bupaR Cheat Sheet

📋 Event Log Components

REQUIRED case_id – Unique process instance

REQUIRED activity_id – Activity name/label

REQUIRED activity_instance_id – Unique event ID

REQUIRED timestamp – When event occurred

OPTIONAL lifecycle_id – start/complete status

OPTIONAL resource_id – Who performed it

📖 Performance Metrics Defined

⏱️ Throughput Time

Total time from case start to end. Includes all waiting + working time. Measures end-to-end duration.

⚙️ Processing Time

Actual working time spent on activities. Excludes waiting/idle periods. Sum of all activity durations.

💤 Idle Time

Time between activities where no work happens. Waiting time = Throughput − Processing time.

📦 Setup

library(bupaR)core

library(bupaverse)all pkgs

library(processmapR)viz

ℹ️ Log Info

summary(log)overview

mapping(log)field map

case_id(log)case col

activity_id(log)act col

🔧 Creating Event Log from DataFrames

df %>% mutate(case_id = paste(id1, id2, sep="-")) %>%
eventlog(case_id="case_id", activity_id="act", timestamp="ts", ...)

🔢 Counts

n_cases(log)# cases

n_activities(log)# acts

n_traces(log)# traces

n_resources(log)# res

🏷️ Labels

activity_labels(log)acts

resource_labels(log)res

activities(log)df

resources(log)df

🔀 Traces

traces(log)all traces

trace_length(log)lengths

trace_coverage(log)coverage

trace_explorer(log,coverage=.8)viz

🗺️ Process Maps

process_map(log)basic

process_map(type=frequency())freq

process_map(type=performance())perf

resource_map(log)resources

⏱️ Performance

throughput_time(log)total time

processing_time(log)work time

idle_time(log)wait time

units = "days"/"hours"/"mins"

👥 Resources

resource_frequency(log)freq

resource_involvement(log)cases

level="resource"/"activity"/"resource-activity"

📐 Structure

start_activities(log)starts

end_activities(log)ends

activity_presence(log)presence

number_of_repetitions(log)rework

↔️ Precedence

precedence_matrix(log)matrix

type="absolute"/"relative"

process_matrix(type=frequency())proc

⏰ Filter: Throughput Time

filter_throughput_time(log, interval=c(5,10), units="days")range

filter_throughput_time(log, percentage=0.5)fastest 50%

filter_throughput_time(log, percentage=0.5, reverse=TRUE)slowest 50%

filter_throughput_time(log, interval=c(5,NA), units="days")>5 days

🎯 Filter: Activity

filter_activity(log, activities=c("A","B"))labels

filter_activity_frequency(log, percentage=0.6)freq

filter_activity_presence(log, c("A"), method="none")absent

🔗 Filter: Precedence

filter_precedence(log, antecedents="A", consequents="B")flow

precedence_type="eventually_follows"/"directly_follows"

filter_method="all"/"none"

🔍 Filter: Trace

filter_trace_frequency(log, percentage=0.2)top 20%

filter_resource_frequency(log, interval=c(2,5))res range

filter_time_period(log, interval, filter_method="trim")time

📦 Aggregate

act_unite(log,"New"=c("A","B"))rename

act_collapse(log,"New"=c("A","B"))merge

✨ Enrich

group_by_case(log)by case

mutate(x = sum(cost))add var

throughput_time(level="case", append=TRUE)add metric

📊 Visualize

plot()pipe any metric

dotted_chart(log, x="relative")timeline

x="absolute"/"relative"/"relative_day"

📏 Granularity Levels

✂️ Slice & Dice

slice(log, n)nth case

filter(col == "val")dplyr

group_by(col)segment

📊 bupaR Cheat Sheet