When we’d like to slice a dataframe by row, we can employ the split() function or the iter() function in the iterators package.

By leveraging the power of parallelism, I wrote an utility function slice() to faster slice the dataframe. In the example shown below, the slice() is 3 times more efficient than the split() or the iter() to select 2 records out of 5,960 rows.

df <- read.csv("hmeq.csv")

nrow(df)
# [1] 5960

slice <- function(df) {
  return(parallel::mcMap(function(i) df[i, ], seq(nrow(df)), mc.cores = parallel::detectCores()))
}

Reduce(rbind, Filter(function(x) x$DEROG == 10, slice(df)))
#     BAD  LOAN MORTDUE VALUE  REASON   JOB YOJ DEROG DELINQ     CLAGE NINQ CLNO  DEBTINC
#3094   1 16800   16204 27781 HomeImp Other   1    10      0 190.57710    0    9 27.14689
#3280   1 17500   76100 98500 DebtCon Other   5    10      1  59.83333    5   16       NA

rbenchmark::benchmark(replications = 10, order = "elapsed", relative = "elapsed",
                        columns = c("test", "replications", "elapsed", "relative"),
  "SPLIT" = Reduce(rbind, Filter(Negate(function(x) x$DEROG != 10), split(df, seq(nrow(df))))),
  "ITER " = Reduce(rbind, Filter(Negate(function(x) x$DEROG != 10), as.list(iterators::iter(df, by = "row")))),
  "SLICE" = Reduce(rbind, Filter(Negate(function(x) x$DEROG != 10), slice(df)))
)
#  test replications elapsed relative
# SLICE           10   2.224    1.000
# SPLIT           10   7.185    3.231
# ITER            10   7.375    3.316