Why is dplyr so slow? - r

Question

Like most people, I'm impressed by Hadley Wickham and what he's done for R -- so i figured that i'd move some functions toward his tidyverse ... having done so i'm left wondering what the point of it all is?
My new dplyr functions are much slower than their base equivalents -- i hope i'm doing something wrong. I'd particularly like some payoff from the effort required to understand non-standard-evaluation.
So, what am i doing wrong? Why is dplyr so slow?
An example:
require(microbenchmark)
require(dplyr)
df <- tibble(
a = 1:10,
b = c(1:5, 4:0),
c = 10:1)
addSpread_base <- function() {
df[['spread']] <- df[['a']] - df[['b']]
df
}
addSpread_dplyr <- function() df %>% mutate(spread := a - b)
all.equal(addSpread_base(), addSpread_dplyr())
microbenchmark(addSpread_base(), addSpread_dplyr(), times = 1e4)
Timing results:
Unit: microseconds
expr min lq mean median uq max neval
addSpread_base() 12.058 15.769 22.07805 24.58 26.435 2003.481 10000
addSpread_dplyr() 607.537 624.697 666.08964 631.19 636.291 41143.691 10000
So using dplyr functions to transform the data takes about 30x longer -- surely this isn't the intention?
I figured that perhaps this is too easy a case -- and that dplyr would really shine if we had a more realistic case where we are adding a column and sub-setting the data -- but this was worse. As you can see from the timings below, this is ~70x slower than the base approach.
# mutate and substitute
addSpreadSub_base <- function(df, col1, col2) {
df[['spread']] <- df[['a']] - df[['b']]
df[, c(col1, col2, 'spread')]
}
addSpreadSub_dplyr <- function(df, col1, col2) {
var1 <- as.name(col1)
var2 <- as.name(col2)
qq <- quo(!!var1 - !!var2)
df %>%
mutate(spread := !!qq) %>%
select(!!var1, !!var2, spread)
}
all.equal(addSpreadSub_base(df, col1 = 'a', col2 = 'b'),
addSpreadSub_dplyr(df, col1 = 'a', col2 = 'b'))
microbenchmark(addSpreadSub_base(df, col1 = 'a', col2 = 'b'),
addSpreadSub_dplyr(df, col1 = 'a', col2 = 'b'),
times = 1e4)
Results:
Unit: microseconds
expr min lq mean median uq max neval
addSpreadSub_base(df, col1 = "a", col2 = "b") 22.725 30.610 44.3874 45.450 53.798 2024.35 10000
addSpreadSub_dplyr(df, col1 = "a", col2 = "b") 2748.757 2837.337 3011.1982 2859.598 2904.583 44207.81 10000

Answer 1

These are micro seconds, your dataset has 10 rows, unless you plan on looping on millions of datasets of 10 rows your benchmark is pretty much irrelevant (and in that case I can't imagine a situation where it wouldn't be wise to bind them together as a first step).
Let's do it with a bigger dataset, like 1 million times bigger :
df <- tibble(
a = 1:10,
b = c(1:5, 4:0),
c = 10:1)
df2 <- bind_rows(replicate(1000000,df,F))
addSpread_base <- function(df) {
df[['spread']] <- df[['a']] - df[['b']]
df
}
addSpread_dplyr <- function(df) df %>% mutate(spread = a - b)
microbenchmark::microbenchmark(
addSpread_base(df2),
addSpread_dplyr(df2),
times = 100)
# Unit: milliseconds
# expr min lq mean median uq max neval cld
# addSpread_base(df2) 25.85584 26.93562 37.77010 32.33633 35.67604 170.6507 100 a
# addSpread_dplyr(df2) 26.91690 27.57090 38.98758 33.39769 39.79501 182.2847 100 a
Still quite fast and not much difference.
As for the "whys" of the result that you got, it's because you're using a much more complex function, so it has overheads.
Commenters have pointed that dplyr doesn't try too hard to be fast and maybe it's true when you compare to data.table, and interface is the first concern, but the authors have been working hard on speed as well. Hybrid evaluation for example allows (if I got it right) to execute C code directly on grouped data when aggregating with common functions, which can be much faster than base code, but simple code will always run faster with simple functions.