Statement of Need

Life data analysis examines the behavior of systems over time. Often termed Weibull data analysis, due to the common use of the Weibull distribution, this field involves iterative data wrangling, modeling, and visualization. Interactive Weibull modeling offers numerous advantages, such as the ability to explore subsamples and uncover hidden data structures. WeibullR.plotly (Govan 2023b) is an open-source software package designed for creating interactive Weibull Probability Plots. It aims to provide more flexibility for exploratory Weibull analysis than traditional static plotting. The primary users of this project are analysts and engineers involved in life data, survival, and reliability analysis.

Description

WeibullR.plotly is developed in R (R Core Team 2023), leveraging WeibullR (Silkworth and Symynck 2022), a package dedicated to Life Data Analysis, and plotly (Sievert 2020), an interactive web-based graphing library. This combination leverages the strengths of both tools, making WeibullR.plotly ideal for embedding in web-based applications, such as learnr (Aden-Buie et al. 2023) interactive tutorials or shiny (Chang et al. 2024) web applications. Examples include WeibullR.learnr (Govan 2023a), a learnr module for Life Data Analysis, and WeibullR.shiny (Govan 2023c), a shiny app designed for Weibull Analysis.

Usage

WeibullR.plotly is available on the Comprehensive R Archive Network (CRAN) as an R package at https://CRAN.R-project.org/package=WeibullR.plotly.

To install the release verion in R, use:

install.packages(“WeibullR.plotly”)

To install the latest development version:

devtools::install_github(‘paulgovan/WeibullR.plotly’)

Weibull Probability Plots

To build a Weibull Probability Plot, fit a wblr object to a life data set using the WeibullR package, then generate plots with the plotly_wblr function.

library(WeibullR)
library(WeibullR.plotly)
failures<-c(30, 49, 82, 90, 96)
obj<-wblr.conf(wblr.fit(wblr(failures)))
plotly_wblr(obj, main='Weibull Probability Plot', xlab='Years', ylab='Failure Probability', confCol='blue', signif=4, grid=FALSE)

Contour Plots

Create contour plots using the plotly_contour function with a wblr object.

plotly_contour(obj, main='Weibull Contour Plot', col='red', signif=4, grid=FALSE)

Customization Options

Customize labels, colors, and grids to tailor the plots to specific needs. Refer to the package documentation for a comprehensive list of options.

Documentation and Resources

Full documentation and working examples are available at: https://paulgovan.github.io/WeibullR.plotly/. The project documentation includes guidance on installing WeibullR.plotly and its dependencies, demonstrations of how to use the package functions. Unit tests for ensuring code reliability and performance, and further learning resources for getting started with the package.

Contribution and Community Engagement

Engineers and analysts are encouraged to use and contribute to the project. The repository includes a Contributor Code of Conduct. Issues and feature requests can be submitted through Issues or Pull Requests here: https://github.com/paulgovan/WeibullR.plotly/issues.

References

Aden-Buie, Garrick, Barret Schloerke, JJ Allaire, and Alexander Rossell Hayes. 2023. Learnr: Interactive Tutorials for r. https://CRAN.R-project.org/package=learnr.
Chang, Winston, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert, and Barbara Borges. 2024. Shiny: Web Application Framework for r. https://CRAN.R-project.org/package=shiny.
Govan, Paul. 2023a. WeibullR.learnr: An Interactive Introduction to Life Data Analysis. https://paulgovan.github.io/WeibullR.learnr/.
———. 2023b. WeibullR.plotly: Interactive Weibull Probability Plots with ’WeibullR’. https://CRAN.R-project.org/package=WeibullR.plotly.
———. 2023c. WeibullR.shiny: A ’Shiny’ App for Weibull Analysis from ’WeibullR’. https://paulgovan.github.io/WeibullR.shiny/.
R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Sievert, Carson. 2020. Interactive Web-Based Data Visualization with r, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.
Silkworth, David, and Jurgen Symynck. 2022. WeibullR: Weibull Analysis for Reliability Engineering. https://CRAN.R-project.org/package=WeibullR.