[FIXED] How to populate Yes and No when applying conditions accros 3 columns only with dplyr?

Issue

I am trying to create a new column, say test, with several conditions based on 3 columns. I am tryiing to achieve this with tidyverse only. Here are my conditions:

• if I have Yes in one column, regardless of other categories (No/Unknown/NA) in the same id across columns: previous_cabg, previous_pci, previous_ami then assign Yes in test variable
• if I have No in all columns for the same id then assign NO for the test variable
• if I have NO for one column and NA/Unknown in the other columns for the same id then assign with No in the test variable
• if I have Yes in all column for the same id then assign Yes in the test variable
• if I haveYes in one column and NA/Unknownfor the same id in each column then assignYes`in test variable

This is the type of dataset I have:

structure(list(id = c(112139L, 43919L, 92430L, 87137L, 95417L, 
66955L, 16293L, 61396L, 25379L, 79229L, 27107L, 63243L, 50627L, 
17968L, 83015L, 96549L, 7332L, 4873L, 98131L, 93506L, 52894L, 
59327L, 85003L, 96623L, 82999L, 65769L, 67063L, 21744L, 62961L, 
2229L, 103673L, 9367L, 60215L, 74044L, 58422L, 57530L, 100399L, 
46483L, 108690L, 62017L, 46467L, 79562L, 4800L, 119158L, 103222L, 
32908L, 14491L, 30293L, 52558L, 122304L, 42281L, 1553L, 111771L, 
23087L, 30147L, 37842L, 51552L, 20148L, 28L, 7477L), previous_cabg = structure(c(1L, 
1L, 1L, NA, 1L, NA, NA, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, NA, 1L, 1L, NA, 1L, NA, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 3L, 
1L, 1L, NA, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L), .Label = c("No", 
"Unknown", "Yes"), class = "factor"), previous_pci = structure(c(1L, 
1L, 2L, NA, 1L, NA, NA, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, NA, 2L, 1L, NA, 2L, NA, 1L, 2L, 1L, 1L, 1L, NA, 2L, 1L, 1L, 
2L, 2L, NA, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 2L, 1L, 1L), .Label = c("No", 
"Yes", "Unknown"), class = "factor"), previous_ami = structure(c(2L, 
2L, 1L, 2L, 2L, NA, 2L, 1L, 2L, 2L, NA, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, NA, 1L, 2L, NA, 1L, NA, 2L, 1L, 2L, 2L, 2L, NA, 1L, 1L, 1L, 
2L, 1L, NA, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 1L, 2L), .Label = c("Yes", 
"No", "Unknown"), class = "factor")), row.names = c(NA, -60L), problems = structure(list(
    row = c(34136L, 121773L, 121779L), col = c("1.01 Hospital identifier", 
    "1.01 Hospital identifier", "1.01 Hospital identifier"), 
    expected = c("value in level set", "value in level set", 
    "value in level set"), actual = c("CMH", "CMH", "CMH"), file = c("'../../data/changed/minap_2020_2021_second.csv'", 
    "'../../data/changed/minap_2020_2021_second.csv'", "'../../data/changed/minap_2020_2021_second.csv'"
    )), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"
)), class = c("tbl_df", "tbl", "data.frame"))

And this is how it looks, but only first 10 rows, if you look in detail, I have different groups of matches across the 3 columns

# A tibble: 60 x 4
       id previous_cabg previous_pci previous_ami
    <int> <fct>         <fct>        <fct>       
 1 112139 No            No           No          
 2  43919 No            No           No          
 3  92430 No            Yes          Yes         
 4  87137 NA            NA           No          
 5  95417 No            No           No          
 6  66955 NA            NA           NA          
 7  16293 NA            NA           No          
 8  61396 No            Yes          Yes         
 9  25379 No            Yes          No          
10  79229 No            No           No        

I am hoping to solve this only with tidyverse or a mix of tidyverse and r base.

This is what I have tried, yet I feel it is not so wise. I believe it is not wise, since this code will be part of automation process and if I will get other categories, than Yes and No, like Unknown as thisn appeared later in the next dataset extracts, then I wish the code will avoid all the other cases from the conditions I have given above.

dplyr::mutate(first_attack = 
                  dplyr::case_when(previous_cabg == 'No'  | previous_pci == 'No'  | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'No'  | previous_pci == 'Yes' | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'Yes' | previous_pci == 'No'  | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'Yes' | previous_ami == 'No' ~  'Yes', 
                                   previous_cabg == 'No'  | previous_pci == 'No'  | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'No'  | previous_pci == 'Yes' | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'Yes' | previous_pci == 'No'  | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'Yes' | previous_ami == 'No' ~  'Yes'
                                   
                                   # deal with the unknown category
                                   previous_cabg == 'Unknown'  | previous_pci == 'Yes' | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'Yes' | previous_pci == 'Unknown'  | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'Yes' | previous_ami == 'No' ~  'Yes', 
                                   previous_cabg == 'Unknown'  | previous_pci == 'Unknown'  | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'Unknown'  | previous_pci == 'Yes' | previous_ami == 'Yes' ~ 'Yes',
                                   previous_cabg == 'Yes' | previous_pci == 'Unknown' | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'Yes' | previous_ami == 'Unknown' ~  'Yes', 
                                   
