df_process_by_row.R

# Comparing different ways of processing a data frame row-by-row:

library(data.table)
df = data.frame(a = runif(10000), b = runif(10000))

system.time(rbindlist(purrrlyr::by_row(df, function(row) { data.frame(a = row$a) }, .labels = FALSE)$.out))
 #   user  system elapsed
 #  4.479   0.008   4.494

# This is fast, but note that 'apply' doesnt preserve types. e.g. character columns will get converted to numeric.
system.time(rbindlist(apply(df, 1, function(row) { data.frame(a = row[['a']]) })))
 #   user  system elapsed
 #  0.857   0.004   0.864

system.time(plyr::adply(df, .margins = 1, .fun = function(row) { data.frame(c = row$a) }))
 #   user  system elapsed
 #  2.175   0.012   2.197