Rclean

Rclean: A Tool for Writing Cleaner, More Transparent Code - Published in JOSS (2020)

https://github.com/ropensci-archive/Rclean

Science Score: 46.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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Found .zenodo.json file
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Academic publication links
Links to: joss.theoj.org, zenodo.org
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Committers with academic emails
2 of 4 committers (50.0%) from academic institutions
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Scientific vocabulary similarity
Low similarity (21.6%) to scientific vocabulary

Scientific Fields

Sociology Social Sciences - 64% confidence

Last synced: 4 months ago · JSON representation

Repository

:warning: ARCHIVED :warning: Isolate essential code to re-produce specific results.

Basic Info

Host: GitHub
Owner: ropensci-archive
License: gpl-3.0
Language: TeX
Default Branch: master
Homepage:
Size: 9.18 MB

Statistics

Stars: 94
Watchers: 8
Forks: 13
Open Issues: 0
Releases: 13

Archived

Created over 8 years ago · Last pushed about 3 years ago

Metadata Files

Readme Changelog Contributing License Code of conduct Codemeta

README-not.md

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Quick Start Guide

Rclean was created to help scientists more easily write “cleaner” code.
Written with research scientists that are results oriented in mind, the package’s primary function provides a simple way to isolate the minimal code you need to produce a specific result, such as a statistical table or a figure. By focusing on specific results (aka. variables), large and/or complicated analytical scripts can be paired down to the essentials and easily re-factored to be more robust and easily shared.
Below, you'll find a brief introduction to get you started using the package. For more details, see vignette("Rclean").

Install

You can install the most up to date version easily with devtools:

install.packages("devtools")
devtools::install_github("MKLau/Rclean")

You will also likely need to install the RGraphViz:

install.packages("BiocManager")
BiocManager::install("Rgraphviz")

Once installed, per usual R practice just load the Rclean package with:

library(Rclean)

Usage

Rclean usage is simple. Have a script with code you want to clean saved to disk. Then, just run the clean function with the path to the script as the input. Here, we can use an example script that is included with the package:

script <- system.file("example", "simple_script.R", package = "Rclean")

Here's a quick look at the code:

readLines(script)
#>  [1] "## Make a data frame"                             
#>  [2] "mat <- matrix(rnorm(400), nrow = 100)"            
#>  [3] "dat <- as.data.frame(mat)"                        
#>  [4] "dat[, \"V2\"] <- dat[, \"V2\"] + runif(nrow(dat))"
#>  [5] "dat[, \"V5\"] <- gl(10, 10)"                      
#>  [6] "## Conduct some analyses"                         
#>  [7] "fit12 <- lm(V1 ~ V2, data = dat)"                 
#>  [8] "fit13 <- lm(V1 ~ V3, data = dat)"                 
#>  [9] "fit14 <- lm(V1 ~ V4, data = dat)"                 
#> [10] "fit15.aov <- aov(V1 ~ V2 + V5, data = dat)"       
#> [11] "## Summarize analyses"                            
#> [12] "summary(fit15.aov)"                               
#> [13] "tab.12 <- summary(fit12)"                         
#> [14] "tab.13 <- summary(fit13)"                         
#> [15] "tab.14 <- summary(fit14)"                         
#> [16] "tab.15 <- append(fit15.aov, tab.14)"              
#> [17] "## Conduct a calculation"                         
#> [18] "dat <- 25 + 2"                                    
#> [19] "dat[2] <- 10"                                     
#> [20] "out <- dat * 2"

You can get a list of the variables found in an object with get_vars.

get_vars(script)
#>  [1] "mat"       "dat"       "fit12"     "fit13"     "fit14"     "fit15.aov"
#>  [7] "tab.12"    "tab.13"    "tab.14"    "tab.15"    "out"

Sometimes for more complicated scripts, it can be helpful to see a network graph showing the interdependencies of variables. code_graph will produce a network diagram showing which lines of code produce or use which variables:

code_graph(script)

