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tabPanel('Inference - 2', value = 'tab_infer2_home',
fluidPage(
fluidRow(
br(),
column(12, align = 'center',
h5('What do you want to do?')
),
br(),
br()
),
fluidRow(
column(12),
br(),
column(3),
column(4, align = 'left',
h5('Independent Sample t Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_it',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Paired Sample t Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_pt',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Binomial Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_binom',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Two Sample Variance Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_var',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Two Sample Proportion Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_prop',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Chi Square Association Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_chi',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('McNemar Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_2_mcnemar',
label = 'Click Here',
width = '120px'
)
),
column(3)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_infer2_home.R |
tabPanel('Inference - 3', value = 'tab_infer3_home',
fluidPage(
fluidRow(
br(),
column(12, align = 'center',
h5('What do you want to do?')
),
br(),
br()
),
fluidRow(
column(12),
br(),
column(3),
column(4, align = 'left',
h5('One Way ANOVA')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_3_anova',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Levene Test')
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_3_levene',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5("Cochran's Q Test")
),
column(2, align = 'left',
actionButton(
inputId = 'inf_menu_3_cochran',
label = 'Click Here',
width = '120px'
)
),
column(3)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_infer3_home.R |
tabPanel('Inference', value = 'tab_infer_home',
fluidPage(
fluidRow(
column(12),
br(),
column(12, align = 'center',
h5('What do you want to do?')
),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Comparison of one group to a hypothetical value')
),
column(2, align = 'left',
actionButton(inputId = 'button_infer_home_1', label = 'Click Here', width = '120px')
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Comparison of two groups')
),
column(2, align = 'left',
actionButton(inputId = 'button_infer_home_2', label = 'Click Here', width = '120px')
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Comparison of three or more groups')
),
column(2, align = 'left',
actionButton(inputId = 'button_infer_home_3', label = 'Click Here', width = '120px')
),
column(3)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_infer_home.R |
tabPanel('Inference', value = 'tab_infer', icon = icon('cogs'),
navlistPanel(id = 'navlist_infer',
well = FALSE,
widths = c(2, 10),
source('ui/ui_ttest.R', local = TRUE)[[1]],
source('ui/ui_indttest.R', local = TRUE)[[1]],
source('ui/ui_ptest.R', local = TRUE)[[1]],
source('ui/ui_binomtest.R', local = TRUE)[[1]],
source('ui/ui_osvar.R', local = TRUE)[[1]],
source('ui/ui_tsvar.R', local = TRUE)[[1]],
source('ui/ui_osprop.R', local = TRUE)[[1]],
source('ui/ui_tsprop.R', local = TRUE)[[1]],
source('ui/ui_anova.R', local = TRUE)[[1]],
source('ui/ui_levene.R', local = TRUE)[[1]],
source('ui/ui_chigof.R', local = TRUE)[[1]],
source('ui/ui_chict.R', local = TRUE)[[1]],
source('ui/ui_cochran.R', local = TRUE)[[1]],
source('ui/ui_runs.R', local = TRUE)[[1]],
source('ui/ui_mcnemar.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_inference.R |
tabPanel('Levene Test', value = 'tab_levtest',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Levene Test'),
p("Levene's robust test statistic for the equality of variances and the
two statistics proposed by Brown and Forsythe that replace the mean in
Levene's formula with alternative location estimators. The first alternative
replaces the mean with the median. The second alternative replaces the mean
with the 10% trimmed mean.")
),
column(6, align = 'right',
actionButton(inputId='levtestlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_levene_test.html', '_blank')"),
actionButton(inputId='levtestlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=Yz5fhDhzMKI', '_blank')")
)
),
hr(),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Using Variables',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variables:')),
column(10, align = 'left',
selectInput("var_levtest", label = '', width = '660px',
choices = "", selected = "", multiple = TRUE,
selectize = TRUE),
bsTooltip("var_levtest", "Select variables.",
"left", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_levtest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_levtest", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('levtest_out')
)
)
)
),
tabPanel('Using Groups',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(2, align = 'left',
selectInput("var_levtestg1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_levtestg1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Grouping Variable:')),
column(2, align = 'left',
selectInput("var_levtestg2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_levtestg2", "Select a grouping variable.",
"left", options = list(container = "body"))),
column(4, align = 'center',
br(),
actionButton(inputId = 'submit_levtestg', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_levtestg", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('levtestg_out')
)
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_levene.R |
tabPanel('McNemar Test', value = 'tab_mcnemar',
fluidPage(
fluidRow(
column(8, align = 'left',
h4('McNemar Test'),
p('Test if the proportions of two dichotomous variables are equal in the same population.')
),
column(4, align = 'right',
actionButton(inputId='mclink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_mcnemar_test.html', '_blank')"),
actionButton(inputId='mclink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=MrbnGGcF6ek', '_blank')")
)
),
hr(),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Using Variables',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable 1:')),
column(2, align = 'left',
selectInput("var_mcnemar1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_mcnemar1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Variable 2:')),
column(2, align = 'left',
selectInput("var_mcnemar2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_mcnemar2", "Select a variable.",
"left", options = list(container = "body"))),
column(4, align = 'center',
br(),
actionButton(inputId = 'submit_mcnemar', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_mcnemar", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('mcnemar_out')
)
)
)
),
tabPanel('Calculator',
fluidPage(
fluidRow(
column(5, align = 'right', br(), h5('0')),
column(1, align = 'right', br(), h5('')),
column(1, align = 'right', br(), h5('1'))
),
fluidRow(
column(4, align = 'right', br(), h5('0')),
column(2, align = 'left',
numericInput("mc_00", label = '', width = '100px',
min = 0, step = 1, value = 1)),
# column(2, align = 'right', br(), h5('')),
column(4, align = 'left',
numericInput('mc_01', label = '', width = '100px',
min = 0, value = 1, step = 1))
),
fluidRow(
column(4, align = 'right', br(), h5('1')),
column(2, align = 'left',
numericInput("mc_10", label = '', width = '100px',
min = 0, step = 1, value = 1)),
# column(2, align = 'right', br(), h5('')),
column(4, align = 'left',
numericInput('mc_11', label = '', width = '100px',
min = 0, value = 1, step = 1))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_mcnemarc', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_mcnemarc", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('mcnemarc_out'))
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_mcnemar.R |
tabPanel('One Sample Proportion', value = 'tab_osproptest',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('One Sample Proportion Test'),
p('Compares proportion in one group to a specified population proportion.')
),
column(6, align = 'right',
actionButton(inputId='osproplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_os_prop_test.html', '_blank')"),
actionButton(inputId='osproplink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=K8BNGJYmvlI', '_blank')")
)
),
hr(),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Using Variables',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(2, align = 'left',
selectInput("var_osproptest", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_osproptest", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Probability:')),
column(2, align = 'left',
numericInput('osproptest_prob', label = '',
min = 0, value = 0.5, max = 1, step = 0.01),
bsTooltip("osproptest_prob", "Probability",
"bottom", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(2, align = 'left',
selectInput('osproptest_type', '',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("osproptest_type", "Alternative hypothesis",
"bottom", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_osproptest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_osproptest", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('osproptest_out')
)
)
)
),
tabPanel('Calculator',
fluidPage(
fluidRow(
column(3, align = 'right', br(), h5('N:')),
column(3, align = 'left',
numericInput("n_ospropcalc", label = '', width = '150px',
min = 0, step = 1, value = 1),
bsTooltip("n_ospropcalc", "Number of observations",
"left", options = list(container = "body"))),
column(3, align = 'right', br(), h5('Hypothesized Proportion:')),
column(3, align = 'left',
numericInput('p_ospropcalc', label = '', width = '150px',
min = 0, value = 0.5, step = 0.1, max = 1),
bsTooltip("p_ospropcalc", "Hypothesized Proportion",
"bottom", options = list(container = "body")))
),
fluidRow(
column(3, align = 'right', br(), h5('Probability:')),
column(3, align = 'left',
numericInput('prob_ospropcalc', label = '', width = '150px',
min = 0, value = 0.5, step = 0.1, max = 1),
bsTooltip("prob_ospropcalc", "Probability",
"bottom", options = list(container = "body"))),
column(3, align = 'right', br(), h5('Alternative:')),
column(3, align = 'left',
selectInput('ospropcalc_type', '', width = '150px',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("ospropcalc_type", "Alternative hypothesis",
"bottom", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_ospropcalc', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_ospropcalc", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('ospropcalc_out')
)
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_osprop.R |
tabPanel('One Sample Variance', value = 'tab_osvartest',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('One Sample Variance Test'),
p('Performs tests on the equality of standard deviations (variances).It
tests that the standard deviation of a sample is equal to a hypothesized value.')
),
column(6, align = 'right',
actionButton(inputId='osvarlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_os_var_test.html', '_blank')"),
actionButton(inputId='osvarlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=00ZCHwMPaFY', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(4, align = 'left',
selectInput("var_osvartest", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_osvartest", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(4, align = 'left',
selectInput('osvartest_type', '', width = '200px',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("osvartest_type", "Alternative hypothesis",
"bottom", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Conf Int')),
column(4, align = 'left',
numericInput('osvartest_conf', label = '', width = '200px',
min = 0, value = 0.95, step = 0.01),
bsTooltip("osvartest_conf", "Confidence Level",
"bottom", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Std. Deviation')),
column(4, align = 'left',
numericInput("sd_osvartest", label = '', width = '200px',
min = 0, step = 0.1, value = 0.5),
bsTooltip("sd_osvartest", "Specify standard deviation",
"left", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_osvartest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_osvartest", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('osvartest_out')
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_osvar.R |
tabPanel('Paired Sample t', value = 'tab_ptest',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Paired Sample t Test'),
p('Tests that two samples have the same mean, assuming paired data.')
),
column(6, align = 'right',
actionButton(inputId='ab1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_ts_paired_ttest.html', '_blank')"),
actionButton(inputId='ab3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=dgMJcgeXLL0', '_blank')")
)
),
hr(),
fluidRow(
br(),
column(2, align = 'right', br(), h5('Variable 1:')),
column(4, align = 'left',
selectInput("var_ptest1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_ptest1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Variable 2:')),
column(4, align = 'left',
selectInput("var_ptest2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_ptest2", "Select a variable.",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Conf Int')),
column(4, align = 'left',
numericInput('ptest_conf', label = '', width = '200px',
min = 0, value = 0.95, step = 0.01),
bsTooltip("ptest_conf", "Confidence Level",
"bottom", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(4, align = 'left',
selectInput('ptest_type', '', width = '200px',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("ptest_type", "Alternative hypothesis",
"bottom", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_ptest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_ptest", "Click here to view t test result.",
"bottom", options = list(container = "body"))
)
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('ptest_out')
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_ptest.R |
tabPanel('Runs Test', value = 'tab_runs',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Runs Test for Randomness'),
p('Tests whether the observations are serially independent i.e. whether
they occur in a random order, by counting how many runs there are above
and below a threshold. By default, the median is used as the threshold.
A small number of runs indicates positive serial correlation; a large
number indicates negative serial correlation.')
),
column(6, align = 'right',
actionButton(inputId='runslink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_runs_test.html', '_blank')"),
actionButton(inputId='runslink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=H12KM_uHbWc', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(4, align = 'left',
selectInput("var_runs", label = '', width = '250px',
choices = "", selected = ""),
bsTooltip("var_runs", "Select a variable.",
"left", options = list(container = "body"))),
column(1, align = 'right', br(), h5('Drop:')),
column(3, align = 'left',
selectInput('runs_drop', '', width = '250px',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("runs_drop", "Drop values equal to threshold.",
"bottom", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Split:')),
column(2, align = 'left',
selectInput("runs_split", label = '',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("runs_split", "Recode data in binary format.",
"left", options = list(container = "body"))),
column(1, align = 'right', br(), h5('Mean:')),
column(2, align = 'left',
selectInput('runs_mean', '',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("runs_mean", "Use mean as the threshold.",
"bottom", options = list(container = "body"))),
column(1, align = 'right', br(), h5('Threshold')),
column(2, align = 'left',
numericInput('runs_thold', label = '',
min = 0, step = 1, value = NA),
bsTooltip("runs_thold", "threshold to be used for counting runs, specify
0 if data is coded as a binary.",
"bottom", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_runs', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_runs", "Click here to view result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('runs_out')
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_runs.R |
tabPanel('Screen', value = 'tab_scr', icon = icon('binoculars'),
navlistPanel(id = 'navlist_scr',
well = FALSE,
widths = c(2, 10),
source('ui/ui_screen.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_scr.R |
tabPanel('Screen', value = 'tab_screen',
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Data Screening'),
p('Screen data for missing values, verify column names and data types.')
),
column(4, align = 'right',
actionButton(inputId='dscreenlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('http://www.rsquaredacademy.com/descriptr/reference/ds_screener.html', '_blank')"),
actionButton(inputId='dscreenlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=lheslEn5icc#t=03m04s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
verbatimTextOutput('screen')
)
),
fluidRow(
br(),
column(12, align = 'center',
actionButton('finalok', 'Approve', width = '120px', icon = icon('sign-out')),
bsTooltip("finalok", "Click here to approve the data.",
"top", options = list(container = "body"))
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_screen.R |
tabPanel('Select', value = 'tab_sel', icon = icon('database'),
navlistPanel(id = 'navlist_up',
well = FALSE,
widths = c(2, 10),
source('ui/ui_seldata.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_sel.R |
tabPanel("Select Data", value = "tab_seldata",
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Select Data Set'),
p('Select a data set from the drop down box and click on submit.')
),
column(6, align = 'right',
actionButton(inputId='seldatalink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=lheslEn5icc#t=01m10s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = "center",
selectInput(
inputId = "sel_data",
label = "Select a data set:",
choices = '',
# choices = c('csv', 'excel', 'json', 'spss', 'stata', 'sas'),
selected = '',
width = '200px',
)
)
),
fluidRow(
column(12, align = 'center',
br(),
actionButton(inputId = 'submit_seldata', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_seldata", "Click here to select data.",
"bottom", options = list(container = "body")))
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_seldata.R |
tabPanel('Select Variables', value = 'tab_selvar',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Select Variables'),
p('Click on Yes to select variables.')
),
column(6, align = 'right',
actionButton(inputId='selvarlink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=lheslEn5icc#t=02m08s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
h4('Do you want to select variables?')
)
),
fluidRow(
column(6, align = 'right',
actionButton(
inputId = 'button_selvar_yes',
label = 'Yes',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'button_selvar_no',
label = 'No',
width = '120px'
)
)
),
fluidRow(
br(),
br(),
uiOutput('show_sel_button')
),
fluidRow(
uiOutput('sub_sel_button')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_select.R |
tabPanel('Transform', value = 'tab_trans', icon = icon('rotate-right'),
navlistPanel(id = 'navlist_trans',
well = FALSE,
widths = c(2, 10),
source('ui/ui_seldata.R', local = TRUE)[[1]],
source('ui/ui_transform2.R', local = TRUE)[[1]],
source('ui/ui_select.R', local = TRUE)[[1]],
source('ui/ui_filter.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_trans.R |
tabPanel('Transform', value = 'tab_transform',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Data Transformation'),
p('Rename variables and modify data types.')
),
column(6, align = 'right',
actionButton(inputId='translink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=lheslEn5icc#t=01m20s', '_blank')")
)
),
hr(),
fluidRow(
column(3, tags$h5('Variable')),
column(3, tags$h5('Rename Variable')),
column(3, tags$h5('Modify Data Type'))
),
column(12, uiOutput('trans_try')),
fluidRow(
tags$br()
),
fluidRow(
column(12, align = 'center',
br(),
actionButton(inputId="apply_changes", label="Apply Changes", icon = icon('thumbs-up')),
bsTooltip("apply_changes", "Click here to apply changes to data.",
"top", options = list(container = "body")),
br(),
br()
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_transform2.R |
tabPanel('Two Sample Proportion', value = 'tab_tsproptest',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Two Sample Proportion Test'),
p('Tests on the equality of proportions using large-sample statistics.
It tests that a sample has the same proportion within two independent
groups or two samples have the same proportion.')
),
column(6, align = 'right',
actionButton(inputId='tsproplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_ts_prop_test.html', '_blank')"),
actionButton(inputId='tsproplink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=0HhnnSzj1JY', '_blank')")
)
),
hr(),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Using Variables',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable 1:')),
column(2, align = 'left',
selectInput("var_tsproptest1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsproptest1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Variable 2:')),
column(2, align = 'left',
selectInput("var_tsproptest2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsproptest2", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(2, align = 'left',
selectInput('tsproptest_type', '',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("tsproptest_type", "Alternative hypothesis",
"bottom", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_tsproptest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_tsproptest", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('tsproptest_out')
)
)
)
),
tabPanel('Using Groups',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(2, align = 'left',
selectInput("var_tsproptestg1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsproptestg1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Grouping Variable:')),
column(2, align = 'left',
selectInput("var_tsproptestg2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsproptestg2", "Select a grouping variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(2, align = 'left',
selectInput('tsproptestg_type', '',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("tsproptestg_type", "Alternative hypothesis",
"bottom", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_tsproptestg', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_tsproptestg", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('tsproptestg_out')
)
)
)
),
tabPanel('Calculator',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('n1:')),
column(2, align = 'left',
numericInput("n1_tspropcalc", label = '', width = '120px',
min = 0, step = 1, value = 1),
bsTooltip("n1_tspropcalc", "Number of observations",
"left", options = list(container = "body"))),
column(1, align = 'right', br(), h5('n2:')),
column(2, align = 'left',
numericInput("n2_tspropcalc", label = '', width = '120px',
min = 0, step = 1, value = 1),
bsTooltip("n2_tspropcalc", "Number of observations",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(3, align = 'left',
selectInput('tspropcalc_type', '', width = '120px',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("tspropcalc_type", "Alternative hypothesis",
"bottom", options = list(container = "body")))
),
fluidRow(
column(4, align = 'right', br(), h5('Proportion 1:')),
column(2, align = 'left',
numericInput('prop_tspropcalc1', label = '', width = '150px',
min = 0, value = 0.5, step = 0.1, max = 1),
bsTooltip("prop_tspropcalc1", "Proportion 1",
"bottom", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Proportion 2:')),
column(4, align = 'left',
numericInput('prop_tspropcalc2', label = '', width = '150px',
min = 0, value = 0.5, step = 0.1, max = 1),
bsTooltip("prop_tspropcalc2", "Proportion 2",
"bottom", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_tspropcalc', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_tspropcalc", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('tspropcalc_out')
)
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_tsprop.R |
tabPanel('Two Sample Variance', value = 'tab_tsvartest',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Two Sample Variance Test'),
p('Performs tests on the equality of standard deviations (variances).')
),
column(6, align = 'right',
actionButton(inputId='tsvarlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_ts_var_test.html', '_blank')"),
actionButton(inputId='tsvarlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=H5XX3wmF1Sc', '_blank')")
)
),
hr(),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Using Variables',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable 1:')),
column(2, align = 'left',
selectInput("var_tsvartest1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsvartest1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Variable 2:')),
column(2, align = 'left',
selectInput("var_tsvartest2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsvartest2", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(2, align = 'left',
selectInput('tsvartest_type', '',
choices = c("less", "greater", "all"),
selected = "all"),
bsTooltip("tsvartest_type", "Alternative hypothesis",
"bottom", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_tsvartest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_tsvartest", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('tsvartest_out')
)
)
)
),
tabPanel('Using Groups',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(2, align = 'left',
selectInput("var_tsvartestg1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsvartestg1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Grouping Variable:')),
column(2, align = 'left',
selectInput("var_tsvartestg2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_tsvartestg2", "Select grouping variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(2, align = 'left',
selectInput('tsvartestg_type', '',
choices = c("less", "greater", "all"),
selected = "all"),
bsTooltip("tsvartestg_type", "Alternative hypothesis",
"bottom", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_tsvartestg', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_tsvartestg", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('tsvartestg_out')
)
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_tsvar.R |
tabPanel('One Sample t', value = 'tab_ttest',
fluidPage(
fluidRow(
column(8, align = 'left',
h4('One Sample t Test'),
p('Performs t tests on the equality of means. It tests the hypothesis
that a sample has a mean equal to a hypothesized value.')
),
column(4, align = 'right',
actionButton(inputId='ostlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://inferr.rsquaredacademy.com/reference/infer_os_t_test.html', '_blank')"),
actionButton(inputId='ostlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=7eNfzplm86Y', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(4, align = 'left',
selectInput("var_ttest", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_ttest", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Alternative:')),
column(4, align = 'left',
selectInput('ttest_type', '', width = '200px',
choices = c("both", "less", "greater", "all"),
selected = "both"),
bsTooltip("ttest_type", "Alternative hypothesis",
"bottom", options = list(container = "body"))
)
),
fluidRow(
column(2, align = 'right', br(), h5('alpha:')),
column(4, align = 'left',
numericInput('ttest_alpha', label = '', width = '200px',
min = 0, value = 0.05, step = 0.01),
bsTooltip("ttest_alpha", "Acceptable tolerance for type 1 error.",
"bottom", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Mu:')),
column(4, align = 'left',
numericInput('ttest_mu', label = '', min = 0, value = 1, step = 1, width = '200px'),
bsTooltip("ttest_mu", "True value of the mean.",
"bottom", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_ttest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_ttest", "Click here to view t test result.",
"bottom", options = list(container = "body"))
)
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('ttest_out')
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_ttest.R |
tabPanel('Get Data', value = 'tab_upload', icon = icon('server'),
navlistPanel(id = 'navlist_up',
well = FALSE,
widths = c(2, 10),
source('ui/ui_dataoptions.R', local = TRUE)[[1]],
source('ui/ui_datafiles.R', local = TRUE)[[1]],
source('ui/ui_datasamples.R', local = TRUE)[[1]]
# source('ui/ui_upload.R', local = TRUE)[[1]],
# source('ui/ui_excel.R', local = TRUE)[[1]],
# source('ui/ui_json.R', local = TRUE)[[1]],
# source('ui/ui_stata.R', local = TRUE)[[1]],
# source('ui/ui_spss.R', local = TRUE)[[1]],
# source('ui/ui_sas.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_up.R |
tabPanel('View', value = 'tab_vi', icon = icon('sort'),
navlistPanel(id = 'navlist_vi',
well = FALSE,
widths = c(2, 10),
source('ui/ui_view.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_vi.R |
tabPanel('View', value = 'tab_view',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='view2getdata', label=" Get Data", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='view2analyze', label="Analyze Data", icon = icon("long-arrow-right"))
)
),
hr(),
fluidRow(
dataTableOutput(outputId = "table")
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui/ui_view.R |
library(shiny)
library(shinyBS)
library(shinythemes)
library(descriptr)
library(dplyr)
shinyUI(
navbarPage(HTML("inferr"), id = 'mainpage',
source('ui/ui_data.R', local = TRUE)[[1]],
source('ui/ui_analyze.R', local = TRUE)[[1]],
source('ui/ui_exit_button.R', local = TRUE)[[1]]
))
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/ui.R |
fg <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
fk <- function(x, w) {
format(x, width = w, justify = "centre", nsmall = 3)
}
fs <- function() {
rep(" ")
}
fl <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "left")
}
fc <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
formatter_t <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
format_cil <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
format_ciu <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
formats_t <- function() {
rep(" ")
}
formatter_pair <- function(x, w) {
x1 <- format(x, nsmall = 2)
x2 <- as.character(x1)
ret <- format(x2, width = w, justify = "centre")
return(ret)
}
fw <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
fn <- function(x, w) {
x %>%
as.character() %>%
format(width = w, justify = "centre")
}
formats <- function() {
rep(" ")
}
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/xpl-format.R |
# one sample t test
xpl_os_t_test <- function(data, x, mu = 0, alpha = 0.05,
alternative = c("both", "less", "greater", "all"), ...) {
xone <- data[[x]]
if (!is.numeric(xone)) {
stop("x must be numeric")
}
if (!is.numeric(mu)) {
stop("mu must be numeric")
}
if (!is.numeric(alpha)) {
stop("alpha must be numeric")
}
type <- match.arg(alternative)
var_name <- x
k <- ttest_comp(xone, mu, alpha, type)
result <-
list(conf = k$conf,
confint = k$confint,
df = k$df,
Mean = k$Mean,
mean_diff = k$mean_diff,
mean_diff_l = k$mean_diff_l,
mean_diff_u = k$mean_diff_u,
mu = k$mu,
n = k$n,
p = k$p,
p_l = k$p_l,
p_u = k$p_u,
stddev = k$stddev,
std_err = k$std_err,
test_stat = k$test_stat,
type = type,
var_name = var_name)
print_ttest(result)
}
ttest_comp <- function(x, mu, alpha, type) {
n <- length(x)
a <- (alpha / 2)
df <- n - 1
conf <- 1 - alpha
Mean <- round(mean(x), 4)
stddev <- round(stats::sd(x), 4)
std_err <- round(stddev / sqrt(n), 4)
test_stat <- round((Mean - mu) / std_err, 3)
if (type == "less") {
cint <- c(-Inf, test_stat + stats::qt(1 - alpha, df))
} else if (type == "greater") {
cint <- c(test_stat - stats::qt(1 - alpha, df), Inf)
} else {
cint <- stats::qt(1 - a, df)
cint <- test_stat + c(-cint, cint)
}
confint <- round(mu + cint * std_err, 4)
mean_diff <- round((Mean - mu), 4)
mean_diff_l <- confint[1] - mu
mean_diff_u <- confint[2] - mu
p_l <- stats::pt(test_stat, df)
p_u <- stats::pt(test_stat, df, lower.tail = FALSE)
if (p_l < 0.5) {
p <- p_l * 2
} else {
p <- p_u * 2
}
out <-
list(conf = conf,
confint = confint,
df = df,
Mean = Mean,
mean_diff = mean_diff,
mean_diff_l = mean_diff_l,
mean_diff_u = mean_diff_u,
mu = mu,
n = n,
p = p,
p_l = p_l,
p_u = p_u,
stddev = stddev,
std_err = std_err,
test_stat = test_stat)
return(out)
}
# one way anova
xpl_oneway_anova <- function(data, x, y, ...) {
fdata <- data[c(x, y)]
sample_mean <- anova_avg(fdata, x)
sample_stats <- anova_split(fdata, x, y, sample_mean)
k <- anova_calc(fdata, sample_stats, x, y)
result <-
list(
adjusted_r2 = round(k$reg$adj.r.squared, 4),
df_btw = k$df_sstr,
df_total = k$df_sst,
df_within = k$df_sse,
fstat = k$f,
group_stats = sample_stats[, c(1, 2, 3, 5)],
ms_btw = k$mstr,
ms_within = k$mse,
obs = k$obs,
pval = k$sig,
r2 = round(k$reg$r.squared, 4),
rmse = round(k$reg$sigma, 4),
ss_between = k$sstr,
ss_total = k$total,
ss_within = k$ssee)
print_owanova(result)
}
anova_split <- function(data, x, y, sample_mean) {
dat <- data[c(y, x)]
dm <- data.table(dat)
by_factor <- dm[, .(length = length(get(x)),
mean = mean(get(x)),
var = stats::var(get(x)),
sd = stats::sd(get(x))),
by = y]
by_factor[, ':='(sst = length * ((mean - sample_mean) ^ 2),
sse = (length - 1) * var)]
setDF(by_factor)
by_factor <- by_factor[order(by_factor[, 1]),]
return(by_factor)
}
anova_avg <- function(data, y) {
mean(data[[y]])
}
anova_calc <- function(data, sample_stats, x, y) {
var_names <- names(data[c(x, y)])
sstr <-
sample_stats %>%
magrittr::use_series(sst) %>%
sum() %>%
round(3)
ssee <-
sample_stats %>%
magrittr::use_series(sse) %>%
sum() %>%
round(3)
total <- round(sstr + ssee, 3)
df_sstr <- nrow(sample_stats) - 1
df_sse <- nrow(data) - nrow(sample_stats)
df_sst <- nrow(data) - 1
mstr <- round(sstr / df_sstr, 3)
mse <- round(ssee / df_sse, 3)
f <- round(mstr / mse, 3)
sig <- round(1 - stats::pf(f, df_sstr, df_sse), 3)
obs <- nrow(data)
regs <- paste(var_names[1], "~ as.factor(", var_names[2], ")")
model <- stats::lm(stats::as.formula(regs), data = data)
reg <- summary(model)
out <- list(
sstr = sstr, ssee = ssee, total = total, df_sstr = df_sstr,
df_sse = df_sse, df_sst = df_sst, mstr = mstr, mse = mse, f = f,
sig = sig, obs = obs, model = model, reg = reg
)
return(out)
}
# chi square association test
xpl_chisq_assoc_test <- function(data, x, y) {
xone <- data[[x]]
yone <- data[[y]]
if (!is.factor(xone)) {
stop("x must be a categorical variable")
}
if (!is.factor(yone)) {
stop("y must be a categorical variable")
}
# dimensions
k <- table(xone, yone)
dk <- dim(k)
ds <- prod(dk)
nr <- dk[1]
nc <- dk[2]
if (ds == 4) {
twoway <- matrix(table(xone, yone), nrow = 2)
df <- df_chi(twoway)
ef <- efmat(twoway)
k <- pear_chsq(twoway, df, ef)
m <- lr_chsq(twoway, df, ef)
n <- yates_chsq(twoway)
p <- mh_chsq(twoway, n$total, n$prod_totals)
} else {
twoway <- matrix(table(xone, yone), nrow = dk[1])
ef <- efm(twoway, dk)
df <- df_chi(twoway)
k <- pear_chi(twoway, df, ef)
m <- lr_chsq2(twoway, df, ef, ds)
}
j <- chigf(xone, yone, k$chi)
result <- if (ds == 4) {
list(
chisquare = k$chi,
chisquare_adjusted = n$chi_y,
chisquare_lr = m$chilr,
chisquare_mantel_haenszel = p$chimh,
contingency_coefficient = j$cc,
cramers_v = j$cv,
df = df,
ds = ds,
phi_coefficient = j$phi,
pval_chisquare = k$sig,
pval_chisquare_adjusted = n$sig_y,
pval_chisquare_lr = m$sig_lr,
pval_chisquare_mantel_haenszel = p$sig_mh
)
} else {
list(
chisquare = k$chi,
chisquare_lr = m$chilr,
contingency_coefficient = j$cc,
cramers_v = j$cv,
df = df,
ds = ds,
phi_coefficient = j$phi,
pval_chisquare = k$sig,
pval_chisquare_lr = m$sig_lr
)
}
print_chisq_test(result)
}
df_chi <- function(twoway) {
(nrow(twoway) - 1) * (ncol(twoway) - 1)
}
efmat <- function(twoway) {
mat1 <- matrix(rowSums(twoway) / sum(twoway), nrow = 2)
mat2 <- matrix(colSums(twoway), nrow = 1)
mat1 %*% mat2
}
pear_chsq <- function(twoway, df, ef) {
chi <- round(sum(((twoway - ef) ^ 2) / ef), 4)
sig <- round(stats::pchisq(chi, df, lower.tail = F), 4)
list(chi = chi, sig = sig)
}
lr_chsq <- function(twoway, df, ef) {
chilr <- round(2 * sum(matrix(log(twoway / ef), nrow = 1) %*% matrix(twoway, nrow = 4)), 4)
sig_lr <- round(stats::pchisq(chilr, df, lower.tail = F), 4)
list(chilr = chilr, sig_lr = sig_lr)
}
lr_chsq2 <- function(twoway, df, ef, ds) {
chilr <- round(2 * sum(matrix(twoway, ncol = ds) %*% matrix(log(twoway / ef), nrow = ds)), 4)
sig_lr <- round(stats::pchisq(chilr, df, lower.tail = F), 4)
list(chilr = chilr, sig_lr = sig_lr)
}
yates_chsq <- function(twoway) {
way2 <- twoway[, c(2, 1)]
total <- sum(twoway)
prods <- prod(diag(twoway)) - prod(diag(way2))
prod_totals <- prod(rowSums(twoway)) * prod(colSums(twoway))
chi_y <- round((total * (abs(prods) - (total / 2)) ^ 2) / prod_totals, 4)
sig_y <- round(stats::pchisq(chi_y, 1, lower.tail = F), 4)
list(chi_y = chi_y, sig_y = sig_y, total = total, prod_totals = prod_totals)
}
mh_chsq <- function(twoway, total, prod_totals) {
num <- twoway[1] - ((rowSums(twoway)[1] * colSums(twoway)[1]) / total)
den <- prod_totals / ((total ^ 3) - (total ^ 2))
chimh <- round((num ^ 2) / den, 4)
sig_mh <- round(stats::pchisq(chimh, 1, lower.tail = F), 4)
list(chimh = chimh, sig_mh = sig_mh)
}
efm <- function(twoway, dk) {
mat1 <- matrix(rowSums(twoway) / sum(twoway), nrow = dk[1])
mat2 <- matrix(colSums(twoway), ncol = dk[2])
mat1 %*% mat2
}
pear_chi <- function(twoway, df, ef) {
chi <- round(sum(((twoway - ef) ^ 2) / ef), 4)
sig <- round(stats::pchisq(chi, df, lower.tail = F), 4)
list(chi = chi, sig = sig)
}
chigf <- function(x, y, chi) {
twoway <- matrix(table(x, y),
nrow = nlevels(as.factor(x)),
ncol = nlevels(as.factor(y))
)
total <- sum(twoway)
phi <- round(sqrt(chi / total), 4)
cc <- round(sqrt(chi / (chi + total)), 4)
q <- min(nrow(twoway), ncol(twoway))
cv <- round(sqrt(chi / (total * (q - 1))), 4)
list(phi = phi, cc = cc, cv = cv)
}
# chi square goodness of fit test
xpl_chisq_gof_test <- function(data, x, y, correct = FALSE) {
xcheck <- data[[x]]
xlen <- length(data[[x]])
xone <- as.vector(table(data[[x]]))
if (!is.factor(xcheck)) {
stop("x must be an object of class factor")
}
if (!is.numeric(y)) {
stop("y must be numeric")
}
if (!is.logical(correct)) {
stop("correct must be either TRUE or FALSE")
}
varname <- names(data[x])
n <- length(xone)
if (length(y) != n) {
stop("Length of y must be equal to the number of categories in x")
}
df <- n - 1
if (sum(y) == 1) {
y <- xlen * y
}
if ((df == 1) || (correct == TRUE)) {
k <- chi_cort(xone, y)
} else {
k <- chigof(xone, y)
}
sig <- round(stats::pchisq(k$chi, df, lower.tail = FALSE), 4)
result <-
list(
categories = levels(xcheck),
chisquare = k$chi,
deviation = format(k$dev, nsmall = 2),
degrees_of_freedom = df,
expected_frequency = y,
n_levels = nlevels(xcheck),
observed_frequency = xone,
pvalue = sig,
sample_size = length(xcheck),
std_residuals = format(k$std, nsmall = 2),
varname = varname
)
print_chisq_gof(result)
}
chi_cort <- function(x, y) {
diff <- x - y - 0.5
dif <- abs(x - y) - 0.5
dif2 <- dif ^ 2
dev <- round((diff / y) * 100, 2)
std <- round(diff / sqrt(y), 2)
chi <- round(sum(dif2 / y), 4)
list(dev = dev, std = std, chi = chi)
}
chigof <- function(x, y) {
dif <- x - y
dif2 <- dif ^ 2
dev <- round((dif / y) * 100, 2)
std <- round(dif / sqrt(y), 2)
chi <- round(sum(dif2 / y), 4)
list(dev = dev, std = std, chi = chi)
}
# cochran's q test
xpl_cochran_qtest <- function(data, vars) {
fdata <- data[vars]
if (ncol(fdata) < 3) {
stop("Please specify at least 3 variables.")
}
if (any(sapply(lapply(fdata, as.factor), nlevels) > 2)) {
stop("Please specify dichotomous/binary variables only.")
