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R tip: consider using radix sort.

The “method = "radix"” option can greatly speed up sorting and ordering tables in R.

For a 1 million row table the speedup is already as much as 35 times (around 9.6 seconds versus 3 tenths of a second). Below is an excerpt from an experiment sorting showing default settings and showing radix sort (full code here).

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,
drop = FALSE],
check = my_check,
times = 10L)

print(timings)
## Unit: milliseconds
##           expr       min        lq      mean    median        uq
##  order_default 9531.2865 9653.6827 9759.8929 9690.6702 9833.2170
##    order_radix  262.1377  263.3226  278.2547  265.1452  274.2476
##         max neval
##  10329.3520    10
##    382.2544    10

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.

Edit/Note: Iñaki Úcar shared at least 2 good points in a follow-up article: if you are using factors you get radix sort for free (for technical reasons I tend to delay/disable conversion to factors), and I didn’t mention the loss of control of collation order. Because of that I am changing the article title from “R tip: Use Radix Sort” to “R Tip: Consider radix Sort”.