Apply dplyr::starts_with() with lambda function

Question

I have below implementation

library(dplyr)
library(tidyr)
dat = data.frame('A' = 1:3, 'C_1' = 1:3, 'C_2' = 1:3, 'M' = 1:3)

Below works

dat %>% rowwise %>% mutate(Anew = list({function(x) c(x[1]^2, x[2] + 5, x[3] + 1)}(c(M, C_1, C_2)))) %>% ungroup %>% unnest_wider(Anew, names_sep = "")

However below does not work when I try find the column names using dplyr::starts_with()

dat %>% rowwise %>% mutate(Anew = list({function(x) c(x[1]^2, x[2] + 5, x[3] + 1)}(c(M, starts_with('C_'))))) %>% ungroup %>% unnest_wider(Anew, names_sep = "")

Any pointer on how to correctly apply starts_with() in this context will be very helpful.

PS : This is continuation from my earlier post Apply custom function that returns multiple values after dplyr::rowwise()

What happens, when the number of `C_` columns are not equal to 2. This seems to be similar to the case I showed i.e. `nm1 <- c(C_1 = 5, C_2 = 1); dat %>% mutate(across(starts_with("C_"), ~ .x + nm1[cur_column()], .names = "{.col}_new"))` — akrun, Dec 18 '22 at 22:00
I have edited my original post to reflect my actual problem more closer — Brian Smith, Dec 18 '22 at 22:08
I guess you need `c_across` i.e. `dat %>% rowwise %>% mutate(Anew = list((function(x) c(x[1]^2, x[2] + 5, x[3] + 1))(c_across(starts_with("C_"))))) %>% unnest_wider(Anew, names_sep = "")` Also, you need `C_3` in the data as the `x[3]` corresponds to the third column — akrun, Dec 18 '22 at 22:14

G. Grothendieck · Answer 1 · 2022-12-19T13:39:07.537

starts_with must be used within a selecting function so we can write this. across is also a selecting function so we could alternately use across(M | starts_with('C_')) in place of select(...) . c_across is also a selecting function but it does not preserve names.

dat %>%
  rowwise %>%
  mutate(Anew = list({function(x) c(x[1]^2, x[2] + 5, x[3] + 1)}
    (select(cur_data(), M, starts_with('C_'))))) %>%
  ungroup %>%
  unnest_wider(Anew, names_sep = "")
## # A tibble: 3 × 7
##       A   C_1   C_2     M AnewM AnewC_1 AnewC_2
##   <int> <int> <int> <int> <dbl>   <dbl>   <dbl>
## 1     1     1     1     1     1       6       2
## 2     2     2     2     2     4       7       3
## 3     3     3     3     3     9       8       4

Here group_modify would also work and allow the use of formula notation to specify an anonymous function. The indexes in the anonymous function have been reordered to correspond to the order in the input.

dat %>%
  group_by(A) %>%
  group_modify(~ cbind(.x, Anew = c(.x[3]^2, .x[1] + 5, .x[2] + 1))) %>%
  ungroup
## # A tibble: 3 × 7
##       A   C_1   C_2     M Anew.M Anew.C_1 Anew.C_2
##   <int> <int> <int> <int>  <dbl>    <dbl>    <dbl>
## 1     1     1     1     1      1        6        2
## 2     2     2     2     2      4        7        3
## 3     3     3     3     3      9        8        4

akrun · Accepted Answer · 2022-12-18T22:26:52.917

If we wrap the starts_with in c_across and assuming there is a third column that starts with C_, then the lambda function on the fly would work

library(dplyr)
library(tidyr)
dat %>%
  rowwise %>%
   mutate(Anew = list((function(x) c(x[1]^2, x[2] + 5, x[3] + 
      1))(c_across(starts_with("C_"))))) %>%
  unnest_wider(Anew, names_sep = "")

-output

# A tibble: 3 × 8
      A   C_1   C_2   C_3     M Anew1 Anew2 Anew3
  <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
1     1     1     1     1     1     1     6     2
2     2     2     2     2     2     4     7     3
3     3     3     3     3     3     9     8     4

Or instead of doing rowwise, we may create a named list of functions and apply column wise with across (would be more efficient)

fns <- list(C_1 = function(x) x^2, C_2 = function(x) x + 5, 
      C_3 = function(x) x + 1)
dat %>%
   mutate(across(starts_with("C_"), 
    ~ fns[[cur_column()]](.x), .names = "Anew{seq_along(.fn)}"))

-output

   A C_1 C_2 C_3 M Anew1 Anew2 Anew3
1 1   1   1   1 1     1     6     2
2 2   2   2   2 2     4     7     3
3 3   3   3   3 3     9     8     4

data

dat <- data.frame('A' = 1:3, 'C_1' = 1:3, 'C_2' = 1:3, C_3 = 1:3, 'M' = 1:3)

Apply dplyr::starts_with() with lambda function

2 Answers2

data