}
k <- cochran_comp(fdata)
result <-
list(
df = k$df,
n = k$n,
pvalue = k$pvalue,
q = k$q)
print_cochran_test(result)
}
coch_data <- function(x, ...) {
if (is.data.frame(x)) {
data <- x %>%
lapply(as.numeric) %>%
as.data.frame() %>%
`-`(1)
} else {
data <- cbind(x, ...) %>%
apply(2, as.numeric) %>%
`-`(1) %>%
as.data.frame()
}
return(data)
}
cochran_comp <- function(data) {
n <- nrow(data)
k <- ncol(data)
df <- k - 1
cs <-
data %>%
lapply(as.numeric) %>%
as.data.frame() %>%
magrittr::subtract(1) %>%
sums()
q <- coch(k, cs$cls_sum, cs$cl, cs$g, cs$gs_sum)
pvalue <- 1 - stats::pchisq(q, df)
list(
df = df,
n = n,
pvalue = round(pvalue, 4),
q = q)
}
sums <- function(data) {
cl <- colSums(data)
cls_sum <- sum(cl ^ 2)
g <- rowSums(data)
gs_sum <- sum(g ^ 2)
list(
cl = cl,
cls_sum = cls_sum,
g = g,
gs_sum = gs_sum)
}
coch <- function(k, cls_sum, cl, g, gs_sum) {
((k - 1) * ((k * cls_sum) - (sum(cl) ^ 2))) / ((k * sum(g)) - gs_sum)
}
# independent sample t test
xpl_ts_ind_ttest <- function(data, x, y, confint = 0.95,
alternative = c("both", "less", "greater", "all"), ...) {
yone <- names(data[y])
if (check_x(data, x)) {
stop("x must be a binary factor variable", call. = FALSE)
}
if (check_level(data, x) > 2) {
stop("x must be a binary factor variable", call. = FALSE)
}
method <- match.arg(alternative)
var_y <- yone
alpha <- 1 - confint
a <- alpha / 2
h <- indth(data, x, y, a)
grp_stat <- h
g_stat <- as.matrix(h)
comb <- indcomb(data, y, a)
k <- indcomp(grp_stat, alpha)
j <- indsig(k$n1, k$n2, k$s1, k$s2, k$mean_diff)
m <- indpool(k$n1, k$n2, k$mean_diff, k$se_dif)
result <- list(alternative = method,
combined = comb,
confint = confint,
conf_diff = round(k$conf_diff, 5),
den_df = k$n2 - 1,
df_pooled = m$df_pooled,
df_satterthwaite = j$d_f,
f = round(k$s1 / k$s2, 4),
f_sig = fsig(k$s1, k$s2, k$n1, k$n2),
levels = g_stat[, 1],
lower = g_stat[, 8],
mean = g_stat[, 3],
mean_diff = round(k$mean_diff, 3),
n = k$n,
num_df = k$n1 - 1,
obs = g_stat[, 2],
sd = g_stat[, 4],
sd_dif = round(k$sd_dif, 3),
se = g_stat[, 5],
se_dif = round(k$se_dif, 3),
sig = j$sig,
sig_l = j$sig_l,
sig_pooled_l = m$sig_pooled_l,
sig_pooled_u = m$sig_pooled_u,
sig_pooled = m$sig_pooled,
sig_u = j$sig_u,
t_pooled = round(m$t_pooled, 4),
t_satterthwaite = round(j$t, 4),
upper = g_stat[, 9],
var_y = var_y)
print_two_ttest(result)
}
indth <- function(data, x, y, a) {
h <- data_split(data, x, y)
h$df <- h$length - 1
h$error <- stats::qt(a, h$df) * -1
h$lower <- h$mean_t - (h$error * h$std_err)
h$upper <- h$mean_t + (h$error * h$std_err)
return(h)
}
data_split <- function(data, x, y) {
dat <- data.table(data[c(x, y)])
out <- dat[, .(length = length(get(y)),
mean_t = mean_t(get(y)),
sd_t = sd_t(get(y)),
std_err = std_err(get(y))),
by = x]
setDF(out)
}
indcomb <- function(data, y, a) {
comb <- da(data, y)
comb$df <- comb$length - 1
comb$error <- stats::qt(a, comb$df) * -1
comb$lower <- round(comb$mean_t - (comb$error * comb$std_err), 5)
comb$upper <- round(comb$mean_t + (comb$error * comb$std_err), 5)
names(comb) <- NULL
return(comb)
}
da <- function(data, y) {
dat <- data[[y]]
data.frame(length = length(dat),
mean_t = mean_t(dat),
sd_t = sd_t(dat),
std_err = std_err(dat))
}
mean_t <- function(x) {
x %>%
mean() %>%
round(3)
}
sd_t <- function(x) {
x %>%
stats::sd() %>%
round(3)
}
std_err <- function(x) {
x %>%
stats::sd() %>%
divide_by(x %>%
length() %>%
sqrt()) %>%
round(3)
}
indcomp <- function(grp_stat, alpha) {
n1 <- grp_stat[1, 2]
n2 <- grp_stat[2, 2]
n <- n1 + n2
means <- grp_stat[, 3]
mean_diff <- means[1] - means[2]
sd1 <- grp_stat[1, 4]
sd2 <- grp_stat[2, 4]
s1 <- grp_stat[1, 4] ^ 2
s2 <- grp_stat[2, 4] ^ 2
sd_dif <- sd_diff(n1, n2, s1, s2)
se_dif <- se_diff(n1, n2, s1, s2)
conf_diff <- conf_int_p(mean_diff, se_dif, alpha = alpha)
list(conf_diff = conf_diff,
mean_diff = mean_diff,
n = n,
n1 = n1,
n2 = n2,
s1 = s1,
s2 = s2,
sd1 = sd1,
sd2 = sd2,
sd_dif = sd_dif,
se_dif = se_dif)
}
sd_diff <- function(n1, n2, s1, s2) {
n1 <- n1 - 1
n2 <- n2 - 1
n <- (n1 + n2) - 2
(n1 * s1) %>%
add(n2 * s2) %>%
divide_by(n) %>%
raise_to_power(0.5)
}
se_diff <- function(n1, n2, s1, s2) {
df <- n1 + n2 - 2
n_1 <- n1 - 1
n_2 <- n2 - 1
(n_1 * s1) %>%
add(n_2 * s2) %>%
divide_by(df) -> v
(1 / n1) %>%
add(1 / n2) %>%
multiply_by(v) %>%
sqrt()
}
conf_int_p <- function(u, se, alpha = 0.05) {
a <- alpha / 2
error <- round(stats::qnorm(a), 3) * -1
lower <- u - (error * se)
upper <- u + (error * se)
c(lower, upper)
}
indsig <- function(n1, n2, s1, s2, mean_diff) {
d_f <- as.vector(df(n1, n2, s1, s2))
t <- mean_diff / (((s1 / n1) + (s2 / n2)) ^ 0.5)
sig_l <- round(stats::pt(t, d_f), 4)
sig_u <- round(stats::pt(t, d_f, lower.tail = FALSE), 4)
if (sig_l < 0.5) {
sig <- round(stats::pt(t, d_f) * 2, 4)
} else {
sig <- round(stats::pt(t, d_f, lower.tail = FALSE) * 2, 4)
}
list(d_f = d_f,
sig_l = sig_l,
sig_u = sig_u,
sig = sig,
t = t)
}
df <- function(n1, n2, s1, s2) {
sn1 <- s1 / n1
sn2 <- s2 / n2
m1 <- 1 / (n1 - 1)
m2 <- 1 / (n2 - 1)
num <- (sn1 + sn2) ^ 2
den <- (m1 * (sn1 ^ 2)) + (m2 * (sn2 ^ 2))
round(num / den)
}
fsig <- function(s1, s2, n1, n2) {
round(min(
stats::pf((s1 / s2), (n1 - 1), (n2 - 1)),
stats::pf((s1 / s2), (n1 - 1), (n2 - 1),
lower.tail = FALSE
)
) * 2, 4)
}
indpool <- function(n1, n2, mean_diff, se_dif) {
df_pooled <- (n1 + n2) - 2
t_pooled <- mean_diff / se_dif
sig_pooled_l <- round(stats::pt(t_pooled, df_pooled), 4)
sig_pooled_u <- round(stats::pt(t_pooled, df_pooled, lower.tail = FALSE), 4)
if (sig_pooled_l < 0.5) {
sig_pooled <- round(stats::pt(t_pooled, df_pooled) * 2, 4)
} else {
sig_pooled <- round(stats::pt(t_pooled, df_pooled, lower.tail = FALSE) * 2, 4)
}
list(df_pooled = df_pooled,
sig_pooled_l = sig_pooled_l,
sig_pooled_u = sig_pooled_u,
sig_pooled = sig_pooled,
t_pooled = t_pooled)
}
check_x <- function(data, x) {
!is.factor(data[[x]])
}
check_level <- function(data, x) {
nlevels(data[[x]])
}
# levene test
xpl_levene_test <- function(data, variables = NULL, group_var = "NULL",
trim_mean = 0.1) {
groupvar <- group_var
varyables <- variables
fdata <- data[varyables]
if (groupvar == "NULL") {
z <- as.list(fdata)
ln <- unlist(lapply(z, length))
ly <- seq_len(length(z))
if (length(z) < 2) {
stop("Please specify at least two variables.", call. = FALSE)
}
out <- xpl_gvar(ln, ly)
fdata <- unlist(z)
groupvars <-
out %>%
unlist() %>%
as.factor()
} else {
fdata <- fdata[[1]]
groupvars <- data[[groupvar]]
if (length(fdata) != length(groupvars)) {
stop("Length of variable and group_var do not match.", call. = FALSE)
}
}
k <- lev_comp(fdata, groupvars, trim_mean)
out <-
list(avg = k$avg,
avgs = k$avgs,
bf = k$bf,
bft = k$bft,
d_df = k$d_df,
lens = k$lens,
lev = k$lev,
levs = k$levs,
n = k$n,
n_df = k$n_df,
p_bf = k$p_bf,
p_bft = k$p_bft,
p_lev = k$p_lev,
sd = k$sd,
sds = k$sds)
print_levene_test(out)
}
lev_metric <- function(cvar, gvar, loc, ...) {
metric <- tapply(cvar, gvar, loc, ...)
y <- abs(cvar - metric[gvar])
result <- stats::anova(stats::lm(y ~ gvar))
list(
fstat = result$`F value`[1],
p = result$`Pr(>F)`[1]
)
}
lev_comp <- function(variable, group_var, trim.mean) {
comp <- stats::complete.cases(variable, group_var)
n <- length(comp)
k <- nlevels(group_var)
cvar <- variable[comp]
gvar <- group_var[comp]
lens <- tapply(cvar, gvar, length)
avgs <- tapply(cvar, gvar, mean)
sds <- tapply(cvar, gvar, stats::sd)
bf <- lev_metric(cvar, gvar, mean)
lev <- lev_metric(cvar, gvar, stats::median)
bft <- lev_metric(cvar, gvar, mean, trim = trim.mean)
list(
avg = round(mean(cvar), 2),
avgs = round(avgs, 2),
bf = round(bf$fstat, 4),
bft = round(bft$fstat, 4),
d_df = (n - k),
lens = lens,
lev = round(lev$fstat, 4),
levs = levels(gvar),
n = n,
n_df = (k - 1),
p_bf = round(bf$p, 4),
p_bft = round(bft$p, 4),
p_lev = round(lev$p, 4),
sd = round(stats::sd(cvar), 2),
sds = round(sds, 2))
}
# mcnemar test
xpl_mcnemar_test <- function(data, x = NULL, y = NULL) {
if (is.matrix(data) | is.table(data)) {
dat <- mcdata(data)
} else {
dat <- table(data[c(x, y)])
}
k <- mccomp(dat)
result <-
list(cases = k$cases,
controls = k$controls,
cpvalue = k$cpvalue,
cstat = k$cstat,
df = k$df,
exactp = k$exactp,
kappa = k$kappa,
kappa_cil = k$kappa_cil,
kappa_ciu = k$kappa_ciu,
odratio = k$odratio,
pvalue = k$pvalue,
ratio = k$ratio,
statistic = k$statistic,
std_err = k$std_err,
tbl = dat)
print_mcnemar_test(result)
}
mcdata <- function(x, y) {
if (!is.matrix(x)) {
stop("x must be either a table or a matrix")
}
if (is.matrix(x)) {
if (length(x) != 4) {
stop("x must be a 2 x 2 matrix")
}
}
dat <- x
return(dat)
}
mctestp <- function(dat) {
retrieve <- matrix(c(1, 2, 2, 1), nrow = 2)
dat[retrieve]
}
tetat <- function(p) {
((p[1] - p[2]) ^ 2) / sum(p)
}
mcpval <- function(test_stat, df) {
1 - stats::pchisq(test_stat, df)
}
mcpex <- function(dat) {
2 * min(stats::pbinom(dat[2], sum(dat[2], dat[3]), 0.5), stats::pbinom(dat[3], sum(dat[2], dat[3]), 0.5))
}
mcstat <- function(p) {
((abs(p[1] - p[2]) - 1) ^ 2) / sum(p)
}
mccpval <- function(cstat, df) {
1 - stats::pchisq(cstat, df)
}
mckappa <- function(dat) {
agreement <- sum(diag(dat)) / sum(dat)
expected <- sum(rowSums(dat) * colSums(dat)) / (sum(dat) ^ 2)
(agreement - expected) / (1 - expected)
}
mcserr <- function(dat, kappa) {
expected <- sum(rowSums(dat) * colSums(dat)) / (sum(dat) ^ 2)
serr(dat, kappa, expected)
}
mcconf <- function(std_err, kappa) {
alpha <- 0.05
interval <- stats::qnorm(1 - (alpha / 2)) * std_err
ci_lower <- kappa - interval
ci_upper <- kappa + interval
list(ci_lower = ci_lower, ci_upper = ci_upper)
}
prop_fact <- function(dat, p) {
dat_per <- dat / sum(dat)
row_sum <- rowSums(dat_per)
col_sum <- colSums(dat_per)
controls <- 1 - col_sum[2]
cases <- 1 - row_sum[2]
ratio <- cases / controls
odds_ratio <- p[1] / p[2]
list(cases = cases,
controls = controls,
odds_ratio = odds_ratio,
ratio = ratio
)
}
serr <- function(dat, kappa, expected) {
dat_per <- dat / sum(dat)
row_sum <- rowSums(dat_per)
row_sum[3] <- sum(row_sum)
col_sum <- colSums(dat_per)
dat_per <- rbind(dat_per, col_sum)
dat_per <- cbind(dat_per, row_sum)
d1 <- dim(dat_per)
dat_per[d1[1], d1[2]] <- 1.0
diagonal <- diag(dat_per)
a <- diagonal[1] * (1 - (row_sum[1] + col_sum[1]) * (1 - kappa)) ^ 2 +
diagonal[2] * (1 - (row_sum[2] + col_sum[2]) * (1 - kappa)) ^ 2
x1 <- dat_per[lower.tri(dat_per)][1]
x2 <- dat_per[upper.tri(dat_per)][1]
b <- ((1 - kappa) ^ 2) * ((x1 * (row_sum[1] + col_sum[2]) ^ 2) +
(x2 * (row_sum[2] + col_sum[1]) ^ 2))
c <- ((kappa) - expected * (1 - kappa)) ^ 2
variance <- ((a + b - c) / ((1 - expected) ^ 2)) / sum(dat)
sqrt(variance)
}
mccomp <- function(dat) {
p <- mctestp(dat)
test_stat <- tetat(p)
df <- nrow(dat) - 1
pvalue <- mcpval(test_stat, df)
exactp <- mcpex(dat)
cstat <- mcstat(p)
cpvalue <- mccpval(cstat, df)
kappa <- mckappa(dat)
std_err <- mcserr(dat, kappa)
clu <- mcconf(std_err, kappa)
k <- prop_fact(dat, p)
list(cases = round(k$cases, 4),
controls = round(k$controls, 4),
cpvalue = cpvalue,
cstat = cstat,
df = df,
exactp = round(exactp, 4),
kappa = round(kappa, 4),
kappa_cil = round(clu$ci_lower, 4),
kappa_ciu = round(clu$ci_upper, 4),
odratio = round(k$odds_ratio, 4),
pvalue = round(pvalue, 4),
ratio = round(k$ratio, 4),
statistic = round(test_stat, 4),
std_err = round(std_err, 4))
}
# one sample proportion test
xpl_os_prop_test <- function(data, variable = NULL, prob = 0.5, phat = 0.5,
alternative = c("both", "less", "greater", "all")) {
if (is.numeric(data)) {
method <- match.arg(alternative)
k <- prop_comp(
data, prob = prob, phat = phat,
alternative = method
)
} else {
fdata <- data[[variable]]
n1 <- length(fdata)
n2 <-
fdata %>%
table() %>%
`[[`(2)
phat <- round(n2 / n1, 4)
prob <- prob
method <- match.arg(alternative)
k <- prop_comp(
n1, prob = prob, phat = phat,
alternative = method
)
}
result <-
list(alt = k$alt,
deviation = k$deviation,
exp = k$exp,
n = k$n,
obs = k$obs,
p = k$p,
phat = k$phat,
sig = k$sig,
std = k$std,
z = k$z)
print_prop_test(result)
}
prop_comp <- function(n, prob, alternative, phat) {
n <- n
phat <- phat
p <- prob
q <- 1 - p
obs <- c(n * (1 - phat), n * phat)
exp <- n * c(q, p)
dif <- obs - exp
dev <- round((dif / exp) * 100, 2)
std <- round(dif / sqrt(exp), 2)
num <- phat - prob
den <- sqrt((p * q) / n)
z <- round(num / den, 4)
lt <- round(stats::pnorm(z), 4)
ut <- round(1 - stats::pnorm(z), 4)
tt <- round((1 - stats::pnorm(abs(z))) * 2, 4)
alt <- alternative
if (alt == "all") {
sig <- c("two-both" = tt, "less" = lt, "greater" = ut)
} else if (alt == "greater") {
sig <- ut
} else if (alt == "less") {
sig <- lt
} else {
sig <- tt
}
out <-
list(alt = alt,
deviation = format(dev, nsmall = 2),
exp = exp,
n = n,
obs = obs,
p = prob,
phat = phat,
sig = sig,
std = format(std, nsmall = 2),
z = z)
return(out)
}
# one sample variance test
xpl_os_var_test <- function(data, x, sd, confint = 0.95,
alternative = c("both", "less", "greater", "all"), ...) {
xone <- data[[x]]
if (!is.numeric(xone)) {
stop("x must be numeric")
}
if (!is.numeric(sd)) {
stop("sd must be numeric")
}
if (!is.numeric(confint)) {
stop("confint must be numeric")
}
type <- match.arg(alternative)
varname <- names(data[x])
k <- osvar_comp(xone, sd, confint)
result <-
list(chi = round(k$chi, 4),
c_lwr = k$c_lwr,
conf = k$conf,
c_upr = k$c_upr,
df = k$df,
n = k$n,
p_lower = k$p_lower,
p_two = k$p_two,
p_upper = k$p_upper,
sd = k$sd,
se = round(k$se, 4),
sigma = round(k$sigma, 4),
type = type,
var_name = varname,
xbar = round(k$xbar, 4))
print_os_vartest(result)
}
osvar_comp <- function(x, sd, confint) {
n <- length(x)
df <- n - 1
xbar <- mean(x)
sigma <- stats::sd(x)
se <- sigma / sqrt(n)
chi <- df * ((sigma / sd) ^ 2)
p_lower <- stats::pchisq(chi, df)
p_upper <- stats::pchisq(chi, df, lower.tail = F)
if (p_lower < 0.5) {
p_two <- stats::pchisq(chi, df) * 2
} else {
p_two <- stats::pchisq(chi, df, lower.tail = F) * 2
}
conf <- confint
a <- (1 - conf) / 2
al <- 1 - a
tv <- df * sigma
c_lwr <- round(tv / stats::qchisq(al, df), 4)
c_upr <- round(tv / stats::qchisq(a, df), 4)
list(chi = chi,
c_lwr = c_lwr,
conf = conf,
c_upr = c_upr,
df = df,
n = n,
p_lower = p_lower,
p_two = p_two,
p_upper = p_upper,
sd = sd,
se = se,
sigma = sigma,
xbar = xbar)
}
# paired t test
xpl_ts_paired_ttest <- function(data, x, y, confint = 0.95,
alternative = c("both", "less", "greater", "all")) {
xone <- data[[x]]
yone <- data[[y]]
method <- match.arg(alternative)
var_names <- names(data[c(x, y)])
k <- paired_comp(xone, yone, confint, var_names)
result <- list(
Obs = k$Obs, b = k$b, conf_int1 = k$conf_int1,
conf_int2 = k$conf_int2, conf_int_diff = k$conf_int_diff, corr = k$corr,
corsig = k$corsig, tstat = k$tstat, p_lower = k$p_lower,
p_upper = k$p_upper, p_two_tail = k$p_two_tail, var_names = var_names,
xy = k$xy, df = k$df, alternative = method, confint = confint
)
print_paired_ttest(result)
}
paired_comp <- function(x, y, confint, var_names) {
n <- length(x)
df <- (n - 1)
xy <- paste(var_names[1], "-", var_names[2])
data_prep <- paired_data(x, y)
b <- paired_stats(data_prep, "key", "value")
corr <- round(stats::cor(x, y), 4)
corsig <- cor_sig(corr, n)
alpha <- 1 - confint
confint1 <- conf_int_t(b[[1, 1]], b[[1, 2]], n, alpha = alpha) %>% round(2)
confint2 <- conf_int_t(b[[2, 1]], b[[2, 2]], n, alpha = alpha) %>% round(2)
confint3 <- conf_int_t(b[[3, 1]], b[[3, 2]], n, alpha = alpha) %>% round(2)
t <- round(b[[3, 1]] / b[[3, 3]], 4)
p_l <- stats::pt(t, df)
p_u <- stats::pt(t, df, lower.tail = FALSE)
p <- stats::pt(abs(t), df, lower.tail = FALSE) * 2
list(
Obs = n, b = b, conf_int1 = confint1, conf_int2 = confint2,
conf_int_diff = confint3, corr = round(corr, 2), corsig = round(corsig, 2),
tstat = t, p_lower = p_l, p_upper = p_u, p_two_tail = p, xy = xy, df = df
)
}
paired_data <- function(x, y) {
j <- data.frame(x = x, y = y)
j$z <- j$x - j$y
val <- data.frame(value = c(j$x, j$y, j$z))
key <- rep(c("x", "y", "z"), each = nrow(j))
cbind(key = key, value = val)
}
paired_stats <- function(data, key, value) {
dat <- data.table(data[c("value", "key")])
out <- dat[, .(length = length(value),
mean = mean(value),
sd = stats::sd(value)),
by = key]
out[, ':='(se = sd / sqrt(length))]
setDF(out)
out[, c(-1, -2)]
}
cor_sig <- function(corr, n) {
t <- corr / ((1 - (corr ^ 2)) / (n - 2)) ^ 0.5
df <- n - 2
sig <- (1 - stats::pt(t, df)) * 2
round(sig, 4)
}
conf_int_t <- function(u, s, n, alpha = 0.05) {
a <- alpha / 2
df <- n - 1
error <- round(stats::qt(a, df), 3) * -1
lower <- u - (error * samp_err(s, n))
upper <- u + (error * samp_err(s, n))
c(lower, upper)
}
samp_err <- function(sigma, n) {
sigma / (n ^ 0.5)
}
# runs test
xpl_runs_test <- function(data, x, drop = FALSE,
split = FALSE, mean = FALSE,
threshold = NA) {
xone <- data[[x]]
n <- length(xone)
if (is.na(threshold)) {
y <- unique(xone)
if (sum(y) == 1) {
stop("Use 0 as threshold if the data is coded as a binary.")
}
}
if (!(is.na(threshold))) {
thresh <- threshold
} else if (mean) {
thresh <- mean(xone)
} else {
thresh <- stats::median(xone, na.rm = TRUE)
}
if (drop) {
xone <- xone[xone != thresh]
}
if (split) {
x_binary <- ifelse(xone > thresh, 1, 0)
} else {
x_binary <-
xone %>%
lapply(nruns2, thresh) %>%
unlist(use.names = FALSE)
}
n_runs <- xpl_nsignC(x_binary)
n1 <- sum(x_binary)
n0 <- length(x_binary) - n1
exp_runs <- expruns(n0, n1)
sd_runs <- sdruns(n0, n1)
test_stat <- (n_runs - exp_runs) / (sd_runs ^ 0.5)
sig <- 2 * (1 - stats::pnorm(abs(test_stat), lower.tail = TRUE))
result <-
list(mean = exp_runs,
n = n,
n_above = n1,
n_below = n0,
n_runs = n_runs,
p = sig,
threshold = thresh,
var = sd_runs,
z = test_stat)
print_runs_test(result)
}
# expected runs
expruns <- function(n0, n1) {
N <- n0 + n1
return(((2 * n0 * n1) / N) + 1)
}
# standard deviation of runs
sdruns <- function(n0, n1) {
N <- n0 + n1
n <- 2 * n0 * n1
return(((n * (n - N)) / ((N ^ 2) * (N - 1))))
}
nruns2 <- function(data, value) {
if (data <= value) {
return(0)
} else {
return(1)
}
}
# two sample proportion test
xpl_ts_prop_test <- function(data, var1, var2,
alternative = c("both", "less", "greater", "all"), ...) {
varone <- data[[var1]]
vartwo <- data[[var2]]
alt <- match.arg(alternative)
k <- prop_comp2(varone, vartwo, alt)
result <-
list(alt = alt,
n1 = k$n1,
n2 = k$n2,
phat1 = k$phat1,
phat2 = k$phat2,
sig = k$sig,
z = k$z)
print_ts_prop_test(result)
}
xpl_ts_prop_grp <- function(data, var, group,
alternative = c("both", "less", "greater", "all")) {
varone <- data[[var]]
groupone <- data[[group]]
if (nlevels(groupone) > 2) {
stop("Grouping variable must be a binary factor variables.", call. = FALSE)
}
n <- tapply(varone, groupone, length)
n1 <- n[[1]]
n2 <- n[[2]]
y <- tapply(varone, groupone, table)
y1 <- y[[1]][[2]]
y2 <- y[[2]][[2]]
phat1 <- y1 / n1
phat2 <- y2 / n2
phat <- sum(y1, y2) / sum(n1, n2)
num <- (phat1 - phat2)
den1 <- phat * (1 - phat)
den2 <- (1 / n1) + (1 / n2)
den <- sqrt(den1 * den2)
z <- num / den
lt <- stats::pnorm(z)
ut <- round(stats::pnorm(z, lower.tail = FALSE), 4)
tt <- round(stats::pnorm(abs(z), lower.tail = FALSE) * 2, 4)
alt <- match.arg(alternative)
if (alt == "all") {
sig <- c("both" = tt, "less" = lt, "greater" = ut)
} else if (alt == "greater") {
sig <- ut
} else if (alt == "less") {
sig <- lt
} else {
sig <- tt
}
out <-
list(alt = alt,
n1 = n1,
n2 = n2,
phat1 = phat1,
phat2 = phat2,
sig = round(sig, 3),
z = round(z, 3))
print_ts_prop_test(out)
}
xpl_ts_prop_calc <- function(n1, n2, p1, p2,
alternative = c("both", "less", "greater", "all"), ...) {
n1 <- n1
n2 <- n2
phat1 <- p1
phat2 <- p2
phat <- sum(n1 * p1, n2 * p2) / sum(n1, n2)
num <- (phat1 - phat2)
den1 <- phat * (1 - phat)
den2 <- (1 / n1) + (1 / n2)
den <- sqrt(den1 * den2)
z <- num / den
lt <- stats::pnorm(z)
ut <- round(stats::pnorm(z, lower.tail = FALSE), 4)
tt <- round(stats::pnorm(abs(z), lower.tail = FALSE) * 2, 4)
alt <- match.arg(alternative)
if (alt == "all") {
sig <- c("both" = tt, "less" = lt, "greater" = ut)
} else if (alt == "greater") {
sig <- ut
} else if (alt == "less") {
sig <- lt
} else {
sig <- tt
}
out <-
list(alt = alt,
n1 = n1,
n2 = n2,
phat1 = round(phat1, 3),
phat2 = round(phat2, 3),
sig = round(sig, 3),
z = round(z, 3))
print_ts_prop_test(out)
}
prop_comp2 <- function(var1, var2, alt) {
n1 <- length(var1)
n2 <- length(var2)
y1 <- table(var1)[[2]]
y2 <- table(var2)[[2]]
phat1 <- round(y1 / n1, 4)
phat2 <- round(y2 / n2, 4)
phat <- sum(y1, y2) / sum(n1, n2)
num <- (phat1 - phat2)
den1 <- phat * (1 - phat)
den2 <- (1 / n1) + (1 / n2)
den <- sqrt(den1 * den2)
z <- round(num / den, 4)
lt <- round(stats::pnorm(z), 4)
ut <- round(stats::pnorm(z, lower.tail = FALSE), 4)
tt <- round(stats::pnorm(abs(z), lower.tail = FALSE) * 2, 4)
if (alt == "all") {
sig <- c("two-tail" = tt, "lower-tail" = lt, "upper-tail" = ut)
} else if (alt == "greater") {
sig <- ut
} else if (alt == "less") {
sig <- lt
} else {
sig <- tt
}
list(n1 = n1,
n2 = n2,
phat1 = phat1,
phat2 = phat2,
sig = round(sig, 3),
z = round(z, 3))
}
# two sample variance test
xpl_ts_var_test <- function(data, variables = NULL, group_var = "NULL",
alternative = c("less", "greater", "all")) {
groupvar <- group_var
varyables <- variables
fdata <- data[varyables]
if (groupvar == "NULL") {
z <- as.list(fdata)
ln <- unlist(lapply(z, length))
ly <- seq_len(length(z))
if (length(z) < 2) {
stop("Please specify at least two variables.", call. = FALSE)
}
out <- xpl_gvar(ln, ly)
fdata <- unlist(z)
groupvars <-
out %>%
unlist() %>%
as.factor()
lev <- names(data[varyables])
} else {
fdata <- fdata[[1]]
groupvars <- data[[groupvar]]
lev <- levels(groupvars)
if (length(fdata) != length(groupvars)) {
stop("Length of variable and group_var do not match.", call. = FALSE)
}
}
type <- match.arg(alternative)
k <- var_comp(fdata, groupvars)
out <- list(avg = k$avg,
avgs = k$avgs,
f = k$f,
len = k$len,
lens = k$lens,
lev = lev,
lower = k$lower,
n1 = k$n1,
n2 = k$n2,
sd = k$sd,
sds = k$sds,
se = k$se,
ses = k$ses,
type = type,
upper = k$upper,
vars = k$vars)
print_var_test(out)
}
var_comp <- function(variable, group_var) {
comp <- stats::complete.cases(variable, group_var)
cvar <- variable[comp]
gvar <- group_var[comp]
d <- data.frame(cvar, gvar)
vals <- tibble_stats(d, "cvar", "gvar")
lass <- tbl_stats(d, "cvar")
lens <- vals[[2]]
vars <- vals[[4]]
f <- vars[1] / vars[2]
n1 <- lens[1] - 1
n2 <- lens[2] - 1
lower <- stats::pf(f, n1, n2)
upper <- stats::pf(f, n1, n2, lower.tail = FALSE)
list(avg = round(lass[2], 2),
avgs = round(vals[[3]], 2),
f = round(f, 4),
len = lass[1],
lens = lens,
lower = round(lower, 4),
n1 = n1,
n2 = n2,
sd = round(lass[3], 2),
sds = round(vals[[5]], 2),
se = round(lass[4], 2),
ses = round(vals[[6]], 2),
upper = round(upper, 4),
vars = round(vars, 2))
}
tibble_stats <- function(data, x, y) {
dat <- data.table(data[c(x, y)])
out <- dat[, .(length = length(get(x)),
mean = mean(get(x)),
var = stats::var(get(x)),
sd = stats::sd(get(x))),
by = y]
out[, ':='(ses = sd / sqrt(length))]
setDF(out)
out <- out[order(out[, 1]),]
return(out)
}
tbl_stats <- function(data, y) {
dat <- data[[y]]
c(length(dat), mean(dat), sd(dat), (sd(dat) / sqrt(length(dat))))
}
# binomial test
xpl_binom_calc <- function(n, success, prob = 0.5, ...) {
if (!is.numeric(n)) {
stop("n must be an integer")
}
if (!is.numeric(success)) {
stop("success must be an integer")
}
if (!is.numeric(prob)) {
stop("prob must be numeric")
}
if ((prob < 0) | (prob > 1)) {
stop("prob must be between 0 and 1")
}
k <- binom_comp(n, success, prob)
out <-
list(
exp_k = k$exp_k,
exp_p = k$exp_p,
k = k$k,
n = n,
obs_p = k$obs_p,
pval_lower = k$lower,
pval_upper = k$upper
)
print_binom(out)
}
xpl_binom_test <- function(data, variable, prob = 0.5) {
varyable <- variable
fdata <- data[[varyable]]
if (!is.factor(fdata)) {
stop("variable must be of type factor", call. = FALSE)
}
if (nlevels(fdata) > 2) {
stop("Binomial test is applicable only to binary data i.e. categorical data with 2 levels.", call. = FALSE)
}
if (!is.numeric(prob)) {
stop("prob must be numeric", call. = FALSE)
}
if ((prob < 0) | (prob > 1)) {
stop("prob must be between 0 and 1", call. = FALSE)
}
n <- length(fdata)
k <- table(fdata)[[2]]
xpl_binom_calc(n, k, prob)
}
binom_comp <- function(n, success, prob) {
n <- n
k <- success
obs_p <- k / n
exp_k <- round(n * prob)
lt <- stats::pbinom(k, n, prob, lower.tail = T)
ut <- stats::pbinom(k - 1, n, prob, lower.tail = F)
p_opp <- round(stats::dbinom(k, n, prob), 9)
i_p <- stats::dbinom(exp_k, n, prob)
i_k <- exp_k
if (k < exp_k) {
while (i_p > p_opp) {
i_k <- i_k + 1
i_p <- round(stats::dbinom(i_k, n, prob), 9)
if (round(i_p) == p_opp) {
break
}
}
ttf <- stats::pbinom(k, n, prob, lower.tail = T) +
stats::pbinom(i_k - 1, n, prob, lower.tail = F)
} else {
while (p_opp <= i_p) {
i_k <- i_k - 1
i_p <- stats::dbinom(i_k, n, prob)
if (round(i_p) == p_opp) {
break
}
}
i_k <- i_k
tt <- stats::pbinom(i_k, n, prob, lower.tail = T) +
stats::pbinom(k - 1, n, prob, lower.tail = F)
ttf <- ifelse(tt <= 1, tt, 1)
}
list(
n = n, k = k, exp_k = exp_k, obs_p = obs_p, exp_p = prob, ik = i_k,
lower = round(lt, 6), upper = round(ut, 6), two_tail = round(ttf, 6)
)
}
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/xpl-helpers.R |
print_owanova <- function(data) {
# width
w1 <- nchar("Between Groups")
w2 <- max(nchar("Squares"), nchar(data$ss_between), nchar(data$ss_within), nchar(data$ss_total))
w3 <- max(nchar("DF"), nchar(data$df_btw), nchar(data$df_btw), nchar(data$df_within), nchar(data$df_total))
w4 <- max(nchar("Mean Square"), nchar(data$ms_btw), nchar(data$ms_within))
w5 <- max(nchar("F"), nchar(data$fstat))
w6 <- max(nchar("Sig."), nchar(format(data$pval, nsmall = 4)))
w <- sum(w1, w2, w3, w4, w5, w6, 21)
w7 <- nchar(data$rmse)
dc <- as.vector(data$group_stats[, 1])
w8 <- max(nchar("Category"), max(nchar(dc)))
w9 <- max(nchar("N"), max(nchar(data$group_stats[[2]])))
w10 <- max(nchar("Mean"), max(nchar(format(data$group_stats[[3]], nsmall = 3))))
w11 <- max(nchar("Std. Dev."), max(nchar(format(data$group_stats[[4]], nsmall = 3))))
wr <- sum(w8, w9, w10, w11, 13)
p <- format(data$pval, nsmall = 4)
q <- nrow(data$group_stats)
s <- length(data$group_stats)
cat(fg("ANOVA", w), "\n")
cat(rep("-", w), sep = "", "\n")
cat(fg("", w1), fs(), fg("Sum of", w2), fs(), fg("", w3), fs(), fg("", w4), fs(), fg("", w5), fs(), fg("", w6), "\n")
cat(fg("", w1), fs(), fg("Squares", w2), fs(), fg("DF", w3), fs(), fg("Mean Square", w4), fs(), fg("F", w5), fs(), fg("Sig.", w6), "\n")
cat(rep("-", w), sep = "", "\n")
cat(fl("Between Groups", w1), fs(), fg(data$ss_between, w2), fs(), fg(data$df_btw, w3), fs(), fg(data$ms_btw, w4), fs(), fg(data$fstat, w5), fs(), fg(data$pval, w6), "\n")
cat(fl("Within Groups", w1), fs(), fg(data$ss_within, w2), fs(), fg(data$df_within, w3), fs(), fg(data$ms_within, w4), fs(), fg("", w5), fs(), fg("", w6), "\n")
cat(fl("Total", w1), fs(), fg(data$ss_total, w2), fs(), fg(data$df_total, w3), fs(), fg("", w4), fs(), fg("", w5), fs(), fg("", w6), "\n")
cat(rep("-", w), sep = "", "\n\n")
cat(fg("Report", wr), "\n")
cat(rep("-", wr), sep = "", "\n")
cat(fg("Category", w8), fs(), fg("N", w9), fs(), fg("Mean", w10), fs(), fg("Std. Dev.", w11), "\n")
cat(rep("-", wr), sep = "", "\n")
for (i in seq_len(q)) {
cat(
fc(data$group_stats[[i, 1]], w8), fs(), fg(data$group_stats[[i, 2]], w9), fs(), fk(format(round(data$group_stats[[i, 3]], 3), nsmall = 3), w10),
fs(), fk(format(round(data$group_stats[[i, 4]], 3), nsmall = 3), w11), "\n"
)
}
cat(rep("-", wr), sep = "", "\n\n")
cat(fl("Number of obs", 13), "=", fl(data$obs, w7), fs(), fl("R-squared", 13), "=", data$r2, "\n")
cat(fl("Root MSE", 13), "=", data$rmse, fs(), fl("Adj R-squared", 13), "=", data$adjusted_r2, "\n\n")
}
print_binom <- function(data) {
# widths
w1 <- nchar("Group")
w2 <- max(nchar("N"), nchar(data$n))
w3 <- max(nchar("Obs. Prop"), nchar(data$obs_p))
w4 <- max(nchar("Exp. Prop"), nchar(data$exp_p))
w <- sum(w1, w2, w3, w4, 13)
k0 <- data$n - data$k
p0 <- 1 - data$obs_p
e0 <- 1 - data$exp_p
cat(format("Binomial Test", width = w, justify = "centre"), "\n")
cat(" ", rep("-", w), sep = "", "\n")
cat(
" ", format("Group", width = w1, justify = "left"), fs(),
format("N", width = w2, justify = "centre"), fs(),
format("Obs. Prop", width = w3, justify = "centre"), fs(),
format("Exp. Prop", width = w4, justify = "centre"), "\n"
)
cat(" ", rep("-", w), sep = "", "\n")
cat(
" ", format("0", width = w1, justify = "centre"), fs(),
format(k0, width = w2, justify = "right"), fs(),
format(p0, width = w3, justify = "centre"), fs(),
format(e0, width = w4, justify = "centre", nsmall = 3), "\n"
)
cat(
" ", format("1", width = w1, justify = "centre"), fs(),
format(data$k, width = w2, justify = "right"), fs(),
format(data$obs_p, width = w3, justify = "centre"), fs(),
format(data$exp_p, width = w4, justify = "centre", nsmall = 3), "\n"
)
cat(" ", rep("-", w), sep = "", "\n")
# test summary widths
w6 <- nchar("Lower")
w7 <- nchar(paste0("Pr(k <= ", data$k, " or k >= ", data$k, ")"))
w8 <- nchar(paste0("Pr(k <= ", data$k, " or k >= ", data$k, ")"))
w9 <- 8
w10 <- sum(w6, w7, w9, 9)
w11 <- sum(w6, w8, w9, 9)
if (data$k < data$exp_k) {
cat("\n\n", format("Test Summary", width = w11, justify = "centre"), "\n")
cat(" ", rep("-", w11), sep = "", "\n")
cat(
" ", format("Tail", width = w6, justify = "left"), fs(), format("Prob", width = w8, justify = "centre"), fs(),
format("p-value", width = w9, justify = "centre"), "\n"
)
cat(" ", rep("-", w11), sep = "", "\n")
cat(
" ", format("Lower", width = w6, justify = "left"), fs(), format(paste0("Pr(k <= ", data$k, ")"), width = w8, justify = "centre"), fs(),
format(as.character(data$pval_lower), width = w9, justify = "centre"), "\n"
)
cat(
" ", format("Upper", width = w6, justify = "left"), fs(), format(paste0("Pr(k >= ", data$k, ")"), width = w8, justify = "centre"), fs(),
format(as.character(data$pval_upper), width = w9, justify = "centre"), "\n"
)
cat(" ", rep("-", w11), sep = "", "\n")
} else {
cat("\n\n", format("Test Summary", width = w10, justify = "centre"), "\n")
cat(" ", rep("-", w10), sep = "", "\n")
cat(
" ", format("Tail", width = w6, justify = "left"), fs(), format("Prob", width = w7, justify = "centre"), fs(),
format("p-value", width = w9, justify = "centre"), "\n"
)
cat(" ", rep("-", w10), sep = "", "\n")
cat(
" ", format("Lower", width = w6, justify = "left"), fs(), format(paste0("Pr(k <= ", data$k, ")"), width = w7, justify = "centre"), fs(),
format(as.character(data$pval_lower), width = w9, justify = "centre"), "\n"
)
cat(
" ", format("Upper", width = w6, justify = "left"), fs(), format(paste0("Pr(k >= ", data$k, ")"), width = w7, justify = "centre"), fs(),
format(as.character(data$pval_upper), width = w9, justify = "centre"), "\n"
)
cat(" ", rep("-", w10), sep = "", "\n")
}
}
print_ttest <- function(data) {
null_l <- paste0("Ho: mean(", data$var_name, ") >=", as.character(data$mu))
alt_l <- paste0(" Ha: mean(", data$var_name, ") <", as.character(data$mu))
null_u <- paste0("Ho: mean(", data$var_name, ") <=", as.character(data$mu))
alt_u <- paste0("Ha: mean(", data$var_name, ") >", as.character(data$mu))
null_t <- paste0("Ho: mean(", data$var_name, ") ~=", as.character(data$mu))
alt_t <- paste0("Ha: mean(", data$var_name, ") !=", as.character(data$mu))
all_l <- paste("Ha: mean <", as.character(data$mu))
all_u <- paste("Ha: mean >", as.character(data$mu))
all_t <- paste("Ha: mean ~=", as.character(data$mu))
char_p_l <- format(data$p_l, digits = 0, nsmall = 4)
char_p_u <- format(data$p_u, digits = 0, nsmall = 4)
char_p <- format(data$p, digits = 0, nsmall = 4)
all_p_l <- paste("P < t =", char_p_l)
all_p_t <- paste("P > |t| =", char_p)
all_p_u <- paste("P > t =", char_p_u)
all_tval <- paste0(" t = ", as.character(data$test_stat))
# formatting output
# compute the characters of each output and decide the overall width
var_width <- max(nchar("Variable"), nchar(data$var_name))
obs_width <- max(nchar("Obs"), nchar(data$n))
mean_width <- max(nchar("Mean"), nchar(data$Mean))
se_width <- max(nchar("Std. Err."), nchar(data$std_err))
sd_width <- max(nchar("Std. Dev."), nchar(data$stddev))
conf_length <- nchar(data$confint[1]) + nchar(data$confint[2])
conf_str <- paste0("[", data$conf * 100, "% Conf. Interval]")
confint_length <- nchar(conf_str)
if (conf_length > confint_length) {
conf_width <- round(conf_length / 2)
} else {
conf_width <- round(confint_length / 2)
}
t_width <- nchar(data$test_stat)
df_width <- max(nchar("DF"), nchar(data$df))
p_width <- max(nchar("2 Tailed"), nchar(round(data$p, 5)))
md_width <- max(nchar("Difference"), nchar(data$mean_diff))
md_length <- nchar(data$mean_diff_l) + nchar(data$mean_diff_u)
if (md_length > confint_length) {
md_conf_width <- floor(md_length / 2)
} else {
md_conf_width <- floor(confint_length / 2)
}
width_1 <- sum(var_width, obs_width, mean_width, se_width, sd_width, ceiling(conf_width * 2), 26)
width_2 <- sum(var_width, t_width, df_width, p_width, md_width, ceiling(md_conf_width * 2), 26)
all_width <- round(width_1 / 3)
cat(
format("One-Sample Statistics", width = width_1, justify = "centre"),
"\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(),
formatter_t("Obs", obs_width), formats_t(),
