Wicked Fast, Accurate Quantiles Using ‘t-Digests’ in R with the {tdigest} Package

[This article was first published on R – rud.is, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

@ted_dunning recently updated the t-Digest algorithm he created back in 2013. What is this “t-digest”? Fundamentally, it is a probabilistic data structure for estimating any percentile of distributed/streaming data. Ted explains it quite elegantly in this short video:

Said video has a full transcript as well.

T-digests have been baked into many “big data” analytics ecosystems for a while but I hadn’t seen any R packages for them (ref any in a comment if you do know of some) so I wrapped one of the low-level implementation libraries by ajwerner into a diminutive R package boringly, but appropriately named tdigest:

There are wrappers for the low-level accumulators and quantile/value extractors along with vectorised functions for creating t-digest objects and retrieving quantiles from them (including a tdigest S3 method for stats::quantile()).


install.packages("tdigest", repos="https://cinc.rud.is/")

will install from source or binaries onto your system(s).

Basic Ops

The low-level interface is more useful in “streaming” operations (i.e. accumulating input over time):


td <- td_create()

for (i in 1:100000) {
  td_add(td, sample(100, 1), 1)

## [1]   1.00000  25.62222  53.09883  74.75522 100.00000

More R-like Ops

Vectorisation is the name of the game in R and we can use tdigest() to work in a vectorised manner:


x <- sample(100, 1000000, replace=TRUE)

td <- tdigest(x)

## [1]   1.00000  25.91914  50.79468  74.76439 100.00000

Need for Speed

The t-digest algorithm was designed for both streaming operations and speed. It’s pretty, darned fast:

  tdigest = tquantile(td, c(0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1)),
  r_quantile = quantile(x, c(0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))
## Unit: microseconds
##        expr      min         lq        mean    median       uq       max neval
##     tdigest    22.81    26.6525    48.70123    53.355    63.31    151.29   100
##  r_quantile 57675.34 59118.4070 62992.56817 60488.932 64731.23 160130.50   100

Note that “accurate” is not the same thing as “precise”, so regular quantile ops in R will be close to what t-digest computes, but not always exactly the same.


This was a quick (but, complete) wrapper and could use some tyre kicking. I’ve a mind to add serialization to the C implementation so I can then enable [de]serialization on the R-side since that would (IMO) make t-digest ops more useful in an R-context, especially since you can merge two different t-digests.

As always, code/PR where you want to and file issues with any desired functionality/enhancements.

Also, whomever started the braces notation for package names (e.g. {ggplot2}): brilliant!

To leave a comment for the author, please follow the link and comment on their blog: R – rud.is.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)