iedup : tempfix to #152

iedup : tempfix to #152

Temporary fix with better error message to issue #152 to be included in the version 5.6

*! version 5.5 26APR2018 DIME Analytics lcardosodeandrad@worldbank.org

	capture program drop ieduplicates
	program ieduplicates , rclass

	qui {

		syntax varname ,  FOLder(string) UNIQUEvars(varlist) [KEEPvars(varlist) tostringok droprest nodaily SUFfix(string)]

		version 11.0

		*Add version of Stata fix
		//Make sure that keepvars are still saved if saved if the duplicates file
		*	is generated on a subset of the data. For example, duplicates from
		*	version 1, 2 . If the command is run on only version 1. Then values
		*	for keeepvars in version 2 and 3 are dropped and not reloaded as those
		*	obs are not in current memory

		**Test that observations have not been deleted from the report before readind
		* it. Deleted in a way that the report does not make sense. Provide an error
		* message to this that is more informative.

		preserve

			/***********************************************************************
			************************************************************************

				Section 1 - Set up locals needed in data

				Saving a version of the data to be used before merging corrections
				back to the original data set and before correcting duplicates

			************************************************************************
			***********************************************************************/

			*Tempfiles to be uses
			tempfile originalData preppedReport datawithreportmerged dataToReturn

			** Save a version of original data that can be brought
			*  back withing the preserve/restore section
			save 	`originalData'

			* Create a local with todays date to date the reports
			local  date = subinstr(c(current_date)," ","",.)

			*Put idvar in a local with a more descriptive name
			local idvar `varlist'

			** Making one macro with all variables that will be
			*  imported and exported from the Excel file
			local argumentVars `idvar' `uniquevars' `keepvars'

			* Create a list of the varaibles created by this command to put in the report
			local excelvars dupListID dateListed dateFixed correct drop newID initials notes

			* Test that nu variable with the name needed for the excel report already exist in the data set
			foreach excelvar of local excelvars {
				cap confirm variable `excelvar'
				if _rc == 0 {
					*Variable exist, output error
					noi display as error "{phang}The data set already have a variable called {inp:`excelvar'} which is a name that this command requires to be availible. Please change the name of the variable already in the data set. Future versions of this command will allow the user to rename the variables used by this command.{p_end}"
					noi di ""
					error 198
					exit
				}
			}

			/***********************************************************************
			************************************************************************

				Section 2 - Test unique vars

				Test the unique vars so that they are identifying the data set and
				are not in a time format that might get corrupted by exporting to
				and importing from Excel. This is needed as the uniquevars are needed
				to merge the correct correction to the correct duplicat

			************************************************************************
			***********************************************************************/

			*Test that the unique vars fully and uniquely identifies the data set
			cap isid `uniquevars'
			if _rc {

				noi display as error "{phang}The variable(s) listed in uniquevars() does not uniquely and fully identifies all observations in the data set.{p_end}"
				isid `uniquevars'
				error 198
				exit
			}

			** Test that no unique vars are time format. Time values might be corrupted
			*  and changed a tiny bit when importing and exporting to Excel, which make merge not possible
			foreach uniquevar of local uniquevars {

				*Test if format is time format
				local format : format `uniquevar'
				if substr("`format'",1,2) == "%t" {

					noi display as error `"{phang}The variable {inp:`uniquevar'} listed in {inp:uniquevars()} is using time format which is not allowed. Stata and Excel stores and displays time slightly differently which can lead to small changes in the value when the value is imported and exported between Stata and Excel, and then the variable can no longer be used to merge the report back to the original data. Use another variable or create a string variable out of your time variable using this code: {inp: generate `uniquevar'_str = string(`uniquevar',"%tc")}.{p_end}"'
					noi di ""
					error 198
					exit
				}
			}

			/***********************************************************************
			************************************************************************

				Section 3 - Test input from Excel file

				If Excel repirt exist, import it and test for invalid corrections
				made in the excel report.

			************************************************************************
			***********************************************************************/

			/******************
				Section 3.1
				Check if report exists from before.
			******************/

			*Check if file exist. Suffix option can be used to create multiple reports in the same folder
			cap confirm file "`folder'/iedupreport`suffix'.xlsx"

			if !_rc {
				local fileExists 1
			}
			else {
				local fileExists 0
			}

			/******************
				Section 3.2
				If report exist, load file and check input, otherwise skip to section 4
			******************/

			if `fileExists' {

				*Load excel file. Load all vars as string and use meta data from Section 1
				import excel "`folder'/iedupreport`suffix'.xlsx"	, clear firstrow

				** All excelvars but dupListID and newID should be string. dupListID
				*  should be numeric and the type of newID should be based on the user input
				foreach excelvar of local excelvars {
					if !inlist("`excelvar'", "dupListID", "newID") {

						* Make original ID var string
						tostring `excelvar' , replace
						replace  `excelvar' = "" if `excelvar' == "."
					}
				}

				/******************
					Section 3.2.1
					Make sure that the ID variable and the uniquevars are
					not changed since last report.
				******************/

				*Copy a list of all variables in the imported report to the local existingexcelvars
				ds
				local existingexcelvars  `r(varlist)'

				*Test that the ID variable is in the imported report
				if `:list idvar in existingexcelvars' == 0 {

					noi display as error "{phang}The ID variable `idvar' does not exist in the previously exported Excle file. If you renamed the ID variable you need to rename it manually in the Excel report or start a new Excel report by renaming or moving the original report, then run the command again and create a new file and manually copy any corrections from the old file to the new. If you changed the ID varaible you need to start with a new report.{p_end}"
					noi di ""
					error 111
					exit

				}

				*Test that the unique variables are in the imported report
				if `:list uniquevars in existingexcelvars' == 0 {

					noi display as error "{phang}One or more unique variables in [`uniquevars'] do not exist in the previously exported Excel file. If you renamed or changed any variable used in uniquevars(), you need to start over with a new file. Rename or move the already existing file. Create a new file and carefully copy any corrections from the old file to the new.{p_end}"
					noi di ""
					error 111
					exit
				}

				/******************
					Section 3.3
					Make sure input is correct
				******************/

				*Temporary variables needed for checking input
				tempvar multiInp inputNotYes maxMultiInp notDrop yesCorrect groupNumCorrect toDrop anyCorrection groupAnyCorrection

				*Locals indicating in which ways input is incorrect (if any)
				local local_multiInp 		0
				local local_multiCorr		0
				local local_inputNotYes		0
				local local_notDrop			0

				/******************
					Section 3.3.1
					Make sure input is yes or y for the correct and drop columns
				******************/

				* Make string input lower case and change "y" to "yes"
				replace correct = lower(correct)
				replace drop 	= lower(drop)
				replace correct = "yes" if correct 	== "y"
				replace drop 	= "yes" if drop 	== "y"

				*Check that varaibles are either empty or "yes"
				gen `inputNotYes' = !((correct  == "yes" | correct == "") & (drop  == "yes" | drop == ""))

				*Set local to 1 if error should be outputted
				cap assert `inputNotYes' == 0
				if _rc local local_inputNotYes 	1

				/******************
					Section 3.3.2
					Make sure there are not too many corrections for a single observation
				******************/

				* Count the number of corrections (correct drop newID) per
				* observation. Only one correction per observation is allowed.
				egen `multiInp' = rownonmiss(correct drop newID), strok

				*Check that all rows have utmost one correction
				cap assert `multiInp' == 0 | `multiInp' == 1

				*Error will be outputted below
				if _rc local local_multiInp 	1

				/******************
					Section 3.3.3
					Test that maximum one duplicate per duplicate group is indicated as correct

				******************/

				*Generate dummy if correct column is set to yes
				gen `yesCorrect' = (correct == "yes")

				*Count number of duplicates within duplicates where that dummy is 1
				bys `idvar' : egen `groupNumCorrect' =  total(`yesCorrect')

				*Test if more than 1 duplicate in that duplicate group is correct
				count if `groupNumCorrect' > 1

				*Output error is more than one duplicate in a duplicate group is yes
				if `r(N)' != 0 local local_multiCorr 1

				/******************
					Section 3.3.4
					Make sure that either option droprest is specified, or that
					drop was correctly indicated for all observations. i.e.; if
					correct or newID was indicated for at least one duplicate in
					a duplicate group, then all other observations should be
					indicated as drop (unless droprest is specified)

				******************/

				*Generate dummy if there is any correction for this observation
				gen `anyCorrection' = !missing(correct) | !missing(newID)

				*Count number of observations with any correction in suplicates group
				bys `idvar' : egen `groupAnyCorrection' =  total(`anyCorrection')

				*Create dummy that indicates each place this error happens
				gen `notDrop' = (missing(drop) & `groupAnyCorrection' > 0 & `anyCorrection' == 0)

				* Check if option droprest is specified
				if "`droprest'" == "" {

					** If option droprest is not used, then all observations in a duplicate
					*  group where at least one observation has a correction must have a
					*  correction or have drop set to yes.
					cap assert `notDrop' == 0

					*Error will be outputted below
					if _rc local local_notDrop 	1

				}
				else {

					** Option -droprest- specified. Drop will be changed to yes
					*  for any observations without drop or any other correction
					*  explicitly specified if the observation is in a duplicate
					*  group with at least one observation has a correction
					replace drop 	= "yes" if `notDrop' == 1

				}

				/******************
					Section 3.4
					Throw errors if any of the tests were not passed
				******************/