formatter_t("Mean", mean_width),
formats_t(), formatter_t("Std. Err.", se_width), formats_t(),
formatter_t("Std. Dev.", sd_width), formats_t(),
formatter_t(conf_str, conf_width), "\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(data$n, obs_width), formats_t(),
formatter_t(data$Mean, mean_width),
formats_t(), formatter_t(data$std_err, sd_width), formats_t(),
formatter_t(data$stddev, se_width), formats_t(),
format_cil(data$confint[1], conf_width),
format_ciu(data$confint[2], conf_width), "\n"
)
cat(rep("-", width_1), sep = "")
# print result
if (data$type == "less") {
cat("\n\n", format("Lower Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"), "\n")
cat("\n", format(null_l, width = width_2, justify = "centre"))
cat("\n", format(alt_l, width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(), formatter_t("t", t_width), formats_t(), formatter_t("DF", df_width), formats_t(),
formatter_t("Sig", p_width), formats_t(), formatter_t("Mean Diff.", md_width), formats_t(), formatter_t(conf_str, md_conf_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(round(data$test_stat, 3), t_width), formats_t(),
formatter_t(data$df, df_width), formats_t(),
formatter_t(round(data$p_l, 5), p_width),
formats_t(), formatter_t(data$mean_diff, md_width), formats_t(),
format_cil(round(data$mean_diff_l, 4), md_conf_width),
format_ciu(round(data$mean_diff_u, 4), md_conf_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else if (data$type == "greater") {
cat("\n\n", format("Upper Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"), "\n")
cat("\n", format(null_u, width = width_2, justify = "centre"))
cat("\n", format(alt_u, width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(), formatter_t("t", t_width), formats_t(), formatter_t("DF", df_width), formats_t(),
formatter_t("Sig", p_width), formats_t(), formatter_t("Mean Diff.", md_width), formats_t(), formatter_t(conf_str, md_conf_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(round(data$test_stat, 3), t_width), formats_t(),
formatter_t(data$df, df_width), formats_t(),
formatter_t(round(data$p_l, 5), p_width),
formats_t(), formatter_t(data$mean_diff, md_width), formats_t(),
format_cil(round(data$mean_diff_l, 4), md_conf_width),
format_ciu(round(data$mean_diff_u, 4), md_conf_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else if (data$type == "both") {
cat("\n\n", format("Two Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"), "\n")
cat("\n", format(null_t, width = width_2, justify = "centre"))
cat("\n", format(alt_t, width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(), formatter_t("t", t_width), formats_t(), formatter_t("DF", df_width), formats_t(),
formatter_t("Sig", p_width), formats_t(), formatter_t("Mean Diff.", md_width), formats_t(), formatter_t(conf_str, md_conf_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(round(data$test_stat, 3), t_width), formats_t(),
formatter_t(data$df, df_width), formats_t(),
formatter_t(round(data$p_l, 5), p_width),
formats_t(), formatter_t(data$mean_diff, md_width), formats_t(),
format_cil(round(data$mean_diff_l, 4), md_conf_width),
format_ciu(round(data$mean_diff_u, 4), md_conf_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else {
cat("\n\n", format(null_t, width = width_2, justify = "centre"))
cat("\n\n", format(all_l, width = all_width, justify = "centre"), format(all_t, width = all_width, justify = "centre"), format(all_u, width = all_width, justify = "centre"), "\n")
cat(format(all_tval, width = all_width, justify = "centre"), format(all_tval, width = all_width, justify = "centre"), format(all_tval, width = all_width, justify = "centre"))
cat("\n", format(all_p_l, width = all_width, justify = "centre"), format(all_p_t, width = all_width, justify = "centre"), format(all_p_u, width = all_width, justify = "centre"))
}
}
print_paired_ttest <- function(data) {
char_p_u <- format(data$p_upper, digits = 0, nsmall = 3)
char_p_l <- format(data$p_lower, digits = 0, nsmall = 3)
char_p <- format(data$p_two_tail, digits = 0, nsmall = 3)
# hypothesis heading
hyp_null <- paste0("Ho: mean(", data$var_names[1], " - ", data$var_names[2], ") = ", "0")
hyp_lt <- paste0("Ha: mean(", data$var_names[1], " - ", data$var_names[2], ") < ", "0")
hyp_ut <- paste0("Ha: mean(", data$var_names[1], " - ", data$var_names[2], ") > ", "0")
hyp_2t <- paste0("Ha: mean(", data$var_names[1], " - ", data$var_names[2], ") ~= ", "0")
conf <- data$confint * 100
conf_char <- paste0("[", conf, "% Conf. Interval]")
# all tests combines
all_null <- paste0("Ho: mean(", data$var_names[1], " - ", data$var_names[2], ") = mean(diff) = ", "0")
all_p_l <- paste("P < t =", char_p_l)
all_p_t <- paste("P > |t| =", char_p)
all_p_u <- paste("P > t =", char_p_u)
all_tval <- paste0(" t = ", as.character(data$tstat))
# formatting output
var_width1 <- max(nchar("Variables"), nchar(data$var_names[1]), nchar(data$var_names[2]), nchar("diff"))
var_width <- max(nchar("Variables"), nchar(data$xy))
obs_width <- max(nchar("Obs"), nchar(data$Obs))
mean_width <- max(nchar("Mean"), nchar(format(max(data$b[["mean"]]), nsmall = 2)))
se_width <- max(nchar("Std. Err."), nchar(format(max(data$b[["se"]]), nsmall = 2)))
sd_width <- max(nchar("Std. Dev."), nchar(format(max(data$b[["sd"]]), nsmall = 2)))
corr_width <- nchar("Correlation")
corsig_width <- max(nchar("Sig."), nchar(data$corsig))
t_width <- nchar(data$tstat)
df_width <- max(nchar("DF"), nchar(data$df))
p_width <- max(nchar("Sig."), nchar(format(data$corsig, nsmall = 3)))
conf_length <- max(sum(nchar(data$conf_int1)), sum(nchar(data$conf_int2)))
if (conf_length > 20) {
conf_width <- conf_length
conf_l_width <- ceiling(conf_width / 2)
conf_u_width <- ceiling(conf_width / 2)
} else {
conf_width <- 20
conf_l_width <- 10
conf_u_width <- 10
}
space1 <- 20
space2 <- 13
space3 <- 13
width_1 <- sum(var_width1, obs_width, mean_width, se_width, sd_width, conf_width, space1)
width_2 <- sum(var_width, obs_width, corr_width, corsig_width, space2)
width_3 <- sum(var_width, t_width, df_width, p_width, space3)
cat(format("Paired Samples Statistics", width = width_1, justify = "centre"), "\n")
cat(rep("-", width_1), sep = "", "\n")
cat(
formatter_pair("Variables", var_width1), formats_t(), formatter_pair("Obs", obs_width), formats_t(), formatter_pair("Mean", mean_width),
formats_t(), formatter_pair("Std. Err.", se_width), formats_t(), formatter_pair("Std. Dev.", sd_width), formats_t(), conf_char, "\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_pair(data$var_names[1], var_width1), formats_t(), formatter_pair(data$Obs, obs_width), formats_t(), formatter_pair(data$b[[1, 1]], mean_width),
formats_t(), formatter_pair(data$b[[1, 3]], se_width), formats_t(), formatter_pair(data$b[[1, 2]], sd_width), formats_t(), format_cil(data$conf_int1[[1]], conf_l_width),
format_ciu(data$conf_int1[[2]], conf_u_width)
)
cat(
"\n", formatter_pair(data$var_names[2], var_width1), formats_t(), formatter_pair(data$Obs, obs_width), formats_t(), formatter_pair(data$b[[2, 1]], mean_width), formats_t(), formatter_pair(data$b[[2, 3]], se_width),
formats_t(), formatter_pair(data$b[[2, 2]], sd_width), formats_t(), format_cil(data$conf_int2[[1]], conf_l_width),
format_ciu(data$conf_int2[[2]], conf_u_width), "\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_pair("diff", var_width1), formats_t(), formatter_pair(data$Obs, obs_width), formats_t(), formatter_pair(data$b[[3, 1]], mean_width), formats_t(), formatter_pair(data$b[[3, 3]], se_width),
formats_t(), formatter_pair(data$b[[3, 2]], sd_width), formats_t(), format_cil(data$conf_int_diff[[1]], conf_l_width),
format_ciu(data$conf_int_diff[[2]], conf_u_width), "\n"
)
cat(rep("-", width_1), sep = "")
cat("\n\n", format("Paired Samples Correlations", width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_pair("Variables", var_width), formats_t(), formatter_pair("Obs", obs_width), formats_t(), formatter_pair("Correlation", corr_width),
formats_t(), formatter_pair("Sig.", corsig_width)
)
cat(
"\n", formatter_pair(paste(data$var_names[1], "&", data$var_names[2]), var_width), formats_t(), formatter_pair(data$Obs, obs_width),
formats_t(), formatter_pair(data$corr, corr_width), formats_t(), format(data$corsig, corsig_width), "\n"
)
cat(rep("-", width_2), sep = "", "\n\n")
# print output
if (data$alternative == "less") {
cat(format("Paired Samples Test", width = width_3, justify = "centre"), "\n")
cat(format("-------------------", width = width_3, justify = "centre"), "\n")
cat(format(hyp_null, width = width_3, justify = "centre"), "\n")
cat(format(hyp_lt, width = width_3, justify = "centre"), "\n\n")
cat(rep("-", width_3), sep = "")
cat(
"\n", formatter_pair("Variables", var_width), formats_t(), formatter_pair("t", t_width),
formats_t(), formatter_pair("df", df_width), formats_t(), formatter_pair("Sig.", p_width), "\n"
)
cat(rep("-", width_3), sep = "")
cat(
"\n", formatter_pair(paste(data$var_names[1], "-", data$var_names[2]), var_width), formats_t(), formatter_pair(data$tstat, t_width), formats_t(), format(data$df, df_width),
formats_t(), formatter_pair(char_p_l, p_width), "\n"
)
cat(rep("-", width_3), sep = "")
} else if (data$alternative == "greater") {
cat(format("Paired Samples Test", width = width_3, justify = "centre"), "\n")
cat(format("-------------------", width = width_3, justify = "centre"), "\n")
cat(format(hyp_null, width = width_3, justify = "centre"), "\n")
cat(format(hyp_ut, width = width_3, justify = "centre"), "\n\n")
cat(rep("-", width_3), sep = "")
cat(
"\n", formatter_pair("Variables", var_width), formats_t(), formatter_pair("t", t_width),
formats_t(), formatter_pair("df", df_width), formats_t(), formatter_pair("Sig.", p_width), "\n"
)
cat(rep("-", width_3), sep = "")
cat(
"\n", formatter_pair(paste(data$var_names[1], "-", data$var_names[2]), var_width), formats_t(), formatter_pair(data$tstat, t_width), formats_t(), format(data$df, df_width),
formats_t(), formatter_pair(char_p_u, p_width), "\n"
)
cat(rep("-", width_3), sep = "")
} else if (data$alternative == "both") {
cat(format("Paired Samples Test", width = width_3, justify = "centre"), "\n")
cat(format("-------------------", width = width_3, justify = "centre"), "\n")
cat(format(hyp_null, width = width_3, justify = "centre"), "\n")
cat(format(hyp_2t, width = width_3, justify = "centre"), "\n\n")
cat(rep("-", width_3), sep = "")
cat(
"\n", formatter_pair("Variables", var_width), formats_t(), formatter_pair("t", t_width),
formats_t(), formatter_pair("df", df_width), formats_t(), formatter_pair("Sig.", p_width), "\n"
)
cat(rep("-", width_3), sep = "")
cat(
"\n", formatter_pair(paste(data$var_names[1], "-", data$var_names[2]), var_width), formats_t(), formatter_pair(data$tstat, t_width), formats_t(), format(data$df, df_width),
formats_t(), formatter_pair(char_p, p_width), "\n"
)
cat(rep("-", width_3), sep = "")
} else {
cat(format(all_null, width = 72, justify = "centre"), "\n\n")
cat(
format("Ha: mean(diff) < 0", width = 24, justify = "centre"), format("Ha: mean(diff) ~= 0", width = 24, justify = "centre"),
format("Ha: mean(diff) > 0", width = 24, justify = "centre"), "\n"
)
cat(format(all_tval, width = 24, justify = "centre"), format(all_tval, width = 24, justify = "centre"), format(all_tval, width = 24, justify = "centre"), "\n")
cat(format(all_p_l, width = 24, justify = "centre"), format(all_p_t, width = 24, justify = "centre"), format(all_p_u, width = 24, justify = "centre"), "\n")
}
}
print_two_ttest <- function(data) {
char_sig <- format(data$sig, digits = 0, nsmall = 4)
char_sig_l <- format(data$sig_l, digits = 0, nsmall = 4)
char_sig_u <- format(data$sig_u, digits = 0, nsmall = 4)
char_sig_pooled <- format(data$sig_pooled, digits = 0, nsmall = 4)
char_sig_pooled_l <- format(data$sig_pooled_l, digits = 0, nsmall = 4)
char_sig_pooled_u <- format(data$sig_pooled_u, digits = 0, nsmall = 4)
# hypothesis heading
hyp_null <- paste0("Ho: mean(", data$levels[1], ") - mean(", data$levels[2], ") = diff = ", "0")
hyp_lt <- paste0("Ha: diff < ", "0")
hyp_2t <- paste0("Ha: diff ~= ", "0")
hyp_ut <- paste0("Ha: diff > ", "0")
conf <- data$confint * 100
conf_char <- paste0("[", conf, "% Conf. Interval]")
# all tests combines
all_p_l <- paste("P < t =", char_sig_pooled_l)
all_p_t <- paste("P > |t| =", char_sig_pooled)
all_p_u <- paste("P > t =", char_sig_pooled_u)
all_s_l <- paste("P < t =", char_sig_l)
all_s_t <- paste("P > |t| =", char_sig)
all_s_u <- paste("P > t =", char_sig_u)
p_tval <- paste0(" t = ", as.character(data$t_pooled))
s_tval <- paste0(" t = ", as.character(data$t_satterthwaite))
# format output
grp_w <- max(nchar(data$levels[1]), nchar(data$levels[2]), nchar("Combined"), 10)
obs_w <- max(nchar("Obs"), nchar(data$obs[1]), nchar(data$obs[2]), nchar(data$n))
mean_w <- max(nchar("Mean"), nchar(data$mean[1]), nchar(data$mean[2]), nchar(data$mean_diff), nchar(data$combined[2]))
se_w <- max(nchar("Std. Err."), nchar(data$se[1]), nchar(data$se[2]), nchar(data$combined[4]), nchar(data$se_dif))
sd_w <- max(nchar("Std. Dev."), nchar(data$sd[1]), nchar(data$sd[2]), nchar(data$combined[3]), nchar(data$sd_dif))
df_w <- max(nchar("DF"), nchar(as.vector(data$df_pooled)), nchar(as.vector(data$df_satterthwaite)))
t_w <- max(nchar("t Value"), nchar(as.vector(data$t_pooled)), nchar(as.vector(data$t_satterthwaite)))
pt_w <- max(
nchar("P > |t|"), nchar(as.vector(char_sig)), nchar(as.vector(char_sig_l)), nchar(as.vector(char_sig_u)),
nchar(as.vector(char_sig_pooled)), nchar(as.vector(char_sig_pooled_l)), nchar(as.vector(char_sig_u))
)
numdf_w <- max(nchar("Num DF"), nchar(as.vector(data$num_df)), nchar(as.vector(data$den_df)))
f_w <- max(nchar("F Value"), nchar(as.vector(data$f)))
fp_w <- max(nchar("P > F"), nchar(as.vector(data$f_sig)))
conf_length <- nchar(data$lower[1]) + nchar(data$upper[1])
if (conf_length > 20) {
conf_width <- conf_length
conf_l_width <- ceiling(conf_width / 2)
conf_u_width <- floor(conf_width / 2)
} else {
conf_width <- 20
conf_l_width <- 10
conf_u_width <- 10
}
w1 <- sum(grp_w, obs_w, mean_w, se_w, sd_w, conf_width, 20)
w2 <- sum(grp_w, 13, 9, df_w, t_w, pt_w, 20)
w3 <- sum(grp_w, 8, numdf_w, numdf_w, f_w, fp_w, 20)
cat(fw("Group Statistics", w = w1), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(
fw("Group", w = grp_w), formats_t(), fw("Obs", w = obs_w), formats_t(),
fw("Mean", w = mean_w), formats_t(), fw("Std. Err.", w = se_w), formats_t(),
fw("Std. Dev.", w = sd_w), formats_t(), conf_char, "\n"
)
cat(rep("-", w1), sep = "", "\n")
cat(
fw((data$levels[1]), w = grp_w), formats_t(), fn(data$obs[1], w = obs_w), formats_t(),
fn(data$mean[1], w = mean_w), formats_t(), fn(data$se[1], w = se_w), formats_t(),
fn(data$sd[1], w = sd_w), formats_t(), fn(data$lower[1], w = conf_l_width), fn(data$upper[1], w = conf_u_width), "\n"
)
cat(
fw((data$levels[2]), w = grp_w), formats_t(), fn(data$obs[2], w = obs_w), formats_t(),
fn(data$mean[2], w = mean_w), formats_t(), fn(data$se[2], w = se_w), formats_t(),
fn(data$sd[2], w = sd_w), formats_t(), fn(data$lower[2], w = conf_l_width), fn(data$upper[2], w = conf_u_width), "\n"
)
cat(rep("-", w1), sep = "", "\n")
cat(
fw("combined", w = grp_w), formats_t(), fn(data$n, w = obs_w), formats_t(),
fn(data$combined[2], w = mean_w), formats_t(), fn(data$combined[4], w = se_w), formats_t(),
fn(data$combined[3], w = sd_w), formats_t(), fn(data$combined[7], w = conf_l_width), fn(data$combined[8], w = conf_u_width), "\n"
)
cat(rep("-", w1), sep = "", "\n")
cat(
fw(("diff"), w = grp_w), formats_t(), fn(data$n, w = obs_w), formats_t(),
fn(data$mean_diff, w = mean_w), formats_t(), fn(as.vector(data$se_dif), w = se_w), formats_t(),
fn(as.vector(data$sd_dif), w = sd_w), formats_t(), fn(as.vector(data$conf_diff[1]), w = conf_l_width),
fn(as.vector(data$conf_diff[2]), w = conf_u_width), "\n"
)
cat(rep("-", w1), sep = "", "\n\n")
if (data$alternative == "less") {
cat(fw("Independent Samples Test", w = w2), "\n")
cat(fw("------------------------", w = w2), "\n\n")
cat(fw(hyp_null, w = w2), "\n")
cat(fw(hyp_lt, w = w2), "\n\n")
cat(rep("-", w2), sep = "", "\n")
cat(
fw("Variable", w = grp_w), formats_t(), fw("Method", w = 13), formats_t(),
fw("Variances", w = 9), formats_t(), fw("DF", w = df_w), formats_t(),
fw("t Value", w = t_w), formats_t(), fw("P < t", w = pt_w), "\n"
)
cat(rep("-", w2), sep = "", "\n")
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Pooled", w = 13), formats_t(),
fw("Equal", w = 9), formats_t(), fn(data$df_pooled, w = df_w), formats_t(),
fw(data$t_pooled, w = t_w), formats_t(), fw(char_sig_pooled_l, w = pt_w), "\n"
)
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Satterthwaite", w = 13), formats_t(),
fw("Unequal", w = 9), formats_t(), fn(data$df_satterthwaite, w = df_w), formats_t(),
fw(data$t_satterthwaite, w = t_w), formats_t(), fw(char_sig_l, w = pt_w), "\n"
)
cat(rep("-", w2), sep = "", "\n\n")
} else if (data$alternative == "greater") {
cat(fw("Independent Samples Test", w = w2), "\n")
cat(fw("------------------------", w = w2), "\n\n")
cat(fw(hyp_null, w = w2), "\n")
cat(fw(hyp_ut, w = w2), "\n\n")
cat(rep("-", w2), sep = "", "\n")
cat(
fw("Variable", w = grp_w), formats_t(), fw("Method", w = 13), formats_t(),
fw("Variances", w = 9), formats_t(), fw("DF", w = df_w), formats_t(),
fw("t Value", w = t_w), formats_t(), fw("P > t", w = pt_w), "\n"
)
cat(rep("-", w2), sep = "", "\n")
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Pooled", w = 13), formats_t(),
fw("Equal", w = 9), formats_t(), fn(data$df_pooled, w = df_w), formats_t(),
fw(data$t_pooled, w = t_w), formats_t(), fw(char_sig_pooled_u, w = pt_w), "\n"
)
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Satterthwaite", w = 13), formats_t(),
fw("Unequal", w = 9), formats_t(), fn(data$df_satterthwaite, w = df_w), formats_t(),
fw(data$t_satterthwaite, w = t_w), formats_t(), fw(char_sig_u, w = pt_w), "\n"
)
cat(rep("-", w2), sep = "", "\n\n")
} else if (data$alternative == "both") {
cat(fw("Independent Samples Test", w = w2), "\n")
cat(fw("------------------------", w = w2), "\n\n")
cat(fw(hyp_null, w = w2), "\n")
cat(fw(hyp_2t, w = w2), "\n\n")
cat(rep("-", w2), sep = "", "\n")
cat(
fw("Variable", w = grp_w), formats_t(), fw("Method", w = 13), formats_t(),
fw("Variances", w = 9), formats_t(), fw("DF", w = df_w), formats_t(),
fw("t Value", w = t_w), formats_t(), fw("P > |t|", w = pt_w), "\n"
)
cat(rep("-", w2), sep = "", "\n")
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Pooled", w = 13), formats_t(),
fw("Equal", w = 9), formats_t(), fn(data$df_pooled, w = df_w), formats_t(),
fw(data$t_pooled, w = t_w), formats_t(), fw(char_sig_pooled, w = pt_w), "\n"
)
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Satterthwaite", w = 13), formats_t(),
fw("Unequal", w = 9), formats_t(), fn(data$df_satterthwaite, w = df_w), formats_t(),
fw(data$t_satterthwaite, w = t_w), formats_t(), fw(char_sig, w = pt_w), "\n"
)
cat(rep("-", w2), sep = "", "\n\n")
} else {
cat(fw("Independent Samples Test", w = 72), "\n")
cat(fw("------------------------", w = w2), "\n\n")
cat(format(hyp_null, width = 72, justify = "centre"), "\n\n")
cat(
format("Ha: diff < 0", width = 24, justify = "centre"), format("Ha: diff ~= 0", width = 24, justify = "centre"),
format("Ha: diff > 0", width = 24, justify = "centre"), "\n\n"
)
cat(
format("", width = 24, justify = "centre"), format("Pooled", width = 24, justify = "centre"),
format("", width = 24, justify = "centre"), "\n"
)
cat(rep("-", 72), sep = "", "\n")
cat(format(p_tval, width = 24, justify = "centre"), format(p_tval, width = 24, justify = "centre"), format(p_tval, width = 24, justify = "centre"), "\n")
cat(format(all_p_l, width = 24, justify = "centre"), format(all_p_t, width = 24, justify = "centre"), format(all_p_u, width = 24, justify = "centre"), "\n\n")
cat(
format("", width = 24, justify = "centre"), format("Satterthwaite", width = 24, justify = "centre"),
format("", width = 24, justify = "centre"), "\n"
)
cat(rep("-", 72), sep = "", "\n")
cat(format(s_tval, width = 24, justify = "centre"), format(s_tval, width = 24, justify = "centre"), format(s_tval, width = 24, justify = "centre"), "\n")
cat(format(all_s_l, width = 24, justify = "centre"), format(all_s_t, width = 24, justify = "centre"), format(all_s_u, width = 24, justify = "centre"), "\n\n\n")
}
cat(fw("Test for Equality of Variances", w = w3), "\n")
cat(rep("-", w3), sep = "", "\n")
cat(
fw("Variable", w = grp_w), formats_t(), fw("Method", w = 8), formats_t(),
fw("Num DF", w = numdf_w), formats_t(), fw("Den DF", w = numdf_w), formats_t(),
fw("F Value", w = f_w), formats_t(), fw("P > F", w = fp_w), "\n"
)
cat(rep("-", w3), sep = "", "\n")
cat(
fw(data$var_y, w = grp_w), formats_t(), fw("Folded F", w = 8), formats_t(),
fn(data$num_df, w = numdf_w), formats_t(), fn(data$den_df, w = numdf_w), formats_t(),
fn(data$f, w = f_w), formats_t(), fn(data$f_sig, w = fp_w), "\n"
)
cat(rep("-", w3), sep = "")
}
print_prop_test <- function(data) {
cwidth <- max(nchar("z"), nchar("DF"), nchar("Pr(|Z| > |z|)"), nchar("Sample Size"), nchar("phat"))
nwidth <- max(nchar(data$z), nchar(data$p0), nchar(data$sig[1]), nchar(data$n), nchar(data$phat))
w1 <- sum(cwidth, nwidth, 6)
lw <- max(nchar("Variable"), nchar(data$varname))
ow <- max(nchar("Observed"), nchar(data$n))
ew <- max(nchar("Expected"), nchar(data$exp))
dw <- max(nchar("% Deviation"), nchar(data$deviation))
rw <- max(nchar("Std. Residuals"), nchar(data$std))
w <- sum(lw, ow, ew, dw, rw, 16)
names <- c(0, 1)
if (data$alt == "less") {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Sample Size", width = cwidth, justify = "left"), formats(), format(data$n, width = nwidth, justify = "right"), "\n")
cat(format("Exp Prop", width = cwidth, justify = "left"), formats(), format(data$p, width = nwidth, justify = "right"), "\n")
cat(format("Obs Prop", width = cwidth, justify = "left"), formats(), format(data$phat, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z < z)", width = cwidth, justify = "left"), formats(), format(data$sig, width = nwidth, justify = "right"), "\n\n")
} else if (data$alt == "greater") {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Sample Size", width = cwidth, justify = "left"), formats(), format(data$n, width = nwidth, justify = "right"), "\n")
cat(format("Exp Prop", width = cwidth, justify = "left"), formats(), format(data$p, width = nwidth, justify = "right"), "\n")
cat(format("Obs Prop", width = cwidth, justify = "left"), formats(), format(data$phat, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z > z)", width = cwidth, justify = "left"), formats(), format(data$sig, width = nwidth, justify = "right"), "\n\n")
} else if (data$alt == "both") {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Sample Size", width = cwidth, justify = "left"), formats(), format(data$n, width = nwidth, justify = "right"), "\n")
cat(format("Exp Prop", width = cwidth, justify = "left"), formats(), format(data$p, width = nwidth, justify = "right"), "\n")
cat(format("Obs Prop", width = cwidth, justify = "left"), formats(), format(data$phat, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(|Z| > |z|)", width = cwidth, justify = "left"), formats(), format(data$sig, width = nwidth, justify = "right"), "\n\n")
} else {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Sample Size", width = cwidth, justify = "left"), formats(), format(data$n, width = nwidth, justify = "right"), "\n")
cat(format("Exp Prop", width = cwidth, justify = "left"), formats(), format(data$p, width = nwidth, justify = "right"), "\n")
cat(format("Obs Prop", width = cwidth, justify = "left"), formats(), format(data$phat, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(|Z| > |z|)", width = cwidth, justify = "left"), formats(), format(unname(data$sig[1]), width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z < z)", width = cwidth, justify = "left"), formats(), format(unname(data$sig[2]), width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z > z)", width = cwidth, justify = "left"), formats(), format(unname(data$sig[3]), width = nwidth, justify = "right"), "\n\n")
}
cat(rep("-", w), sep = "", "\n")
cat(fg("Category", lw), fs(), fg("Observed", ow), fs(), fg("Expected", ew), fs(), fg("% Deviation", dw), fs(), fg("Std. Residuals", rw), "\n")
cat(rep("-", w), sep = "", "\n")
for (i in seq_len(length(data$obs))) {
cat(
fg(names[i], lw), fs(), fg(data$obs[i], ow), fs(), fg(data$exp[i], ew), fs(),
fg(data$deviation[i], dw), fs(), fg(data$std[i], rw), "\n"
)
}
cat(rep("-", w), sep = "", "\n")
}
print_ts_prop_test <- function(data) {
cwidth <- max(nchar("z"), nchar("Pr(|Z| > |z|)"), nchar("Total Observations"))
nwidth <- max(nchar(data$z), nchar(data$sig[1]), nchar(data$n1), nchar(data$n2))
w1 <- sum(cwidth, nwidth, 6)
totobs <- sum(data$n1, data$n2)
if (data$alt == "less") {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Total Observations", width = cwidth, justify = "left"), formats(), format(totobs, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z < z)", width = cwidth, justify = "left"), formats(), format(data$sig, width = nwidth, justify = "right"), "\n\n")
} else if (data$alt == "greater") {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Total Observations", width = cwidth, justify = "left"), formats(), format(totobs, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z > z)", width = cwidth, justify = "left"), formats(), format(data$sig, width = nwidth, justify = "right"), "\n\n")
} else if (data$alt == "both") {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Total Observations", width = cwidth, justify = "left"), formats(), format(totobs, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(|Z| < |z|)", width = cwidth, justify = "left"), formats(), format(data$sig, width = nwidth, justify = "right"), "\n\n")
} else {
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Total Observations", width = cwidth, justify = "left"), formats(), format(totobs, width = nwidth, justify = "right"), "\n")
cat(format("z", width = cwidth, justify = "left"), formats(), format(data$z, width = nwidth, justify = "right"), "\n")
cat(format("Pr(|Z| < |z|)", width = cwidth, justify = "left"), formats(), format(unname(data$sig[1]), width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z < z)", width = cwidth, justify = "left"), formats(), format(unname(data$sig[2]), width = nwidth, justify = "right"), "\n")
cat(format("Pr(Z > z)", width = cwidth, justify = "left"), formats(), format(unname(data$sig[3]), width = nwidth, justify = "right"), "\n\n")
}
}
print_os_vartest <- function(data) {
null_l <- paste0("Ho: sd(", data$var_name, ") >= ", as.character(data$sd))
alt_l <- paste0(" Ha: sd(", data$var_name, ") < ", as.character(data$sd))
null_u <- paste0("Ho: sd(", data$var_name, ") <= ", as.character(data$sd))
alt_u <- paste0("Ha: sd(", data$var_name, ") > ", as.character(data$sd))
null_t <- paste0("Ho: sd(", data$var_name, ") = ", as.character(data$sd))
alt_t <- paste0("Ha: sd(", data$var_name, ") != ", as.character(data$sd))
all_l <- paste("Ha: sd <", as.character(data$sd))
all_u <- paste("Ha: sd >", as.character(data$sd))
all_t <- paste("Ha: sd !=", as.character(data$sd))
char_p_l <- format(data$p_lower, digits = 0, nsmall = 4)
char_p_u <- format(data$p_upper, digits = 0, nsmall = 4)
char_p <- format(data$p_two, digits = 0, nsmall = 4)
all_p_l <- paste("Pr(C < c) =", char_p_l)
if (data$p_lower < 0.5) {
all_p_t <- paste("2 * Pr(C < c) =", char_p)
} else {
all_p_t <- paste("2 * Pr(C > c) =", char_p)
}
all_p_u <- paste("Pr(C > c) =", char_p_u)
all_tval <- paste0(" c = ", as.character(data$chi))
# formatting output
# compute the characters of each output and decide the overall width
var_width <- max(nchar("Variable"), nchar(data$var_name))
obs_width <- max(nchar("Obs"), nchar(data$n))
mean_width <- max(nchar("Mean"), nchar(data$xbar))
se_width <- max(nchar("Std. Err."), nchar(data$se))
sd_width <- max(nchar("Std. Dev."), nchar(data$sigma))
conf_length <- nchar(data$c_lwr) + nchar(data$c_upr)
conf_str <- paste0("[", data$conf * 100, "% Conf. Interval]")
confint_length <- nchar(conf_str)
if (conf_length > confint_length) {
conf_width <- round(conf_length / 2)
} else {
conf_width <- round(confint_length / 2)
}
c_width <- nchar(data$chi)
df_width <- max(nchar("DF"), nchar(data$df))
p_width <- max(nchar("2 Tailed"), nchar(round(data$p_two, 5)))
md_width <- max(nchar("Difference"), nchar(data$mean_diff))
md_length <- nchar(data$mean_diff_l) + nchar(data$mean_diff_u)
width_1 <- sum(var_width, obs_width, mean_width, se_width, sd_width, ceiling(conf_width * 2), 21)
width_2 <- sum(var_width, c_width, df_width, p_width, 12)
all_width <- round(width_1 / 3)
width_3 <- all_width * 3
cat(
format("One-Sample Statistics", width = width_1, justify = "centre"),
"\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(),
formatter_t("Obs", obs_width), formats_t(),
formatter_t("Mean", mean_width),
formats_t(), formatter_t("Std. Err.", se_width), formats_t(),
formatter_t("Std. Dev.", sd_width), formats_t(),
formatter_t(conf_str, conf_width), "\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(data$n, obs_width), formats_t(),
formatter_t(data$xbar, mean_width),
formats_t(), formatter_t(data$se, se_width), formats_t(),
formatter_t(data$sigma, sd_width), formats_t(),
format_cil(data$c_lwr, conf_width),
format_ciu(data$c_upr, conf_width), "\n"
)
cat(rep("-", width_1), sep = "")
# print result
if (data$type == "less") {
cat("\n\n", format("Lower Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"))
cat("\n", format(null_l, width = width_2, justify = "centre"))
cat("\n", format(alt_l, width = width_2, justify = "centre"), "\n\n")
cat(format("Chi-Square Test for Variance", width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(), formatter_t("c", c_width), formats_t(), formatter_t("DF", df_width), formats_t(),
formatter_t("Sig", p_width), formats_t(), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(round(data$chi, 3), c_width), formats_t(),
formatter_t(data$df, df_width), formats_t(),
formatter_t(char_p_l, p_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else if (data$type == "greater") {
cat("\n\n", format("Upper Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"))
cat("\n", format(null_u, width = width_2, justify = "centre"))
cat("\n", format(alt_u, width = width_2, justify = "centre"), "\n\n")
cat(format("Chi-Square Test for Variance", width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(), formatter_t("c", c_width), formats_t(), formatter_t("DF", df_width), formats_t(),
formatter_t("Sig", p_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(round(data$chi, 3), c_width), formats_t(),
formatter_t(data$df, df_width), formats_t(),
formatter_t(char_p_u, p_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else if (data$type == "both") {
cat("\n\n", format("Two Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"))
cat("\n", format(null_t, width = width_2, justify = "centre"))
cat("\n", format(alt_t, width = width_2, justify = "centre"), "\n\n")
cat(format("Chi-Square Test for Variance", width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("Variable", var_width), formats_t(), formatter_t("c", c_width), formats_t(), formatter_t("DF", df_width), formats_t(),
formatter_t("Sig", p_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$var_name, var_width), formats_t(),
formatter_t(round(data$chi, 3), c_width), formats_t(),
formatter_t(data$df, df_width), formats_t(),
formatter_t(char_p, p_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else {
cat("\n\n", format(null_t, width = width_3, justify = "centre"))
cat("\n\n", format(all_l, width = all_width, justify = "centre"), format(all_t, width = all_width, justify = "centre"), format(all_u, width = all_width, justify = "centre"), "\n")
cat(format(all_tval, width = all_width, justify = "centre"), format(all_tval, width = all_width, justify = "centre"), format(all_tval, width = all_width, justify = "centre"))
cat("\n", format(all_p_l, width = all_width, justify = "centre"), format(all_p_t, width = all_width, justify = "centre"), format(all_p_u, width = all_width, justify = "centre"))
}
}
print_chisq_test <- function(x) {
width1 <- nchar("Likelihood Ratio Chi-Square")
width2 <- max(nchar(x$df))
width3 <- max(
nchar(x$chisquare), nchar(x$chisquare_lr), nchar(x$chisquare_mantel_haenszel), nchar(x$chisquare_adjusted), nchar(x$phi_coefficient),
nchar(x$contingency_coefficient), nchar(x$cramers_v)
)
width4 <- 6
widthn <- sum(width1, width2, width3, width4, 12)
if (x$ds == 4) {
cat(format("Chi Square Statistics", width = widthn, justify = "centre"), "\n\n")
cat(
format("Statistics", width = width1, justify = "left"), formats(), format("DF", width = width2, justify = "centre"), formats(),
format("Value", width = width3, justify = "centre"), formats(), format("Prob", width = width4, justify = "centre"), "\n", sep = ""
)
cat(rep("-", widthn), sep = "", "\n")
cat(
format("Chi-Square", width = width1, justify = "left"), formats(), format(x$df, width = width2, justify = "centre"), formats(),
