Split concatenated column to corresponding column positions - r

Question

I have a data frame where a column may contain concatenated characters separated by |:
df <- data.frame(FOO = c('A|B|C', 'A|B', 'B|C', 'A', 'C'))
# df
# FOO
# 1 A|B|C
# 2 A|B
# 3 B|C
# 4 A
# 5 C
I want to split the string and put the individual values into different columns:
df
# X1 X2 X3
# 1 A B C
# 2 A B
# 3 B C
# 4 A
# 5 C
So far I tried with this example: [https://stackoverflow.com/questions/7069076/split-column-at-delimiter-in-data-frame][1] but it is not splitting the columns without repeating values, what I get there is:
df <- data.frame(do.call('rbind', strsplit(as.character(df$FOO),'|',fixed=TRUE)))
> df
X1 X2 X3
1 A B C
2 A B A
3 B C B
4 A A A
5 C C C
And I also get this warning:
Warning message: In rbind(c("A", "B", "C"), c("A", "B"), c("B", "C"),
"A", "C") : number of columns of result is not a multiple of vector
length (arg 2)
What can I do in those cases? Preferably with base R.
[1]: Split column at delimiter in data frame

Answer 1

Simply do:
splt <- strsplit(as.character(df$FOO),"\\|")
all_val <- sort(unique(unlist(splt)))
t(sapply(splt,function(x){all_val[!(all_val %in% x)]<-NA;all_val}))
# [,1] [,2] [,3]
#[1,] "A" "B" "C"
#[2,] "A" "B" NA
#[3,] NA "B" "C"
#[4,] "A" NA NA
#[5,] NA NA "C"
data:
df <- data.frame(FOO = c('A|B|C', 'A|B', 'B|C', 'A', 'C'))
Please note:
My version is base:: (no libraries needed) and general:
It would also work with:
df <- data.frame(FOO = c('A|B|C', 'A|B', 'B|C', 'A', 'C', 'B|D|F'))

Answer 2

Overlooked that OP asked for a base R solution. Please try #AndreElrico's, #r.user.05apr's or #milan's solutions.
This can be done with cSplit_e from the splitstackshape package:
library(splitstackshape)
cSplit_e(
data = df,
split.col = "FOO",
sep = "|",
mode = "value",
type = "character",
fill = " ",
drop = TRUE
)
# FOO_A FOO_B FOO_C
#1 A B C
#2 A B
#3 B C
#4 A
#5 C
Does also work in case of the following df (see OP's comment above).
(df1 <- data.frame(FOO = c('A|B|C', 'A|B', 'B|C', 'A', 'C', 'B|D|F')))
# FOO
#1 A|B|C
#2 A|B
#3 B|C
#4 A
#5 C
#6 B|D|F
cSplit_e(df1, "FOO", "|", "value", "character", TRUE, fill = " ")
# FOO_A FOO_B FOO_C FOO_D FOO_F
#1 A B C
#2 A B
#3 B C
#4 A
#5 C
#6 B D F

Answer 3

In base R:
df <- data.frame(FOO = c('A|B|C', 'A|B', 'B|C', 'A', 'C'))
dummy <- strsplit(as.character(df$FOO), "[|]")
want <- data.frame(values = unlist(dummy),
ids = rep(1:length(dummy), unlist(lapply(dummy, length))),
stringsAsFactors = FALSE)
library(reshape2)
want <- dcast(want, ids ~ values, value.var = "values", fill = " ")[, -1] # first col removed
names(want) <- paste0("X", seq_along(unique(unlist(dummy))))
want
# X1 X2 X3
#1 A B C
#2 A B
#3 B C
#4 A
#5 C

Answer 4

Use unique and strsplit to find all unique values (A, B and C in this case). Use grep to search for the unique values, and return the values when there's a match or character(0) otherwise. cbind the resulting characters. Use apply and ifelse to replace character(0) with NA.
vals <- unique(unlist(sapply(a1, function(x) strsplit(x, '|', fixed = T))))
out <- NULL
for(i in vals){
out <- cbind(out, as.character((lapply(df$FOO, function(x) grep(x, i, value=T)))))
}
apply(out, 2, function(x) ifelse(x=="character(0)", NA, x))
[,1] [,2] [,3]
[1,] "A" "B" "C"
[2,] "A" "B" NA
[3,] NA "B" "C"
[4,] "A" NA NA
[5,] NA NA "C"

Answer 5

You can try a tidyverse as well
library(tidyverse)
df %>%
rownames_to_column() %>%
separate_rows(FOO, sep="[|]") %>%
mutate(L=factor(FOO, labels = paste0("X",1:length(unique(FOO))))) %>%
spread(L, FOO) %>%
select(-1)
X1 X2 X3
1 A B C
2 A B <NA>
3 <NA> B C
4 A <NA> <NA>
5 <NA> <NA> C
It is also generally working e.g. df <- data.frame(FOO = c('A|B|C', 'A|B', 'B|C', 'A', 'C', 'B|D|F')). In addition you can set the levels e.g. B>C>A by yourself using levels = c("B", "C", "A") in the factor function in the mutate step.