Split concatenated column to corresponding column positions

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

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 2

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 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"