				*Output errors if errors in the report were detected
				if `local_multiInp' == 1 | `local_inputNotYes' == 1 | `local_notDrop' == 1 | `local_multiCorr' == 1  {
					noi {
						di ""
						di ""
						di as error "{phang}{ul:The corrections made in the Excel report has the following errors:}{p_end}"
						di ""

						*Error multiple input
						if `local_multiInp' == 1 {
							display as error "{phang}The following observations have more than one correction. Only one correction (correct, drop or newID) per row is allowed{p_end}"
							list `idvar' dupListID correct drop newID `uniquevars' if `multiInp' > 1
							di ""
						}

						*Error multiple correct
						if `local_multiCorr' == 1 {
							display as error "{phang}The following observations are in a duplicate group where more than one observation is listed as correct. Only one observation per duplicate group can be correct{p_end}"
							list `idvar' dupListID correct drop newID `uniquevars' if `groupNumCorrect' > 1
							di ""
						}

						*Error in incorrect string
						if `local_inputNotYes' == 1 {
							display as error "{phang}The following observations have an answer in either correct or drop that is neither yes nor y{p_end}"
							list `idvar' dupListID correct drop `uniquevars' if `inputNotYes' == 1
							di ""
						}

						*Error is not specfied as drop
						if `local_notDrop' == 1 {
							display as error "{phang}The following observations are not explicitly indicated as drop while other duplicates in the same duplicate group are corrected. Either manually indicate as drop or see option droprest{p_end}"
							list `idvar' dupListID correct drop newID `uniquevars' if `notDrop' == 1
							di ""
						}

						*Same error for any incorrect input
						error 198
						exit
					}
				}

				/******************
					Section 3.5
					Save the prepared report to be used later
				******************/

				*Keep only the variables needed for matching and variables used for input in the Excel file
				keep 	`idvar' `uniquevars' `excelvars' `groupAnyCorrection'

				*Save imported data set with all corrections
				save	`preppedReport'
			}

			/***********************************************************************
			************************************************************************

				Section 4 - Merge report to original data

				Merge corrections with original data, test that there are no
				obs in report that are not in main data, and save this data
				in temp file

			************************************************************************
			***********************************************************************/

			*Re-load original data
			use `originalData', clear

			* Merge original data with imported Excel file (if Excel file exists)
			if `fileExists'  {

				*Create a tempvar for merging results
				tempvar iedup_merge

				*Merge the corrections with the data set
				merge 1:1 `uniquevars' using `preppedReport', generate(`iedup_merge')

				*Make sure that obsrevations listed in the duplicate report is still in the data set
				cap assert `iedup_merge' != 2

				*Display error message if assertion is not true and some duplicates in the excle file are no longer in the data set
				if _rc {

					display as error "{phang}One or several observations in the Excel report are no longer found in the data set. Always run ieduplicates on the raw data set that include all the duplicates, both new duplicates and those you have already identified. After removing duplicates, save the data set using a different name. You might also recieve this error if you are using an old ieduplicates Excel report on a new data set.{p_end}"
					error 9
					exit
				}

				*Explicitly drop temporary variable. Temporary variables might
				*be exported to excel so delete explicitly before that. Only
				*using tempvar here to create a name with no conflicts
				drop `iedup_merge'

			}

			*save the data set to be used when correcting the data
			save `datawithreportmerged'

			/***********************************************************************
			************************************************************************

				Section 5 - Generate the Excel report

				Test if there are duplicates in ID var. If any duplicates exist,
				then update the Excel file with new and unaddressed cases

			************************************************************************
			***********************************************************************/

			/******************
				Section 5.1
				Test if there are any duplicates in ID var
			******************/

			* Generate variable that is not 0
			* if observation is a duplicate
			tempvar dup
			duplicates tag `idvar', gen(`dup')

			*Test if there are any duplicates
			cap assert `dup'==0
			if _rc {

				/******************
					Section 5.2
					Keep only duplicates for the report
				******************/

				*Keep if observation is part of duplicate group
				keep if `dup' != 0

				if `fileExists'  {
					* If Excel file exists keep excel vars and
					* variables passed as arguments in the
					* command
					keep 	`argumentVars' `excelvars'
				}
				else {
					* Keep only variables passed as arguments in
					* the command and the string ID var as no Excel file exists
					keep 	`argumentVars'

					*Generate the excel variables used for indicating correction
					foreach excelvar of local excelvars {

						*Create all variables apart from dupListID as string vars
						if inlist("`excelvar'", "dupListID", "newID") {
							gen `excelvar' = .
						}
						else {
							gen `excelvar' = ""
						}
					}
				}

				/******************
					Section 5.3
					Update the excel vars that are not updated manually
				******************/

				* Generate a local that is 1 if there are new duplicates
				local unaddressedNewExcel 0
				count if missing(dateFixed)
				if `r(N)' > 0 local unaddressedNewExcel 1

				/******************
					Section 5.3.1 Date variables
				******************/

				* Add date first time duplicvate was identified
				replace dateListed 	= "`date'" if missing(dateListed)

				** Add today's date to variable dateFixed if dateFixed
				*  is empty and at least one correction is added
				replace dateFixed 	= "`date'" if missing(dateFixed) & (!missing(correct) | !missing(drop) | !missing(newID))

				/******************
					Section 5.3.2 Duplicate report list ID
				******************/

				** Sort after dupListID and after ID var for
				*  duplicates currently without dupListID
				sort dupListID `idvar'

				** Assign dupListID 1 to the top row if no duplicate
				*  list IDs have been generated so far.
				replace dupListID = 1 if _n == 1 & missing(dupListID)

				** Generate new IDs based on the row above instead of directly
				*  from the row number. That prevents duplicates in the list in
				*  case an observation is deleted. The first observation with
				*  missing value will have an ID that is one digit higher than
				*  the highest ID already in the list
				replace dupListID = dupListID[_n - 1] + 1 if missing(dupListID)

				/******************
					Section 5.4
					Keep and order the variables and output the Excel files
				******************/

				* If cases unaddressed then update the Excel file
				if `unaddressedNewExcel'  {

					keep 	`argumentVars' `excelvars'
					order	`idvar' `excelvars' `uniquevars' `keepvars'

					if "`daily'" == "" {

						*Returns 0 if folder does not exist, 1 if it does
						mata : st_numscalar("r(dirExist)", direxists("`folder'/Daily"))

						** If the daily folder is not created, just create it
						if `r(dirExist)' == 0  {

							*Create the folder since it does not exist
							mkdir "`folder'/Daily"
						}

						*Export the daily file
						cap export excel using "`folder'/Daily/iedupreport`suffix'_`date'.xlsx"	, firstrow(variables) replace nolabel

						*Print error if daily report cannot be saved
						if _rc {

							display as error "{phang}The Daily copy could not be saved to the `folder'/Daily folder. Make sure to close any old daily copy or see the option nodaily.{p_end}"
							error 603
							exit
						}

						*Prepare local for output
						local daily_output " and a daily copy have been saved to the Daily folder"
					}

					*Export main report
					export excel using "`folder'/iedupreport`suffix'.xlsx"	, firstrow(variables) replace  nolabel

					*Produce output
					noi di `"{phang}Excel file created at: {browse "`folder'/iedupreport`suffix'.xlsx":`folder'/iedupreport`suffix'.xlsx}`daily_output'.{p_end}"'
					noi di ""
				}
			}

		/***********************************************************************
		************************************************************************

			Section 6

			Update the data set and with the new corrections.

		************************************************************************
		***********************************************************************/

		* Load the original data set merged with correction. Duplicates
		* in all variables are already dropped in this data set
		use 	`datawithreportmerged', clear

		* If excel file exists, apply any corrections indicated (if any)
		if `fileExists' {

			/******************
				Section 6.1
				Drop duplicates listed for drop
			******************/

			drop if drop == "yes"

			/******************
				Section 6.2
				Update new ID. ID var can be either numeric or
				string. All numbers can be made strings but not
				all strings can be numeric. Therefore this
				section is complicated.
			******************/

			/******************
				Section 6.2.1
				ID var in original file is string. Either
				newID was imported as string or the variable
				is made string. Easy.
			******************/

			*Test if there are any corrections by new ID
			cap assert missing(newID)
			if _rc {

				local idtype 	: type `idvar'
				local idtypeNew : type newID

				*If ID var is string but newID is not, then just make it string
				if substr("`idtype'",1,3) == "str" & substr("`idtypeNew'",1,3) != "str" {

					tostring newID , replace
					replace  newID = "" if newID == "."
				}

				*If ID var is numeric but the newID is loaded as string
				else if substr("`idtype'",1,3) != "str" & substr("`idtypeNew'",1,3) == "str" {

					* Check if -tostringok- is specificed:
					if "`tostringok'" != "" {

						* Make original ID var string
						tostring `idvar' , replace
						replace  `idvar' = "" if `idvar' == "."

					}

					* Error, IDvar can not be updated
					else {

						* Create a local with all non-numeric values
						levelsof newID if missing(real(newID)), local(NaN_values) clean

						* Output error message
						di as error "{phang}The ID varaible `idvar' is numeric but newID has thes non-numeric values: `NaN_values'. Update newID to only contain numeric values or see option tostringok.{p_end}"
						error 109
						exit
					}
				}

				*After making sure that type is ok, update the IDs
				replace `idvar' = newID if !missing(newID)

				/******************
					Section 6.3
					Test that values in newID
					were neither used twice
					nor already existed
				******************/

				*Test if there are any duplicates after corrections (excluding observations in duplicate groups not yet corrected)
				tempvar newDup
				duplicates tag `idvar' if `groupAnyCorrection' != 0, generate(`newDup')

				*Consider missing values as no new duplicates, make conditions below easier
				replace `newDup' = 0 if missing(`newDup')

				cap assert `newDup' == 0
				if _rc {

					levelsof `idvar' if `newDup' != 0  , local(newDuplist)
					di as error "{phang}No corrections from the report are applied as it would lead to new duplicates in the following value(s): `newDuplist'.{p_end}"
					error 198
					exit
				}
			}

			/******************
				Section 6.4
				Drop Excel vars
			******************/

			drop `excelvars'
		}

		/***********************************************************************
		************************************************************************

			Section 7

			Return the data set without duplicates and
			output information regarding unresolved duplicates.

