title: Should I impute missing values and then log transform the variables, or the other way around
body: <p>I'd like to train three different models:</p>
  <ol>
  <li>elastic net regression, caret, method = 'glmnet'</li>
  <li>Random Forest, caret, method = 'ranger'</li>
  <li>Simple linear regression</li>
  </ol>
  <p>on a dataset containing relatively high number of missing values.</p>
  <p>I found the package <strong>missRanger</strong> to handle the missing values.</p>
  <p>However, I am not sure whether I should log-transform the data first and then impute the NAs, or the other way around?</p>