https://github.com/djnavarro/tidylsr

A tidy revision to the lsr package
https://github.com/djnavarro/tidylsr

Last synced: 27 days ago
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A tidy revision to the lsr package

• Host: GitHub
• URL: https://github.com/djnavarro/tidylsr
• Owner: djnavarro
• License: other
• Created: 2019-03-03T07:43:04.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-09-06T10:41:56.000Z (about 5 years ago)
• Last Synced: 2024-10-06T17:54:32.840Z (about 1 month ago)
• Language: R
• Homepage: https://djnavarro.github.io/tidylsr/
• Size: 112 KB
• Stars: 10
• Watchers: 2
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# tidylsr

[![Travis build status](https://travis-ci.org/djnavarro/tidylsr.svg?branch=master)](https://travis-ci.org/djnavarro/tidylsr)  [![Codecov test coverage](https://codecov.io/gh/djnavarro/tidylsr/branch/master/graph/badge.svg)](https://codecov.io/gh/djnavarro/tidylsr?branch=master) ![Lifecycle experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg) [![CRAN status](https://www.r-pkg.org/badges/version/tidylsr)](https://cran.r-project.org/package=tidylsr)

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidylsr)

```

Rethinking the `lsr` package that accompanies [Learning Statistics with R](https://learningstatisticswithr.com). In the original book, the goal of the package was to provide a few convenient wrapper functions and simplifications that novice users might find handy. A typical reader of the book might be psychology undergraduate students who encountering R, statistics and programming for the first time, and I found the simplifications useful in some cases. The `tidylsr` package is intended to accompany the next version of *Learning Statistics with R*, in which the goal is to teach a tidyverse-focused data analysis pipeline. It's very much a work in progress