In WinVector/rqdatatable: 'rquery' for 'data.table'

Late 2014 Mac Mini High Sierra 10.13.6, 8 GB 1600 MHz DDR3 RAM, r R.version.string.

library("microbenchmark")
library("WVPlots")
library("rqdatatable")
library("cdata")

set.seed(32523)

mk_data <- function(nrow) {
  alphabet <- paste("sym", seq_len(max(2, floor(nrow^(1/3)))), sep = "_")
  data.frame(col_a = sample(alphabet, nrow, replace=TRUE),
             col_b = sample(alphabet, nrow, replace=TRUE),
             col_c = sample(alphabet, nrow, replace=TRUE),
             col_x = runif(nrow),
             stringsAsFactors = FALSE)
}

d <- mk_data(1e+6)

# adapted from help(microbenchmark)
my_check <- function(values) {
  isTRUE(all(sapply(values[-1], function(x) identical(values[[1]], x))))
}

timings <- microbenchmark(
  order_default = d[order(d$col_a, d$col_b, d$col_c, d$col_x), , 
                    drop = FALSE],
  order_radix = d[order(d$col_a, d$col_b, d$col_c, d$col_x,
                        method = "radix"), ,
                  drop = FALSE],
  check = my_check,
  times = 10L)

print(timings)

timings <- as.data.frame(timings)
timings$seconds <- timings$time/1e+9
timings$method <- as.factor(timings$expr)
timings$method <- reorder(timings$method, -timings$seconds)

WVPlots::ScatterBoxPlotH(as.data.frame(timings),
                         xvar = "seconds",
                         yvar = "method", 
                         title = "task time by method")

timings %.>%
  project_nse(., 
              groupby = "method",
              seconds = mean(seconds)) %.>%
  pivot_to_rowrecs(.,
                   columnToTakeKeysFrom = "method",
                   columnToTakeValuesFrom = "seconds",
                   rowKeyColumns = NULL,
                   sep = '_') %.>%
  extend_nse(.,
             ratio = method_order_default/method_order_radix)

This speedup is possible because Matt Dowle and Arun Srinivasan of the data.table team generously ported their radix sorting code into base-R! Please see help(sort) for details. So data.table is not only the best data manipulation package in R, the team actually works to improve R itself. This is what is meant by "R community" and what is needed to keep R vibrant and alive.