In our last note we used wrapr::qe() to help quote expressions. In this note we will discuss quoting and code-capturing interfaces (interfaces that capture user source code) a bit more.
Category: Coding
Reusable Pipelines in R
Pipelines in R are popular, the most popular one being magrittr as used by dplyr.
This note will discuss the advanced re-usable piping systems: rquery/rqdatatable operator trees and wrapr function object pipelines. In each case we have a set of objects designed to extract extra power from the wrapr dot-arrow pipe %.>%.
Timing Grouped Mean Calculation in R
This note is a comment on some of the timings shared in the dplyr-0.8.0 pre-release announcement.
The original published timings were as follows:
With performance metrics: measurements are marketing. So let’s dig in the above a bit.
Quasiquotation in R via bquote()
In August of 2003 Thomas Lumley added bquote() to R 1.8.1. This gave R and R users an explicit Lisp-style quasiquotation capability. bquote() and quasiquotation are actually quite powerful. Professor Thomas Lumley should get, and should continue to receive, a lot of credit and thanks for introducing the concept into R.
In fact bquote() is already powerful enough to build a version of dplyr 0.5.0 with quasiquotation semantics quite close (from a user perspective) to what is now claimed in tidyeval/rlang.
Let’s take a look at that.
Running the Same Task in Python and R
According to a KDD poll fewer respondents (by rate) used only R in 2017 than in 2016. At the same time more respondents (by rate) used only Python in 2017 than in 2016.
Let’s take this as an excuse to take a quick look at what happens when we try a task in both systems.
Using a Column as a Column Index
We recently saw a great recurring R question: “how do you use one column to choose a different value for each row?” That is: how do you use a column as an index? Please read on for some idiomatic base R, data.table, and dplyr solutions.
R Tip: Consider radix Sort
R Tip: Be Wary of “…”
R Tip: be wary of “...“.
The following code example contains an easy error in using the R function unique().
vec1 <- c("a", "b", "c")
vec2 <- c("c", "d")
unique(vec1, vec2)
# [1] "a" "b" "c"
Notice none of the novel values from vec2 are present in the result. Our mistake was: we (improperly) tried to use unique() with multiple value arguments, as one would use union(). Also notice no error or warning was signaled. We used unique() incorrectly and nothing pointed this out to us. What compounded our error was R‘s “...” function signature feature.
In this note I will talk a bit about how to defend against this kind of mistake. I am going to apply the principle that a design that makes committing mistakes more difficult (or even impossible) is a good thing, and not a sign of carelessness, laziness, or weakness. I am well aware that every time I admit to making a mistake (I have indeed made the above mistake) those who claim to never make mistakes have a laugh at my expense. Honestly I feel the reason I see more mistakes is I check a lot more.
wrapr 1.5.0 available on CRAN
The R package wrapr 1.5.0 is now available on CRAN.
wrapr includes a lot of tools for writing better R code:
let()(let block)%.>%(dot arrow pipe)build_frame()/draw_frame()(data.framebuilders and formatters )qc()(quoting concatenate):=(named map builder)%?%(coalesce) NEW!%.|%(reduce/expand args) NEW!uniques()(safeunique()replacement) NEW!partition_tables()/execute_parallel()NEW!DebugFnW()(function debug wrappers)λ()(anonymous function builder)
I’ll be writing articles on a number of the new capabilities. For now I just leave you with the nifty operator coalesce notation.
R Tip: Use Slices
R has a very powerful array slicing ability that allows for some very slick data processing.