		************************************************************************
		***********************************************************************/

		* Generate a variable that is 1 if the observation is a duplicate in varlist
		tempvar dropDup
		duplicates tag `idvar',  gen(`dropDup')
		* Generate a list of the IDs that are still duplicates
		levelsof `idvar' 			if `dropDup' != 0 , local(dup_ids) clean
		* Drop the duplicates (they are exported in Excel)
		drop 						if `dropDup' != 0

		* Test if varlist is now uniquely and fully identifying the data set
		cap isid `idvar'
		if _rc {

			di as error "{phang}The data set is not returned with `idvar' uniquely and fully identifying the data set. Please report this bug to kbjarkefur@worldbank.org{p_end}"
			error 119
			exit

		}

		*Count number of duplicate groups still in the data
		local numDup	= `:list sizeof dup_ids'

		if `numDup' == 0 {
			noi di	"{phang}There are no unresolved duplicates in this data set. The data set is returned with `idvar' uniquely and fully identifying the data set.{p_end}"
		}
		else {
			noi di	"{phang}There are `numDup' duplicated IDs still unresolved. IDs still containing duplicates: `dup_ids'. The unresolved duplicate observations were exported in the Excel file. The data set is returned without those duplicates and with `idvar' uniquely and fully identifying the data set.{p_end}"
		}

		return scalar numDup	= `numDup'

		/***********************************************************************
		************************************************************************

			Section 8

			Save data set to be returned outside preserve/restore.
			Preserve/restore is used so that original data is returned
			in case an error is thrown.

		************************************************************************
		***********************************************************************/

		save 	`dataToReturn'

		restore

		** Using restore above to return the data to
		*  the orignal data set in case of error.
		use `dataToReturn', clear

	}
end

*! version 5.5 26APR2018 DIME Analytics lcardosodeandrad@worldbank.org

	capture program drop ieduplicates
	program ieduplicates , rclass

	qui {

		syntax varname ,  FOLder(string) UNIQUEvars(varlist) [KEEPvars(varlist) tostringok droprest nodaily SUFfix(string)]

		version 11.0

		*Add version of Stata fix
		//Make sure that keepvars are still saved if saved if the duplicates file
		*	is generated on a subset of the data. For example, duplicates from
		*	version 1, 2 . If the command is run on only version 1. Then values
		*	for keeepvars in version 2 and 3 are dropped and not reloaded as those
		*	obs are not in current memory

		**Test that observations have not been deleted from the report before readind
		* it. Deleted in a way that the report does not make sense. Provide an error
		* message to this that is more informative.

		preserve

			/***********************************************************************
			************************************************************************

				Section 1 - Set up locals needed in data

				Saving a version of the data to be used before merging corrections
				back to the original data set and before correcting duplicates

			************************************************************************
			***********************************************************************/

			*Tempfiles to be uses
			tempfile originalData preppedReport datawithreportmerged dataToReturn

			** Save a version of original data that can be brought
			*  back withing the preserve/restore section
			save 	`originalData'

			* Create a local with todays date to date the reports
			local  date = subinstr(c(current_date)," ","",.)

			*Put idvar in a local with a more descriptive name
			local idvar `varlist'

			** Making one macro with all variables that will be
			*  imported and exported from the Excel file
			local argumentVars `idvar' `uniquevars' `keepvars'

			* Create a list of the varaibles created by this command to put in the report
			local excelvars dupListID dateListed dateFixed correct drop newID initials notes

			/***********************************************************************
			************************************************************************

				Section 2 - Test unique vars

				Test the unique vars so that they are identifying the data set and
				are not in a time format that might get corrupted by exporting to
				and importing from Excel. This is needed as the uniquevars are needed
				to merge the correct correction to the correct duplicat

			************************************************************************
			***********************************************************************/

			*Test that the unique vars fully and uniquely identifies the data set
			cap isid `uniquevars'
			if _rc {

				noi display as error "{phang}The variable(s) listed in uniquevars() does not uniquely and fully identifies all observations in the data set.{p_end}"
				isid `uniquevars'
				error 198
				exit
			}

			** Test that no unique vars are time format. Time values might be corrupted
			*  and changed a tiny bit when importing and exporting to Excel, which make merge not possible
			foreach uniquevar of local uniquevars {

				*Test if format is time format
				local format : format `uniquevar'
				if substr("`format'",1,2) == "%t" {

					noi display as error `"{phang}The variable {inp:`uniquevar'} listed in {inp:uniquevars()} is using time format which is not allowed. Stata and Excel stores and displays time slightly differently which can lead to small changes in the value when the value is imported and exported between Stata and Excel, and then the variable can no longer be used to merge the report back to the original data. Use another variable or create a string variable out of your time variable using this code: {inp: generate `uniquevar'_str = string(`uniquevar',"%tc")}.{p_end}"'
					noi di ""
					error 198
					exit
				}
			}

			/***********************************************************************
			************************************************************************

				Section 3 - Test input from Excel file

				If Excel repirt exist, import it and test for invalid corrections
				made in the excel report.

			************************************************************************
			***********************************************************************/

			/******************
				Section 3.1
				Check if report exists from before.
			******************/

			*Check if file exist. Suffix option can be used to create multiple reports in the same folder
			cap confirm file "`folder'/iedupreport`suffix'.xlsx"

			if !_rc {
				local fileExists 1
			}
			else {
				local fileExists 0
			}

			/******************
				Section 3.2
				If report exist, load file and check input, otherwise skip to section 4
			******************/

			if `fileExists' {

				*Load excel file. Load all vars as string and use meta data from Section 1
				import excel "`folder'/iedupreport`suffix'.xlsx"	, clear firstrow

				** All excelvars but dupListID and newID should be string. dupListID
				*  should be numeric and the type of newID should be based on the user input
				foreach excelvar of local excelvars {
					if !inlist("`excelvar'", "dupListID", "newID") {

						* Make original ID var string
						tostring `excelvar' , replace
						replace  `excelvar' = "" if `excelvar' == "."
					}
				}

				/******************
					Section 3.2.1
					Make sure that the ID variable and the uniquevars are
					not changed since last report.
				******************/

				*Copy a list of all variables in the imported report to the local existingexcelvars
				ds
				local existingexcelvars  `r(varlist)'

				*Test that the ID variable is in the imported report
				if `:list idvar in existingexcelvars' == 0 {

					noi display as error "{phang}The ID variable `idvar' does not exist in the previously exported Excle file. If you renamed the ID variable you need to rename it manually in the Excel report or start a new Excel report by renaming or moving the original report, then run the command again and create a new file and manually copy any corrections from the old file to the new. If you changed the ID varaible you need to start with a new report.{p_end}"
					noi di ""
					error 111
					exit

				}

				*Test that the unique variables are in the imported report
				if `:list uniquevars in existingexcelvars' == 0 {

					noi display as error "{phang}One or more unique variables in [`uniquevars'] do not exist in the previously exported Excel file. If you renamed or changed any variable used in uniquevars(), you need to start over with a new file. Rename or move the already existing file. Create a new file and carefully copy any corrections from the old file to the new.{p_end}"
					noi di ""
					error 111
					exit
				}

				/******************
					Section 3.3
					Make sure input is correct
				******************/

				*Temporary variables needed for checking input
				tempvar multiInp inputNotYes maxMultiInp notDrop yesCorrect groupNumCorrect toDrop anyCorrection groupAnyCorrection

				*Locals indicating in which ways input is incorrect (if any)
				local local_multiInp 		0
				local local_multiCorr		0
				local local_inputNotYes		0
				local local_notDrop			0

				/******************
					Section 3.3.1
					Make sure input is yes or y for the correct and drop columns
				******************/

				* Make string input lower case and change "y" to "yes"
				replace correct = lower(correct)
				replace drop 	= lower(drop)
				replace correct = "yes" if correct 	== "y"
				replace drop 	= "yes" if drop 	== "y"

				*Check that varaibles are either empty or "yes"
				gen `inputNotYes' = !((correct  == "yes" | correct == "") & (drop  == "yes" | drop == ""))

				*Set local to 1 if error should be outputted
				cap assert `inputNotYes' == 0
				if _rc local local_inputNotYes 	1

				/******************
					Section 3.3.2
					Make sure there are not too many corrections for a single observation
				******************/

				* Count the number of corrections (correct drop newID) per
				* observation. Only one correction per observation is allowed.
				egen `multiInp' = rownonmiss(correct drop newID), strok

				*Check that all rows have utmost one correction
				cap assert `multiInp' == 0 | `multiInp' == 1

				*Error will be outputted below
				if _rc local local_multiInp 	1

				/******************
					Section 3.3.3
					Test that maximum one duplicate per duplicate group is indicated as correct

				******************/

				*Generate dummy if correct column is set to yes
				gen `yesCorrect' = (correct == "yes")

