Using dplyr to compute calculated fields depending on multiple columns without explicitly writing column names - r

Question

Consider the following code.
set.seed(56)
library(dplyr)
df <- data.frame(
NUM_1 = sample.int(500, replace = TRUE),
DENOM_1 = sample.int(500, replace = TRUE),
NUM_2 = sample.int(500, replace = TRUE),
DENOM_2 = sample.int(500, replace = TRUE)
)
head(df)
NUM_1 DENOM_1 NUM_2 DENOM_2
1 417 379 154 173
2 160 437 239 154
3 243 315 106 361
4 291 169 393 340
5 170 450 429 421
6 422 131 75 64
Without having to manually specify each of the column names (the actual problem has about 40 of these I need to create), I would like to create columns FRAC_1 and FRAC_2 for which FRAC_X = NUM_X/DENOM_X.
So, this would be what I'm looking for with regard to output, but since I'm dealing with about 40 of these, I don't want to have to manually type out each column:
df_frac <- df %>%
mutate(FRAC_1 = NUM_1 / DENOM_1,
FRAC_2 = NUM_2 / DENOM_2)
head(df_frac)
NUM_1 DENOM_1 NUM_2 DENOM_2 FRAC_1 FRAC_2
1 417 379 154 173 1.1002639 0.8901734
2 160 437 239 154 0.3661327 1.5519481
3 243 315 106 361 0.7714286 0.2936288
4 291 169 393 340 1.7218935 1.1558824
5 170 450 429 421 0.3777778 1.0190024
6 422 131 75 64 3.2213740 1.1718750
I would strongly prefer a dplyr solution to this. I thought maybe I could use mutate() with across(), but it isn't clear to me how to tell across() to pair the NUM_x with the corresponding DENOM_x columns.

Answer 1

Here is one in tidyverse
Loop across the columns with names starts_with 'NUM'
Extract the column name cur_column(), replace the substring from 'NUM' to 'DENOM' in str_replace
get the column value, divide by the NUM column, and change the column name in .names to create the 'FRAC' columns
library(dplyr)
library(stringr)
df <- df %>%
mutate(across(starts_with("NUM"), ~
./get(str_replace(cur_column(), 'NUM', 'DENOM')),
.names = "{str_replace(.col, 'NUM', 'FRAC')}"))
-output
head(df)
NUM_1 DENOM_1 NUM_2 DENOM_2 FRAC_1 FRAC_2
1 417 379 154 173 1.1002639 0.8901734
2 160 437 239 154 0.3661327 1.5519481
3 243 315 106 361 0.7714286 0.2936288
4 291 169 393 340 1.7218935 1.1558824
5 170 450 429 421 0.3777778 1.0190024
6 422 131 75 64 3.2213740 1.1718750