Reviewing The Touchstone R Package

1. Introduction

Touchstone is an R tool which provides accurate benchmarking features for testing other R packages. It provides continuous benchmarking with reliable relative measurement and uncertainty reporting. It is enriched with features which are very useful especially with respect to merging a Pull Request into target branch. Therefore, it is integrated with GitHub Continuous Integration(CI) which helps to automate the whole process.

2. Concept

For your PR branch and the target branch, touchstone will :

• build two versions of the package in isolated libraries.
• measure the accurate relative differences between the branches. The code under experimentation will be run several times in a random order.
• comment the results of the benchmarking on the Pull Request.
• create visualizations to demonstrate the distribution of the timings for both branches.

3. Installation

Installation can be done in two ways :

• CRAN :

  install.packages("touchstone")
  

• GitHub :

   devtools::install_github("lorenzwalthert/touchstone")
  

4. Package Usage

Initialize touchstone by running

touchstone::use_touchstone()

The above line of code will :

• Create a directory known as the touchstone directory in the root of the repository with config.json and script.R. The config.json contains configurations that define how to run your benchmark. The script.R is the script that runs the benchmark. The header.R contains the default PR header whilst the footer.R containing the default PR comment footer.

• Populate the file touchstone-receive.yaml in .github/workflows/. and touchstone-comment.yaml in .github/workflows/ respectively.

• Add the touchstone directory to .Rbuildignore file in the root directory of your repository.

Write the workflow files you need to invoke touchstone in new pull requests into .github/workflows/.

Modify the touchstone script touchstone/script.R to run different benchmarks.

5. Understanding the script

There are three default functions inside the script.R file.

(i) branch_install

touchstone::branch_install()

branch_install installs each branch in a separate library for isolation.

Usage

branch_install(
  branches = c(branch_get_or_fail("GITHUB_BASE_REF"),
    branch_get_or_fail("GITHUB_HEAD_REF")),
  path_pkg = ".",
  install_dependencies = FALSE
)

The arguments :
branches are names of the branches in character vector
path_pkg represents the path to the package which has a default value of "."
install_dependencies is a boolean which enables dependencies to be installed, has a default value of FALSE

(ii) benchmark_run

touchstone::benchmark_run()

benchmark_run runs benchmarks for git branches using function calls from your package.

Usage

benchmark_run(
  expr_before_benchmark = { },
  ...,
  branches = c(branch_get_or_fail("GITHUB_BASE_REF"),
    branch_get_or_fail("GITHUB_HEAD_REF")),
  n = 100,
  path_pkg = "."
)

The arguments :
expr_before_benchmark allows an expression to be executed just before benchmark is run
... is the named expression of length one with code to benchmark
branches are names of the branches in character vector
n is the number of times benchmarks should be run for each of the branches.
path_pkg represents the path to the package

(ii) benchmark_analyze

touchstone::benchmark_analyze()

benchmark_analyze creates artifacts used downstream in the GitHub Action to turn raw benchmark results into text and figures.

Usage

benchmark_analyze(
  branches = c(branch_get_or_fail("GITHUB_BASE_REF"),
    branch_get_or_fail("GITHUB_HEAD_REF")),
  names = NULL,
  ci = 0.95
)

The arguments :
branches are the names of the branches in character vector under consideration
names are the names of the branches which is actually used for the analysis.
ci represents the confidence level which defaults to 0.95 out of 1

6. Running the script

On the R Interactive console, run

touchstone::run_script()

Usage

run_script(
  path = "touchstone/script.R",
  branch = branch_get_or_fail("GITHUB_HEAD_REF")
)

The arguments :
path is the path to the script to run
branch is the name of the branch corresponding to the library

After committing and pushing the workflow files to default branch, Github CI will run the benchmarks on every pull request and on each commit while that pull request is open.