Profiling Performance

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Thomas Lin Pedersen

In order to continuously monitor the performance of gtable the following piece of code is used to generate a profile and inspect it:

library(ggplot2)
library(profvis)

p <- ggplot(mtcars, aes(mpg, disp)) + 
  geom_point() + 
  facet_grid(gear~cyl)

p_build <- ggplot_build(p)

profile <- profvis(for (i in seq_len(100)) ggplot_gtable(p_build))

profile

The use of an empty ggplot2 ensures that the profile is based on real-life use and includes complex gtable assembly. Profiles for old version are kept for reference and can be accessed at the github repository. Care should be taken in not comparing profiles across versions, as changes to code outside of gtable can have profound effect on the results. Thus, the intend of profiling is to identify bottlenecks in the implementation that are ripe for improvement, more then to quantify improvements to performance over time.

Performance focused changes across versions

To keep track of changes focused on improving the performance of gtable they are summarised below:

v0.3.6

Profiling results from gtable v0.2.0 identified a range of areas that could be easily improved by fairly small code changes. These changes resulted in roughly 20% decrease in running time on the profiling code in general, while gtable related functions were between 50 and 80% decrease in running time specifically.

data.frame construction and indexing. gtable now includes a minimal constructor that makes no input checking used for working with the layout data frame. Further, indexing into the layout data frame has been improved by either treating as a list internally or directly calling .subset2
Input validation. stopifnot() was identified as a bottleneck and has removed in favor of a standard if (...) stop()
Dimension querying. The use of nrow() and ncol() has internally been substituted for direct calls to length() of the heights and widths unit vectors

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