+ 
+
+
+
+
+Safely Map Values via dplyr::left_join()
+Stu Field, +SomaLogic Operating Co., Inc.
+ + + Source:vignettes/safely-map-values-join.Rmd
+ safely-map-values-join.Rmd
+
+
+ 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 ->
+
+
+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