Safely Map Values via dplyr::left_join()

Stu Field, SomaLogic Operating Co., Inc.

Introduction

Mapping values in one column to specific values in another (new) column of a data frame is a common task in data science. Doing so safely is often a struggle. There are some existing methods in the tidyverse that are useful, but in my opinion come with some drawbacks:

• dplyr::recode()
  • can be clunky to implement -> LHS/RHS syntax difficult (for me) to remember
• dplyr::case_when()
  • complex syntax -> difficult to remember; overkill for mapping purposes

Below is what I see is a safe way to map (re-code) values in an existing column to a new column.

---

Mapping Example

# wish to map values of 'x'
df <- withr::with_seed(101, {
  data.frame(id    = 1:10L,
             value = rnorm(10),
             x     = sample(letters[1:3L], 10, replace = TRUE)
  )
})
df
#>    id      value x
#> 1   1 -0.3260365 b
#> 2   2  0.5524619 c
#> 3   3 -0.6749438 c
#> 4   4  0.2143595 a
#> 5   5  0.3107692 c
#> 6   6  1.1739663 a
#> 7   7  0.6187899 b
#> 8   8 -0.1127343 a
#> 9   9  0.9170283 a
#> 10 10 -0.2232594 c

# create a [n x 2] lookup-table (aka hash map)
# n = no. values to map
# x = existing values to map
# new_x = new mapped values for each `x`
map <- data.frame(x = letters[1:4L], new_x = c("cat", "dog", "bird", "turtle"))
map
#>   x  new_x
#> 1 a    cat
#> 2 b    dog
#> 3 c   bird
#> 4 d turtle

# use `dplyr::left_join()`
# note: 'turtle' is absent because `d` is not in `df$x` (thus ignored)
dplyr::left_join(df, map)
#> Joining with `by = join_by(x)`
#>    id      value x new_x
#> 1   1 -0.3260365 b   dog
#> 2   2  0.5524619 c  bird
#> 3   3 -0.6749438 c  bird
#> 4   4  0.2143595 a   cat
#> 5   5  0.3107692 c  bird
#> 6   6  1.1739663 a   cat
#> 7   7  0.6187899 b   dog
#> 8   8 -0.1127343 a   cat
#> 9   9  0.9170283 a   cat
#> 10 10 -0.2232594 c  bird

Un-mapped Values -> NAs

Notice that b maps to NA. This is because the mapping object now lacks a mapping for b (compare to row 2 above). Using a slightly different syntax via tibble::enframe().

# note: `b` is missing in the map
map_vec <- c(a = "cat", c = "bird", d = "turtle")
map2 <- tibble::enframe(map_vec, name = "x", value = "new_x")
map2
#> # A tibble: 3 × 2
#>   x     new_x 
#>   <chr> <chr> 
#> 1 a     cat   
#> 2 c     bird  
#> 3 d     turtle

# note: un-mapped values generate NAs: `b -> NA`
dplyr::left_join(df, map2, by = "x")
#>    id      value x new_x
#> 1   1 -0.3260365 b  <NA>
#> 2   2  0.5524619 c  bird
#> 3   3 -0.6749438 c  bird
#> 4   4  0.2143595 a   cat
#> 5   5  0.3107692 c  bird
#> 6   6  1.1739663 a   cat
#> 7   7  0.6187899 b  <NA>
#> 8   8 -0.1127343 a   cat
#> 9   9  0.9170283 a   cat
#> 10 10 -0.2232594 c  bird