Create presence/absence variables from character string for long data - r

Question

Let's say I have a data frame like this:
dat<- data.frame(ID= rep(c("A","B","C","D"),4),
test= rep(c("pre","post"),8),
item= c(rep("item1",8),rep("item2",8))
answer= c("undergraduateeducation_graduateorprofessionalschool_employment",
"graduateorprofessionalschool",
"undergraduateeducation_graduateorprofessionalschool_employment_volunteeractivityoroutreach",
"volunteeractivityoroutreach",
"undergraduateeducation_employment_volunteeractivityoroutreach",
"employment",
"volunteeractivityoroutreach",
"undergraduateeducation_graduateorprofessionalschool_employment_volunteeractivityoroutreach",
"undergraduateeducation_graduateorprofessionalschool_employment",
"graduateorprofessionalschool",
"undergraduateeducation_graduateorprofessionalschool_employment_volunteeractivityoroutreach",
"volunteeractivityoroutreach",
"undergraduateeducation_employment_volunteeractivityoroutreach",
"employment",
"volunteeractivityoroutreach",
"undergraduateeducation_graduateorprofessionalschool_employment_volunteeractivityoroutreach"))
The answer column represents a "select all the apply" answer type- where the underscore separates selected answer options. For each ID, test and item, I would like to change this single variable to multiple presence/absence variables indicating the presence or absence of that answer component in the string. 1 indicates that answer option was present in the respondents answer and 0 represents that component was absent. The variables undergraduate, graduate, employment and volunteer in res correspond to the following strings in answer, respectivley: undergraduateeducation, graduateorprofessionalschool,employment, volunteeractivityoroutreach. White spaces were removed.
The result data frame would look as follows:
res<- data.frame(ID= rep(c("A","B","C","D"),4),
test= rep(c("pre","post"),8),
item= c(rep("item1",8),rep("item2",8)),
undergraduate= c(1,0,1,0,1,0,0,1,1,0,1,0,1,0,0,1),
graduate= c(1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1),
employment=c(1,0,1,0,1,1,0,1,1,0,1,0,1,1,0,1),
volunteer=c(0,0,1,1,1,0,1,1,0,0,1,1,1,0,1,1))

Answer 1

In base R you could do:
new_cols <- c('undergraduate', 'graduate', 'employment', 'volunteer')
cbind(dat[1:3],
as.data.frame(do.call(rbind, lapply(strsplit(dat$answer, "_"),
function(x) {
z <- sapply(new_cols, function(y) as.numeric(grepl(paste0("\\b", y), x)))
if(is.vector(z)) z else colSums(z)
}))))
#> ID test item undergraduate graduate employment volunteer
#> 1 A pre item1 1 1 1 0
#> 2 B post item1 0 1 0 0
#> 3 C pre item1 1 1 1 1
#> 4 D post item1 0 0 0 1
#> 5 A pre item1 1 0 1 1
#> 6 B post item1 0 0 1 0
#> 7 C pre item1 0 0 0 1
#> 8 D post item1 1 1 1 1
#> 9 A pre item2 1 1 1 0
#> 10 B post item2 0 1 0 0
#> 11 C pre item2 1 1 1 1
#> 12 D post item2 0 0 0 1
#> 13 A pre item2 1 0 1 1
#> 14 B post item2 0 0 1 0
#> 15 C pre item2 0 0 0 1
#> 16 D post item2 1 1 1 1
Created on 2022-05-05 by the reprex package (v2.0.1)

Answer 2

One option is to use tidyverse to separate the data into rows on the _, then keep only the keywords (which will be used for column names). Then, we create a value column to note presence, then we can pivot to wide format, and fill the other values with 0.
library(tidyverse)
result <- dat %>%
mutate(rn = row_number()) %>%
separate_rows(answer, sep = "_") %>%
mutate(answer = str_extract(answer, "undergraduate|graduate|employment|volunteer"),
value = 1) %>%
pivot_wider(names_from = "answer", values_from = "value", values_fill = 0) %>%
select(-rn)
Output
ID test item undergraduate graduate employment volunteer
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 A pre item1 1 1 1 0
2 B post item1 0 1 0 0
3 C pre item1 1 1 1 1
4 D post item1 0 0 0 1
5 A pre item1 1 0 1 1
6 B post item1 0 0 1 0
7 C pre item1 0 0 0 1
8 D post item1 1 1 1 1
9 A pre item2 1 1 1 0
10 B post item2 0 1 0 0
11 C pre item2 1 1 1 1
12 D post item2 0 0 0 1
13 A pre item2 1 0 1 1
14 B post item2 0 0 1 0
15 C pre item2 0 0 0 1
16 D post item2 1 1 1 1
Test
identical(result, as_tibble(res))
#[1] TRUE