For the observation toward the end that compressed files seem highly uniform, this makes sense intuitively. If there were any visible patterns, that would imply that the data is further compressible, which would mean the compressor didn't do it's job very well.
x %>%
bind_cols(y, z) %>%
as.tibble() %>%
na.omit()
Makes the order of execution a little clearer (and uses more dplyr conventions). I still nest functions when working interactively with a REPL, but for code I'll have to reread in the future, the pipes save me much head scratching. Death to the pyramid of doom.
For emphasis, bind_cols is a dplyr function (meaning it gets the speed boost from using Rcpp, albeit not much difference when adding a few columns), while cbind is a base function which does not.
dplyr nowadays obsoletes most of the basic building blocks of data frame construction, although they are hidden in the docs.
Hi, author here. I just find it much more intuitive, like unix pipes, or Clojure's threading macros. Nested calls are more confusing to me.
Though it should have probably been better as cbind(...) %>% as.tibble() %>% na.omit...
It's why I adore %>% as code gets reads as a sequence of step-by-step instructions, of one forcing a reader to constantly keep switching between left-to-right and right-to-left.
