mutate(across) to generate multiple new columns in tidyverse - r

Question

I usually have to perform equivalent calculations on a series of variables/columns that can be identified by their suffix (ranging, let's say from _a to _i) and save the result in new variables/columns. The calculations are equivalent, but vary between the variables used in the calculations. These again can be identified by the same suffix (_a to _i). So what I basically want to achieve is the following:
newvar_a = (oldvar1_a + oldvar2_a) - z
...
newvar_i = (oldvar1_i + oldvar2_i) - z
This is the farest I got:
mutate(across(c(oldvar1_a:oldvar1_i), ~ . - z, .names = "{col}_new"))
Thus, I'm able to "loop" over oldvar1_a to oldvar1_i, substract z from them and save the results in new columns named oldvar1_a_new to oldvar1_i_new. However, I'm not able to include oldvar2_a to oldvar2_i in the calculations as R won't loop over them. (Additionally, I'd still need to rename the new columns).
I found a way to achieve the result using a for-loop. However, this definitely doesn't look like the most efficient and straightforward way to do it:
for (i in letters[1:9]) {
oldvar1_x <- paste0("oldvar1_", i)
oldvar2_x <- paste0("oldvar2_", i)
newvar_x <- paste0("newvar_", i)
df <- df %>%
mutate(!!sym(newvar_x) := (!!sym(oldvar1_x) + !!sym(oldvar2_x)) - z)
}
Thus, I'd like to know whether/how to make mutate(across) loop over multiple columns that can be identified by suffixes (as in the example above)

Answer 1

In this case, you can use cur_data() and cur_column() to take advantage that we are wanting to sum together columns that have the same suffix but just need to swap out the numbers.
library(dplyr)
df <- data.frame(
oldvar1_a = 1:3,
oldvar2_a = 4:6,
oldvar1_i = 7:9,
oldvar2_i = 10:12,
z = c(1,10,20)
)
mutate(
df,
across(
starts_with("oldvar1"),
~ (.x + cur_data()[gsub("1", "2", cur_column())]) - z,
.names = "{col}_new"
)
)
#> oldvar1_a oldvar2_a oldvar1_i oldvar2_i z oldvar2_a oldvar2_i
#> 1 1 4 7 10 1 4 16
#> 2 2 5 8 11 10 -3 9
#> 3 3 6 9 12 20 -11 1
If you want to use with case_when, just make sure to index using [[, you can read more here.
df <- data.frame(
oldvar1_a = 1:3,
oldvar2_a = 4:6,
oldvar1_i = 7:9,
oldvar2_i = 10:12,
z = c(1,2,0)
)
mutate(
df,
across(
starts_with("oldvar1"),
~ case_when(
z == 1 ~ .x,
z == 2 ~ cur_data()[[gsub("1", "2", cur_column())]],
TRUE ~ NA_integer_
),
.names = "{col}_new"
)
)
#> oldvar1_a oldvar2_a oldvar1_i oldvar2_i z oldvar1_a_new oldvar1_i_new
#> 1 1 4 7 10 1 1 7
#> 2 2 5 8 11 2 5 11
#> 3 3 6 9 12 0 NA NA

Answer 2

There is a fairly straightforward way to do what I believe you are attempting to do.
# first lets create data
library(dplyr)
df <- data.frame(var1_a=runif(10, min = 128, max = 131),
var2_a=runif(10, min = 128, max = 131),
var1_b=runif(10, min = 128, max = 131),
var2_b=runif(10, min = 128, max = 131),
var1_c=runif(10, min = 128, max = 131),
var2_c=runif(10, min = 128, max = 131))
# taking a wild guess at what your z is
z <- 4
# initialize a list
fnl <- list()
# iterate over all your combo, put in list
for (i in letters[1:3])
{
dc <- df %>% select(ends_with(i))
i <- dc %>% mutate(a = rowSums(dc[1:ncol(dc)]) - z)
fnl <- append(fnl, i)
}
# convert to a dataframe/tibble
final <- bind_cols(fnl)
I left the column names sloppy assuming you had specific requirements here. You can convert this loop into a function and do the whole thin in a single step using purrr.