Output 'SomaScan' Data

library(dplyr)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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

# 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

# use `dplyr::left_join()`
# note: 'turtle' is absent because `d` is not in `df$x` (thus ignored)
dplyr::left_join(df, map)

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

# note: un-mapped values generate NAs: `b -> NA`
dplyr::left_join(df, map2, by = "x")