--- title: "Getting Started with bupaR" output: rmarkdown::html_vignette: keep_md: TRUE toc: TRUE tags: - Event data - Process analysis - R Authors: Gerhardus van Hulzen, Gert Janssenswillen authors: - name: Gerhardus van Hulzen orcid: 0000-0001-8962-9515 affiliation: 1 - name: Gert Janssenswillen orcid: 0000-0002-7474-2088 affiliation: 1 affiliations: - name: Research group Business Informatics, Hasselt University index: 1 date: 04 October 2022 bibliography: references.bib vignette: > %\VignetteIndexEntry{Getting Started with bupaR} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width = "100%" ) options(cli.unicode = FALSE) ``` ![](../man/figures/logo.png) # Getting Started with bupaR The **bupaverse** (alias **bupaR** [@janssenswillenBupaREnablingReproducible2019]) is an open-source, integrated suite of [`R`](https://www.r-project.org/)-packages [@Rcore] for handling and analysing business process data, developed by the [Business Informatics Research Group](https://www.uhasselt.be/binf) at Hasselt University, Belgium. Profoundly inspired by the [**tidyverse**](https://www.tidyverse.org/) [@wickhamWelcomeTidyverse2019] package, the **bupaverse** package is designed to facilitate the installation and loading of multiple **bupaverse** packages in a single step. ## bupaverse Package The **bupaverse** is a collection of packages that can be conveniently installed from [CRAN](https://cran.r-project.org/) using a single `R` command: ```{r install, eval = FALSE} install.packages("bupaverse") ``` This will install the "core" packages that are required to start with business process analytics in `R`. Currently, the "core" contains the following packages: * [**bupaR**](https://bupaverse.github.io/bupaR/): Core package for business process analysis. * [**edeaR**](https://bupaverse.github.io/edeaR/): Exploratory and descriptive analysis of event-based data. * [**eventdataR**](https://bupaverse.github.io/eventdataR/): Repository of sample process data. * [**processcheckR**](https://bupaverse.github.io/processcheckR/): Rule-based conformance checking and filtering. * [**processmapR**](https://bupaverse.github.io/processmapR/): Visualise event-based data using, i.a., process maps. To start using these packages, you can load them all using a single `R` command: ```{r load} library(bupaverse) ``` In addition to attaching the "core" packages, this command also reports which package versions were loaded and conflicts with previously loaded packages. `install.packages("bupaverse")` also installs "non-core" packages which are required for **bupaverse** to function. The "non-core" packages include: [**cli**](https://cli.r-lib.org) [@Rcli], [**glue**](https://glue.tidyverse.org/) [@Rglue], [**magrittr**](https://magrittr.tidyverse.org/) [@Rmagrittr], [**purrr**](https://purrr.tidyverse.org/) [@Rpurrr], and [**rlang**](https://rlang.r-lib.org/) [@Rrlang]. Note that these packages are not attached by `library(bupaverse)`. ## Example After the package has been loaded, you can start analysing process data, e.g., you can analyse and plot the processing time for each activity in the sample dataset `eventdataR::patients`. Learn more about **bupaverse** at the [bupaR.net](https://bupar.net/) homepage. ```{r example} patients %>% processing_time(level = "activity") %>% plot() ``` ## Acknowledgements The **bupaverse** development team would like to warmly thank all users who are actively contributing to the **bupaverse** framework by submitting issues and pull requests on the [GitHub repositories](https://github.com/bupaverse). ## References