				*Count number of duplicates within duplicates where that dummy is 1
				bys `idvar' : egen `groupNumCorrect' =  total(`yesCorrect')

				*Test if more than 1 duplicate in that duplicate group is correct
				count if `groupNumCorrect' > 1

				*Output error is more than one duplicate in a duplicate group is yes
				if `r(N)' != 0 local local_multiCorr 1

				/******************
					Section 3.3.4
					Make sure that either option droprest is specified, or that
					drop was correctly indicated for all observations. i.e.; if
					correct or newID was indicated for at least one duplicate in
					a duplicate group, then all other observations should be
					indicated as drop (unless droprest is specified)

				******************/

				*Generate dummy if there is any correction for this observation
				gen `anyCorrection' = !missing(correct) | !missing(newID)

				*Count number of observations with any correction in suplicates group
				bys `idvar' : egen `groupAnyCorrection' =  total(`anyCorrection')

				*Create dummy that indicates each place this error happens
				gen `notDrop' = (missing(drop) & `groupAnyCorrection' > 0 & `anyCorrection' == 0)

				* Check if option droprest is specified
				if "`droprest'" == "" {

					** If option droprest is not used, then all observations in a duplicate
					*  group where at least one observation has a correction must have a
					*  correction or have drop set to yes.
					cap assert `notDrop' == 0

					*Error will be outputted below
					if _rc local local_notDrop 	1

				}
				else {

					** Option -droprest- specified. Drop will be changed to yes
					*  for any observations without drop or any other correction
					*  explicitly specified if the observation is in a duplicate
					*  group with at least one observation has a correction
					replace drop 	= "yes" if `notDrop' == 1

				}

				/******************
					Section 3.4
					Throw errors if any of the tests were not passed
				******************/

				*Output errors if errors in the report were detected
				if `local_multiInp' == 1 | `local_inputNotYes' == 1 | `local_notDrop' == 1 | `local_multiCorr' == 1  {
					noi {
						di ""
						di ""
						di as error "{phang}{ul:The corrections made in the Excel report has the following errors:}{p_end}"
						di ""

						*Error multiple input
						if `local_multiInp' == 1 {
							display as error "{phang}The following observations have more than one correction. Only one correction (correct, drop or newID) per row is allowed{p_end}"
							list `idvar' dupListID correct drop newID `uniquevars' if `multiInp' > 1
							di ""
						}

						*Error multiple correct
						if `local_multiCorr' == 1 {
							display as error "{phang}The following observations are in a duplicate group where more than one observation is listed as correct. Only one observation per duplicate group can be correct{p_end}"
							list `idvar' dupListID correct drop newID `uniquevars' if `groupNumCorrect' > 1
							di ""
						}

						*Error in incorrect string
						if `local_inputNotYes' == 1 {
							display as error "{phang}The following observations have an answer in either correct or drop that is neither yes nor y{p_end}"
							list `idvar' dupListID correct drop `uniquevars' if `inputNotYes' == 1
							di ""
						}

						*Error is not specfied as drop
						if `local_notDrop' == 1 {
							display as error "{phang}The following observations are not explicitly indicated as drop while other duplicates in the same duplicate group are corrected. Either manually indicate as drop or see option droprest{p_end}"
							list `idvar' dupListID correct drop newID `uniquevars' if `notDrop' == 1
							di ""
						}

						*Same error for any incorrect input
						error 198
						exit
					}
				}

				/******************
					Section 3.5
					Save the prepared report to be used later
				******************/

				*Keep only the variables needed for matching and variables used for input in the Excel file
				keep 	`idvar' `uniquevars' `excelvars' `groupAnyCorrection'

				*Save imported data set with all corrections
				save	`preppedReport'
			}

			/***********************************************************************
			************************************************************************

				Section 4 - Merge report to original data

				Merge corrections with original data, test that there are no
				obs in report that are not in main data, and save this data
				in temp file

			************************************************************************
			***********************************************************************/

			*Re-load original data
			use `originalData', clear

			* Merge original data with imported Excel file (if Excel file exists)
			if `fileExists'  {

				*Create a tempvar for merging results
				tempvar iedup_merge

				*Merge the corrections with the data set
				merge 1:1 `uniquevars' using `preppedReport', generate(`iedup_merge')

				*Make sure that obsrevations listed in the duplicate report is still in the data set
				cap assert `iedup_merge' != 2

				*Display error message if assertion is not true and some duplicates in the excle file are no longer in the data set
				if _rc {

					display as error "{phang}One or several observations in the Excel report are no longer found in the data set. Always run ieduplicates on the raw data set that include all the duplicates, both new duplicates and those you have already identified. After removing duplicates, save the data set using a different name. You might also recieve this error if you are using an old ieduplicates Excel report on a new data set.{p_end}"
					error 9
					exit
				}

				*Explicitly drop temporary variable. Temporary variables might
				*be exported to excel so delete explicitly before that. Only
				*using tempvar here to create a name with no conflicts
				drop `iedup_merge'

			}

			*save the data set to be used when correcting the data
			save `datawithreportmerged'

			/***********************************************************************
			************************************************************************

				Section 5 - Generate the Excel report

				Test if there are duplicates in ID var. If any duplicates exist,
				then update the Excel file with new and unaddressed cases

			************************************************************************
			***********************************************************************/

			/******************
				Section 5.1
				Test if there are any duplicates in ID var
			******************/

			* Generate variable that is not 0
			* if observation is a duplicate
			tempvar dup
			duplicates tag `idvar', gen(`dup')

			*Test if there are any duplicates
			cap assert `dup'==0
			if _rc {

				/******************
					Section 5.2
					Keep only duplicates for the report
				******************/

				*Keep if observation is part of duplicate group
				keep if `dup' != 0

				if `fileExists'  {
					* If Excel file exists keep excel vars and
					* variables passed as arguments in the
					* command
					keep 	`argumentVars' `excelvars'
				}
				else {
					* Keep only variables passed as arguments in
					* the command and the string ID var as no Excel file exists
					keep 	`argumentVars'

					*Generate the excel variables used for indicating correction
					foreach excelvar of local excelvars {

						*Create all variables apart from dupListID as string vars
						if inlist("`excelvar'", "dupListID", "newID") {
							gen `excelvar' = .
						}
						else {
							gen `excelvar' = ""
						}
					}
				}

				/******************
					Section 5.3
					Update the excel vars that are not updated manually
				******************/

				* Generate a local that is 1 if there are new duplicates
				local unaddressedNewExcel 0
				count if missing(dateFixed)
				if `r(N)' > 0 local unaddressedNewExcel 1

				/******************
					Section 5.3.1 Date variables
				******************/

				* Add date first time duplicvate was identified
				replace dateListed 	= "`date'" if missing(dateListed)

				** Add today's date to variable dateFixed if dateFixed
				*  is empty and at least one correction is added
				replace dateFixed 	= "`date'" if missing(dateFixed) & (!missing(correct) | !missing(drop) | !missing(newID))

				/******************
					Section 5.3.2 Duplicate report list ID
				******************/

				** Sort after dupListID and after ID var for
				*  duplicates currently without dupListID
				sort dupListID `idvar'

				** Assign dupListID 1 to the top row if no duplicate
				*  list IDs have been generated so far.
				replace dupListID = 1 if _n == 1 & missing(dupListID)

				** Generate new IDs based on the row above instead of directly
				*  from the row number. That prevents duplicates in the list in
				*  case an observation is deleted. The first observation with
				*  missing value will have an ID that is one digit higher than
				*  the highest ID already in the list
				replace dupListID = dupListID[_n - 1] + 1 if missing(dupListID)

				/******************
					Section 5.4
					Keep and order the variables and output the Excel files
				******************/

				* If cases unaddressed then update the Excel file
				if `unaddressedNewExcel'  {

					keep 	`argumentVars' `excelvars'
					order	`idvar' `excelvars' `uniquevars' `keepvars'

					if "`daily'" == "" {

						*Returns 0 if folder does not exist, 1 if it does
						mata : st_numscalar("r(dirExist)", direxists("`folder'/Daily"))

						** If the daily folder is not created, just create it
						if `r(dirExist)' == 0  {

							*Create the folder since it does not exist
							mkdir "`folder'/Daily"
						}

						*Export the daily file
						cap export excel using "`folder'/Daily/iedupreport`suffix'_`date'.xlsx"	, firstrow(variables) replace nolabel

						*Print error if daily report cannot be saved
						if _rc {

							display as error "{phang}The Daily copy could not be saved to the `folder'/Daily folder. Make sure to close any old daily copy or see the option nodaily.{p_end}"
							error 603
							exit
						}

						*Prepare local for output
						local daily_output " and a daily copy have been saved to the Daily folder"
					}

					*Export main report
					export excel using "`folder'/iedupreport`suffix'.xlsx"	, firstrow(variables) replace  nolabel

					*Produce output
					noi di `"{phang}Excel file created at: {browse "`folder'/iedupreport`suffix'.xlsx":`folder'/iedupreport`suffix'.xlsx}`daily_output'.{p_end}"'
					noi di ""
				}
			}

		/***********************************************************************
		************************************************************************

			Section 6

			Update the data set and with the new corrections.