format(x$chisquare, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(x$pval_chisquare, width = width4, justify = "right", nsmall = 4, scientific = F), "\n", sep = ""
)
cat(
format("Likelihood Ratio Chi-Square", width = width1, justify = "left"), formats(), format(x$df, width = width2, justify = "centre"), formats(),
format(x$chisquare_lr, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(x$pval_chisquare_lr, width = width4, justify = "right", nsmall = 4, scientific = F), "\n", sep = ""
)
cat(
format("Continuity Adj. Chi-Square", width = width1, justify = "left"), formats(), format(x$df, width = width2, justify = "right"), formats(),
format(x$chisquare_adjusted, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(x$pval_chisquare_adjusted, width = width4, justify = "right", nsmall = 4, scientific = F), "\n", sep = ""
)
cat(
format("Mantel-Haenszel Chi-Square", width = width1, justify = "left"), formats(), format(x$df, width = width2, justify = "right"), formats(),
format(x$chisquare_mantel_haenszel, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(x$pval_chisquare_mantel_haenszel, width = width4, justify = "right", nsmall = 4, scientific = F), "\n", sep = ""
)
cat(
format("Phi Coefficient", width = width1, justify = "left"), formats(), format(" ", width = width2, justify = "right"), formats(),
format(x$phi_coefficient, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(" ", width = width4, justify = "right"), "\n", sep = ""
)
cat(
format("Contingency Coefficient", width = width1, justify = "left"), formats(), format(" ", width = width2, justify = "right"), formats(),
format(x$contingency_coefficient, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(" ", width = width4, justify = "right"), "\n", sep = ""
)
cat(
format("Cramer's V", width = width1, justify = "left"), formats(), format(" ", width = width2, justify = "right"), formats(),
format(x$cramers_v, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(" ", width = width4, justify = "right"), "\n", sep = ""
)
cat(rep("-", widthn), sep = "", "\n")
} else {
cat(format("Chi Square Statistics", width = widthn, justify = "centre"), "\n\n")
cat(
format("Statistics", width = width1, justify = "left"), formats(), format("DF", width = width2, justify = "centre"), formats(),
format("Value", width = width3, justify = "centre"), formats(), format("Prob", width = width4, justify = "centre"), "\n", sep = ""
)
cat(rep("-", widthn), sep = "", "\n")
cat(
format("Chi-Square", width = width1, justify = "left"), formats(), format(x$df, width = width2, justify = "centre"), formats(),
format(x$chisquare, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(x$pval_chisquare, width = width4, justify = "right", nsmall = 4, scientific = F), "\n", sep = ""
)
cat(
format("Likelihood Ratio Chi-Square", width = width1, justify = "left"), formats(), format(x$df, width = width2, justify = "centre"), formats(),
format(x$chisquare_lr, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(x$pval_chisquare_lr, width = width4, justify = "right", nsmall = 4, scientific = F), "\n", sep = ""
)
cat(
format("Phi Coefficient", width = width1, justify = "left"), formats(), format(" ", width = width2, justify = "right"), formats(),
format(x$phi_coefficient, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(" ", width = width4, justify = "right"), "\n", sep = ""
)
cat(
format("Contingency Coefficient", width = width1, justify = "left"), formats(), format(" ", width = width2, justify = "right"), formats(),
format(x$contingency_coefficient, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(" ", width = width4, justify = "right"), "\n", sep = ""
)
cat(
format("Cramer's V", width = width1, justify = "left"), formats(), format(" ", width = width2, justify = "right"), formats(),
format(x$cramers_v, width = width3, justify = "centre", nsmall = 4, scientific = F), formats(), format(" ", width = width4, justify = "right"), "\n", sep = ""
)
cat(rep("-", widthn), sep = "", "\n")
}
}
print_chisq_gof <- function(data) {
cwidth <- max(nchar("Chi-Square"), nchar("DF"), nchar("Pr > Chi Sq"), nchar("Sample Size"))
nwidth <- max(nchar(data$chisquare), nchar(data$degrees_of_freedom), nchar(data$pvalue),
nchar(data$sample_size))
w1 <- sum(cwidth, nwidth, 6)
lw <- max(nchar("Variable"), nchar(data$categories))
ow <- max(nchar("Observed"), nchar(data$observed_frequency))
ew <- max(nchar("Expected"), nchar(data$expected_frequency))
dw <- max(nchar("% Deviation"), nchar(data$deviation))
rw <- max(nchar("Std. Residuals"), nchar(data$std_residuals))
w <- sum(lw, ow, ew, dw, rw, 16)
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("Chi-Square", width = cwidth, justify = "left"), formats(), format(data$chisquare, width = nwidth, justify = "right"), "\n")
cat(format("DF", width = cwidth, justify = "left"), formats(), format(data$degrees_of_freedom, width = nwidth, justify = "right"), "\n")
cat(format("Pr > Chi Sq", width = cwidth, justify = "left"), formats(), format(data$pvalue, width = nwidth, justify = "right"), "\n")
cat(format("Sample Size", width = cwidth, justify = "left"), formats(), format(data$sample_size, width = nwidth, justify = "right"), "\n\n")
cat(format(paste("Variable:", data$varname), width = w, justify = "centre"), "\n")
cat(rep("-", w), sep = "", "\n")
cat(fg("Category", lw), fs(), fg("Observed", ow), fs(), fg("Expected", ew), fs(), fg("% Deviation", dw), fs(), fg("Std. Residuals", rw), "\n")
cat(rep("-", w), sep = "", "\n")
for (i in seq_len(data$n_levels)) {
cat(
fg(data$categories[i], lw), fs(), fg(data$observed_frequency[i], ow), fs(),
fg(data$expected_frequency[i], ew), fs(), fg(data$deviation[i], dw), fs(),
fg(data$std_residuals[i], rw), "\n"
)
}
cat(rep("-", w), sep = "", "\n")
}
print_runs_test <- function(x) {
cat(
"Runs Test\n",
"Total Cases: ", x$n, "\n",
"Test Value : ", x$threshold, "\n",
"Cases < Test Value: ", x$n_below, "\n",
"Cases > Test Value: ", x$n_above, "\n",
"Number of Runs: ", x$n_runs, "\n",
"Expected Runs: ", x$mean, "\n",
"Variance (Runs): ", x$var, "\n",
"z Statistic: ", x$z, "\n",
"p-value: ", x$p, "\n"
)
}
print_cochran_test <- function(data) {
cwidth <- max(nchar("N"), nchar("Cochran's Q"), nchar("df"), nchar("p value"))
nwidth <- max(nchar(data$n), nchar(data$q), nchar(data$df), nchar(data$pvalue))
w1 <- sum(cwidth, nwidth, 6)
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("N", width = cwidth, justify = "left"), formats(), format(data$n, width = nwidth, justify = "right"), "\n")
cat(format("Cochran's Q", width = cwidth, justify = "left"), formats(), format(data$q, width = nwidth, justify = "right"), "\n")
cat(format("df", width = cwidth, justify = "left"), formats(), format(data$df, width = nwidth, justify = "right"), "\n")
cat(format("p value", width = cwidth, justify = "left"), formats(), format(data$pvalue, width = nwidth, justify = "right"), "\n")
cat(rep("-", w1), sep = "", "\n")
}
print_mcnemar_test <- function(data) {
cwidth1 <- max(
nchar("McNemar's chi2"), nchar("DF"), nchar("Pr > chi2"),
nchar("Exact Pr >= chi2")
)
nwidth1 <- max(
nchar(data$tatistic), nchar(data$df), nchar(data$pvalue),
nchar(data$exactp)
)
w1 <- sum(cwidth1, nwidth1, 6)
cwidth2 <- max(
nchar("Kappa"), nchar("ASE"), nchar("95% Lower Conf Limit"),
nchar("95% Upper Conf Limit")
)
nwidth2 <- max(
nchar(data$kappa), nchar(data$std_err), nchar(data$kappa_cil),
nchar(data$kappa_ciu)
)
w2 <- sum(cwidth2, nwidth2, 6)
cwidth3 <- max(
nchar("Cases"), nchar("Controls"), nchar("Ratio"),
nchar("Odds Ratio")
)
nwidth3 <- max(
nchar(data$cases), nchar(data$controls), nchar(data$ratio),
nchar(data$odratio)
)
w3 <- sum(cwidth3, nwidth3, 6)
tcs <- colSums(data$tbl)
trs <- rowSums(data$tbl)
twidth1 <- 5
twidth2 <- max(nchar(data$tbl[, 1]), nchar(tcs[1]))
twidth3 <- max(nchar(data$tbl[, 2]), nchar(tcs[2]))
twidth4 <- max(5, nchar(sum(trs)))
w4 <- sum(twidth1, twidth2, twidth3, twidth4, 18)
twidth5 <- sum(twidth2, twidth3)
cat(
format(" ", width = twidth1, justify = "centre"), formats(),
format("Controls", width = twidth5, justify = "left"), "\n"
)
cat(rep("-", w4), sep = "", "\n")
cat(
format("Cases", width = twidth1, justify = "centre"), formats(),
format("0", width = twidth2, justify = "centre"), formats(),
format("1", width = twidth3, justify = "centre"), formats(),
format("Total", width = twidth4, justify = "centre"), "\n"
)
cat(rep("-", w4), sep = "", "\n")
cat(
format("0", width = twidth1, justify = "centre"), formats(),
format(data$tbl[1, 1], width = twidth2, justify = "centre"), formats(),
format(data$tbl[1, 2], width = twidth3, justify = "centre"), formats(),
format(trs[1], width = twidth4, justify = "centre"), "\n"
)
cat(
format("1", width = twidth1, justify = "centre"), formats(),
format(data$tbl[2, 1], width = twidth2, justify = "centre"), formats(),
format(data$tbl[2, 2], width = twidth3, justify = "centre"), formats(),
format(trs[2], width = twidth4, justify = "centre"), "\n"
)
cat(rep("-", w4), sep = "", "\n")
cat(
format("Total", width = twidth1, justify = "centre"), formats(),
format(tcs[1], width = twidth2, justify = "centre"), formats(),
format(tcs[2], width = twidth3, justify = "centre"), formats(),
format(sum(trs), width = twidth4, justify = "centre"), "\n"
)
cat(rep("-", w4), sep = "", "\n\n")
cat(format("McNemar's Test", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(format("McNemar's chi2", width = cwidth1, justify = "left"), formats(), format(data$statistic, width = nwidth1, justify = "right"), "\n")
cat(format("DF", width = cwidth1, justify = "left"), formats(), format(data$df, width = nwidth1, justify = "right"), "\n")
cat(format("Pr > chi2", width = cwidth1, justify = "left"), formats(), format(data$pvalue, width = nwidth1, justify = "right"), "\n")
cat(format("Exact Pr >= chi2", width = cwidth1, justify = "left"), formats(), format(data$exactp, width = nwidth1, justify = "right"), "\n")
cat(rep("-", w1), sep = "", "\n\n")
cat(format("Kappa Coefficient", width = w2, justify = "centre"), "\n")
cat(rep("-", w2), sep = "", "\n")
cat(format("Kappa", width = cwidth2, justify = "left"), formats(), format(data$kappa, width = nwidth2, justify = "right"), "\n")
cat(format("ASE", width = cwidth2, justify = "left"), formats(), format(data$std_err, width = nwidth2, justify = "right"), "\n")
cat(format("95% Lower Conf Limit", width = cwidth2, justify = "left"), formats(), format(data$kappa_cil, width = nwidth2, justify = "right"), "\n")
cat(format("95% Upper Conf Limit", width = cwidth2, justify = "left"), formats(), format(data$kappa_ciu, width = nwidth2, justify = "right"), "\n")
cat(rep("-", w2), sep = "", "\n\n")
cat(format("Proportion With Factor", width = w3, justify = "centre"), "\n")
cat(rep("-", w3), sep = "", "\n")
cat(format("cases", width = cwidth3, justify = "left"), formats(), format(data$cases, width = nwidth3, justify = "right"), "\n")
cat(format("controls", width = cwidth3, justify = "left"), formats(), format(data$controls, width = nwidth3, justify = "right"), "\n")
cat(format("ratio", width = cwidth3, justify = "left"), formats(), format(data$ratio, width = nwidth3, justify = "right"), "\n")
cat(format("odds ratio", width = cwidth3, justify = "left"), formats(), format(data$odratio, width = nwidth3, justify = "right"), "\n")
cat(rep("-", w3), sep = "", "\n")
}
print_levene_test <- function(data) {
lw <- max(nchar("Levels"), nchar(data$levs), nchar("Total"))
ow <- max(nchar("Frequency"), nchar(data$lens), nchar(data$n))
ew <- max(nchar("Mean"), nchar(data$avgs), nchar(data$avg))
dw <- max(nchar("Std. Dev."), nchar(data$sds), nchar(data$sd))
w <- sum(lw, ow, ew, dw, 12)
cwidth <- max(
nchar("Statistic"), nchar("Brown and Forsythe"), nchar("Levene"),
nchar("Brown and Forsythe (Trimmed Mean)")
)
nwidth <- max(nchar("Num DF"), nchar(data$n_df))
dwidth <- max(nchar("Den DF"), nchar(data$d_df))
ewidth <- max(nchar("F"), nchar(data$bf), nchar(data$lev), nchar(data$bft))
fwidth <- max(nchar("Pr > F"), nchar(data$p_bf), nchar(data$p_lev), nchar(data$p_bft))
w1 <- sum(cwidth, nwidth, dwidth, ewidth, fwidth, 16)
cat(format("Summary Statistics", width = w, justify = "centre"), "\n")
cat(
fg("Levels", lw), fs(), fg("Frequency", ow), fs(), fg("Mean", ew), fs(),
fg("Std. Dev", dw), "\n"
)
cat(rep("-", w), sep = "", "\n")
for (i in seq_len(length(data$levs))) {
cat(
fg(data$levs[i], lw), fs(), fg(data$lens[i], ow), fs(), fg(data$avgs[i], ew), fs(),
fg(data$sds[i], dw), "\n"
)
}
cat(rep("-", w), sep = "", "\n")
cat(
fg("Total", lw), fs(), fg(data$n, ow), fs(), fg(data$avg, ew), fs(),
fg(data$sd, dw), "\n"
)
cat(rep("-", w), sep = "", "\n\n")
cat(format("Test Statistics", width = w1, justify = "centre"), "\n")
cat(rep("-", w1), sep = "", "\n")
cat(
format("Statistic", width = cwidth, justify = "left"), fs(),
format("Num DF", width = nwidth, justify = "right"), fs(), format("Den DF", width = dwidth, justify = "right"),
fs(), format("F", width = ewidth, justify = "right"), fs(), format("Pr > F", width = fwidth, justify = "right"), "\n"
)
cat(rep("-", w1), sep = "", "\n")
cat(
format("Brown and Forsythe", width = cwidth, justify = "left"), fs(),
format(data$n_df, width = nwidth, justify = "right"), fs(), format(data$d_df, width = dwidth, justify = "right"),
fs(), format(data$bf, width = ewidth, justify = "right"), fs(), format(data$p_bf, width = fwidth, justify = "right"), "\n"
)
cat(
format("Levene", width = cwidth, justify = "left"), fs(),
format(data$n_df, width = nwidth, justify = "right"), fs(), format(data$d_df, width = dwidth, justify = "right"),
fs(), format(data$lev, width = ewidth, justify = "right"), fs(), format(data$p_lev, width = fwidth, justify = "right"), "\n"
)
cat(
format("Brown and Forsythe (Trimmed Mean)", width = cwidth, justify = "left"), fs(),
format(data$n_df, width = nwidth, justify = "right"), fs(), format(data$d_df, width = dwidth, justify = "right"),
fs(), format(data$bft, width = ewidth, justify = "right"), fs(), format(data$p_bft, width = fwidth, justify = "right"), "\n"
)
cat(rep("-", w1), sep = "", "\n")
}
print_var_test <- function(data) {
var_width <- max(nchar("combined"), nchar(data$lev))
obs_width <- max(nchar("Obs"), nchar(data$lens), nchar(data$len))
mean_width <- max(nchar("Mean"), nchar(data$avgs), nchar(data$avg))
se_width <- max(nchar("Std. Err."), nchar(data$ses), nchar(data$se))
sd_width <- max(nchar("Std. Dev."), nchar(data$sds), nchar(data$sd))
width_1 <- sum(var_width, obs_width, mean_width, se_width, sd_width, 16)
rto <- paste0("ratio = sd(", data$lev[1], ") / (", data$lev[2], ")")
nhyp <- "Ho: ratio = 1"
lhyp <- "Ha: ratio < 1"
uhyp <- "Ha: ratio > 1"
char_p_l <- format(data$lower, digits = 0, nsmall = 4)
char_p_u <- format(data$upper, digits = 0, nsmall = 4)
all_p_l <- paste("Pr(F < f) =", char_p_l)
all_p_u <- paste("Pr(F > f) =", char_p_u)
f_width <- nchar(data$f)
df1_width <- max(nchar("Num DF"), nchar(data$n1))
df2_width <- max(nchar("Den DF"), nchar(data$n2))
p_width <- max(nchar("p"), nchar(char_p_l))
width_2 <- sum(f_width, df1_width, df2_width, p_width, 12)
width_3 <- sum(f_width, df1_width, df2_width, 8)
all_width <- sum(nchar(all_p_l), nchar(all_p_u), 4)
cat(
format("Variance Ratio Test", width = width_1, justify = "centre"),
"\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t("Group", var_width), formats_t(),
formatter_t("Obs", obs_width), formats_t(),
formatter_t("Mean", mean_width),
formats_t(), formatter_t("Std. Err.", se_width), formats_t(),
formatter_t("Std. Dev.", sd_width), "\n"
)
cat(rep("-", width_1), sep = "", "\n")
for (i in seq_len(length(data$avgs))) {
cat(
formatter_t(data$lev[i], var_width), formats_t(),
formatter_t(data$lens[i], obs_width), formats_t(),
formatter_t(data$avgs[i], mean_width),
formats_t(), formatter_t(data$ses[i], se_width), formats_t(),
formatter_t(data$sds[i], sd_width), "\n"
)
}
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t("combined", var_width), formats_t(),
formatter_t(data$len, obs_width), formats_t(),
formatter_t(data$avg, mean_width),
formats_t(), formatter_t(data$se, se_width), formats_t(),
formatter_t(data$sd, sd_width), "\n"
)
cat(rep("-", width_1), sep = "")
if (data$type == "less") {
cat("\n\n", format("Lower Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"))
cat("\n", format(rto, width = width_2, justify = "centre"))
cat("\n", format(nhyp, width = width_2, justify = "centre"))
cat("\n", format(lhyp, width = width_2, justify = "centre"), "\n\n")
cat(format("Variance Ratio Test", width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("F", f_width), formats_t(), formatter_t("Num DF", df1_width),
formats_t(), formatter_t("Den DF", df2_width), formats_t(),
formatter_t("p", p_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$f, f_width), formats_t(),
formatter_t(data$n1, df1_width), formats_t(),
formatter_t(data$n2, df2_width), formats_t(),
formatter_t(char_p_l, p_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else if (data$type == "greater") {
cat("\n\n", format("Upper Tail Test", width = width_2, justify = "centre"))
cat("\n", format("---------------", width = width_2, justify = "centre"))
cat("\n", format(nhyp, width = width_2, justify = "centre"))
cat("\n", format(uhyp, width = width_2, justify = "centre"), "\n\n")
cat(format("Variance Ratio Test", width = width_2, justify = "centre"), "\n")
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t("F", f_width), formats_t(), formatter_t("Num DF", df1_width),
formats_t(), formatter_t("Den DF", df2_width), formats_t(),
formatter_t("p", p_width), "\n"
)
cat(rep("-", width_2), sep = "")
cat(
"\n", formatter_t(data$f, f_width), formats_t(),
formatter_t(data$n1, df1_width), formats_t(),
formatter_t(data$n2, df2_width), formats_t(),
formatter_t(char_p_u, p_width), "\n"
)
cat(rep("-", width_2), sep = "")
} else {
cat("\n\n", format("Variance Ratio Test", width = width_1, justify = "centre"), "\n")
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t("F", (width_1 / 3)), formatter_t("Num DF", (width_1 / 3)),
formatter_t("Den DF", (width_1 / 3)), "\n"
)
cat(rep("-", width_1), sep = "")
cat(
"\n", formatter_t(data$f, (width_1 / 3)),
formatter_t(data$n1, (width_1 / 3)),
formatter_t(data$n2, (width_1 / 3)), "\n"
)
cat(rep("-", width_1), sep = "")
cat("\n\n", format("Null & Alternate Hypothesis", width = all_width, justify = "centre"), "\n")
cat(rep("-", all_width), sep = "", "\n")
cat(format(rto, width = all_width, justify = "centre"))
cat("\n", format(nhyp, width = all_width, justify = "centre"))
cat(
"\n\n", format(lhyp, width = (all_width / 2), justify = "centre"),
format(uhyp, width = (all_width / 2), justify = "centre")
)
cat(
"\n", format(all_p_l, width = (all_width / 2), justify = "centre"),
format(all_p_u, width = (all_width / 2), justify = "centre")
)
cat("\n", rep("-", all_width), sep = "")
}
}
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-inferr/xpl-output.R |
# collinearity diagnostics
d_diag_coll <- eventReactive(input$submit_colldiag, {
# validate(need((input$collin_fmla != ''), 'Please specify model'))
data <- final_split$train
})
diag_coll_mod <- eventReactive(input$submit_colldiag, {
k <- lm(input$collin_fmla, data = d_diag_coll())
k
})
result <- eventReactive(input$submit_colldiag, {
if (input$colldiag_use_prev) {
ols_coll_diag(all_use_n())
} else {
ols_coll_diag(diag_coll_mod())
}
})
output$colldiag <- renderPrint({
result()
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_collin.R |
observeEvent(input$sample_data_yes, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_use_sample')
})
file_upload_options <- eventReactive(input$upload_files_yes, {
fluidRow(
column(3, align = 'center',
actionButton(
inputId = 'upload_csv_file',
label = 'CSV',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_xls_file',
label = 'XLS',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_xlsx_file',
label = 'XLSX',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_json_file',
label = 'JSON',
width = '120px'
)
),
column(12, br()),
column(3, align = 'center',
actionButton(
inputId = 'upload_stata_file',
label = 'STATA',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_spss_file',
label = 'SPSS',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_sas_file',
label = 'SAS',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_rds_file',
label = 'RDS',
width = '120px'
)
)
)
})
output$upload_file_links <- renderUI({
file_upload_options()
})
observeEvent(input$upload_csv_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tab_uploadfile', selected = 'tab_upload_csv')
})
observeEvent(input$upload_xls_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_excel')
})
observeEvent(input$upload_xlsx_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_excel')
})
observeEvent(input$upload_json_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_json')
})
observeEvent(input$upload_stata_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_stata')
})
observeEvent(input$upload_spss_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_spss')
})
observeEvent(input$upload_sas_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_sas')
})
observeEvent(input$upload_rds_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_rds')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_dataoptions.R |
# Exit ---------------------------------------------------------------
observe({
if (isTRUE(input$mainpage == "exit")) {
stopApp()
}
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_exit_button.R |
filt_ui <- eventReactive(input$button_filt_yes, {
fluidRow(
column(12, align = 'center',
selectInput(
inputId = 'dplyr_filter',
label = 'Filter:',
choices = '',
selected = '',
width = '120px'
)
),
column(12, align = 'center',
selectInput(
inputId = 'dplyr_filt_op',
label = 'Select Operator',
choices = c('<', '>', '<=', '>=', '=='),
selected = '',
width = '120px'
)
),
column(12, align = 'center',
textInput(
inputId = 'dplyr_filt_val',
label = 'Value',
value = '20',
width = '120px'
)
),
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dply_filt', label = 'Filter', width = '120px', icon = icon('check')),
bsTooltip("submit_dply_filt", "Click here to filter data.",
"bottom", options = list(container = "body"))
)
)
})
output$filt_render <- renderUI(
filt_ui()
)
observeEvent(input$button_filt_yes, {
updateSelectInput(
session,
inputId = "dplyr_filter",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
})
observeEvent(input$submit_dply_selvar, {
updateSelectInput(
session,
inputId = "dplyr_filter",
choices = names(finalsel()),
selected = names(finalsel())
)
})
filt_data <- reactiveValues(p = NULL)
observeEvent(input$submit_dply_selvar, {
filt_data$p <- final_sel$a
})
observeEvent(input$button_filt_yes, {
filt_data$p <- final_sel$a
})
observeEvent(input$submit_dply_filt, {
filt_data$p <- filt_data$p %>%
filter_(paste(input$dplyr_filter, input$dplyr_filt_op, input$dplyr_filt_val))
})
observeEvent(input$button_filt_no, {
filt_data$p <- final_sel$a
})
observeEvent(input$button_filt_no, {
updateNavbarPage(session, 'mainpage', selected = 'tab_scr')
updateNavlistPanel(session, 'navlist_trans', 'tab_screen')
})
filttrans <- eventReactive(input$button_filt_yes, {
fluidRow(
column(6, align = 'left',
actionButton(inputId='filt2dvarsel', label="Select Variables", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='filt2screen', label="Screen Data", icon = icon("long-arrow-right"))
)
)
})
output$filt_trans <- renderUI({
filttrans()
})
observeEvent(input$filt2dvarsel, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_selvar')
})
observeEvent(input$filt2screen, {
updateNavbarPage(session, 'mainpage', selected = 'tab_scr')
updateNavlistPanel(session, 'navlist_trans', 'tab_screen')
})
# filtered <- eventReactive(input$submit_dply_filt, {
# k <- final_sel() %>%
# filter_(paste(input$dplyr_filter, input$dplyr_filt_op, input$dplyr_filt_val))
# k
# # k <- filter_(final_sel(), paste(input$dplyr_filter, input$dplyr_filt_op, input$dplyr_filt_val))
# # k
# }) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_filter.R |
# Breusch Pagan Test
d_het_bp <- eventReactive(input$submit_het_bp, {
# validate(need((input$het_bp_fmla != ''), 'Please specify model'))
data <- final_split$train
})
mnames <- eventReactive(input$submit_regress, {
k <- colnames(model.matrix(model()$model)[, -1])
k
})
het_bp_mod <- eventReactive(input$submit_het_bp, {
k <- lm(input$het_bp_fmla, data = d_het_bp())
k
})
bpvars <- eventReactive(input$submit_het_bp, {
k <- colnames(model.matrix(het_bp_mod())[, -1])
k
})
observe({
if (input$het_bp_fv == FALSE) {
if (input$bp_use_prev) {
updateSelectInput(session, inputId = "het_bp_vars", choices = mnames(),
selected = mnames()[1])
} else {
updateSelectInput(session, inputId = "het_bp_vars", choices = bpvars(),
selected = bpvars()[1])
}
} else {
if (input$bp_use_prev) {
updateSelectInput(session, inputId = "het_bp_vars", choices = mnames(),
selected = '')
} else {
updateSelectInput(session, inputId = "het_bp_vars", choices = bpvars(),
selected = '')
}
}
})
result_bp <- eventReactive(input$submit_het_bp, {
if (input$het_bp_fv == FALSE) {
if (input$bp_use_prev) {
ols_test_breusch_pagan(all_use_n(), as.logical(input$het_bp_fv), as.logical(input$het_bp_rhs),
as.logical(input$het_bp_mult), as.character(input$het_bp_padj), input$het_bp_vars)
} else {
ols_test_breusch_pagan(het_bp_mod(), as.logical(input$het_bp_fv), as.logical(input$het_bp_rhs),
as.logical(input$het_bp_mult), as.character(input$het_bp_padj), input$het_bp_vars)
}
} else {
if (input$bp_use_prev) {
ols_test_breusch_pagan(all_use_n(), as.logical(input$het_bp_fv), as.logical(input$het_bp_rhs),
as.logical(input$het_bp_mult), as.character(input$het_bp_padj))
} else {
ols_test_breusch_pagan(het_bp_mod(), as.logical(input$het_bp_fv), as.logical(input$het_bp_rhs),
as.logical(input$het_bp_mult), as.character(input$het_bp_padj))
}
}
})
output$het_bp_out <- renderPrint({
result_bp()
})
# f test for heteroskedasticity
d_het_f <- eventReactive(input$submit_het_f, {
# validate(need((input$het_f_fmla != ''), 'Please specify model'))
data <- final_split$train
})
het_f_mod <- eventReactive(input$submit_het_f, {
k <- lm(input$het_f_fmla, data = d_het_f())
k
})
fvars <- eventReactive(input$submit_het_f, {
k <- colnames(model.matrix(het_f_mod())[, -1])
k
})
observe({
if (input$het_f_fv == FALSE) {
if (input$f_use_prev) {
updateSelectInput(session, inputId = "het_f_vars", choices = mnames(),
selected = mnames()[1])
} else {
updateSelectInput(session, inputId = "het_f_vars", choices = fvars(),
selected = fvars()[1])
}
} else {
if (input$f_use_prev) {
updateSelectInput(session, inputId = "het_f_vars", choices = mnames(),
selected = '')
} else {
updateSelectInput(session, inputId = "het_f_vars", choices = fvars(),
selected = '')
}
}
})
result_f <- eventReactive(input$submit_het_f, {
if (input$het_f_fv == FALSE) {
if (input$f_use_prev) {
ols_test_f(all_use_n(), as.logical(input$het_f_fv), as.logical(input$het_f_rhs),
input$het_f_vars)
} else {
ols_test_f(het_f_mod(), as.logical(input$het_f_fv), as.logical(input$het_f_rhs),
input$het_f_vars)
}
} else {
if (input$f_use_prev) {
ols_test_f(all_use_n(), as.logical(input$het_f_fv), as.logical(input$het_f_rhs))
} else {
ols_test_f(het_f_mod(), as.logical(input$het_f_fv), as.logical(input$het_f_rhs))
}
}
})
output$het_f_out <- renderPrint({
result_f()
})
# score test
d_het_score <- eventReactive(input$submit_het_score, {
# validate(need((input$het_score_fmla != ''), 'Please specify model'))
data <- final_split$train
})
het_score_mod <- eventReactive(input$submit_het_score, {
k <- lm(input$het_score_fmla, data = d_het_score())
k
})
scorevars <- eventReactive(input$submit_het_score, {
k <- colnames(model.matrix(het_score_mod())[, -1])
k
})
observe({
if (input$het_score_fv == FALSE) {
if (input$score_use_prev) {
updateSelectInput(session, inputId = "het_score_vars", choices = mnames(),
selected = mnames()[1])
} else {
updateSelectInput(session, inputId = "het_score_vars", choices = scorevars(),
selected = scorevars()[1])
}
} else {
if (input$score_use_prev) {
updateSelectInput(session, inputId = "het_score_vars", choices = mnames(),
selected = '')
} else {
updateSelectInput(session, inputId = "het_score_vars", choices = scorevars(),
selected = '')
}
}
})
result_score <- eventReactive(input$submit_het_score, {
if (input$het_score_fv == FALSE) {
if (input$score_use_prev) {
ols_test_score(all_use_n(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs),
input$het_score_vars)
} else {
ols_test_score(het_score_mod(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs),
input$het_score_vars)
}
} else {
if (input$score_use_prev) {
ols_test_score(all_use_n(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs))
} else {
ols_test_score(het_score_mod(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs))
}
}
})
output$het_score_out <- renderPrint({
result_score()
})
# output$het_score_out <- renderPrint({
# if (input$het_score_fv == FALSE) {
# if (input$score_use_prev) {
# ols_score_test(all_use_n(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs),
# input$het_score_vars)
# } else {
# ols_score_test(het_score_mod(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs),
# input$het_score_vars)
# }
# } else {
# if (input$score_use_prev) {
# ols_score_test(all_use_n(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs))
# } else {
# ols_score_test(het_score_mod(), as.logical(input$het_score_fv), as.logical(input$het_score_rhs))
# }
# }
# })
# bartlett test
observe({
updateSelectInput(session,inputId = "var_bartest",
choices = names(data()), selected = '')
updateSelectInput(session,inputId = "var_bartestg1",
choices = names(data()), selected = '')
updateSelectInput(session,inputId = "var_bartestg2",
choices = names(data()), selected = '')
})
observeEvent(input$button_split_no, {
f_data <- final_split$train[, sapply(final_split$train, is.factor)]
num_data <- final_split$train[, sapply(final_split$train, is.numeric)]
if (is.null(dim(num_data))) {
k <- final_split$train %>% map(is.numeric) %>% unlist()
j <- names(which(k == TRUE))
numdata <- tibble::as_data_frame(num_data)
colnames(numdata) <- j
updateSelectInput(session, 'var_bartest',
choices = names(numdata), selected = names(numdata))
updateSelectInput(session, 'var_bartestg1',
choices = names(numdata), selected = names(numdata))
} else if (ncol(num_data) < 1) {
updateSelectInput(session, 'var_bartest',
choices = '', selected = '')
updateSelectInput(session, 'var_bartestg1',
choices = '', selected = '')
} else {
updateSelectInput(session, 'var_bartest', choices = names(num_data))
updateSelectInput(session, 'var_bartestg1', choices = names(num_data))
}
if (is.null(dim(f_data))) {
k <- final_split$train %>% map(is.factor) %>% unlist()
j <- names(which(k == TRUE))
fdata <- tibble::as_data_frame(f_data)
colnames(fdata) <- j
updateSelectInput(session, 'var_bartestg2',
choices = names(fdata), selected = names(fdata))
} else if (ncol(f_data) < 1) {
updateSelectInput(session, 'var_bartestg2',
choices = '', selected = '')
} else {
updateSelectInput(session, 'var_bartestg2', choices = names(f_data))
}
})
observeEvent(input$submit_part_train_per, {
f_data <- final_split$train[, sapply(final_split$train, is.factor)]
num_data <- final_split$train[, sapply(final_split$train, is.numeric)]
if (is.null(dim(num_data))) {
k <- final_split$train %>% map(is.numeric) %>% unlist()
j <- names(which(k == TRUE))
numdata <- tibble::as_data_frame(num_data)
colnames(numdata) <- j
updateSelectInput(session, 'var_bartest',
choices = names(numdata), selected = names(numdata))
updateSelectInput(session, 'var_bartestg1',
choices = names(numdata), selected = names(numdata))
} else if (ncol(num_data) < 1) {
updateSelectInput(session, 'var_bartest',
choices = '', selected = '')
updateSelectInput(session, 'var_bartestg1',
choices = '', selected = '')
} else {
updateSelectInput(session, 'var_bartest', choices = names(num_data))
updateSelectInput(session, 'var_bartestg1', choices = names(num_data))
}
if (is.null(dim(f_data))) {
k <- final_split$train %>% map(is.factor) %>% unlist()
j <- names(which(k == TRUE))
fdata <- tibble::as_data_frame(f_data)
colnames(fdata) <- j
updateSelectInput(session, 'var_bartestg2',
choices = names(fdata), selected = names(fdata))
} else if (ncol(f_data) < 1) {
updateSelectInput(session, 'var_bartestg2',
choices = '', selected = '')
} else {
updateSelectInput(session, 'var_bartestg2', choices = names(f_data))
}
})
d_bartest <- eventReactive(input$submit_bartest, {
# validate(need((input$var_bartest != ''), 'Please select variables'))
req(input$var_bartest)
data <- final_split$train[, c(input$var_bartest)]
ols_test_bartlett(data)
})
output$bartest_out <- renderPrint({
d_bartest()
})
d_bartestg <- eventReactive(input$submit_bartestg, {
# validate(need((input$var_bartestg1 != '' & input$var_bartestg2 != ''), 'Please select variables'))
req(input$var_bartestg1)
req(input$var_bartestg2)
data <- final_split$train[, c(input$var_bartestg1, input$var_bartestg2)]
k <- ols_test_bartlett(data[, 1], group_var = data[, 2])
k
})
output$bartestg_out <- renderPrint({
d_bartestg()
})
