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 “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.

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 at some point is inevitable.