dplyr mutate column while checking range of columns

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The code below shows how I am accomplishing this right now, but I am wondering if there is a way to do this with less code by using col1:col12, or something like that. I tried googling and couldn't figure it out.

MRE:

a = matrix(1:2,12,12)
a= as.data.frame(a)
a = a %>% mutate(col = case_when(V1 == 1 & V2 == 1 & V3 == 1 & V4 == 1 & V5 == 1 & V6 == 1 & V7 == 1 & V8 == 1 & V9 == 1 & V10 == 1 & V11 == 1 & V12 == 1~0)) %>% mutate(col = ifelse(is.na(col),1,col))

There is a lot of code and I know there must be a faster way to do this. I have 12 columns with varying values and I need to check the values in all 12 (using & and |) to create dummy variables indicating whether each row contains specific values. In my actual data set there is a larger range of values in all 12 columns. Any help would be appreciated.

dplyr r
1 Answers

Here you can use mutate() with c_across() to query all the columns in your condition.

library(tidyverse)

a <- matrix(1:2,12,12) %>% 
  as.data.frame()

a %>% 
  rowwise() %>% 
  mutate(col = as.numeric(!all(c_across(V1:V12) == 1)))
#> # A tibble: 12 x 13
#> # Rowwise: 
#>       V1    V2    V3    V4    V5    V6    V7    V8    V9   V10   V11   V12   col
#>    <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <dbl>
#>  1     1     1     1     1     1     1     1     1     1     1     1     1     0
#>  2     2     2     2     2     2     2     2     2     2     2     2     2     1
#>  3     1     1     1     1     1     1     1     1     1     1     1     1     0
#>  4     2     2     2     2     2     2     2     2     2     2     2     2     1
#>  5     1     1     1     1     1     1     1     1     1     1     1     1     0
#>  6     2     2     2     2     2     2     2     2     2     2     2     2     1
#>  7     1     1     1     1     1     1     1     1     1     1     1     1     0
#>  8     2     2     2     2     2     2     2     2     2     2     2     2     1
#>  9     1     1     1     1     1     1     1     1     1     1     1     1     0
#> 10     2     2     2     2     2     2     2     2     2     2     2     2     1
#> 11     1     1     1     1     1     1     1     1     1     1     1     1     0
#> 12     2     2     2     2     2     2     2     2     2     2     2     2     1

As Ian Campbell points out in the comments, dplyr::if_all() can also be used here.

a %>% 
  mutate(col = as.numeric(!if_all(everything(), ~ . == 1)))
#>    V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 col
#> 1   1  1  1  1  1  1  1  1  1   1   1   1   0
#> 2   2  2  2  2  2  2  2  2  2   2   2   2   1
#> 3   1  1  1  1  1  1  1  1  1   1   1   1   0
#> 4   2  2  2  2  2  2  2  2  2   2   2   2   1
#> 5   1  1  1  1  1  1  1  1  1   1   1   1   0
#> 6   2  2  2  2  2  2  2  2  2   2   2   2   1
#> 7   1  1  1  1  1  1  1  1  1   1   1   1   0
#> 8   2  2  2  2  2  2  2  2  2   2   2   2   1
#> 9   1  1  1  1  1  1  1  1  1   1   1   1   0
#> 10  2  2  2  2  2  2  2  2  2   2   2   2   1
#> 11  1  1  1  1  1  1  1  1  1   1   1   1   0
#> 12  2  2  2  2  2  2  2  2  2   2   2   2   1

Created on 2022-02-26 by the reprex package (v2.0.1)

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