74 lines
2.5 KiB
Plaintext
74 lines
2.5 KiB
Plaintext
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---
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title: "Profiling Performance"
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author: "Thomas Lin Pedersen"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Profiling Performance}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r}
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#| label: setup
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#| include: false
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>",
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eval = FALSE
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)
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```
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In order to continuously monitor the performance of gtable
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the following piece of code is used to generate a profile and inspect it:
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```{r}
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library(ggplot2)
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library(profvis)
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p <- ggplot(mtcars, aes(mpg, disp)) +
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geom_point() +
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facet_grid(gear~cyl)
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p_build <- ggplot_build(p)
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profile <- profvis(for (i in seq_len(100)) ggplot_gtable(p_build))
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profile
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```
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```{r}
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#| eval: false
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#| include: false
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saveRDS(profile, file.path('profilings', paste0(packageVersion('gtable'), '.rds')))
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```
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The use of an empty ggplot2 ensures that the profile is based on real-life
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use and includes complex gtable assembly. Profiles for old version are kept
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for reference and can be accessed at the
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[github repository](https://github.com/r-lib/gtable/tree/master/vignettes/profilings).
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Care should be taken in not comparing profiles across versions, as
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changes to code outside of gtable can have profound effect on the results.
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Thus, the intend of profiling is to identify bottlenecks in the implementation
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that are ripe for improvement, more then to quantify improvements to performance
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over time.
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## Performance focused changes across versions
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To keep track of changes focused on improving the performance of gtable they
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are summarised below:
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### v`r packageVersion('gtable')`
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Profiling results from gtable v0.2.0 identified a range of areas that could be
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easily improved by fairly small code changes. These changes resulted in roughly
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20% decrease in running time on the profiling code in general, while gtable
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related functions were between 50 and 80% decrease in running time specifically.
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- **`data.frame` construction and indexing.** gtable now includes a minimal
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constructor that makes no input checking used for working with the layout data
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frame. Further, indexing into the layout data frame has been improved by
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either treating as a list internally or directly calling `.subset2`
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- **Input validation.** `stopifnot()` was identified as a bottleneck and has
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removed in favor of a standard `if (...) stop()`
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- **Dimension querying.** The use of `nrow()` and `ncol()` has internally been
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substituted for direct calls to `length()` of the `heights` and `widths` unit
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vectors
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