anchor.diff:    |> series
   (**
   Now that we have a time series, we can pass it to R using the `R.as_zoo` function or
  -using `R.assign` to store it in an R variable. As previously, we can use the extension
  -method `Print` to get the output that R prints for the value:
  -more
  +using `R.assign` to store it in an R variable. As previously, the R provider automatically
  +shows the output that R prints for the value:
   *)
   open RProvider.zoo
   
   // Just convert time series to R
  -R.as_zoo(ts).Print()
  +R.as_zoo(ts)
   // Convert and assing to a variable 'ts'
  -R.assign("ts", ts).Print()
  +R.assign("ts", ts)
   // [fsi:val it : string =
   // [fsi: 2013-11-07 05:00:00 2013-11-07 06:00:00 2013-11-07 07:00:00 ...]
   // [fsi: 0.749946652         0.580584353         0.523962789         ...]
  
positive.code: time series.
  *)
  // Rolling mean with window size 20
  R.rollmean(ts, 20).Print()
  
  (**
  This is a simple example - in practice, you can achieve the same thing with `Series.window`
  
negative.code: open RProvider.``base``
  
  // Pass air data to R and print the R output
  R.as_data_frame(air)
  
  // Pass air data to R and get column means
  R.colMeans(air)
  // [fsi:val it : string =]
  // [fsi:  Ozone  Solar.R  Wind  Temp  Month   Day ]
  // [fsi:    NaN      NaN  9.96 77.88   6.99  15.8]
  