d_bartmod <- eventReactive(input$submit_bartestf, {
# validate(need((input$bartest_fmla != ''), 'Please specify a model.'))
data <- final_split$train
k <- lm(input$bartest_fmla, data = data)
ols_test_bartlett(k)
})
# bartmod <- reactive({
# k
# })
output$bartestf_out <- renderPrint({
d_bartmod()
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_hetero.R |
eda_menu <- eventReactive(input$click_descriptive, {
fluidRow(
column(12),
br(),
column(12, align = 'center',
h5('What do you want to do?')
),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate detailed descriptive statistics for a continuous variable: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_1',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate frequency distribution of a categorical variable: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_2',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate frequency distribution a continuous variable: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_3',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate two way table of categorical variables: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_4',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate multiple one way tables of categorical variables: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_5',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate multiple two way tables of categorical variables: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_6',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Generate grouped summary statistics: ')
),
column(2, align = 'left',
actionButton(
inputId = 'button_7',
label = 'Click Here',
width = '120px'
)
),
column(3)
)
})
output$eda_options <- renderUI({
eda_menu()
})
observeEvent(input$click_descriptive, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_eda_home')
})
observeEvent(input$click_distributions, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_dist_home')
})
observeEvent(input$click_inference, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_infer_home')
})
observeEvent(input$click_model, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_model_home')
})
observeEvent(input$click_visualize, {
updateNavbarPage(session, 'mainpage', selected = 'tab_viz_home')
updateNavlistPanel(session, 'navlist_vizmenu', 'tab_home_viz')
})
# observeEvent(input$click_visualize, {
# updateNavbarPage(session, 'mainpage', selected = 'tab_viz_lib')
# })
observeEvent(input$button_1, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_summary')
})
observeEvent(input$button_2, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_fqual')
})
observeEvent(input$button_3, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_fquant')
})
observeEvent(input$button_4, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_cross')
})
observeEvent(input$button_5, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_mult1')
})
observeEvent(input$button_6, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_mult2')
})
observeEvent(input$button_7, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_gsummary')
})
observeEvent(input$button_dist_home_1, {
updateNavbarPage(session, 'mainpage', selected = 'tab_dist')
updateNavlistPanel(session, 'navlist_dist', 'tab_norm')
})
observeEvent(input$button_dist_home_2, {
updateNavbarPage(session, 'mainpage', selected = 'tab_dist')
updateNavlistPanel(session, 'navlist_dist', 'tab_t')
})
observeEvent(input$button_dist_home_3, {
updateNavbarPage(session, 'mainpage', selected = 'tab_dist')
updateNavlistPanel(session, 'navlist_dist', 'tab_chisq')
})
observeEvent(input$button_dist_home_4, {
updateNavbarPage(session, 'mainpage', selected = 'tab_dist')
updateNavlistPanel(session, 'navlist_dist', 'tab_binom')
})
observeEvent(input$button_dist_home_5, {
updateNavbarPage(session, 'mainpage', selected = 'tab_dist')
updateNavlistPanel(session, 'navlist_dist', 'tab_f')
})
observeEvent(input$button_infer_home_1, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_infer1_home')
})
observeEvent(input$button_infer_home_2, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_infer2_home')
})
observeEvent(input$button_infer_home_3, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_infer3_home')
})
# links for inferential statistics
observeEvent(input$inf_menu_1_t, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_ttest')
})
observeEvent(input$inf_menu_1_var, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_osvartest')
})
observeEvent(input$inf_menu_1_prop, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_osproptest')
})
observeEvent(input$inf_menu_1_chi, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_chigof')
})
observeEvent(input$inf_menu_1_runs, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_runs')
})
observeEvent(input$inf_menu_2_it, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_indttest')
})
observeEvent(input$inf_menu_2_pt, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_indttest')
})
observeEvent(input$inf_menu_2_binom, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_binomtest')
})
observeEvent(input$inf_menu_2_var, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_tsvartest')
})
observeEvent(input$inf_menu_2_prop, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_tsproptest')
})
observeEvent(input$inf_menu_2_chi, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_chict')
})
observeEvent(input$inf_menu_2_mcnemar, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_mcnemar')
})
observeEvent(input$inf_menu_3_anova, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_anova')
})
observeEvent(input$inf_menu_3_levene, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_levtest')
})
observeEvent(input$inf_menu_3_cochran, {
updateNavbarPage(session, 'mainpage', selected = 'tab_infer')
updateNavlistPanel(session, 'navlist_infer', 'tab_cochran')
})
## visulization links
observeEvent(input$click_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_viz_home')
updateNavlistPanel(session, 'navlist_vizmenu', 'tab_viz_base')
})
observeEvent(input$click_ggplot2, {
updateNavbarPage(session, 'mainpage', selected = 'tab_viz_home')
updateNavlistPanel(session, 'navlist_vizmenu', 'tab_viz_gg')
})
observeEvent(input$click_prh, {
updateNavbarPage(session, 'mainpage', selected = 'tab_viz_home')
updateNavlistPanel(session, 'navlist_vizmenu', 'tab_viz_others')
})
## link viz libraries to tabs
observeEvent(input$click_bar_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_bar')
})
observeEvent(input$click_bar2_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_bar2')
})
observeEvent(input$click_box_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_box')
})
observeEvent(input$click_box2_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_box2')
})
observeEvent(input$click_line_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_line')
})
observeEvent(input$click_scatter_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_scatter')
})
observeEvent(input$click_hist_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_hist')
})
observeEvent(input$click_pie_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_pie')
})
observeEvent(input$click_pie2_base, {
updateNavbarPage(session, 'mainpage', selected = 'tab_base')
updateNavlistPanel(session, 'navlist_base', 'tab_pie3d')
})
## ggplot2
observeEvent(input$click_bar_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gbar')
})
observeEvent(input$click_bar2_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gbar2')
})
observeEvent(input$click_box_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gbox')
})
observeEvent(input$click_box2_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gbox2')
})
observeEvent(input$click_line_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gline1')
})
observeEvent(input$click_scatter_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gscatter')
})
observeEvent(input$click_hist_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_ghist')
})
observeEvent(input$click_pie_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gpie')
})
observeEvent(input$click_line2_gg, {
updateNavbarPage(session, 'mainpage', selected = 'tab_gg')
updateNavlistPanel(session, 'navlist_gg', 'tab_gline2')
})
## others
observeEvent(input$click_bar_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_bar_plot_1')
})
observeEvent(input$click_bar2_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_bar_plot_2')
})
observeEvent(input$click_box_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_box_plot_1')
})
observeEvent(input$click_box2_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_box_plot_2')
})
observeEvent(input$click_line_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_line_prh')
})
observeEvent(input$click_scatter_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_scatter_prh')
})
observeEvent(input$click_hist_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_hist_prh')
})
observeEvent(input$click_pie_others, {
updateNavbarPage(session, 'mainpage', selected = 'tab_others')
updateNavlistPanel(session, 'navlist_others', 'tab_pie_prh')
})
## model links
observeEvent(input$model_reg_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_regress')
})
observeEvent(input$model_varsel_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_var_select')
})
observeEvent(input$model_resdiag_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_res_diag')
})
observeEvent(input$model_het_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_hetero')
})
observeEvent(input$model_coldiag_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_regcollin')
})
observeEvent(input$model_infl_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_inflobs')
})
observeEvent(input$model_fit_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_mfit')
})
observeEvent(input$model_varcontrib_click, {
updateNavbarPage(session, 'mainpage', selected = 'tab_reg')
updateNavlistPanel(session, 'navlist_reg', 'tab_regvarcont')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_home.R |
output$ui_inflobslink <- renderUI({
if (input$inflobs_select == "Cook's D Bar Plot") {
fluidRow(
column(6, align = 'left',
h4("Cook's D Bar Plot"),
p("Bar Plot of Cook's distance to detect observations that strongly influence fitted values of the model.")
),
column(6, align = 'right',
actionButton(inputId='cdbplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_cooksd_bar.html', '_blank')")
)
)
} else if (input$inflobs_select == "Cook's D Chart") {
fluidRow(
column(6, align = 'left',
h4("Cook's D Chart"),
p("Chart of Cook's distance to detect observations that strongly influence fitted values of the model.")
),
column(6, align = 'right',
actionButton(inputId='cdclink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_cooksd_chart.html', '_blank')")
)
)
} else if (input$inflobs_select == "DFBETAs Panel") {
fluidRow(
column(6, align = 'left',
h4('DFBETAS Panel'),
p("Panel of plots to detect influential observations using DFBETAs.")
),
column(6, align = 'right',
actionButton(inputId='dfblink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_dfbetas.html', '_blank')")
)
)
} else if (input$inflobs_select == "DFFITS Plot") {
fluidRow(
column(6, align = 'left',
h4('DFFITS Plot'),
p("Plot for detecting influential observations using DFFITS.")
),
column(6, align = 'right',
actionButton(inputId='dfitslink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_dffits.html', '_blank')")
)
)
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
fluidRow(
column(6, align = 'left',
h4('Deleted Studentized Residual vs Predicted Plot'),
p('Plot for detecting outliers.')
),
column(6, align = 'right',
actionButton(inputId='dsrvsplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_stud_fit.html', '_blank')")
)
)
} else if (input$inflobs_select == "Hadi Plot") {
fluidRow(
column(6, align = 'left',
h4('Hadi Plot'),
p("Plot for detecting outliers based on Hadi's influence measure.")
),
column(6, align = 'right',
actionButton(inputId='hadiplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_hadi.html', '_blank')")
)
)
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
fluidRow(
column(6, align = 'left',
h4('Studentized Residual vs Leverage Plot'),
p('Graph for detecting influential observations.')
),
column(6, align = 'right',
actionButton(inputId='srvslev1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_lev.html', '_blank')")
)
)
} else if (input$inflobs_select == "Studentized Residual Plot") {
fluidRow(
column(6, align = 'left',
h4('Studentized Residual Plot'),
p('Graph for identifying outliers.')
),
column(6, align = 'right',
actionButton(inputId='srplot1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_stud.html', '_blank')")
)
)
} else if (input$inflobs_select == "Studentized Residual Chart") {
fluidRow(
column(6, align = 'left',
h4('Studentized Residual Chart'),
p('Graph for identifying outliers.')
),
column(6, align = 'right',
actionButton(inputId='srchart1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_stand.html', '_blank')")
)
)
} else if (input$inflobs_select == "Potential Residual Plot") {
fluidRow(
column(6, align = 'left',
h4('Potential Residual Plot'),
p('Graph for identifying outliers.')
),
column(6, align = 'right',
actionButton(inputId='potreslink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_pot.html', '_blank')")
)
)
}
})
output$ui_inflobsfmla <- renderUI({
if (input$inflobs_select == "Cook's D Bar Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("cooksb_fmla", label = '', width = '660px',
value = ""),
bsTooltip("cooksb_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Potential Residual Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("potres_fmla", label = '', width = '660px',
value = ""),
bsTooltip("potres_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Cook's D Chart") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("cooksc_fmla", label = '', width = '660px',
value = ""),
bsTooltip("cooksc_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "DFBETAs Panel") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("dfbetas_fmla", label = '', width = '660px',
value = ""),
bsTooltip("dfbetas_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("dsresp_fmla", label = '', width = '660px',
value = ""),
bsTooltip("dsresp_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "DFFITS Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("dffits_fmla", label = '', width = '660px',
value = ""),
bsTooltip("dffits_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("sreslev_fmla", label = '', width = '660px',
value = ""),
bsTooltip("sreslev_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Studentized Residual Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("studres_fmla", label = '', width = '660px',
value = ""),
bsTooltip("studres_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Studentized Residual Chart") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("sreschart_fmla", label = '', width = '660px',
value = ""),
bsTooltip("sreschart_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$inflobs_select == "Hadi Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("hadiplot_fmla", label = '', width = '660px',
value = ""),
bsTooltip("hadiplot_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
}
})
output$ui_inflobssubmit <- renderUI({
if (input$inflobs_select == "Cook's D Bar Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_cooksb', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_cooksb", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Potential Residual Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_potres_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_potres_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Cook's D Chart") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_cooksc', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_cooksc", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "DFBETAs Panel") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dfbetas', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_dfbetas", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dsresp_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_dsresp_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "DFFITS Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dffits', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_dffits", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_sreslev_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_sreslev_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Studentized Residual Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_sresp_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_cprp_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Studentized Residual Chart") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_sreschart_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_sreschart_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$inflobs_select == "Hadi Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_hadiplot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_hadiplot", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
}
})
output$ui_inflobsprev <- renderUI({
if (input$inflobs_select == "Cook's D Bar Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'cdbp_use_prev', label = '',
value = FALSE),
bsTooltip("cdbp_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Potential Residual Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'potres_use_prev', label = '',
value = FALSE),
bsTooltip("potres_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Cook's D Chart") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'cooksc_use_prev', label = '',
value = FALSE),
bsTooltip("cooksc_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "DFBETAs Panel") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'dfb_use_prev', label = '',
value = FALSE),
bsTooltip("dfb_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'dsresp_use_prev', label = '',
value = FALSE),
bsTooltip("dsresp_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "DFFITS Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'dfits_use_prev', label = '',
value = FALSE),
bsTooltip("dfits_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'sreslev_use_prev', label = '',
value = FALSE),
bsTooltip("sreslev_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Studentized Residual Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'sres_use_prev', label = '',
value = FALSE),
bsTooltip("sres_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Studentized Residual Chart") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'sreschart_use_prev', label = '',
value = FALSE),
bsTooltip("sreschart_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$inflobs_select == "Hadi Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'hadip_use_prev', label = '',
value = FALSE),
bsTooltip("hadip_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
}
})
d_potres_mod <- eventReactive(input$submit_potres_plot, {
# validate(need((input$potres_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_dsresp_mod <- eventReactive(input$submit_dsresp_plot, {
# validate(need((input$dsresp_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_sreslev_mod <- eventReactive(input$submit_sreslev_plot, {
# validate(need((input$sreslev_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_sres_mod <- eventReactive(input$submit_sresp_plot, {
# validate(need((input$studres_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_sreschart_mod <- eventReactive(input$submit_sreschart_plot, {
# validate(need((input$sreschart_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_dffits <- eventReactive(input$submit_dffits, {
# validate(need((input$dffits_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_cdbp <- eventReactive(input$submit_cooksb, {
# validate(need((input$cooksb_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_cdc <- eventReactive(input$submit_cooksc, {
# validate(need((input$cooksc_fmla != ''), 'Please specify model'))
data <- final_split$train
})
d_hadi <- eventReactive(input$submit_hadiplot, {
# validate(need((input$hadiplot_fmla != ''), 'Please specify model'))
data <- final_split$train
})
potres_mod <- reactive({
k <- lm(input$potres_fmla, data = d_potres_mod())
k
})
dsresp_mod <- reactive({
k <- lm(input$dsresp_fmla, data = d_dsresp_mod())
k
})
sreslev_mod <- reactive({
k <- lm(input$sreslev_fmla, data = d_sreslev_mod())
k
})
sres_mod <- reactive({
k <- lm(input$studres_fmla, data = d_sres_mod())
k
})
sreschart_mod <- reactive({
k <- lm(input$sreschart_fmla, data = d_sreschart_mod())
k
})
dfits_mod <- eventReactive(input$submit_dffits, {
k <- lm(input$dffits_fmla, data = d_dffits())
k
})
cdbp_mod <- eventReactive(input$submit_cooksb, {
k <- lm(input$cooksb_fmla, data = d_cdbp())
k
})
cdc_mod <- eventReactive(input$submit_cooksc, {
k <- lm(input$cooksc_fmla, data = d_cdc())
k
})
hadi_mod <- eventReactive(input$submit_hadiplot, {
k <- lm(input$hadiplot_fmla, data = d_hadi())
k
})
d_dfbetas <- eventReactive(input$submit_dfbetas, {
# validate(need((input$dfbetas_fmla != ''), 'Please specify model'))
data <- final_split$train
})
dfbetas_mod <- eventReactive(input$submit_dfbetas, {
k <- lm(input$dfbetas_fmla, data = d_dfbetas())
k
})
plot_n <- reactive({
if (input$dfb_use_prev) {
(length(all_use_n()$coefficients) * 500) / 2
} else {
(length(dfbetas_mod()$coefficients) * 500) / 2
}
})
output$ui_inflobsplot <- renderUI({
if (input$inflobs_select == "Cook's D Bar Plot") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "Potential Residual Plot") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "Cook's D Chart") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "DFBETAs Panel") {
column(12, align = 'center', plotOutput('inflobsplot', height = paste0(plot_n(), 'px')))
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "DFFITS Plot") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "Studentized Residual Plot") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "Studentized Residual Chart") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
} else if (input$inflobs_select == "Hadi Plot") {
column(12, align = 'center', plotOutput('inflobsplot', height = '500px'))
}
})
output$ui_inflobsprint <- renderUI({
if (input$inflobs_select == "Cook's D Bar Plot") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Potential Residual Plot") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Cook's D Chart") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "DFBETAs Panel") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "DFFITS Plot") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Studentized Residual Plot") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Studentized Residual Chart") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
} else if (input$inflobs_select == "Hadi Plot") {
column(12, align = 'center', verbatimTextOutput('inflobs'))
}
})
result_cdbp <- eventReactive(input$submit_cooksb, {
if (input$cdbp_use_prev) {
ols_plot_cooksd_bar(all_use_n())
} else {
ols_plot_cooksd_bar(cdbp_mod())
}
})
result_cdc <- eventReactive(input$submit_cooksc, {
if (input$cooksc_use_prev) {
ols_plot_cooksd_chart(all_use_n())
} else {
ols_plot_cooksd_chart(cdc_mod())
}
})
result_potres <- eventReactive(input$submit_potres_plot, {
if(input$potres_use_prev) {
ols_plot_resid_pot(all_use_n())
} else {
ols_plot_resid_pot(potres_mod())
}
})
result_dfbetas <- eventReactive(input$submit_dfbetas, {
if (input$dfb_use_prev) {
ols_plot_dfbetas(all_use_n())
} else {
ols_plot_dfbetas(dfbetas_mod())
}
})
result_dsrvsp <- eventReactive(input$submit_dsresp_plot, {
if(input$dsresp_use_prev) {
ols_plot_resid_stud_fit(all_use_n())
} else {
ols_plot_resid_stud_fit(dsresp_mod())
}
})
result_dffits <- eventReactive(input$submit_dffits, {
if (input$dfits_use_prev) {
ols_plot_dffits(all_use_n())
} else {
ols_plot_dffits(dfits_mod())
}
})
result_srvslev <- eventReactive(input$submit_sreslev_plot, {
if(input$sreslev_use_prev) {
ols_plot_resid_lev(all_use_n())
} else {
ols_plot_resid_lev(sreslev_mod())
}
})
result_srplot <- eventReactive(input$submit_sresp_plot, {
if(input$sres_use_prev) {
ols_plot_resid_stud(all_use_n())
} else {
ols_plot_resid_stud(sres_mod())
}
})
result_srchart <- eventReactive(input$submit_sreschart_plot, {
if(input$sreschart_use_prev) {
ols_plot_resid_stand(all_use_n())
} else {
ols_plot_resid_stand(sreschart_mod())
}
})
result_hadi <- eventReactive(input$submit_hadiplot, {
if (input$hadip_use_prev) {
ols_plot_hadi(all_use_n())
} else {
ols_plot_hadi(hadi_mod())
}
})
output$inflobsplot <- renderPlot({
if (input$inflobs_select == "Cook's D Bar Plot") {
result_cdbp()
} else if (input$inflobs_select == "Potential Residual Plot") {
result_potres()
} else if (input$inflobs_select == "Cook's D Chart") {
result_cdc()
} else if (input$inflobs_select == "DFBETAs Panel") {
result_dfbetas()
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
result_dsrvsp()
} else if (input$inflobs_select == "DFFITS Plot") {
result_dffits()
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
result_srvslev()
} else if (input$inflobs_select == "Studentized Residual Plot") {
result_srplot()
} else if (input$inflobs_select == "Standardized Residual Chart") {
result_srchart()
} else if (input$inflobs_select == "Hadi Plot") {
result_hadi()
}
})
output$inflobs <- renderPrint({
if (input$inflobs_select == "Cook's D Bar Plot") {
k <- result_cdbp()
k
} else if (input$inflobs_select == "Cook's D Chart") {
k <- result_cdc()
k
} else if (input$inflobs_select == "DFBETAs Panel") {
k <- result_dfbetas()
k
} else if (input$inflobs_select == "Deleted Stud Resid vs Fitted") {
k <- result_dsrvsp()
k
} else if (input$inflobs_select == "DFFITS Plot") {
k <- result_dffits()
k
} else if (input$inflobs_select == "Studentized Residuals vs Leverage") {
k <- result_srvslev()
k
} else if (input$inflobs_select == "Studentized Residual Plot") {
k <- result_srplot()
k
} else if (input$inflobs_select == "Studentized Residual Chart") {
k <- result_srchart()
k
}
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_inflobs2.R |
output$ui_mfitlink <- renderUI({
if (input$mfit_select == "Residual Fit Spread Plot") {
fluidRow(
column(6, align = 'left',
h4('Residual Fit Spread Plot'),
p('Plot to detect non-linearity, influential observations and outliers.')
),
column(6, align = 'right',
actionButton(inputId='rfslink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_fit_spread.html', '_blank')")
)
)
} else if (input$mfit_select == "Part & Partial Correlations") {
fluidRow(
column(6, align = 'left',
h4('Part & Partial Correlations'),
p('Zero-order, part and partial correlations.')
),
column(6, align = 'right',
actionButton(inputId='corlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_correlations.html', '_blank')")
)
)
} else if (input$mfit_select == "Observed vs Fitted Plot") {
fluidRow(
column(6, align = 'left',
h4('Observed vs Fitted Plot'),
p('Plot of observed vs fitted values to assess the fit of the model.')
),
column(6, align = 'right',
actionButton(inputId='ovsplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_obs_fit.html', '_blank')")
)
)
} else if (input$mfit_select == "Lack of Fit F Test") {
fluidRow(
column(6, align = 'left',
h4('Lack of Fit F Test'),
p('Assess how much of the error in prediction is due to lack of model fit.')
),
column(6, align = 'right',
actionButton(inputId='lfitlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_pure_error_anova.html', '_blank')")
)
)
} else if (input$mfit_select == "Diagnostics Panel") {
fluidRow(
column(6, align = 'left',
h4('Diagnostics Panel'),
p('Panel of plots for regression diagnostics.')
),
column(6, align = 'right',
actionButton(inputId='dpanelink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_diagnostics.html', '_blank')")
)
)
}
})
output$ui_mfitfmla <- renderUI({
if (input$mfit_select == "Residual Fit Spread Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("rfs_fmla", label = '', width = '660px',
value = ""),
bsTooltip("rfs_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mfit_select == "Part & Partial Correlations") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("corr_fmla", label = '', width = '660px',
value = ""),
bsTooltip("corr_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mfit_select == "Observed vs Fitted Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("ovsp_fmla", label = '', width = '660px',
value = ""),
bsTooltip("ovsp_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mfit_select == "Lack of Fit F Test") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("lfit_fmla", label = '', width = '660px',
value = ""),
bsTooltip("lfit_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mfit_select == "Diagnostics Panel") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("dpanel_fmla", label = '', width = '660px',
value = ""),
bsTooltip("dpanel_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
}
})
output$ui_mfitsubmit <- renderUI({
if (input$mfit_select == "Residual Fit Spread Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_rfsplot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_rfsplot", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
} else if (input$mfit_select == "Part & Partial Correlations") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_corr', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_corr", "Click here to correlations.",
"bottom", options = list(container = "body")))
)
} else if (input$mfit_select == "Observed vs Fitted Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_ovsplot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_ovsplot", "Click here to view plot.",
"bottom", options = list(container = "body")))
)
} else if (input$mfit_select == "Lack of Fit F Test") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_lfit', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_lfit", "Click here to view test results.",
"bottom", options = list(container = "body")))
)
} else if (input$mfit_select == "Diagnostics Panel") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dpanel', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_dpanel", "Click here to view panel.",
"bottom", options = list(container = "body")))
)
}
})
output$ui_mfitprev <- renderUI({
if (input$mfit_select == "Residual Fit Spread Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'rfs_use_prev', label = '',
value = FALSE),
bsTooltip("rfs_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mfit_select == "Part & Partial Correlations") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'corr_use_prev', label = '',
value = FALSE),
bsTooltip("corr_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mfit_select == "Observed vs Fitted Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'ovsp_use_prev', label = '',
value = FALSE),
bsTooltip("ovsp_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mfit_select == "Lack of Fit F Test") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'lfit_use_prev', label = '',
value = FALSE),
bsTooltip("lfit_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mfit_select == "Diagnostics Panel") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'dpanel_use_prev', label = '',
value = FALSE),
bsTooltip("dpanel_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
}
})
output$ui_mfitout <- renderUI({
if (input$mfit_select == "Residual Fit Spread Plot") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('mfitrfs', height = '500px'))
)
} else if (input$mfit_select == "Part & Partial Correlations") {
fluidRow(
br(),
column(12, align = 'center', verbatimTextOutput('mfitcorr'))
)
} else if (input$mfit_select == "Observed vs Fitted Plot") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('mfitovfp', height = '500px'))
)
} else if (input$mfit_select == "Lack of Fit F Test") {
fluidRow(
br(),
column(12, align = 'center', verbatimTextOutput('mfitlfit'))
)
} else if (input$mfit_select == "Diagnostics Panel") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('mfitdpanel', height = '2500px'))
)
}
})
d_rfs_mod <- eventReactive(input$submit_rfsplot, {
if (input$rfs_use_prev) {
ols_plot_resid_fit_spread(all_use_n())
} else {
k <- lm(input$rfs_fmla, data = final_split$train)
ols_plot_resid_fit_spread(k)
}
})
d_corr_mod <- eventReactive(input$submit_corr, {
if(input$corr_use_prev) {
ols_correlations(all_use_n())
} else {
k <- lm(input$corr_fmla, data = final_split$train)
ols_correlations(k)
}
})
d_ovsp_mod <- eventReactive(input$submit_ovsplot, {
if (input$ovsp_use_prev) {
ols_plot_obs_fit(all_use_n())
} else {
k <- lm(input$ovsp_fmla, data = final_split$train)
ols_plot_obs_fit(k)
}
})
d_lfit_mod <- eventReactive(input$submit_lfit, {
if (input$lfit_use_prev) {
ols_pure_error_anova(all_use_n())
} else {
k <- lm(input$lfit_fmla, data = final_split$train)
ols_pure_error_anova(k)
}
})
d_dpanel_mod <- eventReactive(input$submit_dpanel, {
if(input$dpanel_use_prev) {
ols_plot_diagnostics(all_use_n())
} else {
k <- lm(input$dpanel_fmla, data = final_split$train)
ols_plot_diagnostics(k)
}
})
output$mfitrfs <- renderPlot({
d_rfs_mod()
})
output$mfitcorr <- renderPrint({
print(d_corr_mod())
})
output$mfitovfp <- renderPlot({
d_ovsp_mod()
})
output$mfitlfit <- renderPrint({
print(d_lfit_mod())
})
output$mfitdpanel <- renderPlot({
d_dpanel_mod()
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_mfit2.R |
output$ui_mselectlink <- renderUI({
if (input$mselect == "All Possible") {
fluidRow(
column(6, align = 'left',
h4('All Subset Regression'),
p('Fits all regressions involving one regressor, two regressors,
three regressors, and so on. It tests all possible subsets of
the set of potential independent variables.')
