Create new columns with information extracted from another column on R - r

Question

I have a data frame where one of the columns have several information separated by ";", like the following:
DF = data.frame(a = c(1,1,1,2,2), b = c('aaa','aaa','aba','abc','ccc'),
extra_info = c(
'animal=horse;color=orange;shape=circle',
'animal=monkey;shape=square;value=532',
'animal=horse;color=blue;shape=square;value=321',
'animal=dog;color=green;value=678',
'color=pink;shape=triangle'
))
I can't use read.table because I'm already using a different function to read the data (and also the content of each row in the column extra_info is different, and the columns would be messed up). What I wish to do is separate all this information to different columns, and assign proper names accordingly, such as:
a b animal color shape value
1 aaa horse orange circle NA
1 aaa monkey NA square 532
1 aba horse blue square 321
2 abc dog green NA 678
2 ccc NA pink triangle NA
So far, I've tried:
new_cols = DF %>% separate(extra_info, c(LETTERS[1:4]), sep = ";")
new_cols %>% separate(A, c("key","value"), sep = '=') %>%
separate(B, c("key","value"), sep = '=') %>%
separate(C, c("key","value"), sep = '=') %>%
separate(D, c("key","value"), sep = '=') %>%
pivot_wider(names_from = c("key"), values_from = c("value"))
But it doesn't work as expected.

Answer 1

Here's an approach where I change the syntax of your key-value pairs into valid JSON syntax and use jsonlite::fromJSON to parse it:
library(purrr)
library(dplyr)
library(stringr)
library(jsonlite)
DF %>%
mutate(
json = str_replace_all(extra_info, pattern = "\\b", replacement = '"'),
json = str_replace_all(json, pattern = fixed("="), replacement = ":"),
json = str_replace_all(json, pattern = fixed(";"), replacement = ","),
json = paste("{", json, "}"),
) %>%
pull(json) %>%
map(jsonlite::fromJSON) %>%
map(as.data.frame) %>%
bind_rows %>%
cbind(DF, .)
# a b extra_info animal color shape value
# 1 1 aaa animal=horse;color=orange;shape=circle horse orange circle <NA>
# 2 1 aaa animal=monkey;shape=square;value=532 monkey <NA> square 532
# 3 1 aba animal=horse;color=blue;shape=square;value=321 horse blue square 321
# 4 2 abc animal=dog;color=green;value=678 dog green <NA> 678
# 5 2 ccc color=pink;shape=triangle <NA> pink triangle <NA>

Answer 2

Here is a base R option using gsub + eval + str2expression
v <- DF$extra_info
p <- gsub(";", ",", gsub("(?<=\\=)(\\w+)", "'\\1'", v, perl = TRUE))
nms <- unique(unlist(regmatches(v, gregexpr("\\w+(?=\\=)", v, perl = TRUE))))
q <- unname(Map(function(x) setNames(eval(str2expression(x))[nms], nms), sprintf("c(%s)", p)))
cbind(DF[c("a","b")], type.convert(data.frame(do.call(rbind, q)), as.is = TRUE))
which gives
a b animal color shape value
1 1 aaa horse orange circle NA
2 1 aaa monkey <NA> square 532
3 1 aba horse blue square 321
4 2 abc dog green <NA> 678
5 2 ccc <NA> pink triangle NA

Answer 3

It's a bit neater with the stringr package, but if you just want base R you can use the following. In the pattern structure (?<=animal=)\\w+(?=\\b) here, the \\w+ is what's actually being returned, it is any word character (\\w) and there has to be at least one of them (+). This is swapped with \\d+ for 'value' since digits are required. Alternatively you could replace both with [:alnum:]+.
Then the (?<=animal=) structure is used to specify that it must be preceded by "animal=", and the (?=\\b) structure indicates that it has to be followed by a word boundary (\\b). You could get a bit more specific and replace \\b with (,|;|$) which stands for comma or semicolon or end of line (EDIT: the original question had commas in some places). There might be a nice way of writing a loop over the four words that creates the variable names and patterns dynamically.
pattern <- "(?<=animal=)\\w+(?=\\b)"
DF$animal <- sapply(regmatches(DF$extra_info, regexec(pattern, DF$extra_info , perl=T)), "[", 1)
pattern <- "(?<=color=)\\w+(?=\\b)"
DF$color<- sapply(regmatches(DF$extra_info, regexec(pattern, DF$extra_info , perl=T)), "[", 1)
pattern <- "(?<=shape=)\\w+(?=\\b)"
DF$shape<- sapply(regmatches(DF$extra_info, regexec(pattern, DF$extra_info , perl=T)), "[", 1)
pattern <- "(?<=value=)\\d+(?=\\b)"
DF$value <- sapply(regmatches(DF$extra_info, regexec(pattern, DF$extra_info , perl=T)), "[", 1)
If you're happy to use tidyverse/stringr, here is the code.
DF <- DF %>%
mutate(animal = str_extract(extra_info, "(?<=animal=)\\w+(?=\\b)" )) %>%
mutate(color = str_extract(extra_info, "(?<=color=)\\w+(?=\\b)" )) %>%
mutate(shape = str_extract(extra_info, "(?<=shape=)\\w+(?=\\b)" )) %>%
mutate(value = str_extract(extra_info, "(?<=value=)\\d+(?=\\b)" ))
For more info on string manipulation and regular expressions, see the stringr cheat sheet here: https://github.com/rstudio/cheatsheets/blob/master/strings.pdf

Answer 4

library(stringr)
col_names <- unlist(str_extract_all(DF$extra_info[3], "(?<=^|;)\\w+"))
DF %>%
mutate(animal = str_extract(extra_info, paste0("(?<=", col_names[1], "=)\\w+")),
color = str_extract(extra_info, paste0("(?<=", col_names[2], "=)\\w+")),
shape = str_extract(extra_info, paste0("(?<=", col_names[3], "=)\\w+")),
value = str_extract(extra_info, paste0("(?<=", col_names[4], "=)\\w+"))
a b extra_info animal color shape value
1 1 aaa animal=horse;color=orange;shape=circle horse orange circle <NA>
2 1 aaa animal=monkey;shape=square;value=532 monkey <NA> square 532
3 1 aba animal=horse;color=blue;shape=square;value=321 horse blue square 321
4 2 abc animal=dog;color=green;value=678 dog green <NA> 678
5 2 ccc color=pink;shape=triangle <NA> pink triangle <NA>