I’m trying to create multiple new score columns based on other columns. I’d like to use a function to minimize copy pasting large blocks of code.
I’m trying to do something like:
Myfunction <- function(column){
Column_df <- old_df %>%
mutate(column.score = if_else(column = 1, “yes”, “no”)
)
}
Score_df <- Myfunction(c(math, reading, science)))
But I’m getting an error saying object math is not found
Starting with an example data frame as below
df <- purrr::map_dfc(c('math', 'reading', 'science', 'history'),
~ rlang::list2(!!.x := sample(1:3, 10, TRUE)))
df
#> # A tibble: 10 × 4
#> math reading science history
#> <int> <int> <int> <int>
#> 1 2 1 3 1
#> 2 3 2 3 1
#> 3 2 2 2 2
#> 4 2 3 1 2
#> 5 3 3 1 2
#> 6 1 2 3 2
#> 7 3 3 2 1
#> 8 3 3 3 2
#> 9 1 2 2 1
#> 10 2 2 2 3
You can create new "score" columns with a function by passing your columns argument to across inside {{ }}, and using the .name option to add ".score" to the name.
If you want only the "score" columns in the output, rather than to add them to existing columns, use transmute instead of mutate.
library(dplyr, warn.conflicts = FALSE)
Myfunction <- function(df, columns){
df %>%
mutate(across({{ columns }}, ~ if_else(. == 1, 'yes', 'no'),
.names = '{.col}.score'))
}
df %>%
Myfunction(c(math, reading, science))
#> # A tibble: 10 × 7
#> math reading science history math.score reading.score science.score
#> <int> <int> <int> <int> <chr> <chr> <chr>
#> 1 2 1 3 1 no yes no
#> 2 3 2 3 1 no no no
#> 3 2 2 2 2 no no no
#> 4 2 3 1 2 no no yes
#> 5 3 3 1 2 no no yes
#> 6 1 2 3 2 yes no no
#> 7 3 3 2 1 no no no
#> 8 3 3 3 2 no no no
#> 9 1 2 2 1 yes no no
#> 10 2 2 2 3 no no no
Created on 2022-01-18 by the reprex package (v2.0.1)