),
column(6, align = 'right',
actionButton(inputId='allsub1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_step_all_possible.html', '_blank')")
)
)
} else if (input$mselect == "Best Subset") {
fluidRow(
column(6, align = 'left',
h4('Best Subset Regression'),
p("Select the subset of predictors that do the best at meeting
some well-defined objective criterion, such as having the
largest R2 value or the smallest MSE, Mallow's Cp or AIC.")
),
column(6, align = 'right',
actionButton(inputId='bestsub1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_step_best_subset.html', '_blank')")
)
)
} else if (input$mselect == "Stepwise") {
fluidRow(
column(6, align = 'left',
h4('Stepwise Regression'),
p('Build regression model from a set of candidate predictor
variables by entering and removing predictors based on p
values, in a stepwise manner until there is no variable
left to enter or remove any more.')
),
column(6, align = 'right',
actionButton(inputId='stepwise1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https:/olsrr.rsquaredacademy.com/reference/ols_step_both_p.html', '_blank')")
)
)
} else if (input$mselect == "Forward") {
fluidRow(
column(6, align = 'left',
h4('Stepwise Forward Regression'),
p('Build regression model from a set of candidate predictor
variables by entering predictors based on p values, in a
stepwise manner until there is no variable left to enter
any more.')
),
column(6, align = 'right',
actionButton(inputId='stepf1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_step_forward_p.html', '_blank')")
)
)
} else if (input$mselect == "Backward") {
fluidRow(
column(6, align = 'left',
h4('Stepwise Backward Regression'),
p('Build regression model from a set of candidate predictor
variables by removing predictors based on p values, in a
stepwise manner until there is no variable left to remove
any more.')
),
column(6, align = 'right',
actionButton(inputId='stepb1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_step_backward_p.html', '_blank')")
)
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
column(6, align = 'left',
h4('stepAIC Forward Regression'),
p('Build regression model from a set of candidate predictor
variables by entering predictors based on Akaike Information
Criteria, in a stepwise manner until there is no variable
left to enter any more.')
),
column(6, align = 'right',
actionButton(inputId='stepaicf1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_step_forward_aic.html', '_blank')")
)
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
column(6, align = 'left',
h4('stepAIC Backward Regression'),
p('Build regression model from a set of candidate predictor
variables by removing predictors based on Akaike Information
Criteria, in a stepwise manner until there is no variable
left to remove any more.')
),
column(6, align = 'right',
actionButton(inputId='stepaicb1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_step_backward_aic.html', '_blank')")
)
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
column(6, align = 'left',
h4('stepAIC(Both) Regression'),
p('Build regression model from a set of candidate predictor
variables by entering and removing predictors based on Akaike
Information Criteria, in a stepwise manner until there is no
variable left to enter or remove any more.')
),
column(6, align = 'right',
actionButton(inputId='stepaicbo1', label="Help", icon = icon("question-circle"),
onclick ="window.open('olsrr.rsquaredacademy.com/reference/ols_step_both_aic.html', '_blank')")
)
)
}
})
output$ui_mselectfmla <- renderUI({
if (input$mselect == "All Possible") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("allsub_fmla", label = '', width = '660px',
value = ""),
bsTooltip("allsub_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Best Subset") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("bestsub_fmla", label = '', width = '660px',
value = ""),
bsTooltip("bestsub_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Stepwise") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("stepwise_fmla", label = '', width = '860px',
value = ""),
bsTooltip("stepwise_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("forward_fmla", label = '', width = '660px',
value = ""),
bsTooltip("forward_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("backward_fmla", label = '', width = '660px',
value = ""),
bsTooltip("backrward_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("aicforward_fmla", label = '', width = '660px',
value = ""),
bsTooltip("aicforward_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("aicbackward_fmla", label = '', width = '660px',
value = ""),
bsTooltip("aicbackrward_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("aicboth_fmla", label = '', width = '660px',
value = ""),
bsTooltip("aicboth_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
}
})
output$ui_mselectprev <- renderUI({
if (input$mselect == "All Possible") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'all_use_model', label = '',
value = FALSE),
bsTooltip("all_use_model", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "Best Subset") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'best_use_prev', label = '',
value = FALSE),
bsTooltip("best_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "Stepwise") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'step_use_prev', label = '',
value = FALSE),
bsTooltip("step_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'stepf_use_model', label = '',
value = FALSE),
bsTooltip("stepf_use_model", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'stepb_use_model', label = '',
value = FALSE),
bsTooltip("stepb_use_model", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'stepaicf_use_model', label = '',
value = FALSE),
bsTooltip("stepaicf_use_model", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'stepaicb_use_model', label = '',
value = FALSE),
bsTooltip("stepaicb_use_model", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'saicbo_use_model', label = '',
value = FALSE),
bsTooltip("saicbo_use_model", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
}
})
output$ui_mselectrow1 <- renderUI({
if (input$mselect == "All Possible") {
} else if (input$mselect == "Best Subset") {
} else if (input$mselect == "Stepwise") {
fluidRow(
column(2, align = 'right', br(), h5('Prob (Enter):')),
column(2, align = 'left',
numericInput("stepwise_pent", label = '', width = '200px',
value = 0.3, min = 0, max = 1, step = 0.01),
bsTooltip("stepwise_pent", "Minimum p value for adding variable to model.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Prob (Removal):')),
column(2, align = 'left',
numericInput("stepwise_prem", label = '', width = '200px',
value = 0.3, min = 0, max = 1, step = 0.01),
bsTooltip("stepwise_prem", "Maximum p value for removing variable from model.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Details:')),
column(2, align = 'left',
selectInput('stepwise_details', '',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("stepwise_details", "Print model selection details.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Prob (Enter):')),
column(4, align = 'left',
numericInput("forward_pent", label = '', width = '200px',
value = 0.3, min = 0, max = 1, step = 0.01),
bsTooltip("forward_pent", "Minimum p value for adding variable to model.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Prob (Removal):')),
column(4, align = 'left',
numericInput("backward_prem", label = '', width = '200px',
value = 0.3, min = 0, max = 1, step = 0.01),
bsTooltip("backward_prem", "Minimum p value for removing variables from model.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Details:')),
column(4, align = 'left',
selectInput('aicforward_details', '', width = '200px',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("aicforward_details", "Print model selection details.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Details:')),
column(4, align = 'left',
selectInput('aicbackward_details', '', width = '200px',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("aicbackward_details", "Print model selection details.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
column(2, align = 'right', br(), h5('Details:')),
column(4, align = 'left',
selectInput('aicboth_details', '', width = '200px',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("aicboth_details", "Print model selection details.",
"left", options = list(container = "body")))
)
}
})
output$ui_mselectrow2 <- renderUI({
if (input$mselect == "All Possible") {
} else if (input$mselect == "Best Subset") {
} else if (input$mselect == "Stepwise") {
} else if (input$mselect == "Forward") {
fluidRow(
column(2, align = 'right', br(), h5('Details:')),
column(4, align = 'left',
selectInput('forward_details', '', width = '200px',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("forward_details", "Print model selection details.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "Backward") {
fluidRow(
column(2, align = 'right', br(), h5('Details:')),
column(4, align = 'left',
selectInput('backward_details', '', width = '200px',
choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("backward_details", "Print model selection details.",
"left", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Forward") {
} else if (input$mselect == "stepAIC Backward") {
} else if (input$mselect == "stepAIC Both") {
}
})
output$ui_mselectsubmit <- renderUI({
if (input$mselect == "All Possible") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_allsub', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_allsub", "Click here to view all subsets regression.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "Best Subset") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_bestsub', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_bestsub", "Click here to view best subsets regression.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "Stepwise") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_stepwise', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_stepwise", "Click here to view stepwise regression.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "Forward") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_forward', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_forward", "Click here to view best stepwise forward regression.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "Backward") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_backward', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_backward", "Click here to view stepwise backward elimination.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_aicforward', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_aicforward", "Click here to view forward selection based on AIC.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_aicbackward', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_aicbackward", "Click here to view backward elimination based on AIC.",
"bottom", options = list(container = "body")))
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_aicboth', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_aicboth", "Click here to view stepwise regression based on AIC.",
"bottom", options = list(container = "body")))
)
}
})
output$ui_mseloutput <- renderUI({
if (input$mselect == "All Possible") {
fluidRow(
br(),
uiOutput('all_title1'),
# column(12, align = 'center', h4('All Subset Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('allsub_out')),
hr()
)
} else if (input$mselect == "Best Subset") {
fluidRow(
br(),
uiOutput('best_title1'),
# column(12, align = 'center', h4('Best Subset Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('bestsub_out')),
hr()
)
} else if (input$mselect == "Stepwise") {
fluidRow(
br(),
uiOutput('swise_title1'),
# column(12, align = 'center', h4('Stepwise Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('stepwise_out')),
hr()
)
} else if (input$mselect == "Forward") {
fluidRow(
br(),
uiOutput('forward_title1'),
# column(12, align = 'center', h4('Stepwise Forward Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('forward_out')),
hr()
)
} else if (input$mselect == "Backward") {
fluidRow(
br(),
uiOutput('backward_title1'),
# column(12, align = 'center', h4('Stepwise Backward Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('backward_out')),
hr()
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
br(),
uiOutput('sforward_title1'),
# column(12, align = 'center', h4('stepAIC Forward Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('aicforward_out')),
hr()
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
br(),
uiOutput('sbackward_title1'),
# column(12, align = 'center', h4('stepAIC Backward Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('aicbackward_out')),
hr()
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
br(),
uiOutput('sboth_title1'),
# column(12, align = 'center', h4('stepAIC Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('aicboth_out')),
hr()
)
}
})
output$ui_mselplot <- renderUI({
if (input$mselect == "All Possible") {
fluidRow(
br(),
uiOutput('all_title2'),
# column(12, align = 'center', h4('All Subset Regression Plot')),
hr(),
br(),
column(12, plotOutput('allsub_plot', height = '1500px'))
)
} else if (input$mselect == "Best Subset") {
fluidRow(
br(),
uiOutput('best_title2'),
# column(12, align = 'center', h4('Best Subset Regression Plot')),
hr(),
br(),
column(12, plotOutput('bestsub_plot', height = '1500px'))
)
} else if (input$mselect == "Stepwise") {
fluidRow(
br(),
uiOutput('swise_title2'),
# column(12, align = 'center', h4('Stepwise Regression Plot')),
hr(),
br(),
column(12, plotOutput('stepwise_plot', height = '1500px'))
)
} else if (input$mselect == "Forward") {
fluidRow(
br(),
uiOutput('forward_title2'),
# column(12, align = 'center', h4('Stepwise Forward Regression Plot')),
hr(),
br(),
column(12, plotOutput('forward_plot', height = '1500px'))
)
} else if (input$mselect == "Backward") {
fluidRow(
br(),
uiOutput('backward_title2'),
# column(12, align = 'center', h4('Stepwise Backward Regression Plot')),
hr(),
br(),
column(12, plotOutput('backward_plot', height = '1500px'))
)
} else if (input$mselect == "stepAIC Forward") {
fluidRow(
br(),
uiOutput('sforward_title2'),
# column(12, align = 'center', h4('stepAIC Forward Regression Plot')),
hr(),
br(),
column(12, plotOutput('aicforward_plot', height = '500px'))
)
} else if (input$mselect == "stepAIC Backward") {
fluidRow(
br(),
uiOutput('sbackward_title2'),
# column(12, align = 'center', h4('stepAIC Backward Regression Plot')),
hr(),
br(),
column(12, plotOutput('aicbackward_plot', height = '500px'))
)
} else if (input$mselect == "stepAIC Both") {
fluidRow(
br(),
uiOutput('sboth_title2'),
# column(12, align = 'center', h4('stepAIC Regression Plot')),
hr(),
br(),
column(12, plotOutput('aicboth_plot', height = '500px'))
)
}
})
# # main regression
# all_use_n <- reactive({
# k <- model()
# object <- k$model
# formul <- formula(object)
# data <- eval(object$call$data)
# n <- lm(formul, data = data)
# n
# })
# all subset
# d_allsub <- eventReactive(input$submit_allsub, {
# # validate(need((input$allsub_fmla != ''), 'Please specify model'))
# data <- final_split$train
# })
allsub_model <- eventReactive(input$submit_allsub, {
if (input$all_use_model) {
ols_step_all_possible(all_use_n())
} else {
model <- lm(input$allsub_fmla, data = final_split$train)
ols_step_all_possible(model)
}
})
a1_title <- eventReactive(input$submit_allsub, {
column(12, align = 'center', h4('All Subset Regression'))
})
output$all_title1 <- renderUI({
a1_title()
})
a2_title <- eventReactive(input$submit_allsub, {
column(12, align = 'center', h4('All Subset Regression Plot'))
})
output$all_title2 <- renderUI({
a2_title()
})
output$allsub_out <- renderPrint({
allsub_model()
})
# output$allsub_plot <- renderPlot({
# plot(allsub_model())
# })
bestsub_model <- eventReactive(input$submit_bestsub, {
if (input$best_use_prev) {
ols_step_best_subset(all_use_n())
} else {
data <- final_split$train
model <- lm(input$bestsub_fmla, data = data)
ols_step_best_subset(model)
}
})
b1_title <- eventReactive(input$submit_bestsub, {
column(12, align = 'center', h4('Best Subset Regression'))
})
output$best_title1 <- renderUI({
b1_title()
})
b2_title <- eventReactive(input$submit_allsub, {
column(12, align = 'center', h4('Best Subset Regression Plot'))
})
output$best_title2 <- renderUI({
b2_title()
})
output$bestsub_out <- renderPrint({
bestsub_model()
})
output$bestsub_plot <- renderPlot({
plot(bestsub_model())
})
stepwise_model <- eventReactive(input$submit_stepwise, {
if (input$step_use_prev) {
ols_step_both_p(all_use_n(), input$stepwise_pent, input$stepwise_prem,
as.logical(input$stepwise_details))
} else {
model <- lm(input$stepwise_fmla, data = final_split$train)
ols_step_both_p(model, input$stepwise_pent, input$stepwise_prem,
as.logical(input$stepwise_details))
}
})
s1_title <- eventReactive(input$submit_stepwise, {
column(12, align = 'center', h4('Stepwise Regression'))
})
output$swise_title1 <- renderUI({
s1_title()
})
s2_title <- eventReactive(input$submit_stepwise, {
column(12, align = 'center', h4('Stepwise Regression Plot'))
})
output$swise_title2 <- renderUI({
s2_title()
})
output$stepwise_out <- renderPrint({
print(stepwise_model())
})
output$stepwise_plot <- renderPlot({
plot(stepwise_model())
})
forward_model <- eventReactive(input$submit_forward, {
if (input$stepf_use_model) {
ols_step_forward_p(all_use_n(), input$forward_pent,
as.logical(input$forward_details))
} else {
model <- lm(input$forward_fmla, data = final_split$train)
ols_step_forward_p(model, input$forward_pent,
as.logical(input$forward_details))
}
})
f1_title <- eventReactive(input$submit_forward, {
column(12, align = 'center', h4('Stepwise Forward Regression'))
})
output$forward_title1 <- renderUI({
f1_title()
})
f2_title <- eventReactive(input$submit_forward, {
column(12, align = 'center', h4('Stepwise Forward Regression Plot'))
})
output$forward_title2 <- renderUI({
f2_title()
})
output$forward_out <- renderPrint({
print(forward_model())
})
output$forward_plot <- renderPlot({
plot(forward_model())
})
backward_model <- eventReactive(input$submit_backward, {
if (input$stepb_use_model) {
ols_step_backward_p(all_use_n(), input$backward_prem,
as.logical(input$backward_details))
} else {
model <- lm(input$backward_fmla, data = final_split$train)
ols_step_backward_p(model, input$backward_prem,
as.logical(input$backward_details))
}
})
ba1_title <- eventReactive(input$submit_backward, {
column(12, align = 'center', h4('Stepwise Backward Regression'))
})
output$backward_title1 <- renderUI({
ba1_title()
})
ba2_title <- eventReactive(input$submit_backward, {
column(12, align = 'center', h4('Stepwise Backward Regression Plot'))
})
output$backward_title2 <- renderUI({
ba2_title()
})
output$backward_out <- renderPrint({
print(backward_model())
})
output$backward_plot <- renderPlot({
plot(backward_model())
})
aicforward_model <- eventReactive(input$submit_aicforward, {
if (input$stepaicf_use_model) {
ols_step_forward_aic(all_use_n(), as.logical(input$aicforward_details))
} else {
model <- lm(input$aicforward_fmla, data = final_split$train)
ols_step_forward_aic(model, as.logical(input$aicforward_details))
}
})
af1_title <- eventReactive(input$submit_aicforward, {
column(12, align = 'center', h4('stepAIC Forward Regression'))
})
output$sforward_title1 <- renderUI({
af1_title()
})
af2_title <- eventReactive(input$submit_aicforward, {
column(12, align = 'center', h4('stepAIC Forward Regression Plot'))
})
output$sforward_title2 <- renderUI({
af2_title()
})
output$aicforward_out <- renderPrint({
print(aicforward_model())
})
output$aicforward_plot <- renderPlot({
plot(aicforward_model())
})
aicbackward_model <- eventReactive(input$submit_aicbackward, {
if (input$stepaicb_use_model) {
ols_step_backward_aic(all_use_n(),
as.logical(input$aicbackward_details))
} else {
model <- lm(input$aicbackward_fmla, data = final_split$train)
ols_step_backward_aic(model,
as.logical(input$aicbackward_details))
}
})
ab1_title <- eventReactive(input$submit_aicbackward, {
column(12, align = 'center', h4('stepAIC Backward Regression'))
})
output$sbackward_title1 <- renderUI({
ab1_title()
})
ab2_title <- eventReactive(input$submit_aicbackward, {
column(12, align = 'center', h4('stepAIC Backward Regression Plot'))
})
output$sbackward_title2 <- renderUI({
ab2_title()
})
output$aicbackward_out <- renderPrint({
print(aicbackward_model())
})
output$aicbackward_plot <- renderPlot({
plot(aicbackward_model())
})
aicboth_model <- eventReactive(input$submit_aicboth, {
if (input$saicbo_use_model) {
ols_step_both_aic(all_use_n(),
as.logical(input$aicboth_details))
} else {
model <- lm(input$aicboth_fmla, data = final_split$train)
ols_step_both_aic(model,
as.logical(input$aicboth_details))
}
})
bo1_title <- eventReactive(input$submit_aicboth, {
column(12, align = 'center', h4('stepAIC Regression'))
})
output$sboth_title1 <- renderUI({
bo1_title()
})
bo2_title <- eventReactive(input$submit_aicboth, {
column(12, align = 'center', h4('stepAIC Regression Plot'))
})
output$sboth_title2 <- renderUI({
bo2_title()
})
output$aicboth_out <- renderPrint({
print(aicboth_model())
})
output$aicboth_plot <- renderPlot({
plot(aicboth_model())
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_mselection2.R |
partition_ui <- eventReactive(input$button_split_yes, {
fluidRow(
column(12, align = 'center',
numericInput(
inputId = 'part_train_per',
label = 'Training Set',
min = 0,
max = 1,
value = 0.5,
step = 0.01,
width = '120px'
)
),
column(12, align = 'center',
br(),
actionButton(inputId = 'submit_part_train_per', label = 'Partition', width = '120px', icon = icon('check')),
bsTooltip("submit_part_train_per", "Click here to partition data.",
"bottom", options = list(container = "body"))
),
column(12,
br(),
br(),
column(6, align = 'right', downloadButton("downloadTrain", "Download Training Data")),
column(6, align = 'left', downloadButton("downloadTest", "Download Test Data"))
),
br(),
br(),
column(12, align = 'center',
br(),
actionButton(inputId = 'start_modeling', label = 'Start Modeling', width = '140px', icon = icon('check'))
)
)
})
output$ui_partition <- renderUI({
partition_ui()
})
final_split <- reactiveValues(train = NULL, test = NULL)
trainpart <- eventReactive(input$button_split_yes, {
out <- createDataPartition(
y = final_sample$d[[1]],
p = input$part_train_per,
list = FALSE
)
as.vector(out)
})
observeEvent(input$submit_part_train_per, {
final_split$train <- final_sample$d[trainpart(), ]
final_split$test <- final_sample$d[-trainpart(), ]
})
observeEvent(input$button_split_no, {
final_split$train <- final_sample$d
})
output$downloadTrain <- downloadHandler(
filename = function() {
paste("train_data.csv", sep = "")
},
content = function(file) {
write.csv(final_split$train, file, row.names = FALSE)
}
)
output$downloadTest <- downloadHandler(
filename = function() {
paste("test_data.csv", sep = "")
},
content = function(file) {
write.csv(final_split$test, file, row.names = FALSE)
}
)
observeEvent(input$start_modeling, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_analyze_home')
})
observeEvent(input$button_split_no, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_analyze_home')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_partition.R |
d_cprp_mod <- eventReactive(input$submit_cprp_plot, {
if(input$cprp_use_prev) {
ols_plot_comp_plus_resid(all_use_n())
} else {
k <- lm(input$cprp_fmla, data = final_split$train)
ols_plot_comp_plus_resid(k)
}
})
output$cprplot <- renderPlot({
d_cprp_mod()
})
# added variable plot
d_diag_advar <- eventReactive(input$submit_avplot, {
if (input$advar_use_prev) {
model <- all_use_n()
data <- final_split$train
xnames <- colnames(attr(model$terms, 'factors'))
nl <- xnames %>% length()
resp <- rownames(attr(model$terms, 'factors'))[1]
myplots <- list()
for(i in seq_len(nl)) {
x <- olsrr:::advarx(data, i, xnames)
y <- olsrr:::advary(data, i, resp, xnames)
d <- tibble(x, y)
p <- eval(substitute(ggplot(d, aes(x = x, y = y)) +
geom_point(colour = 'blue', size = 2) +
xlab(paste(xnames[i], " | Others")) +
ylab(paste(resp, " | Others")) +
stat_smooth(method="lm", se=FALSE), list(i = i)))
# print(p)
j <- i
myplots[[j]] <- p
}
do.call(grid.arrange, c(myplots, list(ncol = 2)))
} else {
model <- lm(input$avplot_fmla, data = final_split$train)
data <- eval(model$call$data)
xnames <- colnames(attr(model$terms, 'factors'))
nl <- xnames %>% length()
resp <- rownames(attr(model$terms, 'factors'))[1]
myplots <- list()
for(i in seq_len(nl)) {
x <- olsrr:::advarx(data, i, xnames)
y <- olsrr:::advary(data, i, resp, xnames)
d <- tibble(x, y)
p <- eval(substitute(ggplot(d, aes(x = x, y = y)) +
geom_point(colour = 'blue', size = 2) +
xlab(paste(xnames[i], " | Others")) +
ylab(paste(resp, " | Others")) +
stat_smooth(method="lm", se=FALSE), list(i = i)))
# print(p)
j <- i
myplots[[j]] <- p
}
do.call(grid.arrange, c(myplots, list(ncol = 2)))
}
})
output$avplot <- renderPlot({
print(d_diag_advar())
})
observeEvent(input$button_split_no, {
updateSelectInput(session,
inputId = "resreg_var",
choices = names(final_split$train))
})
observeEvent(input$submit_part_train_per, {
updateSelectInput(session,
inputId = "resreg_var",
choices = names(final_split$train))
})
d_resreg_mod <- eventReactive(input$submit_resreg_plot, {
if(input$resreg_use_prev) {
rvsr_plot_shiny(all_use_n(), final_split$train, as.character(input$resreg_var))
} else {
k <- lm(input$resreg_fmla, data = final_split$train)
rvsr_plot_shiny(k, final_split$train, as.character(input$resreg_var))
}
})
# d_resreg <- eventReactive(input$submit_resreg_plot, {
# # validate(need((input$resreg_var != ''), 'Please select a variable.'))
# data <- tibble::as_data_frame(final_split$train[, c(input$resreg_var)])
# data
# })
output$rvsrplot <- renderPlot({
d_resreg_mod()
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_regdiag.R |
d_regress <- eventReactive(input$submit_regress, {
# validate(need((input$regress_fmla != ''), 'Please specify model'))
data <- final_split$train
k <- ols_regress(input$regress_fmla, data = data)
k
})
model <- reactive({
d_regress()
})
r1_title <- eventReactive(input$submit_regress, {
column(12, align = 'center', h4('Regression Result'))
})
output$reg1_title <- renderUI({
r1_title()
})
output$regress_out <- renderPrint({
d_regress()
})
# main regression
all_use_n <- eventReactive(input$submit_regress, {
data <- final_split$train
k <- lm(input$regress_fmla, data = data)
k
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_regress.R |
output$ui_resdiaglink <- renderUI({
if (input$restrial1 == "Residual vs Predicted Plot") {
fluidRow(
column(6, align = 'left',
h4('Residual vs Predicted Plot'),
p('Plot to detect non-linearity, unequal error variances, and outliers.')
),
column(6, align = 'right',
actionButton(inputId='rvsp1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_fit.html', '_blank')")
)
)
} else if (input$restrial1 == "Residual Box Plot") {
fluidRow(
column(6, align = 'left',
h4('Residual Box Plot')
),
column(6, align = 'right',
actionButton(inputId='rbplot1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_box.html', '_blank')")
)
)
} else if (input$restrial1 == "Residual Histogram") {
fluidRow(
column(6, align = 'left',
h4('Residual Histogram'),
p('Histogram of residuals for detecting violation of normality assumption.')
),
column(6, align = 'right',
actionButton(inputId='rhist1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_hist.html', '_blank')")
)
)
} else if (input$restrial1 == "Residual QQ Plot") {
fluidRow(
column(6, align = 'left',
h4('Residual QQ Plot'),
p('Graph for detecting violation of normality assumption.')
),
column(6, align = 'right',
actionButton(inputId='rqq1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_qq.html', '_blank')")
)
)
} else if (input$restrial1 == "Normality Test") {
fluidRow(
column(6, align = 'left',
h4('Normality Test'),
p('Test for detecting violation of normality assumption.')
),
column(6, align = 'right',
actionButton(inputId='resnorm1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_test_normality.html', '_blank')")
)
)
}
})
output$ui_resdiagfmla <- renderUI({
if (input$restrial1 == "Residual vs Predicted Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("respred_fmla", label = '', width = '660px',
value = ""),
bsTooltip("respred_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$restrial1 == "Residual Box Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("resbox_fmla", label = '', width = '660px',
value = ""),
bsTooltip("resbox_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$restrial1 == "Residual Histogram") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("reshist_fmla", label = '', width = '660px',
value = ""),
bsTooltip("reshist_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$restrial1 == "Residual QQ Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("resqq_fmla", label = '', width = '660px',
value = ""),
bsTooltip("resqq_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
} else if (input$restrial1 == "Normality Test") {
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("resnorm_fmla", label = '', width = '660px',
value = ""),
bsTooltip("resnorm_fmla", "Specify model formula",
"left", options = list(container = "body")))
)
}
})
output$ui_resdiagsubmit <- renderUI({
if (input$restrial1 == "Residual vs Predicted Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_respred_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_respred_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$restrial1 == "Residual Box Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_resbox_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_resbox_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$restrial1 == "Residual Histogram") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_reshist_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_reshist_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$restrial1 == "Residual QQ Plot") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_resqq_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_resqq_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
)
} else if (input$restrial1 == "Normality Test") {
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_resnorm', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_resnorm", "Click here to view normality test result.",
"bottom", options = list(container = "body")))
)
}
})
output$ui_resdiagprev <- renderUI({
if (input$restrial1 == "Residual vs Predicted Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'respred_use_prev', label = '',
value = FALSE),
bsTooltip("respred_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$restrial1 == "Residual Box Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'resbox_use_prev', label = '',
value = FALSE),
bsTooltip("resbox_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$restrial1 == "Residual Histogram") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'reshist_use_prev', label = '',
value = FALSE),
bsTooltip("reshist_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$restrial1 == "Residual QQ Plot") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'resqq_use_prev', label = '',
value = FALSE),
bsTooltip("resqq_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
} else if (input$restrial1 == "Normality Test") {
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'resnorm_use_prev', label = '',
value = FALSE),
bsTooltip("resnorm_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
)
}
})
output$ui_resdiagout <- renderUI({
if (input$restrial1 == "Residual vs Predicted Plot") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('resvsplot', height = '500px'))
)
} else if (input$restrial1 == "Residual Box Plot") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('resboxplot', height = '500px'))
)
} else if (input$restrial1 == "Residual Histogram") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('reshistplot', height = '500px'))
)
} else if (input$restrial1 == "Residual QQ Plot") {
fluidRow(
br(),
column(12, align = 'center', plotOutput('resqqplot', height = '500px'))
)
} else if (input$restrial1 == "Normality Test") {
fluidRow(
br(),
column(12, align = 'center', verbatimTextOutput('resnormtest'))
)
}
})
d_respred_mod <- eventReactive(input$submit_respred_plot, {
if(input$respred_use_prev) {
ols_plot_resid_fit(all_use_n())
} else {
k <- lm(input$respred_fmla, data = final_split$train)
ols_plot_resid_fit(k)
}
})
d_resbox_mod <- eventReactive(input$submit_resbox_plot, {
if(input$resbox_use_prev) {
ols_plot_resid_box(all_use_n())
} else {
k <- lm(input$resbox_fmla, data = final_split$train)
ols_plot_resid_box(k)
}
})
d_reshist_mod <- eventReactive(input$submit_reshist_plot, {
if(input$reshist_use_prev) {
ols_plot_resid_hist(all_use_n())
} else {
k <- lm(input$reshist_fmla, data = final_split$train)
ols_plot_resid_hist(k)
}
})
d_resqq_mod <- eventReactive(input$submit_resqq_plot, {
if(input$resqq_use_prev) {
ols_plot_resid_qq(all_use_n())
} else {
k <- lm(input$resqq_fmla, data = final_split$train)
ols_plot_resid_qq(k)
}
})
d_resnorm_mod <- eventReactive(input$submit_resnorm, {
if(input$resnorm_use_prev) {
ols_test_normality(all_use_n())
} else {
k <- lm(input$resnorm_fmla, data = final_split$train)
ols_test_normality(k)
}
})
output$resvsplot <- renderPlot({
print(d_respred_mod())
})
output$resboxplot <- renderPlot({
print(d_resbox_mod())
})
output$reshistplot <- renderPlot({
print(d_reshist_mod())
})
output$resqqplot <- renderPlot({
print(d_resqq_mod())
})
output$resnormtest <- renderPrint({
print(d_resnorm_mod())
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_resdiagtrial.R |
samp_yes <- eventReactive(input$button_sample_yes, {
fluidRow(
column(12, align = 'center',
tags$div(class = 'header', id = 'samp_div',
tags$h5('How do you want to sample?')
