Joining and replacing columns multiple times - r

Question

I have a dataframe with a lot of columns with abbreviations. I'm trying to replace the columns with their full name.
A minimal reproducible example:
category <- data.frame(short = c("TOM", "BAN", "APP", "PEA"),
name = c("tomato", "banana", "apple", "pear"))
df <- data.frame(col1 = c("TOM", "TOM", "TOM", "APP", "TOM"),
col2 = c("APP", "TOM", "TOM", "PEA", "PEA"),
col3 = c("TOM", "PEA", "PEA", "TOM", "BAN"))
col1 col2 col3
1 TOM APP TOM
2 TOM TOM PEA
3 TOM TOM PEA
4 APP PEA TOM
5 TOM PEA BAN
Now, I would like my dataframe to just contain the full names of the products. I can get it to work with left_joins, selecting and renaming, but this code is getting out of hand pretty rapidly with a lot of columns.
df2 <- df %>%
left_join(category, by = c("col1" = "short")) %>%
select(-col1) %>%
rename(col1 = name) %>%
left_join(category, by = c("col2" = "short")) %>%
select(-col2) %>%
rename(col2 = name) %>%
left_join(category, by = c("col3" = "short")) %>%
select(-col3) %>%
rename(col3 = name)
col1 col2 col3
1 tomato apple tomato
2 tomato tomato pear
3 tomato tomato pear
4 apple pear tomato
5 tomato pear banana
I think (hope?) there's a better solution for it, but I'm unable to find it.

Answer 1

An option is to create a named vector
library(dplyr)
library(tibble)
v1 <- deframe(category)
and then use that to match and replace the values
df1 <- df %>%
mutate(across(everything(), ~ v1[.]))
-output
df1
# col1 col2 col3
#1 tomato apple tomato
#2 tomato tomato pear
#3 tomato tomato pear
#4 apple pear tomato
#5 tomato pear banana
It can be also done with recode using similar way
df %>%
mutate(across(everything(), ~ recode(., !!! v1)))
Or using base R, create the named vector with setNames, loop over the columns with lapply and replace those values and assign it back
v1 <- with(category, setNames(name, short))
df1 <- df
df1[] <- lapply(df, function(x) v1[x])
Or convert to matrix (a matrix is a vector with dim attributes)
df1[1] <- v1[as.matrix(df)]

Answer 2

Another option is using factor
df[] <- factor(
u <- unlist(df),
labels = with(category, name[match(sort(unique(u)), short)])
)
or a shorter one via setNames
df[]<-with(category,setNames(name,short))[unlist(df)]
which gives
> df
col1 col2 col3
1 tomato apple tomato
2 tomato tomato pear
3 tomato tomato pear
4 apple pear tomato
5 tomato pear banana

Answer 3

You can get the data in long format such that all the values are in one column which is easy to join with category dataframe and then get data back in wide format.
library(dplyr)
library(tidyr)
df %>%
mutate(row = row_number()) %>%
pivot_longer(cols = -row, names_to = 'col', values_to = 'short') %>%
left_join(category, 'short') %>%
select(-short) %>%
pivot_wider(names_from = col, values_from = name) %>%
select(-row)
# col1 col2 col3
# <chr> <chr> <chr>
#1 tomato apple tomato
#2 tomato tomato pear
#3 tomato tomato pear
#4 apple pear tomato
#5 tomato pear banana