Wanting to use dplyr and case_when to collapse a series of indicator columns into a single column. The challenge is I want to be able to collapse over an unspecified/dynamic number of columns.
Consider the following dataset, gear has been split into a series of indicator columns.
library(dplyr)
data(mtcars)
mtcars = mtcars %>%
mutate(g2 = ifelse(gear == 2, 1, 0),
g3 = ifelse(gear == 3, 1, 0),
g4 = ifelse(gear == 4, 1, 0)) %>%
select(g2, g3, g4)
I am trying to write a function that does the reverse.
When I know how many cases this can be done as follows:
combine_indices = function(db, cols, vals){
db %>% mutate(new_col = case_when(!!sym(cols[1]) == 1 ~ vals[1],
!!sym(cols[2]) == 1 ~ vals[2],
!!sym(cols[3]) == 1 ~ vals[3]))
}
cols = c("g2", "g3", "g4")
vals = c(2,3,4)
combine_indices(mtcars, cols, vals)
However, I would like the combine_indices function to handle any number of index columns (right now it works for exactly three).
According to the documentation (?case_when), "if your patterns are stored in a list, you can splice that in with !!!". But I can not get this working:
patterns = list(sym(cols[1] == 1 ~ vals[1],
sym(cols[2] == 1 ~ vals[2],
sym(cols[3] == 1 ~ vals[3])
mtcars %>% mutate(new_col = case_when(!!!patterns))
Only produces a new column filled with NAs.
If !!!patterns worked, then it would be straightforward to take the lists cols and vals and generate patterns. However, I can not get the quosures correct. Hoping someone more familiar with quosures knows how.
Note - some similar questions here of SO were solved using joins or other functions. However, I am restricted to using case_when because of how it translates to sql when using dbplyr.
We can create a string of conditions, use parse_exprs and splice it (!!!).
library(dplyr)
library(rlang)
combine_indices = function(db, cols, vals){
db %>% mutate(new_col = case_when(!!!parse_exprs(paste(cols, '== 1 ~', vals))))
}
cols = c("g2", "g3", "g4")
vals = c(2,3,4)
combine_indices(mtcars, cols, vals)
which returns :
# g2 g3 g4 new_col
#1 0 0 1 4
#2 0 0 1 4
#3 0 0 1 4
#4 0 1 0 3
#5 0 1 0 3
#6 0 1 0 3
#....
where paste generates the conditions for case_when dynamically.
paste(cols, '== 1 ~', vals)
#[1] "g2 == 1 ~ 2" "g3 == 1 ~ 3" "g4 == 1 ~ 4"