create_pmap: Create the process map from event log directly

In pmap: Process Map Visualization

Description

Create the process map by analyzing the given eventlog and extract the nodes by generate_nodes() and edges by generate_edges().

Usage

create_pmap(
   eventlog,
   distinct_case = FALSE,
   distinct_repeated_activities = FALSE,
   target_categories = NULL,
   edge_label = c(
     "amount",
     "mean_duration",
     "median_duration",
     "max_duration",
     "min_duration"
   )
 )

Arguments

eventlog	Event log
distinct_case	Whether should count distinct case only. Default is FALSE.
distinct_repeated_activities	Whether should distinct repeat activities. Default is FALSE, which means the repeated activity will be treated as same node. If it's TRUE, the name of the activity will be attached with the sequence number of occurance of the activity.
target_categories	A vector contains the target activity categories
edge_label	Specify which attribute is used for the edge label.

Details

> eventlog <- data.frame(
    timestamp = c(
      as.POSIXct("2017-10-01"),
      as.POSIXct("2017-10-02"),
      as.POSIXct("2017-10-03"),
      as.POSIXct("2017-10-04"),
      as.POSIXct("2017-10-05"),
      as.POSIXct("2017-10-06"),
      as.POSIXct("2017-10-07"),
      as.POSIXct("2017-10-08"),
      as.POSIXct("2017-10-09"),
      as.POSIXct("2017-10-10")
    ),
    case_id = c("c1", "c1", "c1", "c1", "c1", "c1", "c1", "c1", "c1", "c1"),
    activity =  c("a",  "b",  "d",  "a",  "c",  "a",  "b",  "c",  "a",  "d"),
    category =  c("campaign", "campaign", "sale", "campaign", "sale", "campaign", "campaign", "sale", "campaign", "sale"),
    stringsAsFactors = FALSE
  )
> eventlog
    timestamp case_id activity category
1  2017-10-01          c1          a   campaign
2  2017-10-02          c1          b   campaign
3  2017-10-03          c1          d       sale
4  2017-10-04          c1          a   campaign
5  2017-10-05          c1          c       sale
6  2017-10-06          c1          a   campaign
7  2017-10-07          c1          b   campaign
8  2017-10-08          c1          c       sale
9  2017-10-09          c1          a   campaign
10 2017-10-10          c1          d       sale
> p <- create_pmap(eventlog, target_categories = c("sale"))
> render_pmap(p)

Or for more complex event log:

> eventlog <- generate_eventlog(
    size_of_eventlog = 10000,
    number_of_cases = 2000,
    categories = c("campaign", "sale"),
    categories_size = c(8, 2))
> head(eventlog)
            timestamp   case_id         activity category
1 2017-01-01 02:40:20 Case 1204 Activity 7 (campaign)   campaign
2 2017-01-01 03:10:31 Case 1554 Activity 5 (campaign)   campaign
3 2017-01-01 04:01:51  Case 546 Activity 4 (campaign)   campaign
4 2017-01-01 05:04:09 Case 1119     Activity 9 (sale)       sale
5 2017-01-01 06:43:11 Case 1368 Activity 2 (campaign)   campaign
6 2017-01-01 07:43:06  Case 986 Activity 8 (campaign)   campaign
> str(eventlog)
'data.frame':   10000 obs. of  4 variables:
 $ timestamp  : POSIXct, format: "2017-01-01 02:40:20" "2017-01-01 03:10:31" ...
 $ case_id: chr  "Case 1204" "Case 1554" "Case 546" "Case 1119" ...
 $ activity : chr  "Activity 7 (campaign)" "Activity 5 (campaign)" "Activity 4 (campaign)" "Activity 9 (sale)" ...
 $ category : chr  "campaign" "campaign" "campaign" "sale" ...
> p <- create_pmap(eventlog, target_categories = c("sale"))
> render_pmap(p)

See Also

prune_edges

create_pmap_graph

pmap documentation built on March 18, 2018, 2:14 p.m.