aggregation functions, test #2: base, dplyr, data.table

README.md

Here's a simple timing test of aggregation functions in R, using 1.3 million rows and 80,000 groups of real data on a 1.8GHz Intel Core i5. Thanks to Arun Srinivasan for helpful comments.

The fastest function to run through the data.frame benchmark is data.table, which runs twice faster than dplyr, which runs ten times faster than base R.

For a benchmark that includes plyr, see this earlier Gist for a computationally more intensive test on half a million rows, where dplyr still runs 1.5 times faster than aggregate in base R.

Both tests confirm what W. Andrew Barr blogged on dplyr:

the 2 most important improvements in dplyr are

1. a MASSIVE increase in speed, making dplyr useful on big data sets
2. the ability to chain operations together in a natural order

Tony Fischetti has clear examples of the latter, and Erick Gregory shows that easy access to SQL databases should also be added to the list.

results.r

> # data
> system.time(load("integritate.rda"))
   user  system elapsed 
 14.716   0.273  15.173 

> # base
> system.time(aggregate(URL ~ Functie, length, data = data))
   user  system elapsed 
 26.118   0.284  26.510 

> # dplyr
> system.time(as.data.frame(summarise(group_by(data, Functie), n = length(URL))))
   user  system elapsed 
  0.242   0.011   0.254 
> system.time(summarise(group_by(data, Functie), n = length(URL)))
   user  system elapsed 
  0.249   0.006   0.257 
> system.time(tbl <- group_by(data, Functie))
   user  system elapsed 
  0.183   0.005   0.187 
> system.time(summarise(tbl, n = length(URL)))
   user  system elapsed 
  0.050   0.001   0.050 

> # data.table
> system.time(as.data.frame(as.data.table(data)[, .N, by = Functie]))
> library(data.table)
   user  system elapsed 
  1.173   0.038   1.233 
> system.time(as.data.table(data)[, .N, by = Functie])
   user  system elapsed 
  0.080   0.048   0.128 
> system.time(data.table(data)[, .N, by = Functie])
   user  system elapsed 
  3.300   0.171   3.508 
> system.time(data <- as.data.table(data))
   user  system elapsed 
  0.037   0.032   0.069 
> system.time(data <- data.table(data))
   user  system elapsed 
  0.258   0.094   0.353 
> system.time(data[, .N, by = Functie])
   user  system elapsed 
  0.031   0.002   0.034 

> # versions
> sessionInfo()
R version 3.0.3 (2014-03-06)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] data.table_1.9.2 dplyr_0.1.2     

loaded via a namespace (and not attached):
[1] assertthat_0.1 plyr_1.8.1     Rcpp_0.11.0    reshape2_1.2.2 stringr_0.6.2 
[6] tools_3.0.3

test.r

setwd("/Users/fr/Documents/Code/R/integritate")

# data
system.time(load("integritate.rda"))
str(data[, c("Functie", "URL")])

# base
length(unique(data$Functie))
system.time(aggregate(URL ~ Functie, length, data = data))

# plyr (far too long)
# library(plyr)
# system.time(ddply(data, .(Functie), summarise, n = length(URL)))

library(dplyr)
system.time(as.data.frame(summarise(group_by(data, Functie), n = length(URL))))
system.time(summarise(group_by(data, Functie), n = length(URL)))
system.time(tbl <- group_by(data, Functie))
system.time(summarise(tbl, n = length(URL)))

# data.table
library(data.table)
system.time(as.data.frame(as.data.table(data)[, .N, by = Functie]))
system.time(as.data.table(data)[, .N, by = Functie])
system.time(data.table(data)[, .N, by = Functie])
system.time(data <- as.data.table(data))
system.time(data <- data.table(data))
system.time(data[, .N, by = Functie])

# versions
sessionInfo()

I expected you would have already ran lower-level benchmarks, but wanted to benchmark a user workflow more than subscripting alone. It was my mistake not to make it clearer in the text, which was pasted from the tweets and badly updated between updates of the timers.

@briatte, yes that'd be equally interesting as well. However, in that case, there should be one conversion to data.table at first, and then sticking to data.table - meaning, no conversion to as.data.frame(.) after that at all.