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https://github.com/jcrodriguez1989/rco
Package: The R Code Optimizer
https://github.com/jcrodriguez1989/rco
compiler fast gcc hpc optimization optimizer r
Last synced: 3 months ago
JSON representation
Package: The R Code Optimizer
- Host: GitHub
- URL: https://github.com/jcrodriguez1989/rco
- Owner: jcrodriguez1989
- Created: 2019-03-28T17:47:31.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-07-14T12:28:16.000Z (over 3 years ago)
- Last Synced: 2023-11-20T14:46:26.261Z (about 1 year ago)
- Topics: compiler, fast, gcc, hpc, optimization, optimizer, r
- Language: R
- Homepage: https://jcrodriguez1989.github.io/rco/
- Size: 4.16 MB
- Stars: 83
- Watchers: 3
- Forks: 19
- Open Issues: 22
-
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - jcrodriguez1989/rco - Package: The R Code Optimizer (R)
README
---
output: github_document
---
# rco - The R Code Optimizer
[](https://CRAN.R-project.org/package=rco)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://travis-ci.org/jcrodriguez1989/rco)
[](https://ci.appveyor.com/project/jcrodriguez1989/rco)
[](https://codecov.io/github/jcrodriguez1989/rco?branch=master)
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](https://summerofcode.withgoogle.com/archive/2019/projects/6300906386096128/).
Thanks to the kind mentorship of [Dr. Yihui Xie](https://yihui.org/en/) and [Dr. Nicolás Wolovick](https://cs.famaf.unc.edu.ar/~nicolasw/).
```{r echo=FALSE}
library("rco")
site_url <- "https://github.com/jcrodriguez1989/rco/blob/master/"
```
## Installation
Install the current released version of `rco` from [CRAN](https://cran.r-project.org/package=rco):
```{r eval = FALSE}
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 highlited 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
```{r eval = FALSE}
optimize_files(c("file_to_optimize_1.R", "file_to_optimize_2.R"))
```
2. Optimize some code in a character vector
```{r eval = FALSE}
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
```{r eval = FALSE}
optimize_folder("~/myfolder_to_optimize", recursive = FALSE)
```
## Example
Suppose we have the following code:
```{r}
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:
```{r}
opt_code <- optimize_text(code, iterations = 1)
```
```{r echo = FALSE}
cat(opt_code)
```
After one optimization pass we can see that it has only propagated the `years_old` variable. Another pass:
```{r}
opt_code <- optimize_text(opt_code, iterations = 1)
```
```{r echo = FALSE}
cat(opt_code)
```
Now, it has folded the `days_old` variable, and then propagated it. Another pass:
```{r}
opt_code <- optimize_text(opt_code, iterations = 1)
```
```{r echo = FALSE}
cat(opt_code)
```
It has folded the `hours_old` variable, and then propagated it. Another pass:
```{r}
opt_code <- optimize_text(opt_code, iterations = 1)
```
```{r echo = FALSE}
cat(opt_code)
```
It has folded the `seconds_old` variable, and then propagated it into the `if` condition. Another pass:
```{r}
opt_code <- optimize_text(opt_code, iterations = 1)
```
```{r echo = FALSE}
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"](https://jcrodriguez1989.github.io/rco/articles/contributing-an-optimizer.html) 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.