)
# h5('How do you want to sample?')
),
br(),
br(),
column(6, align = 'right',
actionButton(
inputId = 'button_samp_per',
label = 'Percentage',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'button_samp_n',
label = 'Observations',
width = '120px'
)
)
)
})
# samp_no <- eventReactive(input$button_sample_no, {
# fluidRow(
# br(),
# tags$div(class = 'header', id = 'samp_remove_no',
# tags$h6('Click on Analyze in the drop down menu to explore the data.')
# )
# )
# })
output$samp_yes_no <- renderUI({
samp_yes()
})
# output$samp_no_yes <- renderUI({
# samp_no()
# })
samp_per_options <- eventReactive(input$button_samp_per, {
fluidRow(
column(12, align = 'center',
numericInput(
inputId = 'samp_size_per',
label = 'Sample Size',
min = 0,
max = 1,
value = 1,
step = 0.01,
width = '120px'
)
),
column(12, align = 'center',
br(),
actionButton(inputId = 'submit_samp_per_size', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_samp_per_size", "Click here to select variables.",
"bottom", options = list(container = "body"))
)
)
})
samp_obs_options <- eventReactive(input$button_samp_n, {
fluidRow(
column(12, align = 'center',
numericInput(
inputId = 'samp_size_n',
label = 'Sample Size',
min = 0,
value = 0,
step = 1,
width = '120px'
)
),
column(12, align = 'center',
br(),
actionButton(inputId = 'submit_samp_obs_size', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_samp_obs_size", "Click here to select variables.",
"bottom", options = list(container = "body"))
)
)
})
output$samp_per_option <- renderUI({
samp_per_options()
})
output$samp_obs_option <- renderUI({
samp_obs_options()
})
observeEvent(input$button_sample_no, {
removeUI(
selector = "div:has(> #button_samp_per)"
)
removeUI(
selector = "div:has(> #button_samp_n)"
)
removeUI(
selector = "div:has(> #samp_div)"
)
removeUI(
selector = "div:has(> #samp_size_n)"
)
removeUI(
selector = "div:has(> #submit_samp_obs_size)"
)
removeUI(
selector = "div:has(> #samp_size_per)"
)
removeUI(
selector = "div:has(> #submit_samp_per_size)"
)
})
observeEvent(input$button_sample_yes, {
removeUI(
selector = "div:has(> #samp_remove_no)"
)
})
observeEvent(input$button_samp_per, {
removeUI(
selector = "div:has(> #samp_size_n)"
)
removeUI(
selector = "div:has(> #submit_samp_obs_size)"
)
})
observeEvent(input$button_samp_n, {
removeUI(
selector = "div:has(> #samp_size_per)"
)
removeUI(
selector = "div:has(> #submit_samp_per_size)"
)
})
observeEvent(input$button_samp_n, {
updateNumericInput(
session,
inputId = 'samp_size_n',
label = 'Sample Size',
min = 0,
value = nrow(filt_data$p),
max = nrow(filt_data$p),
step = 1
)
})
final_sample <- reactiveValues(d = NULL)
samp1 <- eventReactive(input$submit_samp_per_size, {
final_sample$d <- dplyr::sample_frac(filt_data$p, size = input$samp_size_per, replace = FALSE)
})
samp2 <- eventReactive(input$submit_samp_obs_size, {
final_sample$d <- dplyr::sample_n(filt_data$p, size = input$samp_size_n, replace = FALSE)
})
observeEvent(input$submit_samp_per_size, {
final_sample$d <- samp1()
})
observeEvent(input$submit_samp_obs_size, {
final_sample$d <- samp2()
})
observeEvent(input$button_sample_no, {
final_sample$d <- filt_data$p
})
observeEvent(input$button_sample_no, {
updateNavbarPage(session, 'mainpage', selected = 'tab_partition')
})
observeEvent(input$submit_samp_obs_size, {
updateNavbarPage(session, 'mainpage', selected = 'tab_partition')
})
observeEvent(input$submit_samp_per_size, {
updateNavbarPage(session, 'mainpage', selected = 'tab_partition')
})
# output$samp_type <- renderUI({
# if (input$data_samp == 'Percentage') {
# numericInput(
# inputId = 'samp_size',
# label = 'Sample Size',
# min = 0,
# max = 1,
# value = 0.7,
# step = 0.01
# )
# } else {
# numericInput(
# inputId = 'samp_size',
# label = 'Sample Size',
# min = 0,
# value = 0,
# step = 1
# )
# }
# })
# observeEvent(input$finalok, {
# if (input$data_samp == 'Observations') {
# updateNumericInput(
# inputId = 'samp_size',
# label = 'Sample Size',
# min = 0,
# value = nrow(final()),
# max = nrow(final()),
# step = 1
# )
# }
# })
# final_sample <- eventReactive(input$submit_samp, {
# if (input$data_samp == 'Percentage') {
# out <- dplyr::sample_frac(filtered(), size = input$samp_size, replace = FALSE)
# } else {
# out <- dplyr::sample_n(filtered(), size = input$samp_size, replace = FALSE)
# }
# out
# }) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_sample.R |
# output
output$screen <- renderPrint({
ds_screener(filt_data$p)
})
observeEvent(input$finalok, {
updateNavbarPage(session, 'mainpage', selected = 'tab_sample')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_screen.R |
show_but_sel <- eventReactive(input$button_selvar_yes, {
column(12, align = 'center',
selectInput(
inputId = 'dplyr_selvar',
label = '',
choices = '',
selected = '',
multiple = TRUE,
selectize = TRUE
)
)
})
output$show_sel_button <- renderUI({
show_but_sel()
})
sel_sub_but <- eventReactive(input$button_selvar_yes, {
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dply_selvar', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_seldata", "Click here to select variables.",
"bottom", options = list(container = "body"))
)
})
output$sub_sel_button <- renderUI({
sel_sub_but()
})
observe({
updateSelectInput(
session,
inputId = "dplyr_selvar",
choices = names(data()),
selected = names(data())
)
})
observeEvent(input$button_selvar_yes, {
updateSelectInput(
session,
inputId = "dplyr_selvar",
choices = names(final()),
selected = names(final())
)
})
final_sel <- reactiveValues(a = NULL)
finalsel <- eventReactive(input$submit_dply_selvar, {
k <- final() %>%
select(input$dplyr_selvar)
k
})
observeEvent(input$submit_dply_selvar, {
final_sel$a <- finalsel()
})
observeEvent(input$button_selvar_no, {
final_sel$a <- final()
})
observeEvent(input$button_selvar_no, {
removeUI(
selector = "div:has(> #dplyr_selvar)"
)
removeUI(
selector = "div:has(> #submit_dply_selvar)"
)
})
observeEvent(input$button_selvar_no, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_filter')
})
observeEvent(input$submit_dply_selvar, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_filter')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_select.R |
library(stringr)
output$trans_try <- renderUI({
ncol <- as.integer(ncol(uploadata$t))
lapply(1:ncol, function(i) {
fluidRow(
column(3,
selectInput(paste("n_col_", i), label = '', width = '150px',
choices = names(uploadata$t)[i], selected = names(uploadata$t)[i])
),
column(3,
textInput(paste("new_name_", i),
label = '', width = '150px',
value = names(uploadata$t)[i])
),
column(3,
selectInput(paste0("data_type_", i),
label = '', width = '150px',
choices = c('numeric', 'factor', 'Date', 'character', 'integer'),
selected = class(uploadata$t[[i]]))
),
column(3,
conditionalPanel(condition = paste(paste0("input.data_type_", i), "== 'Date'"),
column(4, br(), tags$h5('Format')),
column(8,
selectInput(paste("date_type_", i),
label = '', width = '150px',
choices = c('%d %m %y', '%d %m %Y', '%y %m %d', '%Y %m %d', '%d %y %m', '%d %Y %m',
'%m %d %y', '%m %d %Y', '%y %d %m', '%Y %d %m', '%m %y %d', '%m %Y %d',
'%d/%m/%y', '%d/%m/%Y', '%y/m /%d', '%Y/%m/%d', '%d/%y/%m', '%d/%Y/%m',
'%m/%d/%y', '%m/%d/%Y', '%y/%d/%m', '%Y/%d/%m', '%m/%y/%d', '%m/%Y/%d',
'%d-%m-%y', '%d-%m-%Y', '%y-m -%d', '%Y-%m-%d', '%d-%y-%m', '%d-%Y-%m',
'%m-%d-%y', '%m-%d-%Y', '%y-%d-%m', '%Y-%d-%m', '%m-%y-%d', '%m-%Y-%d'
),
selected = '%Y %m %d')
)
)
)
)
})
})
original <- reactive({
uploadata$t
})
save_names <- reactive({
names(original())
})
n <- reactive({
length(original())
})
data_types <- reactive({
ncol <- as.integer(ncol(uploadata$t))
collect <- list(lapply(1:ncol, function(i) {
input[[paste0("data_type_", i)]]
}))
colors <- unlist(collect)
})
new_names <- reactive({
ncol <- as.integer(ncol(uploadata$t))
collect <- list(lapply(1:ncol, function(i) {
input[[paste("new_name_", i)]]
}))
colors <- unlist(collect)
colnames <- str_replace(colors, " ", "_")
})
# original <- reactive({
# data()
# })
# save_names <- reactive({
# names(original())
# })
# n <- reactive({
# length(original())
# })
# data_types <- reactive({
# ncol <- as.integer(ncol(data()))
# collect <- list(lapply(1:ncol, function(i) {
# input[[paste0("data_type_", i)]]
# }))
# colors <- unlist(collect)
# })
# new_names <- reactive({
# ncol <- as.integer(ncol(data()))
# collect <- list(lapply(1:ncol, function(i) {
# input[[paste("new_name_", i)]]
# }))
# colors <- unlist(collect)
# colnames <- str_replace(colors, " ", "_")
# })
copy <- eventReactive(input$apply_changes, {
out <- list()
for (i in seq_len(n())) {
if (data_types()[i] == 'Date') {
inp <- eval(parse(text = paste0('input$', paste0('date_type_', i))))
out[[i]] <- eval(parse(text = paste0("as.", data_types()[i], "(original()$", save_names()[i], ", ", inp, ")")))
} else {
out[[i]] <- eval(parse(text = paste0("as.", data_types()[i], "(original()$", save_names()[i], ")")))
}
}
names(out) <- new_names()
return(out)
})
final <- eventReactive(input$apply_changes, {
data.frame(copy(), stringsAsFactors = F)
})
observeEvent(input$apply_changes, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_selvar')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_transform2.R |
# importing data
inFile1 <- reactive({
if(is.null(input$file1)) {
return(NULL)
} else {
input$file1
}
})
data1 <- reactive({
if(is.null(inFile1())) {
return(NULL)
} else {
read.csv(inFile1()$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
}
})
# importing data
inFile2 <- reactive({
if(is.null(input$file2)) {
return(NULL)
} else {
input$file2
}
})
data2 <- reactive({
if(is.null(inFile2())) {
return(NULL)
} else {
ext <- tools::file_ext(inFile2()$name)
file.rename(inFile2()$datapath,
paste(inFile2()$datapath, ext, sep="."))
readxl::read_excel(
path = paste(inFile2()$datapath, ext, sep="."),
sheet = input$sheet_n
)
}
})
# importing data
inFile3 <- reactive({
if(is.null(input$file3)) {
return(NULL)
} else {
input$file3
}
})
data3 <- reactive({
if(is.null(inFile3())) {
return(NULL)
} else {
jsonlite::fromJSON(inFile3()$datapath)
}
})
# importing data
inFile4 <- reactive({
if(is.null(input$file4)) {
return(NULL)
} else {
input$file4
}
})
data4 <- reactive({
if(is.null(inFile4())) {
return(NULL)
} else {
haven::read_sas(inFile4()$datapath)
}
})
inFile5 <- reactive({
if(is.null(input$file5)) {
return(NULL)
} else {
input$file5
}
})
data5 <- reactive({
if(is.null(inFile5())) {
return(NULL)
} else {
haven::read_sav(inFile5()$datapath)
}
})
inFile6 <- reactive({
if(is.null(input$file6)) {
return(NULL)
} else {
input$file6
}
})
data6 <- reactive({
if(is.null(inFile6())) {
return(NULL)
} else {
haven::read_stata(inFile6()$datapath)
}
})
inFile7 <- reactive({
if(is.null(input$file7)) {
return(NULL)
} else {
input$file7
}
})
data7 <- reactive({
if(is.null(inFile7())) {
return(NULL)
} else {
readRDS(inFile7()$datapath)
}
})
observe({
updateSelectInput(
session,
inputId = 'sel_data',
label = '',
choices = c(input$file1$name, input$file2$name, input$file3$name,
input$file4$name, input$file5$name, input$file6$name, input$file7$name),
selected = ''
)
})
ext_type <- reactive({
ext <- tools::file_ext(input$sel_data)
})
# choosing sample data
sampdata <- reactiveValues(s = NULL)
observeEvent(input$german_data, {
data("GermanCredit")
sampdata$s <- GermanCredit
})
observeEvent(input$iris_data, {
sampdata$s <- iris
})
observeEvent(input$mtcars_data, {
sampdata$s <- descriptr::mtcarz
})
observeEvent(input$hsb_data, {
sampdata$s <- inferr::hsb
})
observeEvent(input$mpg_data, {
sampdata$s <- mpg
})
observeEvent(input$diamonds_data, {
sampdata$s <- diamonds
})
uploadata <- reactiveValues(t = NULL)
observeEvent(input$submit_seldata, {
if (ext_type() == 'csv') {
uploadata$t <- data1()
} else if (ext_type() == 'xls') {
uploadata$t <- data2()
} else if (ext_type() == 'xlsx') {
uploadata$t <- data2()
} else if (ext_type() == 'json') {
uploadata$t <- data3()
} else if (ext_type() == 'sas7bdat') {
uploadata$t <- data4()
} else if (ext_type() == 'sav') {
uploadata$t <- uploadata$t <- data5()
} else if (ext_type() == 'dta') {
uploadata$t <- data6()
} else {
uploadata$t <- data7()
}
})
observeEvent(input$use_sample_data, {
uploadata$t <- sampdata$s
})
observeEvent(input$use_sample_data, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_transform')
})
observeEvent(input$submit_seldata, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_transform')
})
observeEvent(input$csv2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$csv2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$excel2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$excel2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$json2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$json2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$stata2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$stata2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$spss2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$spss2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$sas2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$sas2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$rds2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$rds2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$welcomebutton, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_upload.R |
output$table <- renderDataTable({
final_split$train
})
observeEvent(input$view2getdata, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$view2analyze, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_summary')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/logic/logic_view.R |
library(shiny)
library(ggplot2)
library(descriptr)
library(olsrr)
library(dplyr)
library(grid)
library(gridExtra)
library(purrr)
library(tidyr)
library(tibble)
library(readxl)
library(readr)
library(jsonlite)
library(magrittr)
library(tools)
library(caret)
library(lubridate)
library(scales)
library(stringr)
library(inferr)
shinyServer(function(input, output, session) {
source("logic/logic_dataoptions.R", local = T)
source("logic/logic_upload.R", local = T)
source("logic/logic_transform2.R", local = T)
source("logic/logic_select.R", local = T)
source("logic/logic_filter.R", local = T)
source("logic/logic_screen.R", local = T)
source("logic/logic_sample.R", local = T)
source("logic/logic_partition.R", local = T)
source("logic/logic_view.R", local = T)
source("logic/logic_regress.R", local = T)
# source("logic/logic_mselection2.R", local = T)
source("logic/logic_resdiagtrial.R", local = T)
source("logic/logic_hetero.R", local = T)
source("logic/logic_collin.R", local = T)
source("logic/logic_inflobs2.R", local = T)
source("logic/logic_mfit2.R", local = T)
source("logic/logic_regdiag.R", local = T)
source("logic/logic_home.R", local = T)
source("logic/logic_exit_button.R", local = T)
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/server.R |
navbarMenu('Analyze', icon = icon('search-plus'),
source('ui/ui_homes.R', local = TRUE)[[1]],
source('ui/ui_mlr.R', local = TRUE)[[1]]
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_analyze.R |
tabPanel('Collinearity Diagnostics', value = 'tab_regcollin',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Collinearity Diagnostics'),
p('Variance inflation factor, tolerance, eigenvalues and condition indices.')
),
column(6, align = 'right',
actionButton(inputId='cdiaglink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_coll_diag.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("collin_fmla", label = '', width = '660px',
value = ""),
bsTooltip("collin_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'colldiag_use_prev', label = '',
value = FALSE),
bsTooltip("colldiag_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_colldiag', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_colldiag", "Click here to view collinearity diagnostics.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center', verbatimTextOutput('colldiag'))
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_collin.R |
navbarMenu('Data', icon = icon('database'),
source('ui/ui_up.R', local = TRUE)[[1]],
source('ui/ui_trans.R', local = TRUE)[[1]],
source('ui/ui_scr.R', local = TRUE)[[1]],
source('ui/ui_sample.R', local = TRUE)[[1]],
source('ui/ui_partition.R', local = TRUE)[[1]],
source('ui/ui_vi.R', local = TRUE)[[1]]
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_data.R |
tabPanel('Upload File', value = 'tab_uploadfile',
fluidPage(
includeCSS("mystyle.css"),
fluidRow(
column(12,
tabsetPanel(type = 'tabs', id = 'tabset_upload',
tabPanel('CSV', value = 'tab_upload_csv',
fluidPage(
br(),
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a comma or tab separated file.')
),
column(4, align = 'right',
actionButton(inputId='uploadlink2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=00m25s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput('file1', 'Data Set:',
accept = c('text/csv', '.csv',
'text/comma-separated-values,text/plain')
)
)
),
fluidRow(
column(12, align = 'center', checkboxInput('header', 'Header', TRUE))
),
fluidRow(
column(12, align = 'center',
selectInput('sep', 'Separator',
choices = c('Comma' = ',', 'Semicolon' = ';', 'Tab' = '\t'), selected = ',')
)
),
fluidRow(
column(12, align = 'center',
selectInput('quote', 'Quote',
choices = c('None' = '', 'Double Quote' = '"', 'Single Quote' = "'"), selected = '')
)
),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='csv2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='csv2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('Excel', value = 'tab_upload_excel',
fluidPage(
br(),
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .xls or .xlsx file.')
),
column(4, align = 'right',
actionButton(inputId='uploadlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=00m25s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file2',
label = 'Choose file:',
accept = c('.xls', '.xlsx')
)
)
),
fluidRow(
column(12, align = 'center',
numericInput(
inputId = 'sheet_n',
label = 'Sheet',
value = 1,
min = 1,
step = 1,
width = '120px'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='excel2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='excel2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('JSON', value = 'tab_upload_json',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .json file.')
),
column(4, align = 'right',
actionButton(inputId='uploadjson', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=00m25s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file3',
label = 'Choose file:',
accept = '.json'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='json2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='json2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('STATA', value = 'tab_upload_stata',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .dta file.')
),
column(4, align = 'right',
actionButton(inputId='uploadstata', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=00m25s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file6',
label = 'Choose file:',
accept = '.dta'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='stata2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='stata2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('SPSS', value = 'tab_upload_spss',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .sav file.')
),
column(4, align = 'right',
actionButton(inputId='uploadspss', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=00m25s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file5',
label = 'Choose file:',
accept = '.sav'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='spss2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='spss2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('SAS', value = 'tab_upload_sas',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .sas7bdat file.')
),
column(4, align = 'right',
actionButton(inputId='uploadsas', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=00m25s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file4',
label = 'Choose file:',
accept = '.sas7bdat'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='sas2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='sas2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('RDS', value = 'tab_upload_rds',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a RDS file.')
),
column(4, align = 'right',
actionButton(inputId='uploadrds2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file7',
label = 'Choose file:',
accept = ''
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='rds2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='rds2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
)
)
)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_datafiles.R |
tabPanel('Data Sources', value = 'tab_datasources',
fluidPage(theme = shinytheme('cerulean'),
includeCSS("mystyle.css"),
fluidRow(
column(12, align = 'center',
h4('Use sample data or upload a file')
)
),
fluidRow(
column(6, align = 'right',
actionButton(
inputId = 'sample_data_yes',
label = 'Sample Data',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'upload_files_yes',
label = 'Upload File',
width = '120px'
)
)
),
br(),
fluidRow(
column(12, align = 'center',
h6('The app takes a few seconds to load. Please wait for ~12 seconds.')
)
),
br(),
br(),
fluidRow(
uiOutput('upload_file_links')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_dataoptions.R |
tabPanel('Sample Data', value = 'tab_use_sample',
fluidPage(
includeCSS("mystyle.css"),
fluidRow(
column(12, align = 'center',
h5('Click on a sample for more information')
)
),
br(),
fluidRow(
column(4, align = 'center',
actionButton(
inputId = 'german_data',
label = 'German Credit',
width = '200px',
onclick ="window.open('https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)', 'newwindow', 'width=800,height=600')"
)
),
column(4, align = 'center',
actionButton(
inputId = 'iris_data',
label = 'Iris',
width = '200px',
onclick ="window.open('https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/iris.html', 'newwindow', 'width=800,height=600')"
)
),
column(4, align = 'center',
actionButton(
inputId = 'mtcars_data',
label = 'mtcars',
width = '200px',
onclick ="window.open('https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html', 'newwindow', 'width=800,height=600')"
)
)
),
fluidRow(column(12, br())),
fluidRow(
column(4, align = 'center',
actionButton(
inputId = 'mpg_data',
label = 'mpg',
width = '200px',
onclick ="window.open('http://ggplot2.tidyverse.org/reference/mpg.html', 'newwindow', 'width=800,height=600')"
)
),
column(4, align = 'center',
actionButton(
inputId = 'hsb_data',
label = 'hsb',
width = '200px',
onclick ="window.open('http://www.rsquaredacademy.com/descriptr/reference/hsb.html', 'newwindow', 'width=800,height=600')"
)
),
column(4, align = 'center',
actionButton(
inputId = 'diamonds_data',
label = 'diamonds',
width = '200px',
onclick ="window.open('http://ggplot2.tidyverse.org/reference/diamonds.html', 'newwindow', 'width=800,height=600')"
)
)
),
br(),
br(),
br(),
fluidRow(
column(12, align = 'center',
actionButton(
inputId = 'use_sample_data',
label = 'Use Sample Data',
width = '200px'
)
)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_datasamples.R |
# Exit -----------------------------------------------------------
tabPanel("", value = "exit", icon = icon("power-off"),
br(),
br(),
br(),
br(),
br(),
br(),
# In case window does not close, one should see this message
fluidRow(column(3),
column(6, h2("Thank you for using", strong("olsrr"), "!"))),
fluidRow(column(3),
column(6, h4("Now you should close this window.")))
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_exit_button.R |
tabPanel('Filter', value = 'tab_filter',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Filter Data'),
p('Click on Yes to filter data.')
),
column(6, align = 'right',
actionButton(inputId='fildatalink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=02m34s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
h4('Do you want to filter data?')
),
column(6, align = 'right',
actionButton(
inputId = 'button_filt_yes',
label = 'Yes',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'button_filt_no',
label = 'No',
width = '120px'
)
)
),
br(),
br(),
fluidRow(
uiOutput('filt_render')
),
fluidRow(
uiOutput('filt_trans')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_filter.R |
tabPanel('Heteroskedasticity', value = 'tab_hetero',
fluidPage(
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Breusch Pagan Test',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Breusch Pagan Test'),
p('Test for constant variance. It assumes that the error terms are normally distributed.')
),
column(6, align = 'right',
actionButton(inputId='bplink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_test_breusch_pagan.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("het_bp_fmla", label = '', width = '870px',
value = ""),
bsTooltip("het_bp_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'bp_use_prev', label = '',
value = FALSE),
bsTooltip("bp_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(2, align = 'right', br(), h5('Fitted Values:')),
column(4, align = 'left',
selectInput('het_bp_fv', '', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_bp_fv", "Use fitted values of regression model.",
"left", options = list(container = "body"))
),
column(3, align = 'right', br(), h5('p Value Adjustment:')),
column(3, align = 'left',
selectInput('het_bp_padj', '',
choices = c("none", "bonferroni", "sidak", "holm"),
selected = "none"),
bsTooltip("het_bp_padj", "Options for p value adjustment.",
"bottom", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('RHS:')),
column(4, align = 'left',
selectInput('het_bp_rhs', '', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_bp_rhs", "Use explanatory variables of model.",
"left", options = list(container = "body"))
),
column(3, align = 'right', br(), h5('Variables:')),
column(3, align = 'left',
selectInput("het_bp_vars", label = '',
choices = "", selected = "", multiple = TRUE,
selectize = TRUE),
bsTooltip("het_bp_vars", "Select variables for heteroskedasticity test.",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Multiple:')),
column(4, align = 'left',
selectInput('het_bp_mult', '', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_bp_mult", "Perform multiple tests.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(4, offset = 4, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_het_bp', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_het_bp", "Click here to view test results.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, verbatimTextOutput('het_bp_out'))
)
)
),
tabPanel('Bartlett Test',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Bartlett Test'),
p('Test if k samples have equal variances.')
),
column(6, align = 'right',
actionButton(inputId='bartlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_test_bartlett.html', '_blank')")
)
),
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Using Variables',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variables:')),
column(10, align = 'left',
selectInput("var_bartest", label = '', width = '660px',
choices = "", selected = "", multiple = TRUE,
selectize = TRUE),
bsTooltip("var_bartest", "Select variables.",
"left", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_bartest', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_levtest", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('bartest_out')
)
)
)
),
tabPanel('Using Groups',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Variable:')),
column(2, align = 'left',
selectInput("var_bartestg1", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_bartestg1", "Select a variable.",
"left", options = list(container = "body"))),
column(2, align = 'right', br(), h5('Grouping Variable:')),
column(2, align = 'left',
selectInput("var_bartestg2", label = '', width = '200px',
choices = "", selected = ""),
bsTooltip("var_bartestg2", "Select a grouping variable.",
"left", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_bartestg', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_bartestg", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('bartestg_out')
)
)
)
),
tabPanel('Using Formula',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("bartest_fmla", label = '', width = '660px',
value = ""),
bsTooltip("bartest_fmla", "Specify a formula",
"left", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_bartestf', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_bartestf", "Click here to view test result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center',
verbatimTextOutput('bartestf_out')
)
)
)
)
)
)
)
)
),
tabPanel('F Test',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('F Test'),
p('Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.).')
),
column(6, align = 'right',
actionButton(inputId='freglink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_test_f.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("het_f_fmla", label = '', width = '660px',
value = ""),
bsTooltip("het_f_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'f_use_prev', label = '',
value = FALSE),
bsTooltip("f_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(2, align = 'right', br(), h5('Variables:')),
column(10, align = 'left',
selectInput("het_f_vars", label = '', width = '660px',
choices = "", selected = "", multiple = TRUE,
selectize = TRUE),
bsTooltip("het_f_vars", "Select variables for heteroskedasticity test.",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Fitted Values:')),
column(4, align = 'left',
selectInput('het_f_fv', '', width = '200px', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_f_fv", "Use fitted values of regression model.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(2, align = 'right', br(), h5('RHS:')),
column(4, align = 'left',
selectInput('het_f_rhs', '', width = '200px', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_f_rhs", "Use explanatory variables of model.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_het_f', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_het_f", "Click here to view test results.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, verbatimTextOutput('het_f_out'))
)
)
),
tabPanel('Score Test',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Score Test'),
p('Test for heteroskedasticity under the assumption that the errors are independent and identically distributed (i.i.d.).')
),
column(6, align = 'right',
actionButton(inputId='scorelink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_test_score.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("het_score_fmla", label = '', width = '660px',
value = ""),
bsTooltip("het_score_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'score_use_prev', label = '',
value = FALSE),
bsTooltip("score_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(2, align = 'right', br(), h5('Variables:')),
column(10, align = 'left',
selectInput("het_score_vars", label = '', width = '660px',
choices = "", selected = "", multiple = TRUE,
selectize = TRUE),
bsTooltip("het_score_vars", "Select variables for heteroskedasticity test.",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Fitted Values:')),
column(4, align = 'left',
selectInput('het_score_fv', '', width = '200px', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_score_fv", "Use fitted values of regression model.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(2, align = 'right', br(), h5('RHS:')),
column(4, align = 'left',
selectInput('het_score_rhs', '', width = '200px', choices = c("TRUE" = TRUE, "FALSE" = FALSE),
selected = "FALSE"),
bsTooltip("het_score_rhs", "Use explanatory variables of model.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_het_score', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_het_score", "Click here to view test results.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, verbatimTextOutput('het_score_out'))
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_hetero.R |
tabPanel("Home", value = "tab_analyze_home",
fluidPage(
fluidRow(
column(12, align = 'center',
h3('What do you want to do?')
)
),
br(),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'summary1.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Descriptive Statisics'),
p('Generate descriptive/summary statistics.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_descriptive',
label = 'Click Here',
width = '100px'
)
),
column(1)
),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'normal.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Statistical Distributions'),
p('Explore and visualize different statistical distributions.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_distributions',
label = 'Click Here',
width = '100px'
)
),
column(1)
),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'ttest3.jpg', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Hypothesis Testing'),
p('Test hypothesis using parametric and non-parametric tests.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_inference',
label = 'Click Here',
width = '100px'
)
),
column(1)
),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'simple_reg.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Model Building'),
p('Tools for building simple and multiple linear regression models.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_model',
label = 'Click Here',
width = '100px'
)
),
column(1)
),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'visualize2.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Data Visualization'),
p('Visualize data using ggplot2, rbokeh, plotly and highcharts.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_visualize',
label = 'Click Here',
width = '100px'
)
),
column(1)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_home.R |
tabPanel('Home', value = 'tab_home_analyze', icon = icon('home'),
navlistPanel(id = 'navlist_home', well = FALSE, widths = c(2, 10),
source('ui/ui_model_home.R', local = TRUE)[[1]])
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_homes.R |
tabPanel('Measures of Influence', value = 'tab_inflobs',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Select Procedure:')),
column(4, align = 'left',
selectInput('inflobs_select', label = '', width = '300px',
choices = c("Cook's D Bar Plot", "Cook's D Chart",
"DFBETAs Panel", "DFFITS Plot", "Studentized Residual Plot",
"Studentized Residual Chart", "Studentized Residuals vs Leverage",
"Deleted Stud Resid vs Fitted", "Hadi Plot", "Potential Residual Plot"),
selected = "Cook's D Bar Plot")
)
),
hr(),
fluidRow(
column(12, uiOutput("ui_inflobslink"))
),
hr(),
fluidRow(
column(12, uiOutput("ui_inflobsfmla"))
),
fluidRow(
column(12, uiOutput("ui_inflobsprev"))
),
fluidRow(
column(12, uiOutput("ui_inflobssubmit"))
),
fluidRow(
br(),
column(12, uiOutput('ui_inflobsplot'))
),
fluidRow(
br(),
column(12, uiOutput('ui_inflobsprint'))
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_inflobs2.R |
tabPanel('Model Fit Assessment', value = 'tab_mfit',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Select Procedure:')),
column(4, align = 'left',
selectInput('mfit_select', label = '', width = '300px',
choices = c("Residual Fit Spread Plot", "Part & Partial Correlations",
"Observed vs Fitted Plot", "Lack of Fit F Test",
"Diagnostics Panel"),
selected = "Residual Fit Spread Plot")
)
),
hr(),
fluidRow(
column(12, uiOutput("ui_mfitlink"))
),
hr(),
fluidRow(
column(12, uiOutput("ui_mfitfmla"))
),
fluidRow(
column(12, uiOutput("ui_mfitprev"))
),
fluidRow(
column(12, uiOutput("ui_mfitsubmit"))
),
fluidRow(
column(12, uiOutput("ui_mfitout"))
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_mfit2.R |
tabPanel('Linear Regression', value = 'tab_reg', icon = icon('cubes'),
navlistPanel(id = 'navlist_reg',
well = FALSE,
widths = c(2, 10),
source('ui/ui_regress.R', local = TRUE)[[1]],
# source('ui/ui_mselection2.R', local = TRUE)[[1]],
source('ui/ui_resdiagtrial.R', local = TRUE)[[1]],
source('ui/ui_hetero.R', local = TRUE)[[1]],
source('ui/ui_collin.R', local = TRUE)[[1]],
source('ui/ui_inflobs2.R', local = TRUE)[[1]],
source('ui/ui_mfit2.R', local = TRUE)[[1]],
source('ui/ui_regdiag.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_mlr.R |
tabPanel('Model Building', value = 'tab_model_home',
fluidPage(
fluidRow(
br(),
column(12, align = 'center',
h3('What do you want to do?')
),
br(),
br()
),
fluidRow(
column(12),
br(),
column(3),
column(4, align = 'left',
h5('Regression')
),
column(2, align = 'left',
actionButton(
inputId = 'model_reg_click',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Residual Diagnostics')
),
column(2, align = 'left',
actionButton(
inputId = 'model_resdiag_click',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Heteroskedasticity Tests')
),
column(2, align = 'left',
actionButton(
inputId = 'model_het_click',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Collinearity Diagnostics')
),
column(2, align = 'left',
actionButton(
inputId = 'model_coldiag_click',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Measures of Influence')
),
column(2, align = 'left',
actionButton(
inputId = 'model_infl_click',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Model Fit Assessment')
),
column(2, align = 'left',
actionButton(
inputId = 'model_fit_click',
label = 'Click Here',
width = '120px'
)
),
column(3),
br(),
br(),
br(),
column(3),
column(4, align = 'left',
h5('Variable Contribution')
),
column(2, align = 'left',
actionButton(
inputId = 'model_varcontrib_click',
label = 'Click Here',
width = '120px'
)
),
column(3)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_model_home.R |
tabPanel('Variable Selection', value = 'tab_var_select',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Select Procedure:')),
column(4, align = 'left',
selectInput('mselect', label = '', width = '300px',
choices = c("All Possible", "Best Subset", "Stepwise", "Forward",
"Backward", "stepAIC Forward", "stepAIC Backward", "stepAIC Both"),
selected = "Residual vs Predicted Plot")
)
),
hr(),
fluidRow(
column(12, uiOutput("ui_mselectlink"))
),
hr(),
fluidRow(
column(12, uiOutput("ui_mselectfmla"))
),
fluidRow(
column(12, uiOutput("ui_mselectprev"))
),
fluidRow(
column(12, uiOutput("ui_mselectrow1"))
),
fluidRow(
column(12, uiOutput("ui_mselectrow2"))
),
fluidRow(
column(12, uiOutput("ui_mselectsubmit"))
),
fluidRow(
column(12, uiOutput('ui_mseloutput'))
),
fluidRow(
column(12, uiOutput('ui_mselplot'))
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_mselection2.R |
tabPanel('Partition', value = 'tab_partition', icon = icon('cut'),
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Partition Data'),
p('Click on Yes to partition data into training and test set.')
),
column(6, align = 'right',
actionButton(inputId='partitionlink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=04m24s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
h4('Do you want to partition data into training set and testing set?')
)
),
fluidRow(
column(6, align = 'right',
actionButton(
inputId = 'button_split_yes',
label = 'Yes',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'button_split_no',
label = 'No',
width = '120px'
)
)
),
br(),
br(),
fluidRow(
uiOutput('ui_partition')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_partition.R |
tabPanel('Variable Contribution', value = 'tab_regvarcont',
fluidPage(
fluidRow(
column(12,
tabsetPanel(type = 'tabs',
tabPanel('Added Variable Plot',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Added Variable Plot'),
p('Added variable plot provides information about the marginal
importance of a predictor variable, given the other predictor
variables already in the model. It shows the marginal importance
of the variable in reducing the residual variability.')
),
column(6, align = 'right',
actionButton(inputId='advarlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_added_variable.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("avplot_fmla", label = '', width = '660px',
value = ""),
bsTooltip("avplot_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'advar_use_prev', label = '',
value = FALSE),
bsTooltip("advar_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_avplot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_avplot", "Click here to view added variable plot.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center', verbatimTextOutput('avplotdata'))
),
fluidRow(
br(),
column(12, align = 'center', plotOutput('avplot'))
)
)
),
tabPanel('Residual Plus Component Plot',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Residual Plus Component Plot'),
p('The residual plus component plot indicates whether any
non-linearity is present in the relationship between
response and predictor variables and can suggest possible
transformations for linearizing the data.')
),
column(6, align = 'right',
actionButton(inputId='regcprp1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_comp_plus_resid.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("cprp_fmla", label = '', width = '660px',
value = ""),
bsTooltip("cprp_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'cprp_use_prev', label = '',
value = FALSE),
bsTooltip("cprp_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_cprp_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_cprp_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center', plotOutput('cprplot'))
)
)
),
tabPanel('Residual vs Regressor Plot',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Residual vs Regressor Plot'),
p('Graph to determine whether we should add a new predictor
to the model already containing other predictors. The residuals
from the model is regressed on the new predictor and if the plot
shows non random pattern, you should consider adding the new
predictor to the model.')
),
column(6, align = 'right',
actionButton(inputId='rvsrlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_plot_resid_regressor.html', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("resreg_fmla", label = '', width = '660px',
value = ""),
bsTooltip("resreg_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Select Regressor:')),
column(2, align = 'left',
selectInput('resreg_var', 'Select Variable',
choices = "", selected = ""),
bsTooltip("resreg_var", "Select a variable not in the model.",
"left", options = list(container = "body")))
),
fluidRow(
column(2, align = 'right', br(), h5('Use previous model:')),
column(2, align = 'left', br(),
checkboxInput(inputId = 'resreg_use_prev', label = '',
value = FALSE),
bsTooltip("resreg_use_prev", "Use model from Regression Tab.",
"left", options = list(container = "body"))
)
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_resreg_plot', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_resreg_plot", "Click here to view regression result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
column(12, align = 'center', plotOutput('rvsrplot'))
)
)
)
)
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_regdiag.R |
tabPanel('Regression', value = 'tab_regress',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
h4('Multiple Linear Regression'),
p('Ordinary least squares regression.')
),
column(6, align = 'right',
actionButton(inputId='mlr1', label="Help", icon = icon("question-circle"),
onclick ="window.open('https://olsrr.rsquaredacademy.com/reference/ols_regress.html', '_blank')"),
actionButton(inputId='mlr3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=04m50s', '_blank')")
)
),
hr(),
fluidRow(
column(2, align = 'right', br(), h5('Model Formula:')),
column(10, align = 'left',
textInput("regress_fmla", label = '', width = '660px',
value = ""),
bsTooltip("regress_fmla", "Specify model formula",
"left", options = list(container = "body")))
),
fluidRow(
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_regress', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_regress", "Click here to view regression result.",
"bottom", options = list(container = "body")))
),
fluidRow(
br(),
uiOutput('reg1_title'),
# column(12, align = 'center', h4('Regression Result')),
hr(),
column(12, align = 'center', verbatimTextOutput('regress_out')),
hr()
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_regress.R |
tabPanel('Residual Diagnostics', value = 'tab_res_diag',
fluidPage(
fluidRow(
column(2, align = 'right', br(), h5('Select Procedure:')),
column(4, align = 'left',
selectInput('restrial1', label = '', width = '300px',
choices = c("Residual vs Predicted Plot", "Residual Box Plot",
"Residual Histogram", "Residual QQ Plot", "Normality Test"),
selected = "Residual vs Predicted Plot")
)
),
hr(),
fluidRow(
column(12, uiOutput("ui_resdiaglink"))
),
hr(),
fluidRow(
column(12, uiOutput("ui_resdiagfmla"))
),
fluidRow(
column(12, uiOutput("ui_resdiagprev"))
),
fluidRow(
column(12, uiOutput("ui_resdiagsubmit"))
),
fluidRow(
br(),
column(12, uiOutput("ui_resdiagout"))
# column(12, align = 'center', plotOutput('resdiagplot', height = '500px'))
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_resdiagtrial.R |
tabPanel('Sample', value = 'tab_sample', icon = icon('random'),
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Sample Data'),
p('Click on Yes to create a random sample of data.')
),
column(6, align = 'right',
actionButton(inputId='samplelink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=03m47s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
h4('Draw a random sample of the data?')
)
),
fluidRow(
column(6, align = 'right',
actionButton(
inputId = 'button_sample_yes',
label = 'Yes',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'button_sample_no',
label = 'No',
width = '120px'
)
)
),
br(),
br(),
fluidRow(
column(12, align = 'center',
uiOutput('samp_yes_no')
),
br(),
br()
# column(12, align = 'center',
# uiOutput('samp_no_yes')
# )
),
fluidRow(
br(),
br(),
uiOutput('samp_per_option')
),
fluidRow(
uiOutput('samp_obs_option')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_sample.R |
tabPanel('Screen', value = 'tab_scr', icon = icon('binoculars'),
navlistPanel(id = 'navlist_scr',
well = FALSE,
widths = c(2, 10),
source('ui/ui_screen.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_scr.R |
tabPanel('Screen', value = 'tab_screen',
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Data Screening'),
p('Screen data for missing values, verify variable names and data types.')