                                   
                                   previous_cabg == 'Yes' |  previous_pci == 'No'  | previous_ami == 'Yes' ~  'Yes', 
                                   previous_cabg == 'Yes' |  previous_pci == 'No'  | previous_ami == 'No'  ~  'Yes',
                                   previous_cabg == 'No'  |  previous_pci == 'No'  | previous_ami == 'Yes' ~  'Yes',
                                   previous_cabg == 'No'  | previous_pci == 'Yes'  | previous_ami == 'No'  ~ 'Yes', 
                                   
                                   
                                   previous_cabg == 'Yes' |  previous_pci == 'Unknown'   | previous_ami == 'Yes' ~  'Yes', 
                                   previous_cabg == 'Yes' |  previous_pci == 'Unknown'   | previous_ami == 'Unknown'   ~  'Yes',
                                   previous_cabg == 'Unknown'   |  previous_pci == 'Unknown'   | previous_ami == 'Yes' ~  'Yes',
                                   previous_cabg == 'Unknown'   | previous_pci == 'Yes'  | previous_ami == 'Unknown'   ~ 'Yes', 
                                   
                                   
                                   previous_cabg == 'Yes' | previous_pci == 'Unknown' | previous_ami == 'Unknown' ~ 'Yes', 
                                   previous_cabg == 'Unknown'  | previous_pci == 'Yes'| previous_ami == 'Unknown' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'No' | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'Unknown'  | previous_pci == 'Yes'| previous_ami == 'Yes' ~ 'Yes', 
                                   
                                   previous_cabg == 'Yes' | previous_pci == 'No' | previous_ami == 'No' ~ 'Yes', 
                                   previous_cabg == 'No'  | previous_pci == 'Yes'| previous_ami == 'No' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'No' | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'No'  | previous_pci == 'Yes'| previous_ami == 'Yes' ~ 'Yes', 
                                   
                                   previous_cabg == 'Yes' | previous_pci == 'Unknown' | previous_ami == 'Unknown' ~ 'Yes', 
                                   previous_cabg == 'Unknown'  | previous_pci == 'Yes'| previous_ami == 'Unknown' ~ 'Yes', 
                                   previous_cabg == 'Yes' | previous_pci == 'Unknown' | previous_ami == 'Yes' ~ 'Yes', 
                                   previous_cabg == 'Unknown' | previous_pci == 'Yes'| previous_ami == 'Yes' ~ 'Yes', 
                                   
                                   
                                   previous_cabg == 'No'  | previous_pci == 'No'  |  previous_ami == 'No' ~ 'No', 
                                   previous_cabg == 'Yes' | previous_pci == 'Yes' |  previous_ami == 'Yes' ~'Yes'
                                   
                  ))

Solution

These operations are rowwise(), so they're not very efficient, but this solution in the tidyverse should cleanly achieve what you want.

Let us call your sample dataset by the name dataset. Then the following workflow

library(tidyverse)


# ...
# Code to generate your 'dataset'.
# ...


# Define custom logic across a single row.
get_first_attack <- function(values_across_row) {
  # "Yes" overrides all other values.
  if(isTRUE(any(values_across_row == "Yes"))){
    return("Yes")
  }
  # "No" overrides all missing values: 'NA' and "Unknown".
  else if(isTRUE(any(values_across_row == "No"))) {
    return("No")
  }
  # "Unknown" overrides all other missing values: 'NA'.
  else if(isTRUE(any(values_across_row == "Unknown"))) {
    return("Unknown")
  }
  # All values are missing: 'NA'.
  else {
    return(as.character(NA))
  }
}


dataset %>%
  # Examine row by row.
  dplyr::rowwise() %>%
  # Compare values across each row according to the logic in 'get_first_attack()'.
  dplyr::mutate(first_attack = get_first_attack(across(previous_cabg:previous_ami))) %>%
  # Exit row-wise approach, to restore efficiency.
  dplyr::ungroup() %>%
  # Factor 'first_attack' exactly like its neighboring column.
  dplyr::mutate(first_attack = factor(first_attack, levels = levels(previous_ami)))

should give you these results

# A tibble: 60 x 5
       id previous_cabg previous_pci previous_ami first_attack
    <int> <fct>         <fct>        <fct>        <fct>       
 1 112139 No            No           No           No          
 2  43919 No            No           No           No          
 3  92430 No            Yes          Yes          Yes         
 4  87137 NA            NA           No           No          
 5  95417 No            No           No           No          
 6  66955 NA            NA           NA           NA          
 7  16293 NA            NA           No           No          
 8  61396 No            Yes          Yes          Yes         
 9  25379 No            Yes          No           Yes         
10  79229 No            No           No           No          
# ... with 50 more rows

where the first_attack column is fittingly defined as a factor with three levels: "Yes", "No", and "Unknown".

Answered By - Greg

Answer Checked By - Mildred Charles (PHPFixing Admin)