Getting Started with bupaR
04 October 2022
Source:vignettes/getting_started.Rmd
getting_started.Rmd
Getting Started with bupaR
The bupaverse (alias bupaR (Janssenswillen et al. 2019)) is an open-source, integrated suite of R-packages (R Core Team 2022) for handling and analysing business process data, developed by the Business Informatics Research Group at Hasselt University, Belgium. Profoundly inspired by the tidyverse (Wickham et al. 2019) 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 using a single R command:
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: Core package for business process analysis.
- edeaR: Exploratory and descriptive analysis of event-based data.
- eventdataR: Repository of sample process data.
- processcheckR: Rule-based conformance checking and filtering.
- 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:
library(bupaverse)
#>
#> .______ __ __ .______ ___ ____ ____ _______ .______ _______. _______
#> | _ \ | | | | | _ \ / \ \ \ / / | ____|| _ \ / || ____|
#> | |_) | | | | | | |_) | / ^ \ \ \/ / | |__ | |_) | | (----`| |__
#> | _ < | | | | | ___/ / /_\ \ \ / | __| | / \ \ | __|
#> | |_) | | `--' | | | / _____ \ \ / | |____ | |\ \----.----) | | |____
#> |______/ \______/ | _| /__/ \__\ \__/ |_______|| _| `._____|_______/ |_______|
#>
#> -- Attaching packages --------------------------------------- bupaverse 0.1.0 --
#> v bupaR 0.5.2 v processcheckR 0.1.4
#> v edeaR 0.9.1 v processmapR 0.5.2
#> v eventdataR 0.3.1
#> -- Conflicts ------------------------------------------ bupaverse_conflicts() --
#> x bupaR::filter() masks stats::filter()
#> x processmapR::frequency() masks stats::frequency()
#> x edeaR::setdiff() masks base::setdiff()
#> x bupaR::timestamp() masks utils::timestamp()
#> x processcheckR::xor() masks base::xor()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 (Csárdi 2022), glue (Hester and Bryan 2022), magrittr (Bache and Wickham 2022), purrr (Henry and Wickham 2022a), and rlang (Henry and Wickham 2022b). 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 homepage.
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.