		************************************************************************
		***********************************************************************/

		* Load the original data set merged with correction. Duplicates
		* in all variables are already dropped in this data set
		use 	`datawithreportmerged', clear

		* If excel file exists, apply any corrections indicated (if any)
		if `fileExists' {

			/******************
				Section 6.1
				Drop duplicates listed for drop
			******************/

			drop if drop == "yes"

			/******************
				Section 6.2
				Update new ID. ID var can be either numeric or
				string. All numbers can be made strings but not
				all strings can be numeric. Therefore this
				section is complicated.
			******************/

			/******************
				Section 6.2.1
				ID var in original file is string. Either
				newID was imported as string or the variable
				is made string. Easy.
			******************/

			*Test if there are any corrections by new ID
			cap assert missing(newID)
			if _rc {

				local idtype 	: type `idvar'
				local idtypeNew : type newID

				*If ID var is string but newID is not, then just make it string
				if substr("`idtype'",1,3) == "str" & substr("`idtypeNew'",1,3) != "str" {

					tostring newID , replace
					replace  newID = "" if newID == "."
				}

				*If ID var is numeric but the newID is loaded as string
				else if substr("`idtype'",1,3) != "str" & substr("`idtypeNew'",1,3) == "str" {

					* Check if -tostringok- is specificed:
					if "`tostringok'" != "" {

						* Make original ID var string
						tostring `idvar' , replace
						replace  `idvar' = "" if `idvar' == "."

					}

					* Error, IDvar can not be updated
					else {

						* Create a local with all non-numeric values
						levelsof newID if missing(real(newID)), local(NaN_values) clean

						* Output error message
						di as error "{phang}The ID varaible `idvar' is numeric but newID has thes non-numeric values: `NaN_values'. Update newID to only contain numeric values or see option tostringok.{p_end}"
						error 109
						exit
					}
				}

				*After making sure that type is ok, update the IDs
				replace `idvar' = newID if !missing(newID)

				/******************
					Section 6.3
					Test that values in newID
					were neither used twice
					nor already existed
				******************/

				*Test if there are any duplicates after corrections (excluding observations in duplicate groups not yet corrected)
				tempvar newDup
				duplicates tag `idvar' if `groupAnyCorrection' != 0, generate(`newDup')

				*Consider missing values as no new duplicates, make conditions below easier
				replace `newDup' = 0 if missing(`newDup')

				cap assert `newDup' == 0
				if _rc {

					levelsof `idvar' if `newDup' != 0  , local(newDuplist)
					di as error "{phang}No corrections from the report are applied as it would lead to new duplicates in the following value(s): `newDuplist'.{p_end}"
					error 198
					exit
				}
			}

			/******************
				Section 6.4
				Drop Excel vars
			******************/

			drop `excelvars'
		}

		/***********************************************************************
		************************************************************************

			Section 7

			Return the data set without duplicates and
			output information regarding unresolved duplicates.

		************************************************************************
		***********************************************************************/

		* Generate a variable that is 1 if the observation is a duplicate in varlist
		tempvar dropDup
		duplicates tag `idvar',  gen(`dropDup')
		* Generate a list of the IDs that are still duplicates
		levelsof `idvar' 			if `dropDup' != 0 , local(dup_ids) clean
		* Drop the duplicates (they are exported in Excel)
		drop 						if `dropDup' != 0

		* Test if varlist is now uniquely and fully identifying the data set
		cap isid `idvar'
		if _rc {

			di as error "{phang}The data set is not returned with `idvar' uniquely and fully identifying the data set. Please report this bug to kbjarkefur@worldbank.org{p_end}"
			error 119
			exit

		}

		*Count number of duplicate groups still in the data
		local numDup	= `:list sizeof dup_ids'

		if `numDup' == 0 {
			noi di	"{phang}There are no unresolved duplicates in this data set. The data set is returned with `idvar' uniquely and fully identifying the data set.{p_end}"
		}
		else {
			noi di	"{phang}There are `numDup' duplicated IDs still unresolved. IDs still containing duplicates: `dup_ids'. The unresolved duplicate observations were exported in the Excel file. The data set is returned without those duplicates and with `idvar' uniquely and fully identifying the data set.{p_end}"
		}

		return scalar numDup	= `numDup'

		/***********************************************************************
		************************************************************************

			Section 8

			Save data set to be returned outside preserve/restore.
			Preserve/restore is used so that original data is returned
			in case an error is thrown.

		************************************************************************
		***********************************************************************/

		save 	`dataToReturn'

		restore

		** Using restore above to return the data to
		*  the orignal data set in case of error.
		use `dataToReturn', clear

	}
end


minor change

minor change

clear all
set more off
set matsize 11000
program drop _all

qui adopath + "/mnt/nfs6/wikipedia.proj/jmp/jmpscripts/stata/ado"
cd ${path}

// this declares global vars
declare_global

// 0. Data

// 0.1 make cleancnty, changesets data
clear
preparebasic

// 0.2 make relevant dependent variables
use ${stash}cleanchangeset1, clear
makedv fips
balancepanel fips
mergebasic
qui destring, replace
save ${stash}panelfips, replace

// 1. Summary Stats
program drop _all
use ${stash}panelfips, clear
makesummary

// 2. Treatment Control Balance
use ${stash}panelfips, clear
makehist cntypop kdensity "Population"
makehist emp_earnings kdensity "Earnings"
makehist age_median kdensity "Median Age"
makehist educ_college kdensity "Num. College Educated"

** makehist region hist

// 2. TODO: TTest or regressions treatment / control

//  3.1 Meanline charts (raw data)
makemeanline numcontrib quarter 2011 "Contributions"
makemeanline numuser quarter 2011 "Users"
makemeanline numserious90 quarter 2011 "Super Users"

* makemeanline numnewusers quarter 2011 "New Users"
* makemeanline numnewusers6 quarter 2011 "New Users (Stay for 6+ Months)"
* makemeanline numnewusers90 quarter 2011 "New Users (who become super users)"
* makemeanline numchanges quarter 2011 "Changes"

// 3.2 Cross Sectional Regressions
crossreg

// 3.3 Diff in Diff --- Baseline
local outcomes "numcontrib numuser numserious90"
dd_simple "`outcomes'" run xtreg
dd_simple "`outcomes'" run xtpoisson
dd_simple "`outcomes'" write ddsimple_baseline

// 3.4 Diff in Diff --- Additional
local outcomes "numnewusers numnewusers6 numnewusers90"
dd_simple "`outcomes'" run xtreg
dd_simple "`outcomes'" run xtpoisson
dd_simple "`outcomes'" write ddsimple_additional

// 4. How do results vary?
program drop _all
use ${stash}panelfips, clear
gen large = (cntypop > 100000)

local outcomes "numcontrib numuser numserious90"
diffindiff2 large LARGE hetero1 run `outcomes'
diffindiff2 large LARGE hetero1 write `outcomes'

////////// OLD //
 ** Analysis

// 0. Data

// 0.1 make fips dataset
use ${stash}mergemaster1, clear

balancepanel fips
save ${stash}panelfips, replace

// 0.2 make node dataset
preparenode

use ${stash}mergemaster_node, clear
makedv fips node
balancepanel fips node
save ${stash}panelnode, replace

// 0.3 make way dataset
prepareway
makedv fips way
balancepanel fips way
save ${stash}panelway, replace

// 3. Baseline effects (xtpoisson, cluster at unit level)

// 3.2 FIPS Sample -- DD

/// 3.2.1 -- contrib, users, superu
program drop _all

clear
local dv "numcontrib numuser numserious90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.1 "Contributions" "Users" "Super Users"

/// 3.2.2 -- newusers, newusers6, newuserssuper
clear
local dv "numnewusers numnewusers6 numnewusers90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.2 "New Users" "New Users(6+)" "New Super Users"

/// 3.2.3 -- streetcontrib, nonstreet contrib, amenities
// TODO

use ${stash}panelnode, clear
program drop _all

diffindiff xtpoisson fips run tab_test numcontrib numuser

/// 3.2.4 -- completeness
// TODO

// 3.2.5 -- Diff in Diff charts
// TODO : make this more flexible
// TODO : this works only for fips
ddchart

// 3.3 Robustness : Drop empty counties
// TODO
//  e. Repeat (a) by dropping empty counties
//  f. Repeat (a) by dropping west
//  g. Repeat (a) for Tile Sample, MSA sample
//  h. Repeat (a) with matched estimators
//  i. Repeat (a) with spatially clustered se, Region X Time trends

// 4 Heterogenous Effects

// 1. Large vs. small
use ${stash}panelfips, clear
gen large = (cntypop > 100000)

diffindiff2 large LARGE tab_4.1 run numcontrib numuser numserious90 numnewusers6

diffindiff2 large LARGE tab_4.1 write numcontrib numuser numserious90 numnewusers6

// 2. engaged vs. non-engaged county

use ${stash}panelfips, clear

bysort fips post: egen tmp = total(numcontrib > 0)
replace tmp = . if post == 1
bysort fips: egen precontrib = max(tmp)
bysort fips: egen totcontrib = total(numcontrib)
drop tmp

drop if precontrib == 0
gen active = (prec > 1)

diffindiff2 active ACTIVE tab_4.2 run numcontrib numuser numserious90 numnewusers6

diffindiff2 active ACTIVE tab_4.2 write numcontrib numuser numserious90 numnewusers6

// 3. populated vs. not

use ${stash}panelfips, clear

gen iseast = (region==1 | region == 2)

diffindiff2 iseast EAST tab_4.3 run numcontrib numuser numserious90 numnewusers6

diffindiff2 iseast EAST tab_4.3 write numcontrib numuser numserious90 numnewusers6

// 3. urban vs rural
// 4. university vs non university
// 5. rich vs poor

// 5 Street Level Regressions

// 6 Person Level Regressions

// [A] outcome vars: County sample -- DD spec

   // a. Contributions
   // b. Street, Non Street
   // b. TIGER vs. Non Tiger
   // d. Home county, Num Counties

