I use unix in the same way and for the same purpose as the described in the blog, but I have come to the opinion that once you get into the describe and visualize phase, it's much easier to just drop into R. Reading in the kind of file being worked on here is often as simple as

foo <-read.csv("foo.csv")

Getting summary descriptive statistics, item counts, scatter plots and histograms is often as easy as

summary(foo)

table(foo$col)

plot(foo$xcol, foo$ycol)

hist(foo$col).

I think that is lot simpler than a 4 or 5 command pipeline that can be mistake-prone to edit when you want to change column names or things like that. I still do these kinds of things in the shell sometimes, and I don't know if I can put my finger on when exactly I would drop into R vs write out a pipeline, but there IS a line somewhere...