I have a data.frame where some cells contain strings of comma separate values:
d <- data.frame(a=c(1:3),
b=c("name1, name2, name3", "name4", "name5, name6"),
c=c("name7","name8, name9", "name10" ))
I want to separate those strings where each name is split into its own cell. This is easy with
tidyr::separate_rows(d, b, sep=",")
if it is done for one column a time. But I can't do this for both columns "b" and "c" at the same time, since it requires that the number of names in each string is the same. Instead of writing
tidyr::separate_rows(d, b, sep=",")
tidyr::separate_rows(d, c, sep=",")
Is there a way to do this in a one-liner, for e.g. with apply? Something like
apply(d, 2, separate_rows(...))
Not sure how to pass the arguments to the separate_rows() function.
You can use a pipe. Note that sep = ", " is automatically detected.
d %>% separate_rows(b) %>% separate_rows(c)
# a b c
# 1 1 name1 name7
# 2 1 name2 name7
# 3 1 name3 name7
# 4 2 name4 name8
# 5 2 name4 name9
# 6 3 name5 name10
# 7 3 name6 name10
Note: Using tidyr version 0.6.0, where the %>% operator is included in the package.
Update: Using @doscendodiscimus comment, we could use a for() loop and reassign d in each iteration. This way we can have as many columns as we like. We will use a character vector of column names, so we'll need to switch to the standard evaluation version, separate_rows_.
cols <- c("b", "c")
for(col in cols) {
d <- separate_rows_(d, col)
}
which gives the updated d
a b c
1 1 name1 name7
2 1 name2 name7
3 1 name3 name7
4 2 name4 name8
5 2 name4 name9
6 3 name5 name10
7 3 name6 name10
Here's an alternative approach using splitstackshape::cSplit and zoo::na.locf.
library(splitstackshape)
library(zoo)
df <- cSplit(d, 1:ncol(d), "long", sep = ",")
na.locf(df[rowSums(is.na(df)) != ncol(df),])
# a b c
#1: 1 name1 name7
#2: 1 name2 name7
#3: 1 name3 name7
#4: 2 name4 name8
#5: 2 name4 name9
#6: 3 name5 name10
#7: 3 name6 name10
