How to find if ANY column has a specific value I am looking for?

Question

   id first  middle  last       Age
    1 Carol  Jenny   Smith      15
    2 Sarah  Carol   Roberts    20
    3 Josh   David   Richardson 22

I am t

Answer 1

Using tidyverse

library(tidyverse)
f1 <- function(data, wordToCompare, colsToCompare) {
          wordToCompare <- enquo(wordToCompare)
          data %>%
              select(colsToCompare) %>%
              mutate(!! wordToCompare :=  map(.,  ~ 
       .x == as_label(wordToCompare)) %>% 
           reduce(`|`) %>%
           as.integer)
              }
          
f1(df1, Carol, c("first", 'middle', 'last'))
# first middle       last Carol
#1 Carol  Jenny      Smith     1
#2 Sarah  Carol    Roberts     1
#3  Josh  David Richardson     0

f1(df1, Sarah, c("first", 'middle', 'last'))
#   first middle       last Sarah
#1 Carol  Jenny      Smith     0
#2 Sarah  Carol    Roberts     1
#3  Josh  David Richardson     0

---

Or this can also be done with pmap

df1 %>%
  mutate(Carol = pmap_int(.[c('first', 'middle', 'last')],
          ~ +('Carol' %in% c(...))))
#   id first middle       last Age Carol
#1  1 Carol  Jenny      Smith  15     1
#2  2 Sarah  Carol    Roberts  20     1
#3  3  Josh  David Richardson  22     0

which can be wrapped into a function

f2 <- function(data, wordToCompare, colsToCompare) {
      wordToCompare <- enquo(wordToCompare)
      data %>%
           mutate(!! wordToCompare := pmap_int(.[colsToCompare],
          ~ +(as_label(wordToCompare) %in% c(...))))
  } 

f2(df1, Carol, c("first", 'middle', 'last'))
#  id first middle       last Age Carol
#1  1 Carol  Jenny      Smith  15     1
#2  2 Sarah  Carol    Roberts  20     1
#3  3  Josh  David Richardson  22     0

NOTE: Both the tidyverse methods doesn't require any reshaping

---

With base R, we can loop through the 'first', 'middle', 'last' column and use == for comparison to get a list of logical vectors, which we Reduce to a single logical vector with | and coerce it to binary with +

df1$Carol <- +(Reduce(`|`, lapply(df1[2:4], `==`, 'Carol')))
df1
#  id first middle       last Age Carol
#1  1 Carol  Jenny      Smith  15     1
#2  2 Sarah  Carol    Roberts  20     1 
#3  3  Josh  David Richardson  22     0

---

NOTE: There are dupes for this post. For e.g. here

data

df1 <- structure(list(id = 1:3, first = c("Carol", "Sarah", "Josh"), 
middle = c("Jenny", "Carol", "David"), last = c("Smith", 
"Roberts", "Richardson"), Age = c(15L, 20L, 22L)),
  class = "data.frame", row.names = c(NA, 
 -3L))