Canonical tidyverse method to update some values of a vector from a look-up table

Question

I frequently need to recode some (not all!) values in a data frame column based off of a look-up table. I'm not satisfied by the ways I know of to solve the problem. I'd like to be able to do it in a clear, stable, and efficient way. Before I write my own function, I'd want to make sure I'm not duplicating something standard that's already out there.

## Toy example data = data.frame(   id = 1:7,   x = c("A", "A", "B", "C", "D", "AA", ".") )  lookup = data.frame(   old = c("A", "D", "."),   new = c("a", "d", "!") )  ## desired result #   id  x # 1  1  a # 2  2  a # 3  3  B # 4  4  C # 5  5  d # 6  6 AA # 7  7  ! 

I can do it with a join, coalesce, unselect as below, but this isn't as clear as I'd like - too many steps.

## This works, but is more steps than I want library(dplyr) data %>%   left_join(lookup, by = c("x" = "old")) %>%   mutate(x = coalesce(new, x)) %>%   select(-new) 

It can also be done with dplyr::recode, as below, converting the lookup table to a named lookup vector. I prefer lookup as a data frame, but I'm okay with the named vector solution. My concern here is that recode is the Questioning lifecycle phase, so I'm worried that this method isn't stable.

lookup_v = pull(lookup, new) %>% setNames(lookup$old) data %>%   mutate(x = recode(x, !!!lookup_v)) 

It could also be done with, say, stringr::str_replace, but using regex for whole-string matching isn't efficient. I suppose there is forcats::fct_recode is a stable version of recode, but I don't want a factor output (though mutate(x = as.character(fct_recode(x, !!!lookup_v))) is perhaps my favorite option so far...).

I had hoped that the new-ish rows_update() family of dplyr functions would work, but it is strict about column names, and I don't think it can update the column it's joining on. (And it's Experimental, so doesn't yet meet my stability requirement.)

Summary of my requirements:

• A single data column is updated based off of a lookup data frame (preferably) or named vector (allowable)
• Not all values in the data are included in the lookup--the ones that are not present are not modified
• Must work on character class input. Working more generally is a nice-to-have.
• No dependencies outside of base R and tidyverse packages (though I'd also be interested in seeing a data.table solution)
• No functions used that are in lifecycle phases like superseded or questioning. Please note any experimental lifecycle functions, as they have future potential.
• Concise, clear code
• I don't need extreme optimization, but nothing wildly inefficient (like regex when it's not needed)

Answer 1

A direct data.table solution, without %in%.
Depending on the length of the lookup / data tables, adding keys could improve performance substantially, but this isn't the case on this simple example.

library(data.table)  setDT(data) setDT(lookup)  ## If needed # setkey(data,x) # setkey(lookup,old)  data[lookup, x:=new, on=.(x=old)] data      id  x 1:  1  a 2:  2  a 3:  3  B 4:  4  C 5:  5  d 6:  6 AA 7:  7  !