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rco - The R Code Optimizer

Make your R code run faster!
rco analyzes your code and applies different optimization strategies that return an R code that runs faster.

The rco project, from its start to version 1.0.0, was made possible by a Google Summer of Code 2019 project.

Thanks to the kind mentorship of Dr. Yihui Xie and Dr. Nicolás Wolovick.

library("rco")
site_url <- "https://github.com/jcrodriguez1989/rco/blob/master/"

Installation

Install the current released version of rco from CRAN:

install.packages("rco")

Or install the development version from GitHub:

if (!require("remotes")) {
  install.packages("remotes")
}
remotes::install_github("jcrodriguez1989/rco", dependencies = TRUE)

Usage

rco can be used in three ways:

• Using the RStudio Addins

  1. Optimize active file: Optimizes the file currently open in RStudio. It will apply the optimizers present in all_optimizers.

  2. Optimize selection: Optimizes the code currently highlighted in the RStudio Source Pane. It will apply the optimizers present in all_optimizers.

• Using the shiny GUIs

  1. rco_gui("code_optimizer") opens a shiny interface in a browser. This GUI allows to easily optimize chunks of code.

  2. rco_gui("pkg_optimizer") opens a shiny interface in a browser. This GUI allows to easily optimize R packages that are hosted at CRAN or GitHub.

• Using the R functions

  1. Optimize some .R code files

  optimize_files(c("file_to_optimize_1.R", "file_to_optimize_2.R"))
  

  2. Optimize some code in a character vector

  code <- paste(
    "code_to_optimize <- 8 ^ 8 * 1918",
    "cto <- code_to_optimize * 2",
    sep = "\n"
  )
  optimize_text(code)
  

  3. Optimize all .R code files into a folder

  optimize_folder("~/myfolder_to_optimize", recursive = FALSE)
  

Example

Suppose we have the following code:

code <- paste(
  "# I want to know my age in seconds!",
  "years_old <- 29",
  "days_old <- 365 * years_old # leap years don't exist",
  "hours_old <- 24 * days_old",
  "seconds_old <- 60 * 60 * hours_old",
  "",
  "if (seconds_old > 10e6) {",
  '  print("Whoa! More than a million seconds old, what a wise man!")',
  "} else {",
  '  print("Meh!")',
  "}",
  sep = "\n"
)

We can automatically optimize it by doing:

opt_code <- optimize_text(code, iterations = 1)

cat(opt_code)

After one optimization pass we can see that it has only propagated the years_old variable. Another pass:

opt_code <- optimize_text(opt_code, iterations = 1)

cat(opt_code)

Now, it has folded the days_old variable, and then propagated it. Another pass:

opt_code <- optimize_text(opt_code, iterations = 1)

cat(opt_code)

It has folded the hours_old variable, and then propagated it. Another pass:

opt_code <- optimize_text(opt_code, iterations = 1)

cat(opt_code)

It has folded the seconds_old variable, and then propagated it into the if condition. Another pass:

opt_code <- optimize_text(opt_code, iterations = 1)

cat(opt_code)

Now, it has folded the if condition, and as it was TRUE it just kept its body, as testing the condition or the else clause were dead code.
So, optimize_text function has automatically detected constant variables, constant foldable operations, and dead code. And returned an optimized R code.

Guidelines for contributing

rco is an open source package, and the contributions to the development of the library are more than welcome. Please see our [CONTRIBUTING.md](r paste0(site_url, ".github/CONTRIBUTING.md")) file and “Contributing an Optimizer” article for detailed guidelines of how to contribute.

Code of Conduct

Please note that the ‘rco’ project is released with a [Contributor Code of Conduct](r paste0(site_url, "CODE_OF_CONDUCT.md")).

By contributing to this project, you agree to abide by its terms.