https://jbryer.github.io/likert/

Package to analyze likert based items.
https://jbryer.github.io/likert/

data-science r visualization

Last synced: about 1 month ago
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Package to analyze likert based items.

• Host: GitHub
• URL: https://jbryer.github.io/likert/
• Owner: jbryer
• Created: 2012-05-04T14:02:08.000Z (about 13 years ago)
• Default Branch: master
• Last Pushed: 2025-03-25T18:24:52.000Z (3 months ago)
• Last Synced: 2025-03-25T18:26:52.256Z (3 months ago)
• Topics: data-science, r, visualization
• Language: HTML
• Homepage: https://jbryer.github.io/likert/
• Size: 15.3 MB
• Stars: 310
• Watchers: 20
• Forks: 122
• Open Issues: 29
• Metadata Files:
  • Readme: README.md

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README

        


### Analysis and Visualization of Likert Based Items



**Author: [Jason Bryer, Ph.D.](mailto:[email protected])**  

**Website: **

[![](https://www.r-pkg.org/badges/version/likert?color=orange)](https://cran.r-project.org/package=likert)

[![](https://img.shields.io/badge/devel%20version-2.0.1-blue.svg)](https://github.com/jbryer/likert)

[![R build

status](https://github.com/jbryer/likert/workflows/R-CMD-check/badge.svg)](https://github.com/jbryer/likert/actions)

![Downloads](http://cranlogs.r-pkg.org/badges/likert)

`likert` is an R package designed to help analyzing and visualizing

Likert type items. More information can be obtained at

. Also, the [included

demo](https://github.com/jbryer/likert/blob/master/demo/likert.R)

demonstrates many of the features.

Download the 2013 useR! Conference

[abstract](https://github.com/jbryer/likert/raw/master/slides/useR%202013/Abstract/Speerschneider.Bryer.likert.pdf)

and

[slides](https://github.com/jbryer/likert/raw/master/slides/useR%202013/Slides/Slides.pdf).

![Reading Attitude](https://bryer.org/images/likert/centeredPlot1.png)

![Reading Attitude with

Histogram](https://bryer.org/images/likert/centeredPlot2.png)

The latest development version can be downloaded using the `devtools`

package.

    remotes::install_github('jbryer/likert')

To get started take a look at the [likert

demo](https://github.com/jbryer/likert/blob/master/demo/likert.R) or

from within R:

    demo('likert', package='likert')

Or run the [Shiny app](http://rstudio.com/shiny):

    shinyLikert()