Getting Started with bupaR

04 October 2022

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:

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 0.2.0
#> 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.

References

Bache, Stefan Milton, and Hadley Wickham. 2022. magrittr: A Forward-Pipe Operator for R. Boston, MA, USA: RStudio. https://magrittr.tidyverse.org/.
Csárdi, Gábor. 2022. cli: Helpers for Developing Command Line Interfaces. Boston, MA, USA: RStudio. https://cli.r-lib.org.
Henry, Lionel, and Hadley Wickham. 2022a. purrr: Functional Programming Tools. Boston, MA, USA: RStudio. https://purrr.tidyverse.org/.
———. 2022b. rlang: Functions for Base Types and Core r and “Tidyverse” Features. Boston, MA, USA: RStudio. https://rlang.r-lib.org/.
Hester, Jim, and Jennifer Bryan. 2022. glue: Interpreted String Literals. Boston, MA, USA: RStudio. https://glue.tidyverse.org/.
Janssenswillen, Gert, Benoît Depaire, Marijke Swennen, Mieke Julie Jans, and Koen Vanhoof. 2019. “bupaR: Enabling Reproducible Business Process Analysis.” Knowledge-Based Systems 163: 927–30. https://doi.org/10.1016/j.knosys.2018.10.018.
R Core Team. 2022. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.