I’m doing a lot of programming in the statistics
language R,
as I translate an economic model into R from Python.
This is a big project, and I’ll blog about it more
in later posts, as I share useful bits of
code I’ve written. But in this post, I want to mention
a kind of add-on to R: not part of the base language,
but widely used and respected. This is
Hadley Wickham‘s
Tidyverse.

I’ve had to decide whether to use the Tidyverse
or to stick to the base language: to so-called
“base R”. Why? Reality is complicated, and in engineering, we
evolve alternative sets of tools for
manipulating it. For example, computing has
“functional programming”, “object-oriented
programming”, and “logic programming”. These
are different notations for describing
reality, and conflicts
may occur if we try to think in
more than one at the same time. When designing
a program that my colleague will also
work on, I have to decide whether the benefits
of another set of tools justify the conflict he’ll
face in learning them.

I hope Hadley Wickham will forgive me for saying that
his Tidyverse libraries set up such a conflict. As
Bob Muenchen notes in
“The
Tidyverse Curse”,
learners often comment that base R functions
and Tidyverse ones feel like two separate
languages. Navigating the balance between
base R and the Tidyverse can be a challenge.

But as Bob also notes when discussing dplyr,
a package that, together with its relatives
makes up the Tidyverse, learning it is well worth
the effort. Conflicts between the Tidyverse and
base R are not there for the hell of it, but
because of decisions made by R’s original
designers. These probably seemed like a good
idea at the time, but conflict with
better ways of doing things. The Tidyverse
functions are just doing the best they
can with the existing architecture.

As for what the Tidyverse contains, Bob
talks about some of its features. And there
are tutorials scattered around the web: I like
monashbioinformaticsplatform.github.io’s
“The
tidyverse: dplyr, ggplot2, and friends”.
Highlights for me so far include: tibbles,
a reimplementation of R data frames;
pipes notation, which makes it easy to
write sequences of data transformations; and
various functions for rearranging data, including
spread, gather, nest,
and unnest.