Now, we can pick the result we want to focus on for cleaning:

clean(script, "tab.15")
#> mat <- matrix(rnorm(400), nrow = 100)
#> dat <- as.data.frame(mat)
#> dat[, "V2"] <- dat[, "V2"] + runif(nrow(dat))
#> dat[, "V5"] <- gl(10, 10)
#> fit14 <- lm(V1 ~ V4, data = dat)
#> fit15.aov <- aov(V1 ~ V2 + V5, data = dat)
#> tab.14 <- summary(fit14)
#> tab.15 <- append(fit15.aov, tab.14)
#> dat <- 25 + 2
#> dat[2] <- 10

We can also select several variables at the same time:

my.vars <- c("tab.12", "tab.15")
clean(script, my.vars)
#> mat <- matrix(rnorm(400), nrow = 100)
#> dat <- as.data.frame(mat)
#> dat[, "V2"] <- dat[, "V2"] + runif(nrow(dat))
#> dat[, "V5"] <- gl(10, 10)
#> fit12 <- lm(V1 ~ V2, data = dat)
#> fit14 <- lm(V1 ~ V4, data = dat)
#> fit15.aov <- aov(V1 ~ V2 + V5, data = dat)
#> tab.12 <- summary(fit12)
#> tab.14 <- summary(fit14)
#> tab.15 <- append(fit15.aov, tab.14)
#> dat <- 25 + 2
#> dat[2] <- 10

While just taking a look at the simplified code can be very helpful, you can also save the code for later use or sharing (e.g. creating a reproducible example for getting help) with keep:

my.code <- clean(script, my.vars)
keep(my.code, file = "results_tables.R")

If you would like to copy your code to your clipboard, you can do that by not specifying a file path. You can now paste the simplified as needed.

keep(my.code)

Contributing

This is an open-source project. If you would like to contribute to the project, please check out CONTRIBUTING.md.

Please note that the 'Rclean' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

$ropensci\_footer$

Owner

Name: rOpenSci Archive
Login: ropensci-archive
Kind: organization
Email: info@ropensci.org

Website: ropensci.org
Repositories: 259
Profile: https://github.com/ropensci-archive

Abandoned rOpenSci projects -- email info@ropensci.org if you have questions!

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GitHub Events

Total

Last Year

Committers

Last synced: 5 months ago

All Time

Total Commits: 547
Total Committers: 4
Avg Commits per committer: 136.75
Development Distribution Score (DDS): 0.009

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
mklau	m**u@f**u	542
Maëlle Salmon	m**n@y**e	2
Daniel S. Katz	d**z@i**g	2
glomus	g**s@p**n	1

Committer Domains (Top 20 + Academic)

ieee.org: 1 fas.harvard.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

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Total issues: 73
Total pull requests: 27
Average time to close issues: 6 months
Average time to close pull requests: 4 days
Total issue authors: 4
Total pull request authors: 4
Average comments per issue: 0.41
Average comments per pull request: 1.44
Merged pull requests: 24
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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MKLau (68)
maelle (3)
javimarlop (1)
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MKLau (23)
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maelle (1)
annakrystalli (1)

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ropensci (26) enhancement (14) priority (3) bug (1) question (1) help wanted (1) BLOCKED (1)

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Dependencies

DESCRIPTION cran

R >= 3.5.0 depends
CodeDepends * imports
Rgraphviz * imports
clipr * imports
igraph * imports
methods * imports
styler * imports
covr * suggests
knitr * suggests
roxygen2 * suggests
testthat * suggests

ecosyste.ms

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Open Source Science

Rclean

Science Score: 46.0%

Scientific Fields

Repository

Basic Info

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Metadata Files

README-not.md

Quick Start Guide

Install

Usage

Contributing

Owner

CodeMeta (codemeta.json)

GitHub Events

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