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arsenal

An Arsenal of 'R' Functions for Large-Scale Statistical Summaries

https://github.com/mayoverse/arsenal

Science Score: 23.0%

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    4 of 5 committers (80.0%) from academic institutions
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    Low similarity (16.0%) to scientific vocabulary

Keywords

baseline-characteristics cran descriptive-statistics modeling paired-comparisons r r-package reporting statistics tableone
Last synced: 6 months ago · JSON representation

Repository

An Arsenal of 'R' Functions for Large-Scale Statistical Summaries

Basic Info
Statistics
  • Stars: 225
  • Watchers: 7
  • Forks: 13
  • Open Issues: 35
  • Releases: 0
Topics
baseline-characteristics cran descriptive-statistics modeling paired-comparisons r r-package reporting statistics tableone
Created over 8 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog

README.md

The arsenal Package Arsenal logo

CRAN_Status_Badge Total Downloads Downloads R-CMD-check <!-- badges: end -->

Overview

The goal of library(arsenal) is to make statistical reporting easy. It includes many functions which the useR will find useful to have in his/her "arsenal" of functions. There are, at this time, 6 main functions, documented below. Each of these functions is motivated by a local SAS macro or procedure of similar functionality.

Note that arsenal v3.0.0 is not backwards compatible with previous versions (mainly because compare() got renamed to comparedf()). See the NEWS file for more details.

arsenal now has a pkgdown site: https://mayoverse.github.io/arsenal/

The tableby() Function

tableby() is a function to easily summarize a set of independent variables by one or more categorical variables. Optionally, an appropriate test is performed to test the distribution of the independent variables across the levels of the categorical variable. Options for this function are easily controlled using tableby.control().

The tableby() output is easily knitted in an Rmarkdown document or displayed in the command line using the summary() function. Other S3 methods are implemented for objects from tableby(), including print(), [, as.data.frame(), sort(), merge(), padjust(), head(), and tail().

The paired() Function

paired() is a function to easily summarize a set of independent variables across two time points. Optionally, an appropriate test is performed to test the distribution of the independent variables across the time points. Options for this function are easily controlled using paired.control().

The paired() output is easily knitted in an Rmarkdown document or displayed in the command line using the summary() function. It has the same S3 methods as tableby(), since it's a special case of the tableby() object.

The modelsum() Function

modelsum() is a function to fit and summarize models for each independent variable with one or more response variables, with options to adjust for covariates for each model. Options for this function are easily controlled using modelsum.control().

The modelsum output is easily knitted in an Rmarkdown document or displayed in the command line using the summary() function. Other S3 methods are implemented for objects from modelsum(), including print(), [, as.data.frame(), and merge().

The freqlist() Function

freqlist() is a function to approximate the output from SAS's PROC FREQ procedure when using the /list option of the TABLE statement. Options for this function are easily controlled using freq.control().

The freqlist() output is easily knitted in an Rmarkdown document or displayed in the command line using the summary() function. Other S3 methods are implemented for objects from freqlist(), including print(), [, as.data.frame(), sort(), and merge(). Additionally, the summary() output can be used with head() or tail().

The comparedf() Function

comparedf() compares two data.frames and reporting any differences between them, much like SAS's PROC COMPARE procedure.

The comparedf() output is easily knitted in an Rmarkdown document or displayed in the command line using the summary() function. Other S3 methods are implemented for objects of class "comparedf", including print(), n.diffs(), n.diff.obs(), and diffs().

The write2*() Family of Functions

write2word(), write2pdf(), and write2html() are functions to output a table into a document, much like SAS's ODS procedure. The S3 method behind them is write2(). There are methods implemented for tableby(), modelsum(), freqlist(), and comparedf(), and also methods for knitr::kable(), xtable::xtable(), and pander::pander_return(). Another option is to coerce an object using verbatim() to print out the results monospaced (as if they were in the terminal)--the default method does this automatically. To output multiple tables into a document, simply make a list of them and call the same function as before. A YAML header can be added using yaml(). Code chunks can be written using code.chunk().

