http://stackoverflow.com/questions/34206003/how-to-flatten-r-data-frame-that-contains-lists

flatten.R

flatten <- function(indt, cols, drop = FALSE) {
  if (!is.data.table(indt)) indt <- as.data.table(indt)
  x <- unlist(indt[, lapply(.SD, function(x) max(lengths(x))), .SDcols = cols])
  nams <- paste(rep(cols, x), sequence(x), sep = "_")
  indt[, (nams) := unlist(lapply(.SD, transpose), recursive = FALSE), .SDcols = cols]
  if (isTRUE(drop)) {
    indt[, (nams) := unlist(lapply(.SD, transpose), recursive = FALSE), 
         .SDcols = cols][, (cols) := NULL]
  }
  indt[]
}

flatten_data.R

library(splitstackshape)
df <- cSplit_l(cSplit_l(cSplit_l(
  concat.test, "Likes", ",", drop = TRUE), 
  "Siblings", " , ", drop = TRUE), 
  "Hates", ";", drop = TRUE)
names(df) <- c("Name", "Likes", "Siblings", "Hates")

## Bigger data...
df2 <- do.call(rbind, replicate(1000, df, FALSE)) 

flatten_tests.R

## Tests
system.time(flatten(df, c("Likes", "Siblings", "Hates")))
system.time(flatten(df2, c("Likes", "Siblings", "Hates"), TRUE))

rawrFun <- function(indf) {
  indf[] <- lapply(indf, f)
  do.call(data.frame, indf)
}

system.time(rawrFun(df))
system.time(rawrFun(df2))


HerokaFun <- function(indf) {
  do.call(cbind.data.frame, lapply(indf, function(x) {
    # check if it is a list, otherwise just return as is
    if (is.list(x)) {
      data.frame(t(sapply(x,'[', seq(max(lengths(x))))))
    } else {
      x
    }
  }))
}

system.time(HerokaFun(df))
system.time(HerokaFun(df2))

Timing:

## Tests
system.time(flatten(df, c("Likes", "Siblings", "Hates")))
#    user  system elapsed 
#       0       0       0 
system.time(flatten(df2, c("Likes", "Siblings", "Hates"), TRUE))
#    user  system elapsed 
#    0.08    0.00    0.06 

system.time(rawrFun(df))
#    user  system elapsed 
#       0       0       0 
system.time(rawrFun(df2))
#    user  system elapsed 
#   46.65    0.00   46.72 

system.time(HerokaFun(df))
#    user  system elapsed 
#       0       0       0 
system.time(HerokaFun(df2))
#    user  system elapsed 
#    0.29    0.00    0.29