Recoding mutilple columns in 1 shot

[Frank]

The function has a few issues:

• It needs to return something at the end (probably x). See ?return.
• We cannot write x$y programmatically passing y, unfortunately. Instead x[[y]] <- should work where y is a string column name with quotes.
• if and else are for control flow (determining which pieces of code get run), while you you want this to apply in a vectorized way to each element of x$y.

I'd suggest approaching this with a lookup table and a left join. The tidyverse way is shown here:

Quickly recoding large(ish) vectors in a tibble

Does anyone have any recommendations for efficiently recoding data in a tibble? I regularly work with coded values that need to be converted into human-readable names. I like the explicitness that dplyr::recode() provides, but it becomes cumbersome if there are many different coded values and it can be slow with large datasets (see the benchmark() tests below). An alternative I sometimes use is to create a named vector of the human-readable values and assign the code values to the names attribute. This works well if there's a 1:1 relationship between codes and human-readable values and it's faster than the recode method, but it's still slow enough that it has become a bottleneck for my dai…

This also looks related, using case_when as a substitute for if/else:

Thought process to choose a Tidyverse approach: join, mutate + map, or other

Sometimes I feel I use a Tidyverse approach, but not the right one or perhaps a non Tidyverse process altogether is better. Here is an example of such a situation: I have some standard data, ie. mpg and cyl from mtcars. I also have some label summary statistics that says Bad, Medium or good for a level of mpg for cars with certain cyl. Note: In the example pretend that the summary labels came from elsewhere. I'm not interested in calculating summary stats in order to label the data. The data: library(dplyr) library(tibble) library(purrr) cars <- rownames_to_column(mtcars[1:2]) %>% as_data_frame() [image] mpg_label <- data_frame( cyl_l = rep(c(4, 6, 8), each = 3), label_l = rep(…