For more information, see vignette("write2").

Other Notable Functions

  • keep.labels() keeps the 'label' attribute on an R object when subsetting. loosen.labels() allows the labels to drop again.

  • formulize() is a shortcut to collapse variable names into a formula.

  • mdy.Date() and Date.mdy() convert numeric dates for month, day, and year to Date object, and vice versa.

  • is.Date: tests if an object is a date.

  • %nin% tests for "not in", the negation of %in%.

  • allNA() tests for all elements being NA, and includeNA() makes NAs explicit values.

Owner

  • Name: mayoverse
  • Login: mayoverse
  • Kind: organization

A collection of R packages from Mayo Clinic

GitHub Events

Total
  • Issues event: 3
  • Watch event: 3
Last Year
  • Issues event: 3
  • Watch event: 3

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 809
  • Total Committers: 5
  • Avg Commits per committer: 161.8
  • Development Distribution Score (DDS): 0.056
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Ethan Heinzen h****n@m****u 764
Jason Sinnwell s****n@m****u 16
Ethan Heinzen H****n@m****u 16
Yihui Xie x****e@y****e 8
Beth Atkinson a****n@m****u 5
Committer Domains (Top 20 + Academic)
mayo.edu: 4 yihui.name: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 108
  • Total pull requests: 5
  • Average time to close issues: 2 months
  • Average time to close pull requests: about 8 hours
  • Total issue authors: 51
  • Total pull request authors: 2
  • Average comments per issue: 1.51
  • Average comments per pull request: 0.6
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • eheinzen (33)
  • alexflaris (8)
  • farhadsalimi (4)
  • guilhermeparreira (4)
  • vksssag (4)
  • kekec3778 (3)
  • ghost (3)
  • jmbarbone (2)
  • peterdalle (2)
  • overdodactyl (2)
  • ashirwad (2)
  • abruegger (1)
  • fabones1 (1)
  • fersalme (1)
  • sheramin (1)
Pull Request Authors
  • yihui (3)
  • eheinzen (2)
Top Labels
Issue Labels
tableby/paired (50) feature (37) bug (14) modelsum (11) wontfix (9) freqlist (3) write2 (3) comparedf (2) tabled (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • cran 5,340 last-month
  • Total docker downloads: 20,878
  • Total dependent packages: 2
    (may contain duplicates)
  • Total dependent repositories: 14
    (may contain duplicates)
  • Total versions: 51
  • Total maintainers: 1
proxy.golang.org: github.com/mayoverse/arsenal
  • Versions: 29
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
cran.r-project.org: arsenal

An Arsenal of 'R' Functions for Large-Scale Statistical Summaries

  • Versions: 22
  • Dependent Packages: 2
  • Dependent Repositories: 14
  • Downloads: 5,340 Last month
  • Docker Downloads: 20,878
Rankings
Stargazers count: 1.9%
Forks count: 5.5%
Downloads: 5.9%
Dependent repos count: 7.7%
Average: 11.1%
Dependent packages count: 18.1%
Docker downloads count: 27.4%
Maintainers (1)
heinzen.ethan@mayo.edu
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4.0 depends
  • stats >= 3.4.0 depends
  • glue * imports
  • knitr >= 1.29 imports
  • utils >= 3.4.0 imports
  • MASS * suggests
  • broom >= 0.7.1 suggests
  • coin * suggests
  • geepack * suggests
  • magrittr * suggests
  • pROC * suggests
  • pander * suggests
  • rmarkdown * suggests
  • rpart * suggests
  • splines * suggests
  • stddiff * suggests
  • survival >= 2.43 suggests
  • testthat * suggests
  • xtable * suggests
  • yaml * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • actions/upload-artifact main composite
  • r-lib/actions/setup-pandoc v1 composite
  • r-lib/actions/setup-r v1 composite
  • r-lib/actions/setup-tinytex v1 composite