How to convert one (comma split) column into multiple columns in R? [duplicate]

Question

For example, I have this data:

data <- data.frame(person=paste0("person_", 1:5),
                   keyword=sapply(1:5, function(x) paste0(sample(letters, sample(1:5, 1)), collapse = ","))
                   )

> data
    person keyword
1 person_1 k,f,p,w
2 person_2     y,j
3 person_3     y,r
4 person_4     g,w
5 person_5 u,x,c,n

I would like to split the keywords into multiple columns and eventually convert them to binary data, like this:

    person k f p w y j r g w u x c n 
1 person_1 1 1 1 1 0 0 0 0 0 0 0 0 0
2 person_2 0 0 0 0 1 1 0 0 0 0 0 0 0 
3 person_3 0 0 0 0 1 0 1 0 0 0 0 0 0
4 person_4 0 0 0 0 0 0 0 1 1 0 0 0 0
5 person_5 0 0 0 0 0 0 0 0 0 1 1 1 1

What is the best way to achieve this?

Thank you.

Answer 1

You could use

library(tidyr)
library(dplyr)

data %>% 
  mutate(keyword = strsplit(keyword, ",")) %>% 
  unnest(keyword) %>% 
  mutate(value = 1) %>% 
  pivot_wider(names_from = keyword, values_fill = 0)

which returns

# A tibble: 5 x 16
  person       p     f     i     u     r     v     q     j     d     k     x     o     c     s     b
  <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 person_1     1     1     1     0     0     0     0     0     0     0     0     0     0     0     0
2 person_2     0     0     0     1     1     1     1     1     0     0     0     0     0     0     0
3 person_3     0     0     0     0     0     1     0     1     1     1     0     0     0     0     0
4 person_4     0     0     0     0     0     0     0     0     0     0     1     0     0     0     0
5 person_5     0     0     0     0     0     0     0     0     0     0     1     1     1     1     1

Answer 2

Using cSplit_e

library(splitstackshape)
out <- cSplit_e(data, 'keyword', sep= ',', type = 
      'character', fill = 0, drop = TRUE)
names(out) <- sub('keyword_', '', names(out))

-output

> out
    person c f g j k n p r u w x y
1 person_1 0 1 0 0 1 0 1 0 0 1 0 0
2 person_2 0 0 0 1 0 0 0 0 0 0 0 1
3 person_3 0 0 0 0 0 0 0 1 0 0 0 1
4 person_4 0 0 1 0 0 0 0 0 0 1 0 0
5 person_5 1 0 0 0 0 1 0 0 1 0 1 0

data

data <- structure(list(person = c("person_1", "person_2", "person_3", 
"person_4", "person_5"), keyword = c("k,f,p,w", "y,j", "y,r", 
"g,w", "u,x,c,n")), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5"))