Modify a vector to replace old with new values from a lookup table.

Use this function inside dplyr::recode() to recode a vector based on values from two other vectors, where old and new codes are looked up. These lookup vectors are commonly stored in a dataframe and come from a .csv or spreadsheet file.

lookup(old, new)

Arguments

old, new	Vectors of equal length giving old and new codes.

Value

A "spliced" list with names from old and values from new. The kind of data structure that you can feed to ... in dplyr::recode()`.

Acknowledgment

Thanks to David Kenfack for inspiring this function.

See also

dplyr::recode()

Other general functions to edit data in place: fill_na

Examples

library(dplyr, warn.conflicts = FALSE)

look <- tibble::tibble(
  old = c("spp1", "unknown"),
  new = c("spp3", "spp4")
)

sp <- c("spp1", "spp2", "spp3", "unknown", "spp3", "unknown", "spp1", "spp1")
recode(sp, lookup(look$old, look$new))
#> [1] "spp3" "spp2" "spp3" "spp4" "spp3" "spp4" "spp3" "spp3"

census <- tibble::tibble(sp = sp)
mutate(census, new_sp = recode(sp, lookup(look$old, look$new)))
#> # A tibble: 8 x 2
#>   sp      new_sp
#>   <chr>   <chr> 
#> 1 spp1    spp3  
#> 2 spp2    spp2  
#> 3 spp3    spp3  
#> 4 unknown spp4  
#> 5 spp3    spp3  
#> 6 unknown spp4  
#> 7 spp1    spp3  
#> 8 spp1    spp3  

# Overwrite
mutate(census, sp = recode(sp, lookup(look$old, look$new)))
#> # A tibble: 8 x 1
#>   sp   
#>   <chr>
#> 1 spp3 
#> 2 spp2 
#> 3 spp3 
#> 4 spp4 
#> 5 spp3 
#> 6 spp4 
#> 7 spp3 
#> 8 spp3