R Interface to the PM4Py Process Mining Library

The goal of the R package ‘pm4py’ is to provide a bridge between bupaR and the Python library PM4Py.

Installation

You can install the development version of pm4py with:

remotes::install_github("fmannhardt/pm4py")

Then, automatically install the pm4py package in a virtual or Conda environment:

pm4py::install_pm4py()

See the ‘reticulate’ documentation for more information on the available options or how to specify an existing Python environment: https://rstudio.github.io/reticulate/

Example

library(pm4py)

# Most of the data structures are converted in their bupaR equivalents
library(bupaR)

# As Inductive Miner of PM4PY is not life-cycle aware, keep only `complete` events:
patients_completes <- patients[patients$registration_type == "complete", ]

# Discovery with Inductive Miner
pn <- discovery_inductive(patients_completes)

# This results in an auto-converted bupaR Petri net and markings
str(pn)
class(pn$petrinet)

# Render with bupaR
render_PN(pn$petrinet)

# Render with  PM4PY and DiagrammeR
library(DiagrammeR)
viz <- reticulate::import("pm4py.visualization.petrinet")

# Convert back to Python
py_pn <- r_to_py(pn$petrinet)
class(py_pn)

# Render to DOT with PMP4Y
dot <- viz$factory$apply(py_pn)$source
grViz(diagram = dot)

# Compute alignment
alignment <- conformance_alignment(patients_completes, pn$petrinet, pn$initial_marking, pn$final_marking)

# # Alignment is returned in long format as data frame
head(alignment)

# Evaluate model quality
quality <- evaluation_all(patients_completes, pn$petrinet, pn$initial_marking, pn$final_marking)