// [B] heterogenous

   // a. Junior vs Senior

// 7 Impacts on Craigslist

preparecl

// PENDING

// GEOID Level

// TASKS for tomorrow

// STASH

 // 1.1 Mean Charts

 // 1.2 Produce Diff in diff Latex tables
 // rundd -> batchreg.sh -> runreg.do -> diffindiff.ado
 program drop _all

 // TODO: fix manual process
 // have to do this manually

 // 1.3 Produce Diff in diff Pictures

 ** person level regressions
 // person level
 makeperson
 ddperson maketables
 ddperson makechart

// TODOs for Tuesday Jun 3
// GEOID dataset
// TILEID diff in diff
// What happened to previous people?
// What happened at the street/amenity level?
// Urban vs Rural (framework for third level diff)

// 1.4 Robust: product diff in diff for GEOID

use ${stash}panelfips_geoid10, clear

** Analysis Stage 2
//

// PROGRAMS

clear all
set more off
set matsize 11000
program drop _all

qui adopath + "/mnt/nfs6/wikipedia.proj/jmp/jmpscripts/stata/ado"
cd ${path}

// this declares global vars
declare_global

// 0. Data

// 0.1 make cleancnty, changesets data
clear
preparebasic

// 0.2 make relevant dependent variables
use ${stash}cleanchangeset1, clear
makedv fips
balancepanel fips
mergebasic
qui destring, replace
save ${stash}panelfips, replace

// 1. Summary Stats
program drop _all
use ${stash}panelfips, clear
makesummary

// 2. Treatment Control Balance
use ${stash}panelfips, clear
makehist cntypop kdensity "Population"
makehist emp_earnings kdensity "Earnings"
makehist age_median kdensity "Median Age"
makehist educ_college kdensity "Num. College Educated"

** makehist region hist

// 2. TODO: TTest or regressions treatment / control

//  3.1 Meanline charts (raw data)
makemeanline numcontrib quarter 2011 "Contributions"
makemeanline numuser quarter 2011 "Users"
makemeanline numserious90 quarter 2011 "Super Users"

* makemeanline numnewusers quarter 2011 "New Users"
* makemeanline numnewusers6 quarter 2011 "New Users (Stay for 6+ Months)"
* makemeanline numnewusers90 quarter 2011 "New Users (who become super users)"
* makemeanline numchanges quarter 2011 "Changes"

// 3.2 Cross Sectional Regressions
crossreg

// 3.3 Diff in Diff --- Baseline
local outcomes "numcontrib numuser numserious90"
dd_simple "`outcomes'" run xtreg
dd_simple "`outcomes'" run xtpoisson
dd_simple "`outcomes'" write ddsimple_baseline

// 3.4 Diff in Diff --- Additional
local outcomes "numnewusers numnewusers6 numnewusers90"
dd_simple "`outcomes'" run xtreg
dd_simple "`outcomes'" run xtpoisson
dd_simple "`outcomes'" write ddsimple_additional

// 4. How do results vary?
program drop _all
use ${stash}panelfips, clear
gen large = (cntypop > 100000)

diffindiff2 large LARGE hetero1 run numcontrib

numuser numserious90 numnewusers6

diffindiff2 large LARGE tab_4.1 write numcontrib numuser numserious90 numnewusers6

////////// OLD //
 ** Analysis

// 0. Data

// 0.1 make fips dataset
use ${stash}mergemaster1, clear

balancepanel fips
save ${stash}panelfips, replace

// 0.2 make node dataset
preparenode

use ${stash}mergemaster_node, clear
makedv fips node
balancepanel fips node
save ${stash}panelnode, replace

// 0.3 make way dataset
prepareway
makedv fips way
balancepanel fips way
save ${stash}panelway, replace

// 3. Baseline effects (xtpoisson, cluster at unit level)

// 3.2 FIPS Sample -- DD

/// 3.2.1 -- contrib, users, superu
program drop _all

clear
local dv "numcontrib numuser numserious90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.1 "Contributions" "Users" "Super Users"

/// 3.2.2 -- newusers, newusers6, newuserssuper
clear
local dv "numnewusers numnewusers6 numnewusers90"
local unit fips
rundd panelfips `unit' "`dv'"
diffindiff xtpoisson "`dv'" `unit' 2014 write tab_3.2.2 "New Users" "New Users(6+)" "New Super Users"

/// 3.2.3 -- streetcontrib, nonstreet contrib, amenities
// TODO

use ${stash}panelnode, clear
program drop _all

diffindiff xtpoisson fips run tab_test numcontrib numuser

/// 3.2.4 -- completeness
// TODO

// 3.2.5 -- Diff in Diff charts
// TODO : make this more flexible
// TODO : this works only for fips
ddchart

// 3.3 Robustness : Drop empty counties
// TODO
//  e. Repeat (a) by dropping empty counties
//  f. Repeat (a) by dropping west
//  g. Repeat (a) for Tile Sample, MSA sample
//  h. Repeat (a) with matched estimators
//  i. Repeat (a) with spatially clustered se, Region X Time trends

// 4 Heterogenous Effects

// 1. Large vs. small
use ${stash}panelfips, clear
gen large = (cntypop > 100000)

diffindiff2 large LARGE tab_4.1 run numcontrib numuser numserious90 numnewusers6

diffindiff2 large LARGE tab_4.1 write numcontrib numuser numserious90 numnewusers6

// 2. engaged vs. non-engaged county

use ${stash}panelfips, clear

bysort fips post: egen tmp = total(numcontrib > 0)
replace tmp = . if post == 1
bysort fips: egen precontrib = max(tmp)
bysort fips: egen totcontrib = total(numcontrib)
drop tmp

drop if precontrib == 0
gen active = (prec > 1)

diffindiff2 active ACTIVE tab_4.2 run numcontrib numuser numserious90 numnewusers6

diffindiff2 active ACTIVE tab_4.2 write numcontrib numuser numserious90 numnewusers6

// 3. populated vs. not

use ${stash}panelfips, clear

gen iseast = (region==1 | region == 2)

diffindiff2 iseast EAST tab_4.3 run numcontrib numuser numserious90 numnewusers6

diffindiff2 iseast EAST tab_4.3 write numcontrib numuser numserious90 numnewusers6

// 3. urban vs rural
// 4. university vs non university
// 5. rich vs poor

// 5 Street Level Regressions

// 6 Person Level Regressions

// [A] outcome vars: County sample -- DD spec

   // a. Contributions
   // b. Street, Non Street
   // b. TIGER vs. Non Tiger
   // d. Home county, Num Counties

// [B] heterogenous

   // a. Junior vs Senior

// 7 Impacts on Craigslist

preparecl

// PENDING

// GEOID Level

// TASKS for tomorrow

// STASH

 // 1.1 Mean Charts

 // 1.2 Produce Diff in diff Latex tables
 // rundd -> batchreg.sh -> runreg.do -> diffindiff.ado
 program drop _all

 // TODO: fix manual process
 // have to do this manually

 // 1.3 Produce Diff in diff Pictures

 ** person level regressions
 // person level
 makeperson
 ddperson maketables
 ddperson makechart

// TODOs for Tuesday Jun 3
// GEOID dataset
// TILEID diff in diff
// What happened to previous people?
// What happened at the street/amenity level?
// Urban vs Rural (framework for third level diff)

// 1.4 Robust: product diff in diff for GEOID

use ${stash}panelfips_geoid10, clear

** Analysis Stage 2
//

// PROGRAMS


iegraph : small file ext fixes

iegraph : small file ext fixes

cap	program drop	iegraph
	program define 	iegraph
	preserve
	syntax varlist, [noconfbars TItle(string) save(string) confbarsnone(varlist) GREYscale yzero *]
	mat beta_ = e(b)

	local counter = 0
	//local textfile = "Mrijan"
	qui{
		if "`confbars'" 			!= "" {
		local CONFINT_BAR 	= 0
		}
		else if "`confbars'" 			== "" {
		local CONFINT_BAR 	= 1
		}

		local varTest : list confbarsnone in varlist

		if `varTest' == 0 {
					noi display as error "{phang} Variables defined in confbarsnone cannot be found in the graph variable list. {p_end}"
					noi display ""
					error 111
					 }

		foreach var of local varlist{
			cap assert inlist(`var',0,1) | missing(`var')
				if _rc {
					noi display as error "{phang} The variable `var' is not a dummy. Treatment variable needs to be a dummy(0 or 1) variable. {p_end}"
					noi display ""
					error 149
					}
				else {
					}

		local counter = `counter' + 1
		di `counter'

		scalar coeff_`var' = _b[`var']
		//noi display coeff_`var'

		scalar coeff_se_`var' = _se[`var']
		//noi display coeff_se_`var'

		count if e(sample) == 1 & `var' == 1 //Count one tmt group at the time
		scalar obs_`var' = r(N)
		//noi display obs_`var'

			}
	if "`save'" != "" {

        *Find index for where the file type suffix start
        local dot_index     = strpos("`save'",".") + 1

        *Extract the file index

        local file_suffix   = substr("`save'", `dot_index', .)
		noi di "`file_suffix'"

        local nonGPH_formats png tiff gph ps eps pdf wmf emf

        *If no file format suffix is specified, use the default .xlsx

        if `dot_index' == 1 | "`file_suffix'" == "gph" {
                   local save_export = 0
				}

        *If a file format suffix is specified make sure that it is one of the two allowed.

