title: How to change type of target column when doing := by group in a data.table in R?
body: <p>I'm trying to do := by group for an existing column of type 'integer' where the new values are of type 'double', which fails.</p>
  
  <p>My scenario is mutating a column representing time into a POSIXct based on values in other columns. I could modify the creating of the data.table as a work around, but I'm still interested in how to go about actually changing the type of a column, as it is suggested in the error message. </p>
  
  <p>Here's a simple toy example of my problem:</p>
  
  <pre><code>db = data.table(id=rep(1:2, each=5), x=1:10, y=runif(10))
  db
  id  x          y
   1:  1  1 0.47154470
   2:  1  2 0.03325867
   3:  1  3 0.56784494
   4:  1  4 0.47936031
   5:  1  5 0.96318208
   6:  2  6 0.83257416
   7:  2  7 0.10659533
   8:  2  8 0.23103810
   9:  2  9 0.02900567
  10:  2 10 0.38346531
  
  db[, x:=mean(y), by=id]   
  
  Error in `[.data.table`(db, , `:=`(x, mean(y)), by = id) : 
  Type of RHS ('double') must match LHS ('integer'). To check and coerce would impact performance too much for the fastest cases. Either change the type of the target column, or coerce the RHS of := yourself (e.g. by using 1L instead of 1)
  </code></pre>