The Inductive miner algorithm is provided through the pm4py package.

library(bupaR)
library(pm4py)
library(petrinetR)

It can be executed through the discovery_inductive function.

#use only complete timestamp
patients_completes <- patients %>% filter_lifecycle("complete")

discovery_inductive(patients_completes, variant = variant_inductive_only_dfg()) -> PN
## Warning: 'variant_inductive_only_dfg' is deprecated.
## Use 'variant_inductive_imdfb' instead.
## See help("Deprecated")

The resulting object consist of three elements, a net, an initial marking, and a final marking. The net can be visualized using petrinetR as follows.

PN %>% str
## List of 3
##  $ petrinet       :List of 4
##   ..$ places     :'data.frame':  8 obs. of  1 variable:
##   .. ..$ id: chr [1:8] "p_3" "p_7" "p_8" "p_6" ...
##   ..$ transitions:'data.frame':  10 obs. of  2 variables:
##   .. ..$ id   : chr [1:10] "Triage and Assessment" "Discuss Results" "X-Ray" "Registration" ...
##   .. ..$ label: chr [1:10] "Triage and Assessment" "Discuss Results" "X-Ray" "Registration" ...
##   ..$ flows      :'data.frame':  20 obs. of  2 variables:
##   .. ..$ from: chr [1:20] "MRI SCAN" "skip_1" "p_5" "skip_2" ...
##   .. ..$ to  : chr [1:20] "p_6" "p_5" "MRI SCAN" "p_6" ...
##   ..$ marking    : chr "source"
##   ..- attr(*, "class")= chr "petrinet"
##  $ initial_marking: chr "source"
##  $ final_marking  : chr "sink"
PN$petrinet %>% render_PN()

Currently, the only supported variant is variant_indutice_imdfb.