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address review feedback
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@IndrajeetPatil
IndrajeetPatil committed Sep 17, 2024
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10 changes: 7 additions & 3 deletions paper/paper.Rmd
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# Statement of Need

R is an interpreted, dynamically-typed programming language [@base2023]. It is a popular choice for statistical analysis and visualization, and is used by a wide range of researchers and data scientists. The `{lintr}` package is an open-source R package that provides static code analysis [@enwiki:1218663830] to check for a variety of common problems related to readability, efficiency, consistency, style, etc. In particular, by default it enforces the tidyverse style guide [@Wickham2023]. It is designed to be easy to use and integrate into existing workflows, and can be used as part of an automated build or continuous integration process. `{lintr}` also integrates with a number of popular IDEs and text editors, such as RStudio and Visual Studio Code, making it convenient for users to run `{lintr}` checks on their code as they work.
A linter is a tool that automatically analyzes code to identify potential errors, stylistic issues, or deviations from coding standards. It helps ensure consistency, readability, and best practices by flagging common mistakes, such as syntax errors, unused variables, or improper formatting. Linters are essential for improving code quality, preventing bugs, and maintaining a clean codebase, especially in collaborative development environments [@enwiki:1218663830]. `{lintr}` is an open-source package that provides linters for R programming language, which is an interpreted, dynamically-typed programming language [@base2023]. It is a popular choice for statistical analysis and visualization, and is used by a wide range of researchers and data scientists. Therefore, `{lintr}` is a valuable tool for R users to help improve the quality and reliability of their code.

# Features

By default, `{lintr}` enforces the tidyverse style guide [@Wickham2023,@Müller2024]. In this respect, it differs from other static code analysis tools in R (like `{codetools}` [@Tierney2024]), which are not opinionated and don't enforce any particular style of writing code, but, rather, check R code for possible problems.

