Benchmarks
In tidylog: Logging for 'dplyr' and 'tidyr' Functions

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Using tidylog adds a small overhead to each function call. For instance, because tidylog needs to figure out how many rows were dropped when you use tidylog::filter, this call will be a bit slower than using dplyr::filter directly. The overhead is usually not noticeable, but can be for larger datasets, especially when using joins. The benchmarks below give some impression of how large the overhead is.

library("dplyr")
library("tidylog", warn.conflicts = FALSE)
library("bench")
library("knitr")

filter

On a small dataset:

bench::mark(
    dplyr::filter(mtcars, cyl == 4),
    tidylog::filter(mtcars, cyl == 4), iterations = 100
) %>%
    dplyr::select(expression, min, median, n_itr) %>%
    kable()

On a larger dataset:

df <- tibble(x = rnorm(100000))

bench::mark(
    dplyr::filter(df, x > 0),
    tidylog::filter(df, x > 0), iterations = 100
) %>%
    dplyr::select(expression, min, median, n_itr) %>%
    kable()

mutate

On a small dataset:

bench::mark(
    dplyr::mutate(mtcars, cyl = as.factor(cyl)),
    tidylog::mutate(mtcars, cyl = as.factor(cyl)), iterations = 100
) %>%
    dplyr::select(expression, min, median, n_itr) %>%
    kable()

On a larger dataset:

df <- tibble(x = round(runif(10000) * 10))

bench::mark(
    dplyr::mutate(df, x = as.factor(x)),
    tidylog::mutate(df, x = as.factor(x)), iterations = 100
) %>%
    dplyr::select(expression, min, median, n_itr) %>%
    kable()

joins

Joins are the most expensive operation, as tidylog has to do two additional joins behind the scenes.

On a small dataset:

bench::mark(
    dplyr::inner_join(band_members, band_instruments, by = "name"),
    tidylog::inner_join(band_members, band_instruments, by = "name"), iterations = 100
) %>%
    dplyr::select(expression, min, median, n_itr) %>%
    kable()

On a larger dataset (with many row duplications):

N <- 1000
df1 <- tibble(x1 = rnorm(N), key = round(runif(N) * 10))
df2 <- tibble(x2 = rnorm(N), key = round(runif(N) * 10))

bench::mark(
    dplyr::inner_join(df1, df2, by = "key"),
    tidylog::inner_join(df1, df2, by = "key"), iterations = 100
) %>%
    dplyr::select(expression, min, median, n_itr) %>%
    kable()