Several alternatives:
1) two ways with data.table:
library(data.table)
# method 1 (preferred)
setDT(v)[, lapply(.SD, function(x) unlist(tstrsplit(x, ",", fixed=TRUE))), by = AB
][!is.na(director)]
# method 2
setDT(v)[, strsplit(as.character(director), ",", fixed=TRUE), by = .(AB, director)
][,.(director = V1, AB)]
2) a dplyr / tidyr combination:
library(dplyr)
library(tidyr)
v %>%
mutate(director = strsplit(as.character(director), ",")) %>%
unnest(director)
3) with tidyr only: With tidyr 0.5.0
(and later), you can also just use separate_rows
:
separate_rows(v, director, sep = ",")
You can use the convert = TRUE
parameter to automatically convert numbers into numeric columns.
4) with base R:
# if 'director' is a character-column:
stack(setNames(strsplit(df$director,','), df$AB))
# if 'director' is a factor-column:
stack(setNames(strsplit(as.character(df$director),','), df$AB))