title: I'm getting a warning message in R I don't understand
body: <p>** When I train my SVM models I get this warning message:  </p>
  
  <p>Warning message: In .local(x, ...): Variable(s) `' constant. Cannot scale data.</p>
  
  <p>Could someone shine some light on this warning? Is this something I should be concerned with? I was thinking the warning maybe because of my dummy variable coding I did. If so, does anyone know a way around this warning message? Thanks for y'all's time!    **</p>
  
  <pre><code># importing libraries
  library(tidyverse)
  library(caret)
  
  # importing the data and changing data types ----
  loan &lt;- read_csv("loan_data.csv", col_types = cols(
      credit.policy    = col_factor(),
      inq.last.6mths = col_factor(),
      delinq.2yrs    = col_factor(),
      pub.rec        = col_factor(),
      not.fully.paid = col_factor(),
      purpose        = col_factor()
  ))
  
  # dummy coding purpose column for SVM models
  loan_dum &lt;- loan %&gt;% 
      fastDummies::dummy_cols(select_columns = "purpose",
                              remove_first_dummy  = TRUE,
                              remove_selected_columns = TRUE)
  loan_dum %&gt;% View()
  
  # Checking classes of new dummy variables 
  loan_dum %&gt;% glimpse()
  
  # Changing the data type of new dummy variables from int to factors
  loan_dum &lt;- loan_dum %&gt;% 
      mutate(
          purpose_credit_card      = as.factor(purpose_credit_card),
          purpose_all_other        = as.factor(purpose_all_other),
          purpose_home_improvement = as.factor(purpose_home_improvement),
          purpose_small_business   = as.factor(purpose_small_business),
          purpose_major_purchase   = as.factor(purpose_major_purchase),
          purpose_educational      = as.factor(purpose_educational)
      )
  loan_dum %&gt;% glimpse()
  
  
  
  # standardizing continuous values
  loan_dum_stand &lt;- loan_dum %&gt;% 
      mutate(
          int.rate          = as.vector(scale(int.rate,center = TRUE,scale = TRUE)),
          installment       = as.vector(scale(installment,center = TRUE,scale = TRUE)),
          log.annual.inc    = as.vector(scale(log.annual.inc,center = TRUE,scale = TRUE)),
          dti               = as.vector(scale(dti,center = TRUE,scale = TRUE)),
          fico              = as.vector(scale(fico,center = TRUE,scale = TRUE)),
          days.with.cr.line = as.vector(scale(days.with.cr.line,center = TRUE,scale = TRUE)),
          revol.bal         = as.vector(scale(revol.bal,center = TRUE,scale = TRUE)),
          revol.util        = as.vector(scale(revol.util,center = TRUE,scale = TRUE))
      )
  loan_dum_stand %&gt;% view()
  
  
  # splitting the data into train test ----
  set.seed(123)
  trainIndexDum &lt;- createDataPartition(loan_dum_stand$not.fully.paid,p = 0.70,list = FALSE,times = 1)
  loan_dum_train &lt;- loan_dum_stand[trainIndexDum,]
  loan_dum_test  &lt;- loan_dum_stand[-trainIndexDum,]
  
  loan_dum_train %&gt;% glimpse()
  
  
  
  #  SVM model with default settings ----
  ####################################################################################
  # Runing model building in parallel
  cl &lt;- makePSOCKcluster(10)
  registerDoParallel(cl)
  
  
  # fitting svm model with radial kernel no tuning 
  set.seed(123)
  svm_model_radial &lt;- train(
      form = not.fully.paid ~.,
      data = loan_dum_train,
      method = "svmRadial"
  )
  svm_model_radial
  plot(svm_model_radial)
  </code></pre>