anchor.diff:  and missing data for other columns are turned into `NA`. Here, we use `R.assign` which
   stores the data frame in a varaible available in the current R environment:
   *)
  -R.assign("x",  df).Print()
  +R.assign("x",  df)
   // [fsi:val it : string = ]
   // [fsi:     Floats   Names ]
   // [fsi: 1       10     one ] 
  
positive.code: open RProvider.``base``
  
  // Pass air data to R and print the R output
  R.as_data_frame(air).Print()
  
  // Pass air data to R and get column means
  R.colMeans(air).Print()
  // [fsi:val it : string =]
  // [fsi:  Ozone  Solar.R  Wind  Temp  Month   Day ]
  // [fsi:    NaN      NaN  9.96 77.88   6.99  15.8]
  
negative.code: time series.
  *)
  // Rolling mean with window size 20
  R.rollmean(ts, 20)
  
  (**
  This is a simple example - in practice, you can achieve the same thing with `Series.window`
  