R rowSums and select inside mutate function dplyr - r

Question

I am trying to select columns in my dataframe and add them together if they exist. I tried
newdf4 %>% select(any_of(contains(c("adSize_300 x 600","adSize_160 x 600","adSize_120 x 600","adSize_125 x 600")))) %>% mutate(vertical_sizes=rowSums(.))
It gives me a separate output of the two columns that exist and the new vertical_sizes column created:
adSize_300 x 600 adSize_160 x 600 vertical_sizes
1 1 0 1
2 0 0 0
3 0 0 0
4 0 0 0
5 0 1 1
6 0 0 0
7 1 0 1
8 0 0 0
9 0 0 0
10 0 0 0
11 0 0 0
However, I want the new vertical sizes column to be added to my original newdf4 dataframe.
I am trying:
newdf4 %>% mutate(vertical_sizes = rowSums(select(any_of(contains(c("adSize_300 x 600","adSize_160 x 600","adSize_120 x 600","adSize_125 x 600"))))))
But i receive this error:
Error in `mutate_cols()`:
! Problem with `mutate()` column `vertical_sizes`.
i `vertical_sizes = rowSums(...)`.
x `any_of()` must be used within a *selecting* function.
i See <https://tidyselect.r-lib.org/reference/faq-selection-context.html>.
Caused by error:
! `any_of()` must be used within a *selecting* function.
i See <https://tidyselect.r-lib.org/reference/faq-selection-context.html>.
Run `rlang::last_error()` to see where the error occurred.
Please let me know if there is another approach to this. Thank you!

Answer 1

select inside of mutate requires data as its first argument, it will not infer or assume that it should look in the enclosing environment. For instance, when one does something like
mtcars %>%
select(cyl, mpg, disp)
The %>% is placing the data in the first argument of the RHS function, so this is more-aptly shown as
mtcars %>%
select(., cyl, mpg, disp)
where . indicates where the data is going. %>% allows one to specify where in the RHS expression the data should be placed (generally it works so long as the . is not placed within nested expressions).
Having said that, your use of select places the any_of in the first argument, which is (obviously) not going to work.
I suggest we use cur_data() (one of dplyr's context-dependent expressions) as the first argument.
Data:
read.table(text = '
adSize_300x600 adSize_160x600 vertical_sizes
1 1 0 1
2 0 0 0
3 0 0 0
4 0 0 0
5 0 1 1
6 0 0 0
7 1 0 1
8 0 0 0
9 0 0 0
10 0 0 0
11 0 0 0')
Code:
newdf4 %>%
mutate(
vertical_sizes = rowSums(
select(cur_data(),
contains(c("adSize_300x600", "adSize_160x600", "adSize_120x600", "adSize_125x600")))
)
)
# adSize_300x600 adSize_160x600 vertical_sizes
# 1 1 0 1
# 2 0 0 0
# 3 0 0 0
# 4 0 0 0
# 5 0 1 1
# 6 0 0 0
# 7 1 0 1
# 8 0 0 0
# 9 0 0 0
# 10 0 0 0
# 11 0 0 0