Is there a way to collapse related variables into a single based on a condition?

user_13734451 0
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Lets say I have multiple variables that measure substance abuse i.e a1 is on alcohal usage, a2 is on bhang and a3 is on cocaine. I would like to generate variable afin that indicates engaged in substance abuse if any of the the three is yes.

Is there a way to shorten the code so I don't specify use multiple ifelse statements as below? Trying to find the best way to do it because I have more than 10 variables to collapse into one and writing ifelse may not be ideal.

# Anymatch
library(tidyverse)

set.seed(2021)

mydata <- tibble(
  a1 = factor(round(runif(20, 1, 3)),
              labels = c("Yes", "No", "N/A")),
  a2 = factor(round(runif(20, 1, 3)),
              labels = c("Yes", "No", "N/A")),
  a3 = factor(round(runif(20, 1, 3)),
              labels = c("Yes", "No", "N/A")),
  b1 = round(rnorm(20, 10, 2)))
mydata

mydata <- mydata %>%
  mutate(afin = ifelse(a1 == "Yes"|a2=="Yes"|a3=="Yes", "Yes", "No"))
dplyr r
4 Answers

We could do this without an ifelse as well. Just convert the logical column to numeric index and pass a vector to replace the values

library(dplyr)
mydata %>%
     mutate(afin = c("no", "yes")[1 + (rowSums(select(cur_data(), 
        starts_with('a')) == 'Yes') > 0)])

-output

# A tibble: 20 x 5
   a1    a2    a3       b1 afin 
   <fct> <fct> <fct> <dbl> <chr>
 1 No    Yes   Yes       6 yes  
 2 N/A   N/A   N/A       7 no   
 3 No    No    No       12 no   
 4 No    No    N/A       7 no   
 5 No    No    Yes       9 yes  
 6 No    N/A   N/A       7 no   
 7 No    N/A   N/A       7 no   
 8 No    N/A   Yes       7 yes  
 9 N/A   N/A   Yes      10 yes  
10 N/A   N/A   N/A      11 no   
11 Yes   Yes   No       10 yes  
12 N/A   N/A   No       14 no   
13 No    N/A   Yes       9 yes  
14 No    N/A   No       14 no   
15 N/A   No    No       10 no   
16 No    Yes   Yes       8 yes  
17 No    N/A   No       13 no   
18 N/A   Yes   No        9 yes  
19 N/A   N/A   N/A      11 no   
20 No    No    N/A      11 no

Or use c_across

mydata %>% 
   rowwise %>%
   mutate(afin = c("no", "yes")[1+ 
          any(c_across(starts_with('a')) == "Yes")]) %>% 
   ungroup
user_37322710

We can also use the following solution:

library(dplyr)
library(purrr)

mydata %>% 
  mutate(afin = pmap_chr(mydata %>% select(where(is.factor)), 
                         ~ {if(any(c(...) == "Yes")) "Yes" else "No"}))


# A tibble: 20 x 5
   a1    a2    a3       b1 afin 
   <fct> <fct> <fct> <dbl> <chr>
 1 No    Yes   Yes       6 Yes  
 2 N/A   N/A   N/A       7 No   
 3 No    No    No       12 No   
 4 No    No    N/A       7 No   
 5 No    No    Yes       9 Yes  
 6 No    N/A   N/A       7 No   
 7 No    N/A   N/A       7 No   
 8 No    N/A   Yes       7 Yes  
 9 N/A   N/A   Yes      10 Yes  
10 N/A   N/A   N/A      11 No   
11 Yes   Yes   No       10 Yes  
12 N/A   N/A   No       14 No   
13 No    N/A   Yes       9 Yes  
14 No    N/A   No       14 No   
15 N/A   No    No       10 No   
16 No    Yes   Yes       8 Yes  
17 No    N/A   No       13 No   
18 N/A   Yes   No        9 Yes  
19 N/A   N/A   N/A      11 No   
20 No    No    N/A      11 No
user_143145200

A data.table option

setDT(mydata)[, afin := c("no", "yes")[1 + (rowSums(.SD == "Yes") > 0)], .SDcols = patterns("a")]

gives

     a1  a2  a3 b1 afin
 1:  No Yes Yes  6  yes
 2: N/A N/A N/A  7   no
 3:  No  No  No 12   no
 4:  No  No N/A  7   no
 5:  No  No Yes  9  yes
 6:  No N/A N/A  7   no
 7:  No N/A N/A  7   no
 8:  No N/A Yes  7  yes
 9: N/A N/A Yes 10  yes
10: N/A N/A N/A 11   no
11: Yes Yes  No 10  yes
12: N/A N/A  No 14   no
13:  No N/A Yes  9  yes
14:  No N/A  No 14   no
15: N/A  No  No 10   no
16:  No Yes Yes  8  yes
17:  No N/A  No 13   no
18: N/A Yes  No  9  yes
19: N/A N/A N/A 11   no
20:  No  No N/A 11   no
user_121587570

You can use rowSums -

library(dplyr)

mydata %>% 
   mutate(afin = ifelse(rowSums(select(., a1:a3) == 'Yes') > 0, 'yes', 'no'))

#   a1    a2    a3       b1 afin 
#   <fct> <fct> <fct> <dbl> <chr>
# 1 No    Yes   Yes       6 yes  
# 2 N/A   N/A   N/A       7 no   
# 3 No    No    No       12 no   
# 4 No    No    N/A       7 no   
# 5 No    No    Yes       9 yes  
# 6 No    N/A   N/A       7 no   
# 7 No    N/A   N/A       7 no   
# 8 No    N/A   Yes       7 yes  
# 9 N/A   N/A   Yes      10 yes  
#10 N/A   N/A   N/A      11 no   
#11 Yes   Yes   No       10 yes  
#12 N/A   N/A   No       14 no   
#13 No    N/A   Yes       9 yes  
#14 No    N/A   No       14 no   
#15 N/A   No    No       10 no   
#16 No    Yes   Yes       8 yes  
#17 No    N/A   No       13 no   
#18 N/A   Yes   No        9 yes  
#19 N/A   N/A   N/A      11 no   
#20 No    No    N/A      11 no
user_39629140
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