Code
::pkg_install("lintr") pak
Tony D
March 15, 2025
An introduction to lintr
and styler
for R code optimization, with examples of how to use them for static code analysis and formatting.
This document introduces two essential R packages for code optimization: lintr
and styler
. It explains how lintr
performs static code analysis to help you identify and fix style inconsistencies, syntax errors, and other potential issues in your R code. The document also demonstrates how styler
can automatically format your code to adhere to the tidyverse style guide, ensuring consistency and readability. You’ll find practical examples of how to use both packages to improve the quality of your R code.
pacakge for R code optimization
{lintr} provides static code analysis for R. It checks for adherence to a given style, identifying syntax errors and possible semantic issues, then reports them to you so you can take action. Watch lintr in action in the following animation:
https://github.com/r-lib/lintr
<text>:2:3: style: [assignment_linter] Use one of <-, <<- for assignment, not =.
a = 123
^
<text>:3:4: style: [indentation_linter] Indentation should be 0 spaces but is 4 spaces.
c=5
~~~^
<text>:3:6: style: [assignment_linter] Use one of <-, <<- for assignment, not =.
c=5
^
<text>:3:6: style: [infix_spaces_linter] Put spaces around all infix operators.
c=5
^
/Users/jinchaoduan/Documents/Project/Tech-blog/posts/r_code_optimization/test.R:3:1: style: [object_name_linter] Variable and function name style should match snake_case or symbols.
Good <- 1
^~~~
/Users/jinchaoduan/Documents/Project/Tech-blog/posts/r_code_optimization/test.R:4:1: style: [object_name_linter] Variable and function name style should match snake_case or symbols.
applePie <- Good + 1
^~~~~~~~
/Users/jinchaoduan/Documents/Project/Tech-blog/posts/r_code_optimization/test.R:5:1: style: [object_name_linter] Variable and function name style should match snake_case or symbols.
Peter <- d + 1
^~~~~
styler formats your code according to the tidyverse style guide (or your custom style guide) so you can direct your attention to the content of your code. It helps to keep the coding style consistent across projects and facilitate collaboration. You can access styler through
https://github.com/r-lib/styler
Styling 1 files:
test.R ✔
────────────────────────────────────────
Status Count Legend
✔ 1 File unchanged.
ℹ 0 File changed.
✖ 0 Styling threw an error.
────────────────────────────────────────
After
---
title: "R code optimization with lintr and styler"
author: "Tony D"
execute:
warning: false
error: false
date: "2025-03-15"
categories:
- Tool
- R
image: "images/my screenshots 2.png"
---
An introduction to `lintr` and `styler` for R code optimization, with examples of how to use them for static code analysis and formatting.
This document introduces two essential R packages for code optimization: `lintr` and `styler`. It explains how `lintr` performs static code analysis to help you identify and fix style inconsistencies, syntax errors, and other potential issues in your R code. The document also demonstrates how `styler` can automatically format your code to adhere to the tidyverse style guide, ensuring consistency and readability. You'll find practical examples of how to use both packages to improve the quality of your R code.
pacakge for R code optimization
# lintr
{lintr} provides static code analysis for R. It checks for adherence to a given style, identifying syntax errors and possible semantic issues, then reports them to you so you can take action. Watch lintr in action in the following animation:
https://github.com/r-lib/lintr
```{r}
pak::pkg_install("lintr")
```
```{r}
library(lintr)
```
```{r}
lint("
a = 123
c=5
")
```
```{r}
lint("test.R")
```
# styler
styler formats your code according to the tidyverse style guide (or your custom style guide) so you can direct your attention to the content of your code. It helps to keep the coding style consistent across projects and facilitate collaboration. You can access styler through
https://github.com/r-lib/styler
```{r}
pak::pkg_install("styler")
```
```{r}
library(styler)
```
```{r}
style_text("
a = 3
small_Car='Toyota'
Big=330
")
```
## Before
```{r}
#| eval: false
library("dplyr")
Good <- 1
applePie <- Good + 1
Peter <- d + 1
```
## Auto formating
```{r filename="test.R"}
style_file("test.R")
```
After
```{r}
#| eval: false
library("dplyr")
Good <- 1
applePie <- Good + 1
Peter <- d + 1
```
# reference