        else if `:list file_suffix in nonGPH_formats' != 0  {
            local save_export = 1

			if ("`file_suffix'" == "wmf" | "`file_suffix'" == "emf") & "`c(os)'" != "Windows" {
				di as error ""
                error
                   }
			}

        else {

            di as error "You are not using a allowed file format in save(`save'). Only the following formats are allowed: gph `nonGPH_formats'"
            error
        }
    }

	//noi di `"iegraph `varlist', `noconfbars' title(`title') save(`save') `confbarsnone' `yzero' `anything'"'

	local count: word count `varlist'
	local graphCount = `count' + 1
	//tokenize "`varlist'"
	//noi di `count'

	//Make all vars tempvars (maybe do later)
	//Make sure that missing is properly handled
	tempvar anyTMT
	egen `anyTMT' = rowmax(`varlist') if e(sample) == 1
	tempvar control
	gen `control' = (`anyTMT' == 0) if !missing(`anyTMT')

	sum `e(depvar)' if e(sample) == 1 & `control' == 1
	scalar ctl_N		= r(N)
	scalar ctl_mean	  	= r(mean)
	scalar ctl_mean_sd 	= r(sd)

		foreach var of local varlist {
			scalar  conf_int_min_`var'   =	coeff_`var'-(1.96*coeff_se_`var') + ctl_mean
			scalar  conf_int_max_`var'   =	coeff_`var'+(1.96*coeff_se_`var') + ctl_mean

			test `var'
			local star_`var' " "

			scalar  pvalue =r(p)
			if pvalue < 0.10 {
				local star_`var' "*"
			}
			if pvalue < 0.05 {
				local star_`var' "**"
			}
			if pvalue < 0.01 {
				local star_`var' "***"
			}
			*noi display "`star_`var''"
			scalar tmt_mean_`var' = ctl_mean + coeff_`var'
		}

	tempname 	newHandle
	tempfile	newTextFile
	cap file close 	`newHandle'
	file open  	`newHandle' using "`newTextFile'", text write append

	*Write headers and control value
	file write `newHandle' ///
		"order" 	_tab "xLabeled"	_tab "mean" _tab "coeff" _tab "conf_int_min" _tab "conf_int_max" _tab "obs" _tab "star" _n 	///
		%9.3f (1) _tab "Control"  _tab %9.3f (ctl_mean)      _tab  		 			_tab 		 			  	  _tab 			 				_tab %9.3f (ctl_N) 	   _tab			  	  _n

	tempvar newCounter
	gen `newCounter' = 2  //First tmt group starts at 2 (1 is control)

	foreach var in `varlist' {
	     if `: list var in confbarsnone' {
		file write `newHandle' ///
			%9.3f (`newCounter') _tab "`var'" _tab %9.3f (tmt_mean_`var') _tab  %9.3f (coeff_`var')  _tab %9.3f  _tab _tab %9.3f (obs_`var')   _tab "`star_`var''"  _n

		replace `newCounter' = `newCounter' + 1
			}
		else {
		file write `newHandle' ///
			%9.3f (`newCounter') _tab "`var'" _tab %9.3f (tmt_mean_`var') _tab  %9.3f (coeff_`var')  _tab %9.3f (conf_int_min_`var') _tab %9.3f (conf_int_max_`var') _tab %9.3f (obs_`var')   _tab "`star_`var''"  _n

		replace `newCounter' = `newCounter' + 1
		}
	}

	file close `newHandle'

	copy "`newTextFile'"  "mainMasterDofile.txt" , replace

	/*************************************

		Create the graph

	*************************************/

	insheet using mainMasterDofile.txt, clear

	local tmtGroupBars 	""
	local xAxisLabels 	`"xlabel( "'
	local legendLabels	`"lab(1 "`obsLabelControl'")"'
	local legendNumbers	""

	forval tmtGroupCount = 1/`graphCount' {

		************
		*Create the bar for this group

		if "`greyscale'" == "" {
			colorPicker `tmtGroupCount' `graphCount'
		}
		else {
			greyPicker `tmtGroupCount' `graphCount'
		}

		local tmtGroupBars `"`tmtGroupBars' (bar mean order if order == `tmtGroupCount', color("`r(color)'") lcolor(black) ) "'

		************
		*Create labels etc. for this group

		local obs 			= obs[`tmtGroupCount']
		local stars 		= star[`tmtGroupCount']
		local legendLabel 	= xlabeled[`tmtGroupCount']

		local xAxisLabels 	`"`xAxisLabels' `tmtGroupCount' "(N = `obs') `stars'" "'
		local legendLabels 	`"`legendLabels' lab(`tmtGroupCount' "`legendLabel'") "'
		local legendNumbers	`"`legendNumbers' `tmtGroupCount'"'
	}

	*Close or comple some strings
	local xAxisLabels `"`xAxisLabels' ,noticks labsize(medsmall)) "'
	local legendOption `"legend(order(`legendNumbers') `legendLabels')"'

	*Create the confidence interval bars
	if `CONFINT_BAR' == 0 {
		//local confIntGraph == `"(scatter mean order,  msym(none)  mlabs(medium) mlabpos(10) mlabcolor(black)), xtitle("") ytitle("`e(depvar)'") "'
		local confIntGraph = ""
	}
		else if `CONFINT_BAR' == 1 {
		local confIntGraph = `"(rcap conf_int_max conf_int_min order, lc(gs)) (scatter mean order,  msym(none)  mlabs(medium) mlabpos(10) mlabcolor(black))"'
	}

	local titleOption `" , xtitle("") ytitle("`e(depvar)'") "'

	if "`save'" != "" {
		local saveOption saving("`save'", replace)
	}

	if "`yzero'" != "" {

		gen maxvalue = max(mean , conf_int_max)

		sum maxvalue
		local max = `r(max)'

		local logmax = log10(`max')

		local logmax = round(`logmax') - 1

		local tenpower = 10 ^ (`logmax')

		local up = `tenpower' * ceil(`max' / `tenpower')

		local quarter = (`up') / 4

		noi di "up: `up'"
		noi di "quarter: `quarter'"

		local yzero_option ylabel(0(`quarter')`up')
	}

		//noi di `" graph twoway `tmtGroupBars' `confIntGraph' `titleOption' `options' `legendOption' `xAxisLabels' `saveOption' title("`title'") `yzero_option'  "'
	if `save_export' == 0 {
		graph twoway `tmtGroupBars' `confIntGraph' `titleOption'  `legendOption' `xAxisLabels' `saveOption' title("`title'") `yzero_option' `options'
		noi di `" graph twoway `tmtGroupBars' `confIntGraph' `titleOption' `options' `legendOption' `xAxisLabels' `saveOption' title("`title'") `yzero_option'  "'

		}
	else if `save_export' == 1 {
		graph twoway `tmtGroupBars' `confIntGraph' `titleOption'  `legendOption' `xAxisLabels' title("`title'") `yzero_option' `options'
		graph export "`save'", replace
		}

	restore
}

end

cap	program drop	colorPicker
	program define 	colorPicker , rclass

	args groupCount totalNumGroups

	if `totalNumGroups' == 2 {

		if `groupCount' == 1 return local color "215 25 28"
		if `groupCount' == 2 return local color "43 123 182"
	}
	else if `totalNumGroups' == 3 {

		if `groupCount' == 1 return local color "215 25 28"
		if `groupCount' == 2 return local color "255 255 191"
		if `groupCount' == 3 return local color "43 123 182"
	}
	else if `totalNumGroups' == 4 {

		if `groupCount' == 1 return local color "215 25 28"
		if `groupCount' == 2 return local color "255 255 191"
		if `groupCount' == 3 return local color "171 217 233"
		if `groupCount' == 4 return local color "43 123 182"
	}
	else {

		*For five or more colors we repeat the same pattern

		local colourNum = mod(`groupCount', 5)
		if `colourNum' == 1 return local color "215 25 28"
		if `colourNum' == 2 return local color "253 174 93"
		if `colourNum' == 3 return local color "255 255 191"
		if `colourNum' == 4 return local color "171 217 233"
		if `colourNum' == 0 return local color "43 123 182"

	}

end

cap	program drop	greyPicker
	program define 	greyPicker , rclass

	args groupCount totalNumGroups

	if `groupCount' == 1 {

		return local color "black"
	}
	else if `groupCount' == 2 & `totalNumGroups' <= 3 {

		return local color "gs14"
	}
	else {

		local grayscale =  round( (`groupCount'-1) * (100 / (`totalNumGroups'-1) ))

		return local color "`grayscale' `grayscale' `grayscale' `grayscale'"
	}

end

cap	program drop	iegraph
	program define 	iegraph
	preserve
	syntax varlist, [noconfbars TItle(string) save(string) confbarsnone(varlist) GREYscale yzero *]
	mat beta_ = e(b)

	local counter = 0
	//local textfile = "Mrijan"
	qui{
		if "`confbars'" 			!= "" {
		local CONFINT_BAR 	= 0
		}
		else if "`confbars'" 			== "" {
		local CONFINT_BAR 	= 1
		}

		local varTest : list confbarsnone in varlist

		if `varTest' == 0 {
					noi display as error "{phang} Variables defined in confbarsnone cannot be found in the graph variable list. {p_end}"
					noi display ""
					error 111
					 }

		foreach var of local varlist{
			cap assert inlist(`var',0,1) | missing(`var')
				if _rc {
					noi display as error "{phang} The variable `var' is not a dummy. Treatment variable needs to be a dummy(0 or 1) variable. {p_end}"
					noi display ""
					error 149
					}
				else {
					}

		local counter = `counter' + 1
		di `counter'

		scalar coeff_`var' = _b[`var']
		//noi display coeff_`var'

		scalar coeff_se_`var' = _se[`var']
		//noi display coeff_se_`var'

		count if e(sample) == 1 & `var' == 1 //Count one tmt group at the time
		scalar obs_`var' = r(N)
		//noi display obs_`var'

