Getting started

After installing all required packages, we can load the whole "bupaverse" by loading the bupaR package.

library(ggplot2)
library(bupaR)

## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'xesreadR'

## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'processmonitR'

## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'petrinetR'

library(processmapR)

Now, our dataset is already in eventlog format, but typically this not the case. Here's how to turn a data.frame into an object of class eventlog:

patients <- eventdataR::patients

df <- eventlog(patients,
               case_id = "patient",
               activity_id = "handling",
               activity_instance_id = "handling_id",
               lifecycle_id = "registration_type",
               timestamp = "time",
               resource_id = "employee")

## Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## Please use the `.add` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Let's check it out.

summary(df)

## Number of events:  5442
## Number of cases:  500
## Number of traces:  7
## Number of distinct activities:  7
## Average trace length:  10.884
## 
## Start eventlog:  2017-01-02 11:41:53
## End eventlog:  2018-05-05 07:16:02

##                   handling      patient          employee  handling_id       
##  Blood test           : 474   Length:5442        r1:1000   Length:5442       
##  Check-out            : 984   Class :character   r2:1000   Class :character  
##  Discuss Results      : 990   Mode  :character   r3: 474   Mode  :character  
##  MRI SCAN             : 472                      r4: 472                     
##  Registration         :1000                      r5: 522                     
##  Triage and Assessment:1000                      r6: 990                     
##  X-Ray                : 522                      r7: 984                     
##  registration_type      time                         .order    
##  complete:2721     Min.   :2017-01-02 11:41:53   Min.   :   1  
##  start   :2721     1st Qu.:2017-05-06 17:15:18   1st Qu.:1361  
##                    Median :2017-09-08 04:16:50   Median :2722  
##                    Mean   :2017-09-02 20:52:34   Mean   :2722  
##                    3rd Qu.:2017-12-22 15:44:11   3rd Qu.:4082  
##                    Max.   :2018-05-05 07:16:02   Max.   :5442  
## 

So we learn that there are 500 "cases", i.e. patients. There are 7 different activities.

Let's check out the data for a single patient:

df %>% filter(patient == 1) %>% 
  arrange(handling_id) #%>% 

## Log of 12 events consisting of:
## 1 trace 
## 1 case 
## 6 instances of 6 activities 
## 6 resources 
## Events occurred from 2017-01-02 11:41:53 until 2017-01-09 19:45:45 
##  
## Variables were mapped as follows:
## Case identifier:     patient 
## Activity identifier:     handling 
## Resource identifier:     employee 
## Activity instance identifier:    handling_id 
## Timestamp:           time 
## Lifecycle transition:        registration_type 
## 
## # A tibble: 12 x 7
##    handling patient employee handling_id registration_ty… time               
##    <fct>    <chr>   <fct>    <chr>       <fct>            <dttm>             
##  1 Registr… 1       r1       1           start            2017-01-02 11:41:53
##  2 Registr… 1       r1       1           complete         2017-01-02 12:40:20
##  3 Blood t… 1       r3       1001        start            2017-01-05 08:59:04
##  4 Blood t… 1       r3       1001        complete         2017-01-05 14:34:27
##  5 MRI SCAN 1       r4       1238        start            2017-01-05 21:37:12
##  6 MRI SCAN 1       r4       1238        complete         2017-01-06 01:54:23
##  7 Discuss… 1       r6       1735        start            2017-01-07 07:57:49
##  8 Discuss… 1       r6       1735        complete         2017-01-07 10:18:08
##  9 Check-o… 1       r7       2230        start            2017-01-09 17:09:43
## 10 Check-o… 1       r7       2230        complete         2017-01-09 19:45:45
## 11 Triage … 1       r2       501         start            2017-01-02 12:40:20
## 12 Triage … 1       r2       501         complete         2017-01-02 22:32:25
## # … with 1 more variable: .order <int>

 # select(handling, handling_id, registration_type) # does not work

We learn that each "handling" has a separate start and complete timestamp.

Traces

The summary info of the event log also counts so-called "traces". A trace is defined a unique sequence of events in the event log. Apparently, there are only seven different traces (possible sequences). Let's visualize them.

To visualize all traces, we set coverage to 1.0.

df %>% processmapR::trace_explorer(type = "frequent", coverage = 1.0)

## Warning: `rename_()` is deprecated as of dplyr 0.7.0.
## Please use `rename()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

So there are a few traces (0.6%) that do not end with a check-out. Ignoring these rare cases, we find that there are two types of cases:

• Cases that get an X-ray
• Cases that get a blood test followed by an MRI scan

The dotted chart

A really powerful visualization in process mining comes in the form of a "dotted chart". The dotted chart function produces a ggplot graph, which is nice, because so we can actually tweak the graph as we can with regular ggplot objects.

It has two nice use cases. The first is when we plot actual time on the x-axis, and sort the cases by starting date.

df %>% dotted_chart(x = "absolute", sort = "start") + ggtitle("All cases") +
  theme_gray()

## Joining, by = "patient"

The slope of this graphs learns us the rate of new cases, and if this changes over time. Here it appears constant, with 500 cases divided over five quarter years.

The second is to align all cases relative to the first event, and sort on duration of the whole sequence of events.

df %>% dotted_chart(x = "relative", sort = "duration") + ggtitle("All cases") +
  theme_gray()

## Joining, by = "patient"

A nice pattern emerges, where all cases start with registration, then quickly proceed to triage and assessment, after that, a time varying period of 1-10 days follows where either the blood test + MRI scan, or the X-ray is performed, followed by discussing the results. Finally, check out occurs.

Conclusion

To conclude, the process mining approach to analyze time series event data appears highly promising. The dotted chart is a great addition to my data visualization repertoire, and the process mining folks appear to have at lot more goodies, such as Trace Alignment.