I have 4 different variables in my raw data (all starting with the prefix raw_), followed by combinations of hours/money and school/social. In my real data, I have many more options for each value of variable, so it ends up being quite a long chunk of code to have to iterate through 4 times.
There must be a simple way to do the equivalent of a foreach loop in Stata (which I use more frequently for data cleaning) in R to condense the 4 iterations to just 1.
In Stata, the loop would look something like:
foreach suffix in hours_school hours_social money_school money_social {
gen olderch_`suffix' = .
replace olderch_`suffix' = 90 if raw_`suffix' == "90% or more 10% or less"
replace olderch_`suffix' = 10 if raw_`suffix' == "10% or less 90% or more"
}
And here's my current version of R code that I'd love to condense similarly:
d <- d %>%
mutate(olderch_hours_school = case_when (
raw_hours_school == "90% or more 10% or less" ~ 90,
raw_hours_school == "10% or less 90% or more" ~ 10
))
d <- d %>%
mutate(olderch_hours_social = case_when (
raw_hours_social == "90% or more 10% or less" ~ 90,
raw_hours_social == "10% or less 90% or more" ~ 10
))
d <- d %>%
mutate(olderch_money_school = case_when (
raw_money_school == "90% or more 10% or less" ~ 90,
raw_money_school == "10% or less 90% or more" ~ 10
))
d <- d %>%
mutate(olderch_money_social = case_when (
raw_money_social == "90% or more 10% or less" ~ 90,
raw_money_social == "10% or less 90% or more" ~ 10
))
Edit: As requested in a comment, answer examples create new columns based on the originals.
dplyr::across() (docs) allows you to apply a function across multiple columns in your data frame. You can adapt your case_when statements by adding ~ to make a lambda function and using . to represent each column.
You can provide a vector of all columns being transformed, or use a selection helper, such as everything() or starts_with().
Test data follows the examples.
library(dplyr)
glimpse(df)
#> Rows: 10
#> Columns: 4
#> $ raw_hours_school <chr> "90% or more 10% or less", "90% or more 10% or less",…
#> $ raw_hours_social <chr> "10% or less 90% or more", "10% or less 90% or more",…
#> $ raw_money_school <chr> "90% or more 10% or less", "10% or less 90% or more",…
#> $ raw_money_social <chr> "90% or more 10% or less", "10% or less 90% or more",…
df |>
mutate(across(
c(raw_hours_school, raw_hours_social,
raw_money_school, raw_money_social),
~ case_when(. == "90% or more 10% or less" ~ 90,
. == "10% or less 90% or more" ~ 10),
.names = "new_{.col}")) %>%
setNames(gsub('_raw', '', names(.)))
#> raw_hours_school raw_hours_social raw_money_school
#> 1 90% or more 10% or less 10% or less 90% or more 90% or more 10% or less
#> 2 90% or more 10% or less 10% or less 90% or more 10% or less 90% or more
#> 3 90% or more 10% or less 10% or less 90% or more 90% or more 10% or less
#> 4 10% or less 90% or more 90% or more 10% or less 90% or more 10% or less
#> 5 90% or more 10% or less 10% or less 90% or more 90% or more 10% or less
#> 6 10% or less 90% or more 90% or more 10% or less 90% or more 10% or less
#> 7 10% or less 90% or more 10% or less 90% or more 10% or less 90% or more
#> 8 10% or less 90% or more 90% or more 10% or less 10% or less 90% or more
#> 9 90% or more 10% or less 90% or more 10% or less 90% or more 10% or less
#> 10 90% or more 10% or less 90% or more 10% or less 10% or less 90% or more
#> raw_money_social new_hours_school new_hours_social new_money_school
#> 1 90% or more 10% or less 90 10 90
#> 2 10% or less 90% or more 90 10 10
#> 3 90% or more 10% or less 90 10 90
#> 4 10% or less 90% or more 10 90 90
#> 5 10% or less 90% or more 90 10 90
#> 6 90% or more 10% or less 10 90 90
#> 7 90% or more 10% or less 10 10 10
#> 8 90% or more 10% or less 10 90 10
#> 9 90% or more 10% or less 90 90 90
#> 10 10% or less 90% or more 90 90 10
#> new_money_social
#> 1 90
#> 2 10
#> 3 90
#> 4 10
#> 5 10
#> 6 90
#> 7 90
#> 8 90
#> 9 90
#> 10 10
df |>
mutate(across(
starts_with("raw"),
~ case_when(. == "90% or more 10% or less" ~ 90,
. == "10% or less 90% or more" ~ 10),
.names = "new_{.col}")) %>%
setNames(gsub('_raw', '', names(.)))
#> raw_hours_school raw_hours_social raw_money_school
#> 1 90% or more 10% or less 10% or less 90% or more 90% or more 10% or less
#> 2 90% or more 10% or less 10% or less 90% or more 10% or less 90% or more
#> 3 90% or more 10% or less 10% or less 90% or more 90% or more 10% or less
#> 4 10% or less 90% or more 90% or more 10% or less 90% or more 10% or less
#> 5 90% or more 10% or less 10% or less 90% or more 90% or more 10% or less
#> 6 10% or less 90% or more 90% or more 10% or less 90% or more 10% or less
#> 7 10% or less 90% or more 10% or less 90% or more 10% or less 90% or more
#> 8 10% or less 90% or more 90% or more 10% or less 10% or less 90% or more
#> 9 90% or more 10% or less 90% or more 10% or less 90% or more 10% or less
#> 10 90% or more 10% or less 90% or more 10% or less 10% or less 90% or more
#> raw_money_social new_hours_school new_hours_social new_money_school
#> 1 90% or more 10% or less 90 10 90
#> 2 10% or less 90% or more 90 10 10
#> 3 90% or more 10% or less 90 10 90
#> 4 10% or less 90% or more 10 90 90
#> 5 10% or less 90% or more 90 10 90
#> 6 90% or more 10% or less 10 90 90
#> 7 90% or more 10% or less 10 10 10
#> 8 90% or more 10% or less 10 90 10
#> 9 90% or more 10% or less 90 90 90
#> 10 10% or less 90% or more 90 90 10
#> new_money_social
#> 1 90
#> 2 10
#> 3 90
#> 4 10
#> 5 10
#> 6 90
#> 7 90
#> 8 90
#> 9 90
#> 10 10
set.seed(123)
df <- data.frame(
raw_hours_school = sample(c("90% or more 10% or less",
"10% or less 90% or more"),
10, replace = TRUE),
raw_hours_social = sample(c("90% or more 10% or less",
"10% or less 90% or more"),
10, replace = TRUE),
raw_money_school = sample(c("90% or more 10% or less",
"10% or less 90% or more"),
10, replace = TRUE),
raw_money_social = sample(c("90% or more 10% or less",
"10% or less 90% or more"),
10, replace = TRUE)
)