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.
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)
rco can be used in three ways:
-
Using the RStudio Addins
-
Optimize active file: Optimizes the file currently open in RStudio. It will apply the optimizers present inall_optimizers. -
Optimize selection: Optimizes the code currently highlighted in the RStudio Source Pane. It will apply the optimizers present inall_optimizers.
-
-
Using the
shinyGUIs-
rco_gui("code_optimizer")opens ashinyinterface in a browser. This GUI allows to easily optimize chunks of code. -
rco_gui("pkg_optimizer")opens ashinyinterface in a browser. This GUI allows to easily optimize R packages that are hosted at CRAN or GitHub.
-
-
Using the R functions
- Optimize some
.Rcode files
optimize_files(c("file_to_optimize_1.R", "file_to_optimize_2.R"))
- 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)
- Optimize all
.Rcode files into a folder
optimize_folder("~/myfolder_to_optimize", recursive = FALSE)
- Optimize some
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)## Optimization number 1
## # I want to know my age in seconds!
## years_old <- 29
## days_old <- 365 * 29 # leap years don't exist
## hours_old <- 24 * days_old
## seconds_old <- 3600 * hours_old
##
## if (seconds_old > 10e6) {
## print("Whoa! More than a million seconds old, what a wise man!")
## } else {
## print("Meh!")
## }
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)## Optimization number 1
## # I want to know my age in seconds!
## years_old <- 29
## days_old <- 10585 # leap years don't exist
## hours_old <- 24 * 10585
## seconds_old <- 3600 * hours_old
##
## if (seconds_old > 10e6) {
## print("Whoa! More than a million seconds old, what a wise man!")
## } else {
## print("Meh!")
## }
Now, it has folded the days_old variable, and then propagated it.
Another pass:
opt_code <- optimize_text(opt_code, iterations = 1)## Optimization number 1
## # I want to know my age in seconds!
## years_old <- 29
## days_old <- 10585 # leap years don't exist
## hours_old <- 254040
## seconds_old <- 3600 * 254040
##
## if (seconds_old > 10e6) {
## print("Whoa! More than a million seconds old, what a wise man!")
## } else {
## print("Meh!")
## }
It has folded the hours_old variable, and then propagated it. Another
pass:
opt_code <- optimize_text(opt_code, iterations = 1)## Optimization number 1
## # I want to know my age in seconds!
## years_old <- 29
## days_old <- 10585 # leap years don't exist
## hours_old <- 254040
## seconds_old <- 914544000
##
## if (914544000 > 10e6) {
## print("Whoa! More than a million seconds old, what a wise man!")
## } else {
## print("Meh!")
## }
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)## Optimization number 1
## # I want to know my age in seconds!
## years_old <- 29
## days_old <- 10585 # leap years don't exist
## hours_old <- 254040
## seconds_old <- 914544000
##
## print("Whoa! More than a million seconds old, what a wise man!")
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.
rco is an open source package, and the contributions to the
development of the library are more than welcome. Please see our
CONTRIBUTING.md
file and “Contributing an
Optimizer”
article for detailed guidelines of how to contribute.
Please note that the ‘rco’ project is released with a Contributor Code of Conduct.
By contributing to this project, you agree to abide by its terms.