https://github.com/paulgovan/weibullr.learnr

An Interactive Introduction to Life Data Analysis
https://github.com/paulgovan/weibullr.learnr

life-data-analysis r reliability tutorial weibull-analysis

Last synced: 12 months ago
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An Interactive Introduction to Life Data Analysis

• Host: GitHub
• URL: https://github.com/paulgovan/weibullr.learnr
• Owner: paulgovan
• License: apache-2.0
• Created: 2023-05-22T17:00:09.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2025-03-06T14:57:18.000Z (about 1 year ago)
• Last Synced: 2025-03-29T01:33:06.735Z (about 1 year ago)
• Topics: life-data-analysis, r, reliability, tutorial, weibull-analysis
• Language: HTML
• Homepage: https://paulgovan.github.io/WeibullR.learnr/
• Size: 9.47 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: ReadMe.md
  • Changelog: news.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md

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## WeibullR.learnr: An Interactive Introduction to Life Data Analysis

Welcome to `WeibullR.learnr`! This package contains a set of interactive learning modules for life data analysis, focusing on reliability, availability, and maintainability (RAM). It is designed for beginners, including university students and early-career professionals.

## Motivation

Life data analysis is the study of how systems function over time, from machines to people. While various learning resources exist, many rely on proprietary software that can be inaccessible to students and early-career professionals.

`WeibullR.learnr` [@WeibullRlearnr] is an open-source collection of interactive learning modules, exercises, and functions designed for introductory life data analysis. The primary goal of this project is to introduce fundamental concepts while providing an open-source alternative for analyzing life data. 

## Installation Instructions

`WeibullR.learnr` is written in R and is built using [WeibullR](https://CRAN.R-project.org/package=WeibullR) by David Silkworth and Jurgen Symynck (2022), a R package for Weibull Analysis, and [learnr](https://CRAN.R-project.org/package=learnr) by Garrick Aden-Buie et al. (2023), a framework for building interactive learning modules in R. 

To install WeibullR.learnr in R:

``` r

install.packages('WeibullR.learnr')

```

To install the development version:

``` r

devtools::install_github('paulgovan/weibullr.learnr')

```

## Usage

Currently, three primary learning modules exist. These modules can be taken in either order and can be taken separately or together. The learning modules are designed to be plug-and-play, but changes can be made by forking the software repository and modifying the fork.

* `WeibullR.learnr()` - An interactive introduction to Life Data Analysis (estimated duration ~2 hours)

* `RAMR.learnr()` - A quick reference for common Reliability, Availability, and Maintainability concepts (estimated duration ~ 1 hour)

* `TestR.learnr()`- An interactive introduction to Reliability Testing (estimated duration ~ 2 hours)

The modules can also be accessed in a browser at [WeibullR.learnr](https://paulgovan.shinyapps.io/weibullrlearnr/) and [RAMR.learnr](https://paulgovan.shinyapps.io/ramrlearnr/),

[TestR.learnr](https://govan.shinyapps.io/TestRlearnr/)

![](https://github.com/paulgovan/WeibullR.learnr/blob/master/inst/paper/WeibullRlearnr.png?raw=true)

Several helper functions for common RAM calculations are also included:

* `rel()` - reliability function

* `avail()` - availability function

* `mttf()` - mean time to failure

* `mtbf()` - mean time between failure

* `serv()` - serviceability factor

* `fr()` - failure rate

## Code of Conduct

Please note that the WeibullR.learnr project is released with a [Contributor Code of Conduct](https://paulgovan.github.io/WeibullR.learnr/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

## More resources

[WeibullR.plotly](https://paulgovan.github.io/WeibullR.plotly/) is a package for building interactive Weibull models. 

[WeibullR.shiny](https://paulgovan.github.io/WeibullR.shiny/) is a web application for life data analysis.