As of this writing, `{lintr}` offers `r length(all_linters())` linters.

```{r all_linters}
Expand Down Expand Up @@ -103,7 +105,7 @@ lint(

- **Efficiency**

Sometimes the users might not be aware of a more efficient way offered by R for carrying out a computation. `{lintr}` offers linters to improve code efficiency by avoiding common inefficient patterns.
Sometimes users might not be aware of a more efficient way offered by R for carrying out a computation. `{lintr}` offers linters to improve code efficiency by avoiding common inefficient patterns.

For example, the `any_is_na_linter()` linter detects usages of `any(is.na(x))` and suggests `anyNA(x)` as a more efficient alternative to detect presence of *any* missing values.

Expand Down Expand Up @@ -217,11 +219,13 @@ There are two main ways to customize it:

- Create new linters (by leveraging functions like `lintr::make_linter_from_xpath()`) tailored to match project- or organization-specific coding standards.

Indeed, `{goodpractice}` [@Padgham2024] bundles a set of custom linters that are not part of the default set of `{lintr}` linters, while `{flint}` [@Bacher2024] provides a way to fix the lints detected by `{lintr}`.

# Benefits of using `{lintr}`

There are several benefits to using `{lintr}` to analyze and improve R code. One of the most obvious is that it can help users identify and fix problems in their code, which can save time and effort during the development process. By catching issues early on, `{lintr}` can help prevent bugs and other issues from creeping into code, which can save time and effort when it comes to debugging and testing.

Another benefit of `{lintr}` is that it can help users write more readable and maintainable code. By enforcing a consistent style and highlighting potential issues, `{lintr}` can help users write code that is easier to understand and work with. This is especially important for larger projects or teams, where multiple contributors may be working on the same codebase and it is important to ensure that code is easy to follow and understand, particularly when frequently switching context among code primarily authored by different people.
Another benefit of `{lintr}` is that it can help users write more readable and maintainable code. By enforcing a consistent style and highlighting potential issues, `{lintr}` can help users write code that is easier to understand and work with. This is especially important for larger projects or teams, where multiple contributors may be working on the same codebase and it is important to ensure that code is easy to follow and understand, particularly when frequently switching context among code primarily authored by different people. `{lintr}` is designed to be easy to use and integrate into existing workflows, and can be used as part of an automated build or continuous integration process. `{lintr}` also integrates with a number of popular IDEs and text editors, such as RStudio and Visual Studio Code, making it convenient for users to run `{lintr}` checks on their code as they work.

It can also be a useful tool for teaching and learning R. By providing feedback on code style and potential issues, it can help users learn good coding practices and improve their skills over time. This can be especially useful for beginners, who may not yet be familiar with all of the best practices for writing R code.

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36 changes: 35 additions & 1 deletion paper/paper.bib
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Expand Up @@ -32,10 +32,44 @@ @book{mcconnell2004code
publisher={Pearson Education}
}

@misc{ enwiki:1218663830,
@misc{ enwiki:1218663830,
author = "{Wikipedia contributors}",
title = "Static program analysis --- {Wikipedia}{,} The Free Encyclopedia",
year = "2024",
url = "https://en.wikipedia.org/w/index.php?title=Static_program_analysis&oldid=1218663830",
note = "[Online; accessed 7-May-2024]"
}

@Manual{Tierney2024,
title = {codetools: Code Analysis Tools for R},
author = {Luke Tierney},
year = {2024},
note = {R package version 0.2-20},
url = {https://CRAN.R-project.org/package=codetools},
}

@Manual{Bacher2024,
title = {flint: Find and Fix Lints in R Code},
author = {Etienne Bacher},
year = {2024},
note = {R package version 0.0.2,
https://github.com/etiennebacher/flint},
url = {https://flint.etiennebacher.com},
}

@Manual{Müller2024,
title = {styler: Non-Invasive Pretty Printing of R Code},
author = {Kirill Müller and Lorenz Walthert and Indrajeet Patil},
year = {2024},
note = {R package version 1.10.3.9000, commit 6d2f0b34245b6bc712bf2fcabf240d9ca814f0ef},
url = {https://github.com/r-lib/styler},
}

@Manual{Padgham2024,
title = {goodpractice: Advice on R Package Building},
author = {Mark Padgham and Karina Marks and Daniel {de Bortoli} and Gabor Csardi and Hannah Frick and Owen Jones and Hannah Alexander},
year = {2024},
note = {R package version 1.0.5,
https://github.com/ropensci-review-tools/goodpractice},
url = {https://docs.ropensci.org/goodpractice/},
}
52 changes: 34 additions & 18 deletions paper/paper.md
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@@ -1,6 +1,6 @@
---
title: "Static Code Analysis for R"
date: "2024-09-05"
date: "2024-09-17"
tags: ["R", "linter", "tidyverse"]
authors:
- name: Jim Hester
Expand Down Expand Up @@ -51,22 +51,28 @@ link-citations: yes

# Statement of Need

R is an interpreted, dynamically-typed programming language [@base2023].
It is a popular choice for statistical analysis and visualization, and
is used by a wide range of researchers and data scientists. The
`{lintr}` package is an open-source R package that provides static code
analysis [@enwiki:1218663830] to check for a variety of common problems
related to readability, efficiency, consistency, style, etc. In
particular, by default it enforces the tidyverse style guide
[@Wickham2023]. It is designed to be easy to use and integrate into
existing workflows, and can be used as part of an automated build or
continuous integration process. `{lintr}` also integrates with a number
of popular IDEs and text editors, such as RStudio and Visual Studio
Code, making it convenient for users to run `{lintr}` checks on their
code as they work.
A linter is a tool that automatically analyzes code to identify
potential errors, stylistic issues, or deviations from coding standards.
It helps ensure consistency, readability, and best practices by flagging
common mistakes, such as syntax errors, unused variables, or improper
formatting. Linters are essential for improving code quality, preventing
bugs, and maintaining a clean codebase, especially in collaborative
development environments [@enwiki:1218663830]. `{lintr}` is an
open-source package that provides linters for R programming language,
which is an interpreted, dynamically-typed programming language
[@base2023]. It is a popular choice for statistical analysis and
visualization, and is used by a wide range of researchers and data
scientists. Therefore, `{lintr}` is a valuable tool for R users to help
improve the quality and reliability of their code.

# Features

By default, `{lintr}` enforces the tidyverse style guide
[@Wickham2023,@Müller2024]. In this respect, it differs from other
static code analysis tools in R (like `{codetools}` [@Tierney2024]),
which are not opinionated and don't enforce any particular style of
writing code, but, rather, check R code for possible problems.

As of this writing, `{lintr}` offers 113 linters.

``` r
Expand Down Expand Up @@ -115,9 +121,9 @@ lint(

- **Efficiency**

Sometimes the users might not be aware of a more efficient way offered
by R for carrying out a computation. `{lintr}` offers linters to improve
code efficiency by avoiding common inefficient patterns.
Sometimes users might not be aware of a more efficient way offered by R
for carrying out a computation. `{lintr}` offers linters to improve code
efficiency by avoiding common inefficient patterns.

For example, the `any_is_na_linter()` linter detects usages of
`any(is.na(x))` and suggests `anyNA(x)` as a more efficient alternative
Expand Down Expand Up @@ -285,6 +291,11 @@ lint(
`lintr::make_linter_from_xpath()`) tailored to match project- or
organization-specific coding standards.

Indeed, `{goodpractice}` [@Padgham2024] bundles a set of custom linters
that are not part of the default set of `{lintr}` linters, while
`{flint}` [@Bacher2024] provides a way to fix the lints detected by
`{lintr}`.

# Benefits of using `{lintr}`

There are several benefits to using `{lintr}` to analyze and improve R
Expand All @@ -301,7 +312,12 @@ is easier to understand and work with. This is especially important for
larger projects or teams, where multiple contributors may be working on
the same codebase and it is important to ensure that code is easy to
follow and understand, particularly when frequently switching context
among code primarily authored by different people.
among code primarily authored by different people. `{lintr}` is designed
to be easy to use and integrate into existing workflows, and can be used
as part of an automated build or continuous integration process.
`{lintr}` also integrates with a number of popular IDEs and text
editors, such as RStudio and Visual Studio Code, making it convenient
for users to run `{lintr}` checks on their code as they work.

It can also be a useful tool for teaching and learning R. By providing
feedback on code style and potential issues, it can help users learn
Expand Down

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