			}
	if "`save'" != "" {

        *Find index for where the file type suffix start
        local dot_index     = strpos("`save'",".")
		local newdot_index = `dot_index' + 1
        *Extract the file index

        local file_suffix   = substr("`save'", `newdot_index', .)
		noi di "`file_suffix'"

        local nonGPH_formats png tiff gph ps eps pdf wmf emf

        *If no file format suffix is specified, use the default .xlsx

        if "`file_suffix'" == "" | "`file_suffix'" == "gph" {
                   local save_export = 0
				}

        *If a file format suffix is specified make sure that it is one of the two allowed.

        else if `:list file_suffix in nonGPH_formats' != 0  {
            local save_export = 1

			if ("`file_suffix'" == "wmf" | "`file_suffix'" == "emf") & "`c(os)'" != "Windows" {
				di as error ""
                error
                   }
			}

        else {

            di as error "You have not using a allowed file format in save(`save'). Only the following formats are allowed: gph `nonGPH_formats'"
            error
        }
    }

	//noi di `"iegraph `varlist', `noconfbars' title(`title') save(`save') `confbarsnone' `yzero' `anything'"'

	local count: word count `varlist'
	local graphCount = `count' + 1
	//tokenize "`varlist'"
	//noi di `count'

	//Make all vars tempvars (maybe do later)
	//Make sure that missing is properly handled
	tempvar anyTMT
	egen `anyTMT' = rowmax(`varlist') if e(sample) == 1
	tempvar control
	gen `control' = (`anyTMT' == 0) if !missing(`anyTMT')

	sum `e(depvar)' if e(sample) == 1 & `control' == 1
	scalar ctl_N		= r(N)
	scalar ctl_mean	  	= r(mean)
	scalar ctl_mean_sd 	= r(sd)

		foreach var of local varlist {
			scalar  conf_int_min_`var'   =	coeff_`var'-(1.96*coeff_se_`var') + ctl_mean
			scalar  conf_int_max_`var'   =	coeff_`var'+(1.96*coeff_se_`var') + ctl_mean

			test `var'
			local star_`var' " "

			scalar  pvalue =r(p)
			if pvalue < 0.10 {
				local star_`var' "*"
			}
			if pvalue < 0.05 {
				local star_`var' "**"
			}
			if pvalue < 0.01 {
				local star_`var' "***"
			}
			*noi display "`star_`var''"
			scalar tmt_mean_`var' = ctl_mean + coeff_`var'
		}

	tempname 	newHandle
	tempfile	newTextFile
	cap file close 	`newHandle'
	file open  	`newHandle' using "`newTextFile'", text write append

	*Write headers and control value
	file write `newHandle' ///
		"order" 	_tab "xLabeled"	_tab "mean" _tab "coeff" _tab "conf_int_min" _tab "conf_int_max" _tab "obs" _tab "star" _n 	///
		%9.3f (1) _tab "Control"  _tab %9.3f (ctl_mean)      _tab  		 			_tab 		 			  	  _tab 			 				_tab %9.3f (ctl_N) 	   _tab			  	  _n

	tempvar newCounter
	gen `newCounter' = 2  //First tmt group starts at 2 (1 is control)

	foreach var in `varlist' {
	     if `: list var in confbarsnone' {
		file write `newHandle' ///
			%9.3f (`newCounter') _tab "`var'" _tab %9.3f (tmt_mean_`var') _tab  %9.3f (coeff_`var')  _tab %9.3f  _tab _tab %9.3f (obs_`var')   _tab "`star_`var''"  _n

		replace `newCounter' = `newCounter' + 1
			}
		else {
		file write `newHandle' ///
			%9.3f (`newCounter') _tab "`var'" _tab %9.3f (tmt_mean_`var') _tab  %9.3f (coeff_`var')  _tab %9.3f (conf_int_min_`var') _tab %9.3f (conf_int_max_`var') _tab %9.3f (obs_`var')   _tab "`star_`var''"  _n

		replace `newCounter' = `newCounter' + 1
		}
	}

	file close `newHandle'

	copy "`newTextFile'"  "mainMasterDofile.txt" , replace

	/*************************************

		Create the graph

	*************************************/

	insheet using mainMasterDofile.txt, clear

	local tmtGroupBars 	""
	local xAxisLabels 	`"xlabel( "'
	local legendLabels	`"lab(1 "`obsLabelControl'")"'
	local legendNumbers	""

	forval tmtGroupCount = 1/`graphCount' {

		************
		*Create the bar for this group

		if "`greyscale'" == "" {
			colorPicker `tmtGroupCount' `graphCount'
		}
		else {
			greyPicker `tmtGroupCount' `graphCount'
		}

		local tmtGroupBars `"`tmtGroupBars' (bar mean order if order == `tmtGroupCount', color("`r(color)'") lcolor(black) ) "'

		************
		*Create labels etc. for this group

		local obs 			= obs[`tmtGroupCount']
		local stars 		= star[`tmtGroupCount']
		local legendLabel 	= xlabeled[`tmtGroupCount']

		local xAxisLabels 	`"`xAxisLabels' `tmtGroupCount' "(N = `obs') `stars'" "'
		local legendLabels 	`"`legendLabels' lab(`tmtGroupCount' "`legendLabel'") "'
		local legendNumbers	`"`legendNumbers' `tmtGroupCount'"'
	}

	*Close or comple some strings
	local xAxisLabels `"`xAxisLabels' ,noticks labsize(medsmall)) "'
	local legendOption `"legend(order(`legendNumbers') `legendLabels')"'

	*Create the confidence interval bars
	if `CONFINT_BAR' == 0 {
		//local confIntGraph == `"(scatter mean order,  msym(none)  mlabs(medium) mlabpos(10) mlabcolor(black)), xtitle("") ytitle("`e(depvar)'") "'
		local confIntGraph = ""
	}
		else if `CONFINT_BAR' == 1 {
		local confIntGraph = `"(rcap conf_int_max conf_int_min order, lc(gs)) (scatter mean order,  msym(none)  mlabs(medium) mlabpos(10) mlabcolor(black))"'
	}

	local titleOption `" , xtitle("") ytitle("`e(depvar)'") "'

	if "`save'" != "" {
		local saveOption saving("`save'", replace)
	}

	if "`yzero'" != "" {

		gen maxvalue = max(mean , conf_int_max)

		sum maxvalue
		local max = `r(max)'

		local logmax = log10(`max')

		local logmax = round(`logmax') - 1

		local tenpower = 10 ^ (`logmax')

		local up = `tenpower' * ceil(`max' / `tenpower')

		local quarter = (`up') / 4

		noi di "up: `up'"
		noi di "quarter: `quarter'"

		local yzero_option ylabel(0(`quarter')`up')
	}

		//noi di `" graph twoway `tmtGroupBars' `confIntGraph' `titleOption' `options' `legendOption' `xAxisLabels' `saveOption' title("`title'") `yzero_option'  "'
	if `save_export' == 0 {
		graph twoway `tmtGroupBars' `confIntGraph' `titleOption'  `legendOption' `xAxisLabels' `saveOption' title("`title'") `yzero_option' `options'
		noi di `" graph twoway `tmtGroupBars' `confIntGraph' `titleOption' `options' `legendOption' `xAxisLabels' `saveOption' title("`title'") `yzero_option'  "'

		}
	else if `save_export' == 1 {
		graph twoway `tmtGroupBars' `confIntGraph' `titleOption'  `legendOption' `xAxisLabels' title("`title'") `yzero_option' `options'
		graph export "`save'", replace
		}

	restore
}

end

cap	program drop	colorPicker
	program define 	colorPicker , rclass

	args groupCount totalNumGroups

	if `totalNumGroups' == 2 {

		if `groupCount' == 1 return local color "215 25 28"
		if `groupCount' == 2 return local color "43 123 182"
	}
	else if `totalNumGroups' == 3 {

		if `groupCount' == 1 return local color "215 25 28"
		if `groupCount' == 2 return local color "255 255 191"
		if `groupCount' == 3 return local color "43 123 182"
	}
	else if `totalNumGroups' == 4 {

		if `groupCount' == 1 return local color "215 25 28"
		if `groupCount' == 2 return local color "255 255 191"
		if `groupCount' == 3 return local color "171 217 233"
		if `groupCount' == 4 return local color "43 123 182"
	}
	else {

		*For five or more colors we repeat the same pattern

		local colourNum = mod(`groupCount', 5)
		if `colourNum' == 1 return local color "215 25 28"
		if `colourNum' == 2 return local color "253 174 93"
		if `colourNum' == 3 return local color "255 255 191"
		if `colourNum' == 4 return local color "171 217 233"
		if `colourNum' == 0 return local color "43 123 182"

	}

end

cap	program drop	greyPicker
	program define 	greyPicker , rclass

	args groupCount totalNumGroups

	if `groupCount' == 1 {

		return local color "black"
	}
	else if `groupCount' == 2 & `totalNumGroups' <= 3 {

		return local color "gs14"
	}
	else {

		local grayscale =  round( (`groupCount'-1) * (100 / (`totalNumGroups'-1) ))

		return local color "`grayscale' `grayscale' `grayscale' `grayscale'"
	}

end