-
Notifications
You must be signed in to change notification settings - Fork 19
/
README.Rmd
168 lines (117 loc) · 5.08 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
---
output: github_document
---
# rco - The R Code Optimizer
<!-- badges: start -->
[](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)
<!-- badges: end -->
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.