Slicing In R

John Mount recommends learning about the array slicing system in R:

R has a very powerful array slicing ability that allows for some very slick data processing.

Suppose we have a data.frame “d“, and for every row where d$n_observations < 5 we wish to “NA-out” some other columns (mark them as not yet reliably available). Using slicing techniques this can be done quite quickly as follows.

library("wrapr")

d[d$n_observations < 5, 
  qc(mean_cost, mean_revenue, mean_duration)] <- NA

Read on for more.  In general, I prefer the pipeline mechanics offered with the Tidyverse for readability.  But this is a good example of why you should know both styles.