),
column(4, align = 'right',
actionButton(inputId='dscreenlink1', label="Help", icon = icon("question-circle"),
onclick ="window.open('www.rsquaredacademy.com/descriptr/reference/ds_screener.html', '_blank')"),
actionButton(inputId='dscreenlink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=03m09s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
verbatimTextOutput('screen')
)
),
fluidRow(
br(),
column(12, align = 'center',
actionButton('finalok', 'Approve', width = '120px', icon = icon('sign-out')),
bsTooltip("finalok", "Click here to approve the data.",
"top", options = list(container = "body"))
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_screen.R |
tabPanel('Select', value = 'tab_sel', icon = icon('database'),
navlistPanel(id = 'navlist_up',
well = FALSE,
widths = c(2, 10),
source('ui/ui_seldata.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_sel.R |
tabPanel("Select Data", value = "tab_seldata",
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Select Data Set'),
p('Select a data set from the drop down box and click on submit.')
),
column(6, align = 'right',
actionButton(inputId='seldatalink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=01m11s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = "center",
selectInput(
inputId = "sel_data",
label = "Select a data set:",
choices = '',
# choices = c('csv', 'excel', 'json', 'spss', 'stata', 'sas'),
selected = '',
width = '200px',
)
)
),
fluidRow(
column(12, align = 'center',
br(),
actionButton(inputId = 'submit_seldata', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_seldata", "Click here to select data.",
"bottom", options = list(container = "body")))
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_seldata.R |
tabPanel('Select Variables', value = 'tab_selvar',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Select Variables'),
p('Click on Yes to select variables.')
),
column(6, align = 'right',
actionButton(inputId='selvarlink', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=02m13s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
h4('Do you want to select variables?')
)
),
fluidRow(
column(6, align = 'right',
actionButton(
inputId = 'button_selvar_yes',
label = 'Yes',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'button_selvar_no',
label = 'No',
width = '120px'
)
)
),
fluidRow(
br(),
br(),
uiOutput('show_sel_button')
),
fluidRow(
uiOutput('sub_sel_button')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_select.R |
tabPanel('Transform', value = 'tab_trans', icon = icon('rotate-right'),
navlistPanel(id = 'navlist_trans',
well = FALSE,
widths = c(2, 10),
source('ui/ui_seldata.R', local = TRUE)[[1]],
source('ui/ui_transform2.R', local = TRUE)[[1]],
source('ui/ui_select.R', local = TRUE)[[1]],
source('ui/ui_filter.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_trans.R |
tabPanel('Transform', value = 'tab_transform',
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Data Transformation'),
p('Rename variables and modify data types.')
),
column(6, align = 'right',
actionButton(inputId='translink3', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=X8b0beNJ64A#t=01m20s', '_blank')")
)
),
hr(),
fluidRow(
column(3, tags$h5('Variable')),
column(3, tags$h5('Rename Variable')),
column(3, tags$h5('Modify Data Type'))
),
column(12, uiOutput('trans_try')),
fluidRow(
tags$br()
),
fluidRow(
column(12, align = 'center',
br(),
actionButton(inputId="apply_changes", label="Apply Changes", icon = icon('thumbs-up')),
bsTooltip("apply_changes", "Click here to apply changes to data.",
"top", options = list(container = "body")),
br(),
br()
)
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_transform2.R |
tabPanel('Get Data', value = 'tab_upload', icon = icon('server'),
navlistPanel(id = 'navlist_up',
well = FALSE,
widths = c(2, 10),
source('ui/ui_dataoptions.R', local = TRUE)[[1]],
source('ui/ui_datafiles.R', local = TRUE)[[1]],
source('ui/ui_datasamples.R', local = TRUE)[[1]]
# source('ui/ui_upload.R', local = TRUE)[[1]],
# source('ui/ui_excel.R', local = TRUE)[[1]],
# source('ui/ui_json.R', local = TRUE)[[1]],
# source('ui/ui_stata.R', local = TRUE)[[1]],
# source('ui/ui_spss.R', local = TRUE)[[1]],
# source('ui/ui_sas.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_up.R |
tabPanel('View', value = 'tab_vi', icon = icon('sort'),
navlistPanel(id = 'navlist_vi',
well = FALSE,
widths = c(2, 10),
source('ui/ui_view.R', local = TRUE)[[1]]
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_vi.R |
tabPanel('View', value = 'tab_view',
fluidPage(
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='view2getdata', label=" Get Data", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='view2analyze', label="Analyze Data", icon = icon("long-arrow-right"))
)
),
hr(),
fluidRow(
dataTableOutput(outputId = "table")
)
)
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui/ui_view.R |
library(shiny)
library(shinyBS)
library(shinythemes)
library(descriptr)
library(dplyr)
shinyUI(
navbarPage(HTML("olsrr"), id = 'mainpage',
source('ui/ui_data.R', local = TRUE)[[1]],
source('ui/ui_analyze.R', local = TRUE)[[1]],
source('ui/ui_exit_button.R', local = TRUE)[[1]]
))
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-olsrr/ui.R |
observeEvent(input$sample_data_yes, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_use_sample')
})
file_upload_options <- eventReactive(input$upload_files_yes, {
fluidRow(
column(3, align = 'center',
actionButton(
inputId = 'upload_csv_file',
label = 'CSV',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_xls_file',
label = 'XLS',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_xlsx_file',
label = 'XLSX',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_json_file',
label = 'JSON',
width = '120px'
)
),
column(12, br()),
column(3, align = 'center',
actionButton(
inputId = 'upload_stata_file',
label = 'STATA',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_spss_file',
label = 'SPSS',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_sas_file',
label = 'SAS',
width = '120px'
)
),
column(3, align = 'center',
actionButton(
inputId = 'upload_rds_file',
label = 'RDS',
width = '120px'
)
)
)
})
output$upload_file_links <- renderUI({
file_upload_options()
})
observeEvent(input$upload_csv_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tab_uploadfile', selected = 'tab_upload_csv')
})
observeEvent(input$upload_xls_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_excel')
})
observeEvent(input$upload_xlsx_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_excel')
})
observeEvent(input$upload_json_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_json')
})
observeEvent(input$upload_stata_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_stata')
})
observeEvent(input$upload_spss_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_spss')
})
observeEvent(input$upload_sas_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_sas')
})
observeEvent(input$upload_rds_file, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_uploadfile')
updateTabsetPanel(session, 'tabset_upload', selected = 'tab_upload_rds')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_dataoptions.R |
# Exit ---------------------------------------------------------------
observe({
if (isTRUE(input$mainpage == "exit")) {
stopApp()
}
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_exit_button.R |
observeEvent(input$click_transaction, {
updateNavbarPage(session, 'mainpage', selected = 'tab_rfm')
updateNavlistPanel(session, 'navlist_rfm', 'tab_rfm_transaction_score')
})
observeEvent(input$click_customer_1, {
updateNavbarPage(session, 'mainpage', selected = 'tab_rfm')
updateNavlistPanel(session, 'navlist_rfm', 'tab_rfm_customer_score')
})
observeEvent(input$click_customer_2, {
updateNavbarPage(session, 'mainpage', selected = 'tab_rfm')
updateNavlistPanel(session, 'navlist_rfm', 'tab_rfm_customer_score_2')
})
observeEvent(input$welcomebutton, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_home.R |
observeEvent(input$finalok, {
updateSelectInput(
session,
inputId = "rfm_customer_id_c",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
updateSelectInput(
session,
inputId = "rfm_n_transactions_c",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
updateSelectInput(
session,
inputId = "rfm_recency_days_c",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
updateSelectInput(
session,
inputId = "rfm_total_revenue_c",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
})
observeEvent(input$finalok, {
updateSelectInput(
session,
inputId = "rfm_customer_id_t",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
updateSelectInput(
session,
inputId = "rfm_order_date_t",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
updateSelectInput(
session,
inputId = "rfm_revenue_t",
choices = names(final_sel$a),
selected = names(final_sel$a)
)
})
comp_rfm_transaction_score <- eventReactive(input$submit_rfm_transaction_score, {
rfm_table_order(data = final_sel$a, customer_id = !! sym(as.character(input$rfm_customer_id_t)),
order_date = !! sym(as.character(input$rfm_order_date_t)),
revenue = !! sym(as.character(input$rfm_revenue_t)),
analysis_date = input$rfm_analysis_date_t, recency_bins = input$rfm_recency_bins_t,
frequency_bins = input$rfm_frequency_bins_t, monetary_bins = input$rfm_monetary_bins_t)
})
comp_rfm_customer_score <- eventReactive(input$submit_rfm_customer_score, {
rfm_table_customer(data = final_sel$a, customer_id = !! sym(as.character(input$rfm_customer_id_c)),
n_transactions = !! sym(as.character(input$rfm_n_transactions_c)),
recency_days = !! sym(as.character(input$rfm_recency_days_c)),
total_revenue = !! sym(as.character(input$rfm_total_revenue_c)),
analysis_date = input$rfm_analysis_date_c, recency_bins = input$rfm_recency_bins_c,
frequency_bins = input$rfm_frequency_bins_c, monetary_bins = input$rfm_monetary_bins_c)
})
comp_rfm_customer_score_2 <- eventReactive(input$submit_rfm_customer_score_2, {
rfm_table_customer(data = final_sel$a, customer_id = !! sym(as.character(input$rfm_customer_id_c_2)),
n_transactions = !! sym(as.character(input$rfm_n_transactions_c_2)),
recency_days = !! sym(as.character(input$rfm_order_date_c)),
total_revenue = !! sym(as.character(input$rfm_total_revenue_c_2)),
analysis_date = input$rfm_analysis_date_c_2, recency_bins = input$rfm_recency_bins_c_2,
frequency_bins = input$rfm_frequency_bins_c_2, monetary_bins = input$rfm_monetary_bins_c_2)
})
output$rfm_transaction_score_out <- renderDataTable({
comp_rfm_transaction_score() %>%
use_series(rfm) %>%
as.data.frame()
})
output$rfm_customer_score_out <- renderDataTable({
comp_rfm_customer_score() %>%
use_series(rfm) %>%
as.data.frame()
})
output$rfm_customer_score_out_2 <- renderDataTable({
comp_rfm_customer_score_2() %>%
use_series(rfm) %>%
as.data.frame()
})
rfm_final_score <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_final_score$a <- comp_rfm_transaction_score()
})
observeEvent(input$submit_rfm_customer_score, {
rfm_final_score$a <- comp_rfm_customer_score()
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_final_score$a <- comp_rfm_customer_score_2()
})
rfm_heatmap_generate <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_heatmap_generate$a <- rfm_heatmap(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score, {
rfm_heatmap_generate$a <- rfm_heatmap(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_heatmap_generate$a <- rfm_heatmap(rfm_final_score$a)
})
output$plot_heatmap <- renderPlot({
print(rfm_heatmap_generate$a)
})
rfm_barchart_generate <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_barchart_generate$a <- rfm_bar_chart(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score, {
rfm_barchart_generate$a <- rfm_bar_chart(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_barchart_generate$a <- rfm_bar_chart(rfm_final_score$a)
})
output$plot_barchart <- renderPlot({
print(rfm_barchart_generate$a)
})
rfm_histogram_generate <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_histogram_generate$a <- rfm_histograms(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score, {
rfm_histogram_generate$a <- rfm_histograms(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_histogram_generate$a <- rfm_histograms(rfm_final_score$a)
})
output$plot_histogram <- renderPlot({
print(rfm_histogram_generate$a)
})
rfm_scatter_1_generate <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_scatter_1_generate$a <- rfm_rm_plot(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score, {
rfm_scatter_1_generate$a <- rfm_rm_plot(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_scatter_1_generate$a <- rfm_rm_plot(rfm_final_score$a)
})
output$plot_scatter_1 <- renderPlot({
print(rfm_scatter_1_generate$a)
})
rfm_scatter_2_generate <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_scatter_2_generate$a <- rfm_fm_plot(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score, {
rfm_scatter_2_generate$a <- rfm_fm_plot(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_scatter_2_generate$a <- rfm_fm_plot(rfm_final_score$a)
})
output$plot_scatter_2 <- renderPlot({
print(rfm_scatter_2_generate$a)
})
rfm_scatter_3_generate <- reactiveValues(a = NULL)
observeEvent(input$submit_rfm_transaction_score, {
rfm_scatter_3_generate$a <- rfm_rf_plot(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score, {
rfm_scatter_3_generate$a <- rfm_rf_plot(rfm_final_score$a)
})
observeEvent(input$submit_rfm_customer_score_2, {
rfm_scatter_3_generate$a <- rfm_rf_plot(rfm_final_score$a)
})
output$plot_scatter_3 <- renderPlot({
print(rfm_scatter_3_generate$a)
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_rfm_score.R |
# output
output$screen <- renderPrint({
ds_screener(final_sel$a)
})
observeEvent(input$finalok, {
updateNavbarPage(session, 'mainpage', selected = 'tab_home_analyze')
updateNavlistPanel(session, 'navlist_home', 'tab_rfm_home')
})
final_split <- reactiveValues(train = NULL)
observeEvent(input$finalok, {
final_split$train <- final_sel$a
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_screen.R |
library(purrr)
library(ggplot2)
output$segment_prep <- renderUI({
ncol <- as.integer(input$n_segments)
lapply(1:ncol, function(i) {
fluidRow(
column(3,
textInput(paste("segment_name_", i),
label = '', width = '150px',
value = "")
),
column(3,
sliderInput(paste("recency_interval_", i),
label = '', min = 1, max = 5, value = c(2, 4), step = 1)
),
column(3,
sliderInput(paste("frequency_interval_", i),
label = '', min = 1, max = 5, value = c(2, 4), step = 1)
),
column(3,
sliderInput(paste("monetary_interval_", i),
label = '', min = 1, max = 5, value = c(2, 4), step = 1)
)
)
})
})
segment_names <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("segment_name_", i)]]
}))
unlist(collect)
})
recency_lower <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("recency_interval_", i)]]
}))
collect[[1]] %>%
map_int(1)
})
recency_upper <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("recency_interval_", i)]]
}))
collect[[1]] %>%
map_int(2)
})
frequency_lower <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("frequency_interval_", i)]]
}))
collect[[1]] %>%
map_int(1)
})
frequency_upper <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("frequency_interval_", i)]]
}))
collect[[1]] %>%
map_int(2)
})
monetary_lower <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("monetary_interval_", i)]]
}))
collect[[1]] %>%
map_int(1)
})
monetary_upper <- reactive({
ncol <- as.integer(input$n_segments)
collect <- list(lapply(1:ncol, function(i) {
input[[paste("monetary_interval_", i)]]
}))
collect[[1]] %>%
map_int(2)
})
prep_segment <- eventReactive(input$button_create_segments, {
rfm_score_table <-
rfm_final_score$a %>%
use_series(rfm)
for (i in seq_len(input$n_segments)) {
rfm_score_table$segment[((rfm_score_table$recency_score %>% between(recency_lower()[i], recency_upper()[i])) &
(rfm_score_table$frequency_score %>% between(frequency_lower()[i], frequency_upper()[i])) &
(rfm_score_table$monetary_score %>% between(monetary_lower()[i], monetary_upper()[i])))] <- segment_names()[i]
}
rfm_score_table$segment[is.na(rfm_score_table$segment)] <- "Others"
rfm_score_table %>%
select(
customer_id, segment, rfm_score, transaction_count, recency_days,
amount
)
})
output$segment_out <- renderDataTable({
prep_segment()
})
output$segment_size_out <- renderPrint({
prep_segment() %>%
count(segment) %>%
arrange(desc(n)) %>%
rename(Segment = segment, Count = n) %>%
kable() %>%
kable_styling(full_width = TRUE, font_size = 30)
})
fill_segments <- reactive({
input$n_segments + 1
})
output$segment_average_recency <- renderPlot({
data <-
prep_segment() %>%
group_by(segment) %>%
select(segment, recency_days) %>%
summarize(median(recency_days)) %>%
rename(segment = segment, avg_recency = `median(recency_days)`) %>%
arrange(avg_recency)
n_fill <- nrow(data)
ggplot(data, aes(segment, avg_recency)) +
geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
xlab("Segment") + ylab("Median Recency") +
ggtitle("Median Recency by Segment") +
coord_flip() +
theme(
plot.title = element_text(hjust = 0.5)
)
})
output$segment_average_frequency <- renderPlot({
data <-
prep_segment() %>%
group_by(segment) %>%
select(segment, transaction_count) %>%
summarize(median(transaction_count)) %>%
rename(segment = segment, avg_frequency = `median(transaction_count)`) %>%
arrange(avg_frequency)
n_fill <- nrow(data)
ggplot(data, aes(segment, avg_frequency)) +
geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
xlab("Segment") + ylab("Median Frequency") +
ggtitle("Median Frequency by Segment") +
coord_flip() +
theme(
plot.title = element_text(hjust = 0.5)
)
})
output$segment_average_monetary <- renderPlot({
data <-
prep_segment() %>%
group_by(segment) %>%
select(segment, amount) %>%
summarize(median(amount)) %>%
rename(segment = segment, avg_monetary = `median(amount)`) %>%
arrange(avg_monetary)
n_fill <- nrow(data)
ggplot(data, aes(segment, avg_monetary)) +
geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
xlab("Segment") + ylab("Median Monetary Value") +
ggtitle("Median Monetary Value by Segment") +
coord_flip() +
theme(
plot.title = element_text(hjust = 0.5)
)
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_segments.R |
show_but_sel <- eventReactive(input$button_selvar_yes, {
column(12, align = 'center',
selectInput(
inputId = 'dplyr_selvar',
label = '',
choices = '',
selected = '',
multiple = TRUE,
selectize = TRUE
)
)
})
output$show_sel_button <- renderUI({
show_but_sel()
})
sel_sub_but <- eventReactive(input$button_selvar_yes, {
column(12, align = 'center',
br(),
br(),
actionButton(inputId = 'submit_dply_selvar', label = 'Submit', width = '120px', icon = icon('check')),
bsTooltip("submit_seldata", "Click here to select variables.",
"bottom", options = list(container = "body"))
)
})
output$sub_sel_button <- renderUI({
sel_sub_but()
})
observe({
updateSelectInput(
session,
inputId = "dplyr_selvar",
choices = names(data()),
selected = names(data())
)
})
observeEvent(input$button_selvar_yes, {
updateSelectInput(
session,
inputId = "dplyr_selvar",
choices = names(final()),
selected = names(final())
)
})
final_sel <- reactiveValues(a = NULL)
finalsel <- eventReactive(input$submit_dply_selvar, {
k <-
final() %>%
select(input$dplyr_selvar)
k
})
observeEvent(input$submit_dply_selvar, {
final_sel$a <- finalsel()
})
observeEvent(input$button_selvar_no, {
final_sel$a <- final()
})
observeEvent(input$button_selvar_no, {
removeUI(
selector = "div:has(> #dplyr_selvar)"
)
removeUI(
selector = "div:has(> #submit_dply_selvar)"
)
})
observeEvent(input$button_selvar_no, {
updateNavbarPage(session, 'mainpage', selected = 'tab_scr')
updateNavlistPanel(session, 'navlist_trans', 'tab_screen')
})
observeEvent(input$submit_dply_selvar, {
updateNavbarPage(session, 'mainpage', selected = 'tab_scr')
updateNavlistPanel(session, 'navlist_trans', 'tab_screen')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_select.R |
library(stringr)
output$trans_try <- renderUI({
ncol <- as.integer(ncol(uploadata$t))
lapply(1:ncol, function(i) {
fluidRow(
column(3,
selectInput(paste("n_col_", i), label = '', width = '150px',
choices = names(uploadata$t)[i], selected = names(uploadata$t)[i])
),
column(3,
textInput(paste("new_name_", i),
label = '', width = '150px',
value = names(uploadata$t)[i])
),
column(3,
selectInput(paste0("data_type_", i),
label = '', width = '150px',
choices = c('numeric', 'factor', 'Date', 'character', 'integer'),
selected = class(uploadata$t[[i]]))
),
column(3,
conditionalPanel(condition = paste(paste0("input.data_type_", i), "== 'Date'"),
column(4, br(), tags$h5('Format')),
column(8,
selectInput(paste("date_type_", i),
label = '', width = '150px',
choices = c('%d %m %y', '%d %m %Y', '%y %m %d', '%Y %m %d', '%d %y %m', '%d %Y %m',
'%m %d %y', '%m %d %Y', '%y %d %m', '%Y %d %m', '%m %y %d', '%m %Y %d',
'%d/%m/%y', '%d/%m/%Y', '%y/m /%d', '%Y/%m/%d', '%d/%y/%m', '%d/%Y/%m',
'%m/%d/%y', '%m/%d/%Y', '%y/%d/%m', '%Y/%d/%m', '%m/%y/%d', '%m/%Y/%d',
'%d-%m-%y', '%d-%m-%Y', '%y-m -%d', '%Y-%m-%d', '%d-%y-%m', '%d-%Y-%m',
'%m-%d-%y', '%m-%d-%Y', '%y-%d-%m', '%Y-%d-%m', '%m-%y-%d', '%m-%Y-%d'
),
selected = '%Y %m %d')
)
)
)
)
})
})
original <- reactive({
uploadata$t
})
save_names <- reactive({
names(original())
})
n <- reactive({
length(original())
})
data_types <- reactive({
ncol <- as.integer(ncol(uploadata$t))
collect <- list(lapply(1:ncol, function(i) {
input[[paste0("data_type_", i)]]
}))
colors <- unlist(collect)
})
new_names <- reactive({
ncol <- as.integer(ncol(uploadata$t))
collect <- list(lapply(1:ncol, function(i) {
input[[paste("new_name_", i)]]
}))
colors <- unlist(collect)
colnames <- str_replace(colors, " ", "_")
})
# original <- reactive({
# data()
# })
# save_names <- reactive({
# names(original())
# })
# n <- reactive({
# length(original())
# })
# data_types <- reactive({
# ncol <- as.integer(ncol(data()))
# collect <- list(lapply(1:ncol, function(i) {
# input[[paste0("data_type_", i)]]
# }))
# colors <- unlist(collect)
# })
# new_names <- reactive({
# ncol <- as.integer(ncol(data()))
# collect <- list(lapply(1:ncol, function(i) {
# input[[paste("new_name_", i)]]
# }))
# colors <- unlist(collect)
# colnames <- str_replace(colors, " ", "_")
# })
copy <- eventReactive(input$apply_changes, {
out <- list()
for (i in seq_len(n())) {
if (data_types()[i] == 'Date') {
inp <- eval(parse(text = paste0('input$', paste0('date_type_', i))))
out[[i]] <- eval(parse(text = paste0("as.", data_types()[i], "(original()$", save_names()[i], ", ", inp, ")")))
} else {
out[[i]] <- eval(parse(text = paste0("as.", data_types()[i], "(original()$", save_names()[i], ")")))
}
}
names(out) <- new_names()
return(out)
})
final <- eventReactive(input$apply_changes, {
data.frame(copy(), stringsAsFactors = F)
})
observeEvent(input$apply_changes, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_selvar')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_transform2.R |
library(rfm)
# importing data
inFile1 <- reactive({
if(is.null(input$file1)) {
return(NULL)
} else {
input$file1
}
})
data1 <- reactive({
if(is.null(inFile1())) {
return(NULL)
} else {
read.csv(inFile1()$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
}
})
# importing data
inFile2 <- reactive({
if(is.null(input$file2)) {
return(NULL)
} else {
input$file2
}
})
data2 <- reactive({
if(is.null(inFile2())) {
return(NULL)
} else {
ext <- tools::file_ext(inFile2()$name)
file.rename(inFile2()$datapath,
paste(inFile2()$datapath, ext, sep="."))
readxl::read_excel(
path = paste(inFile2()$datapath, ext, sep="."),
sheet = input$sheet_n
)
}
})
# importing data
inFile3 <- reactive({
if(is.null(input$file3)) {
return(NULL)
} else {
input$file3
}
})
data3 <- reactive({
if(is.null(inFile3())) {
return(NULL)
} else {
jsonlite::fromJSON(inFile3()$datapath)
}
})
# importing data
inFile4 <- reactive({
if(is.null(input$file4)) {
return(NULL)
} else {
input$file4
}
})
data4 <- reactive({
if(is.null(inFile4())) {
return(NULL)
} else {
haven::read_sas(inFile4()$datapath)
}
})
inFile5 <- reactive({
if(is.null(input$file5)) {
return(NULL)
} else {
input$file5
}
})
data5 <- reactive({
if(is.null(inFile5())) {
return(NULL)
} else {
haven::read_sav(inFile5()$datapath)
}
})
inFile6 <- reactive({
if(is.null(input$file6)) {
return(NULL)
} else {
input$file6
}
})
data6 <- reactive({
if(is.null(inFile6())) {
return(NULL)
} else {
haven::read_stata(inFile6()$datapath)
}
})
inFile7 <- reactive({
if(is.null(input$file7)) {
return(NULL)
} else {
input$file7
}
})
data7 <- reactive({
if(is.null(inFile7())) {
return(NULL)
} else {
readRDS(inFile7()$datapath)
}
})
observe({
updateSelectInput(
session,
inputId = 'sel_data',
label = '',
choices = c(input$file1$name, input$file2$name, input$file3$name,
input$file4$name, input$file5$name, input$file6$name, input$file7$name),
selected = ''
)
})
ext_type <- reactive({
ext <- tools::file_ext(input$sel_data)
})
# choosing sample data
sampdata <- reactiveValues(s = NULL)
observeEvent(input$orders_data, {
sampdata$s <- rfm_data_orders
})
observeEvent(input$customer_data, {
sampdata$s <- rfm_data_customer
})
uploadata <- reactiveValues(t = NULL)
observeEvent(input$submit_seldata, {
if (ext_type() == 'csv') {
uploadata$t <- data1()
} else if (ext_type() == 'xls') {
uploadata$t <- data2()
} else if (ext_type() == 'xlsx') {
uploadata$t <- data2()
} else if (ext_type() == 'json') {
uploadata$t <- data3()
} else if (ext_type() == 'sas7bdat') {
uploadata$t <- data4()
} else if (ext_type() == 'sav') {
uploadata$t <- uploadata$t <- data5()
} else if (ext_type() == 'dta') {
uploadata$t <- data6()
} else {
uploadata$t <- data7()
}
})
observeEvent(input$use_sample_data, {
uploadata$t <- sampdata$s
})
observeEvent(input$use_sample_data, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_transform')
})
observeEvent(input$submit_seldata, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_transform')
})
observeEvent(input$csv2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$csv2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$excel2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$excel2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$json2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$json2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$stata2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$stata2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$spss2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$spss2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$sas2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$sas2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$rds2datasrc, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$rds2datatrans, {
updateNavbarPage(session, 'mainpage', selected = 'tab_trans')
updateNavlistPanel(session, 'navlist_trans', 'tab_seldata')
})
observeEvent(input$welcomebutton, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_upload.R |
output$table <- renderDataTable({
final_sel$a
})
observeEvent(input$view2getdata, {
updateNavbarPage(session, 'mainpage', selected = 'tab_upload')
updateNavlistPanel(session, 'navlist_up', 'tab_datasources')
})
observeEvent(input$view2analyze, {
updateNavbarPage(session, 'mainpage', selected = 'tab_eda')
updateNavlistPanel(session, 'navlist_eda', 'tab_summary')
}) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/logic_view.R |
tabPanel("Home", value = "tab_rfm_home",
fluidPage(
fluidRow(
column(12, align = 'center',
h3('What type of data do you have?')
)
),
br(),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'summary1.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Transaction Data'),
p('Each row represents a transaction/order.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_transaction',
label = 'Click Here',
width = '100px'
)
),
column(1)
),
br(),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'summary1.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Customer Data'),
p('Each row represents transactions of a customer. The data includes
the number of days since the last transaction.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_customer_1',
label = 'Click Here',
width = '100px'
)
),
column(1)
),
fluidRow(
column(1),
column(2, align = 'right',
img(src = 'summary1.png', width = '100px', height = '100px')
),
column(6, align = 'center',
h4('Customer Data'),
p('Each row represents transactions of a customer. The data includes
the date of the latest transaction.')
),
column(2, align = 'left',
br(),
actionButton(
inputId = 'click_customer_2',
label = 'Click Here',
width = '100px'
)
),
column(1)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/logic/ui_rfm_home.R |
library(rfm)
library(descriptr)
library(dplyr)
library(tibble)
library(readxl)
library(haven)
library(readr)
library(jsonlite)
library(magrittr)
library(tools)
library(lubridate)
library(scales)
library(stringr)
library(rlang)
library(ggplot2)
library(forcats)
library(purrr)
library(RColorBrewer)
library(knitr)
library(kableExtra)
shinyServer(function(input, output, session) {
source("logic/logic_dataoptions.R", local = T)
source("logic/logic_upload.R", local = T)
source("logic/logic_transform2.R", local = T)
source("logic/logic_select.R", local = T)
source("logic/logic_screen.R", local = T)
source("logic/logic_view.R", local = T)
source("logic/logic_home.R", local = T)
source("logic/logic_exit_button.R", local = T)
source("logic/logic_rfm_score.R", local = T)
source("logic/logic_segments.R", local = T)
})
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/server.R |
navbarMenu('Analyze', icon = icon('search-plus'),
source('ui/ui_home.R', local = TRUE)[[1]],
source('ui/ui_rfm.R', local = TRUE)[[1]],
source('ui/ui_segment.R', local = TRUE)[[1]]
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_analyze.R |
tabPanel("Median Frequency", value = "tab_average_frequency",
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Median Frequency by Segment')
)
),
hr(),
fluidRow(
br(),
br(),
column(2),
column(8, align = 'center',
plotOutput('segment_average_frequency') %>%
withSpinner()
),
column(2)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_average_frequency.R |
tabPanel("Median Monetary Value", value = "tab_average_monetary",
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Median Monetary Value by Segment')
)
),
hr(),
fluidRow(
br(),
br(),
column(2),
column(8, align = 'center',
plotOutput('segment_average_monetary') %>%
withSpinner()
),
column(2)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_average_monetary.R |
tabPanel("Median Recency", value = "tab_average_recency",
fluidPage(
fluidRow(
column(6, align = 'left',
h4('Median Recency by Segment')
)
),
hr(),
fluidRow(
br(),
br(),
column(2),
column(8, align = 'center',
plotOutput('segment_average_recency') %>%
withSpinner()
),
column(2)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_average_recency.R |
navbarMenu('Data', icon = icon('database'),
source('ui/ui_up.R', local = TRUE)[[1]],
source('ui/ui_trans.R', local = TRUE)[[1]],
source('ui/ui_scr.R', local = TRUE)[[1]],
source('ui/ui_vi.R', local = TRUE)[[1]]
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_data.R |
tabPanel('Upload File', value = 'tab_uploadfile',
fluidPage(
includeCSS("mystyle.css"),
fluidRow(
column(12,
tabsetPanel(type = 'tabs', id = 'tabset_upload',
tabPanel('CSV', value = 'tab_upload_csv',
fluidPage(
br(),
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a comma or tab separated file.')
),
column(4, align = 'right',
actionButton(inputId='uploadlink2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput('file1', 'Data Set:',
accept = c('text/csv', '.csv',
'text/comma-separated-values,text/plain')
)
)
),
fluidRow(
column(12, align = 'center', checkboxInput('header', 'Header', TRUE))
),
fluidRow(
column(12, align = 'center',
selectInput('sep', 'Separator',
choices = c('Comma' = ',', 'Semicolon' = ';', 'Tab' = '\t'), selected = ',')
)
),
fluidRow(
column(12, align = 'center',
selectInput('quote', 'Quote',
choices = c('None' = '', 'Double Quote' = '"', 'Single Quote' = "'"), selected = '')
)
),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='csv2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='csv2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('Excel', value = 'tab_upload_excel',
fluidPage(
br(),
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .xls or .xlsx file.')
),
column(4, align = 'right',
actionButton(inputId='uploadexcel2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file2',
label = 'Choose file:',
accept = c('.xls', '.xlsx')
)
)
),
fluidRow(
column(12, align = 'center',
numericInput(
inputId = 'sheet_n',
label = 'Sheet',
value = 1,
min = 1,
step = 1,
width = '120px'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='excel2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='excel2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('JSON', value = 'tab_upload_json',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .json file.')
),
column(4, align = 'right',
actionButton(inputId='uploadjson2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file3',
label = 'Choose file:',
accept = '.json'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='json2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='json2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('STATA', value = 'tab_upload_stata',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .dta file.')
),
column(4, align = 'right',
actionButton(inputId='uploadstata2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file6',
label = 'Choose file:',
accept = '.dta'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='stata2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='stata2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('SPSS', value = 'tab_upload_spss',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .sav file.')
),
column(4, align = 'right',
actionButton(inputId='uploadspss2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file5',
label = 'Choose file:',
accept = '.sav'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='spss2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='spss2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('SAS', value = 'tab_upload_sas',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a .sas7bdat file.')
),
column(4, align = 'right',
actionButton(inputId='uploadsas2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file4',
label = 'Choose file:',
accept = '.sas7bdat'
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='sas2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='sas2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
),
tabPanel('RDS', value = 'tab_upload_rds',
br(),
fluidPage(
fluidRow(
column(8, align = 'left',
h4('Upload Data'),
p('Upload data from a RDS file.')
),
column(4, align = 'right',
actionButton(inputId='uploadrds2', label="Demo", icon = icon("video-camera"),
onclick ="window.open('https://www.youtube.com/watch?v=IckaPr19Bvc#t=00m29s', '_blank')")
)
),
hr(),
fluidRow(
column(12, align = 'center',
fileInput(
inputId = 'file7',
label = 'Choose file:',
accept = ''
)
)
),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
br(),
fluidRow(
column(6, align = 'left',
actionButton(inputId='rds2datasrc', label="Data Sources", icon = icon("long-arrow-left"))
),
column(6, align = 'right',
actionButton(inputId='rds2datatrans', label="Data Selection", icon = icon("long-arrow-right"))
)
)
)
)
)
)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_datafiles.R |
tabPanel('Data Sources', value = 'tab_datasources',
fluidPage(theme = shinytheme('cerulean'),
includeCSS("mystyle.css"),
fluidRow(
column(12, align = 'center',
h4('Use sample data or upload a file')
)
),
fluidRow(
column(6, align = 'right',
actionButton(
inputId = 'sample_data_yes',
label = 'Sample Data',
width = '120px'
)
),
column(6, align = 'left',
actionButton(
inputId = 'upload_files_yes',
label = 'Upload File',
width = '120px'
)
)
),
br(),
fluidRow(
column(12, align = 'center',
h6('The app takes a few seconds to load. Please wait for ~12 seconds.')
)
),
br(),
br(),
fluidRow(
uiOutput('upload_file_links')
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_dataoptions.R |
tabPanel('Sample Data', value = 'tab_use_sample',
fluidPage(
includeCSS("mystyle.css"),
fluidRow(
column(12, align = 'center',
h5('Click on a sample for more information')
)
),
br(),
fluidRow(
column(6, align = 'center',
actionButton(
inputId = 'orders_data',
label = 'Transaction Data',
width = '200px',
onclick ="window.open('https://rfm.rsquaredacademy.com/reference/rfm_data_orders.html', 'newwindow', 'width=800,height=600')"
)
),
column(6, align = 'center',
actionButton(
inputId = 'customer_data',
label = 'Customer Data',
width = '200px',
onclick ="window.open('https://rfm.rsquaredacademy.com/reference/rfm_data_customer.html', 'newwindow', 'width=800,height=600')"
)
)
),
br(),
br(),
br(),
fluidRow(
column(12, align = 'center',
actionButton(
inputId = 'use_sample_data',
label = 'Use Sample Data',
width = '200px'
)
)
)
)
) | /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_datasamples.R |
# Exit -----------------------------------------------------------
tabPanel("", value = "exit", icon = icon("power-off"),
br(),
br(),
br(),
br(),
br(),
br(),
# In case window does not close, one should see this message
fluidRow(column(3),
column(6, h2("Thank you for using", strong("rfm"), "!"))),
fluidRow(column(3),
column(6, h4("Now you should close this window.")))
)
| /scratch/gouwar.j/cran-all/cranData/xplorerr/inst/app-rfm/ui/ui_exit_button.R |
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