(This article was originally published at Statistics – Win-Vector Blog, and syndicated at StatsBlogs.)
This article is on writing sweet R code using the wrapr package.
The problem
Consider the following R puzzle. You are given: a data.frame, the name of a column that you wish to find missing values (NA) in, and the name of a column to land the result. For instance:
d <- data.frame(x = c(1, NA)) print(d) # x # 1 1 # 2 NA cname <- 'x' print(cname) # [1] "x" rname <- paste(cname, 'isNA', sep = '_') print(rname) # [1] "x_isNA"
How do you write generic code to populate the column x_isNA with which rows of x are missing?
The “base R” solution
In “base R” (R without additional packages) this is easy.
When you know the column names while writing the code:
d2 <- d d2$x_isNA <- is.na(d2$x) print(d2) # x x_isNA # 1 1 FALSE # 2 NA TRUE
And when you don’t know the column names while writing the code (but know they will arrive in variables later):
d2 <- d d2[[rname]] <- is.na(d2[[cname]])
The “base R” solution really is quite elegant.
The “all in” non-standard evaluation dplyr::mutate solution
As far as I can tell the “all in” non-standard evaluation dplyr::mutate solution is something like the following.
When you know the column names while writing the code:
library("dplyr")
d %>% mutate(x_isNA = is.na(x))
And when you don’t know the column names while writing the code (but know they will arrive in variables later):
d %>%
mutate_(.dots =
stats::setNames(list(lazyeval::interp(
~ is.na(VAR),
VAR = as.name(cname)
)),
rname))
The sweet wrapr::let dplyr::mutate solution
We will only work the harder “when you don’t yet know the column name” (or parametric) version:
library("wrapr")
let(list(COL = cname, RES = rname),
d %>% mutate(RES = is.na(COL))
)
I think that this is pretty sweet, and can really level up your dplyr game.
wrapr::let is available from CRAN and already has a number of satisfied users:
If function behavior depends on variable names, then convenient control of functions is eventually going to require convenient control of variable names; so needing to re-map variable names at some point is inevitable.
Please comment on the article here: Statistics – Win-Vector Blog
