Vtyjkyuk

I have a df like this:

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

And I would like to have an output like this:

> 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: Split column at delimiter in data frame but it is not spliting 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 baseR

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'))

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

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

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" 

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.