In zeehio/ggpipe: ggplot with the pipe

This document compares the performance of two simple plots, built with ggplot2 with respect to ggpipe, to show the performance penalty of ggpipe.

ggpipe has some performance penalty with respect to ggplot2 because it uses tryCatch and evaluates twice one of the arguments, to determine if it is a ggplot object, and therefore to know if we are piping or adding.

knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(ggplot2)
library(ggpipe)

We define two tasks:

Task 1 defines a plot and saves it to a variable.
Task 2 defines the plot and builds it.

Task 1 is where most of the overhead of ggpipe is supposed to be, as the plot building step is done by ggplot2 in any case. However Task 2 reflects the most common use case (we usually want to print the plots we define).

Task 1 results: Define a plot

b1 <- microbenchmark::microbenchmark(
  ggpipe = {p <- ggpipe::ggplot(iris) %>% ggpipe::geom_point(ggpipe::aes(x = Sepal.Length, y = Sepal.Width)) %>% ggpipe::scale_x_continuous("test")},
  ggplot2 = {p <- ggplot2::ggplot(iris) + ggplot2::geom_point(ggplot2::aes(x = Sepal.Length, y = Sepal.Width)) + ggplot2::scale_x_continuous("test")})
b1$task <- "Define Plot"

print(b1)

Task 2 results: Define + Build plot

b2 <- microbenchmark::microbenchmark(
  ggpipe = {ggplot2::ggplot_build(ggpipe::ggplot(iris) %>% ggpipe::geom_point(ggpipe::aes(x = Sepal.Length, y = Sepal.Width)) %>% ggpipe::scale_x_continuous("test"))},
  ggplot2 = {ggplot2::ggplot_build(ggplot2::ggplot(iris) + ggplot2::geom_point(ggplot2::aes(x = Sepal.Length, y = Sepal.Width)) + ggplot2::scale_x_continuous("test"))})
b2$task <- "Define+Build Plot"

print(b2)

This is the median time represented in a bar plot:

b <- dplyr::bind_rows(b1, b2)

b %>%
  group_by(expr, task) %>%
  summarize(median_time = median(time)/1E6) %>%
  ungroup %>%
  ggplot %>%
  geom_col(aes(x = expr, y = median_time, fill = expr)) %>%
  xlab('Package') %>%
  ylab("Median time (ms) (less is better)") %>%
  facet_wrap(~task) %>%
  guides(fill = FALSE)

And the region where most of the data points are:

ggplot(b) %>%
  geom_jitter(aes(x = expr, y = time/1E6, color = expr)) %>%
  xlab('Package') %>%
  ylab("Time (ms) (less is better)") %>%
  ylim(0, 20) %>%
  facet_wrap(~task) %>%
  guides(colour = FALSE)