R: Custom Function - Mutate Existing Column - r

Question

I'd like to create a custom function to try and standardise strings in multiple different columns, in multiple different data frames, with the ultimate intention of joining data from them together.
In order to do this, I'd like to be able to pass a column name into a custom function and have the function carry out operations on that column. With the example beneath, I'd like to clean columns a and c before joining them together to look like this:
library(tidyverse)
df1 <- tibble(a = c("apple & pear", "kiwi", "plum"), b = c("cat", "dog", "cow"))
df2 <- tibble(c = c("apple and pear", "kiwi.", "plum"), d = c("car", "bike", "truck"))
full_join(df1, df2, by = c("a" = "c") )
a b d
1 apple & pear cat car
2 kiwi dog bike
3 plum cow truck
Instead of how it currently turns out like, which is this:
# A tibble: 5 x 3
a b d
1 apple & pear cat NA
2 kiwi dog NA
3 plum cow truck
4 apple and pear NA car
5 kiwi. NA bike
To do this, I know I need to build custom functions, which I'd be relatively inexperienced at doing, especially with curly-curly. The two functions beneath should change the symbols and remove the trailing punctuation, and ideally these should be combined into the one function, with the flexibility to be able to add more if necessary, like this:
add_symbol <- function(col.name){
mutate({{col.name}} = gsub(" & ", " and ", {{col.name}}))
}
rm_trail_punc <- function(col.name){
mutate({{col.name}} = gsub("[[:punct:]]$", "", {{col.name}}))
}
standardise_col <- function(df, col.name){
df %>%
add_symbol({{col.name}}) %>%
rm_trail_punc({{col.name}})
}
df1 <- standardise_col(df1)
standardise_col(df2) %>%
full_join(., df1, by = c("a" = "c"))
However, these functions can't be created, and return an error unexpected '=' because the column name can't be passed to the left-hand side of the equal sign. Is there any way of passing these values to the mutate without hard-coding them?

Answer 1

I think you can achieve this more simply using with the following:
library(dplyr)
clean_func <- function(df){
df %>% mutate(across(everything(), ~gsub(" & ", " and ", .) %>%
gsub("[[:punct:]]$", "", .)))
}
df1 <- clean_func(df1)
df2 <- clean_func(df2)
You can make updates to the function by adding additional gsub, str_replace, or other calls as needed.
Edit:
Based on update, you can do something like this to target your variables specifically:
add_symbol <- function(col.name){
gsub(" & ", " and ", col.name)
}
rm_trail_punc <- function(col.name){
gsub("[[:punct:]]$", "", col.name)
}
standardise_col <- function(df, col.name){
col.name <- enquo(col.name)
df %>%
mutate(!!col.name := add_symbol(!!col.name),
!!col.name := rm_trail_punc(!!col.name))
}
Your code won't ever work as written, but you could do something like this:
new_df <- standardise_col(df1, a) %>%
left_join(., standardise_col(df2, c), by = c("a"="c"))
Which gives us:
# A tibble: 3 x 3
a b d
<chr> <chr> <chr>
1 apple and pear cat car
2 kiwi dog bike
3 plum cow truck
You can read up on tidy evaluation here: https://tidyeval.tidyverse.org/dplyr.html

Answer 2

As said in the comment by #1k monkeys and a single PC, your example data are different from what you show, so maybe the results could be different, but let's assume you've some data like this:
df1 <- tibble(a = c("apple & pear", "kiwi", "plum"),
b = c("cat","dog","cow"))
df2 <- tibble(c = c("apple and pear", "kiwi.", "orange"),
d = c("truck","bike","car"))
You can manage to use the package fuzzyjoin to merge them:
library(fuzzyjoin)
library(dplyr)
df1 %>%
stringdist_full_join(df2, by = c(a = "c") ,
max_dist = 3,
distance_col = "DIST")
# A tibble: 4 x 5
a b c d DIST
<chr> <chr> <chr> <chr> <dbl>
1 apple & pear cat apple and pear truck 3
2 kiwi dog kiwi. bike 1
3 plum cow <NA> <NA> NA
4 <NA> <NA> orange car NA
The result is different because I've based the data on your example and "plum" and "orange" doesn't match (so cow and car are not aligned). Clearly with a select() you can select the column you need, or with mutate() you can rename them.