R Bupar：获取每个 case 的踪迹

发布于01月08日

我使用BUPAR包进行流程分析.假设我存储在CSV文件中的数据如下所示(该文件已经按CaseID和时间戳正确排序):

STATUS;timestamp;CASEID
created;16-02-2023 09:46:32;1
revised;13-04-2023 23:58:59;1
accepted;13-04-2023 23:59:59;1
created;16-02-2023 09:46:32;2
accepted;13-04-2023 23:59:59;2
created;14-12-2022 13:17:54;3
revised;02-01-2023 23:59:59;3
accepted;28-02-2023 19:37:01;3
submitted;03-03-2023 23:59:59;3
created;02-01-2023 07:45:43;5
created;24-01-2022 16:05:58;6
accepted;03-02-2022 23:59:59;6
created;24-01-2022 15:52:53;7
accepted;03-02-2022 23:59:59;7
created;15-08-2022 12:54:23;8
rejected;18-08-2022 23:59:59;8
created;21-03-2022 15:32:05;9
accepted;26-04-2022 23:59:59;9
created;21-03-2022 15:42:39;10

ID为1的第一个 case 的跟踪为"Created-Revision-Accept".因此，首先是事件创建，然后修改，然后接受.

我现在使用以下代码创建流程图:

library(bupaR)
library(processmapR)
library(edeaR)

datafile <- read.csv(file="pathtofile\\testfile.csv",header=T, sep=";")
datafile$timestampcolumn <- as.POSIXct(datafile$timestamp, format="%d-%m-%Y %H:%M:%S")

mytest <- simple_eventlog(datafile, case_id = "CASEID", activity_id = "STATUS", timestamp = "timestampcolumn")

process_map(mytest, type = frequency("absolute"))

这提供了:

现在，我想将每个 case 的踪迹添加到我的原始文件中.当然，案件的痕迹总是相同的.因此，输出应如下所示(跟踪中的每个事件由示例"-"分隔):

STATUS;timestamp;CASEID;trace
created;16-02-2023 09:46:32;1;created-revised-accepted
revised;13-04-2023 23:58:59;1;created-revised-accepted
accepted;13-04-2023 23:59:59;1;created-revised-accepted
created;16-02-2023 09:46:32;2;created-accepted
accepted;13-04-2023 23:59:59;2;created-accepted
created;14-12-2022 13:17:54;3;created-revised-accepted-submitted
revised;02-01-2023 23:59:59;3;created-revised-accepted-submitted
accepted;28-02-2023 19:37:01;3;created-revised-accepted-submitted
submitted;03-03-2023 23:59:59;3;created-revised-accepted-submitted
created;02-01-2023 07:45:43;5;created
created;24-01-2022 16:05:58;6;created-accepted
accepted;03-02-2022 23:59:59;6;created-accepted
created;24-01-2022 15:52:53;7;created-accepted
accepted;03-02-2022 23:59:59;7;created-accepted
created;15-08-2022 12:54:23;8;created-rejected
rejected;18-08-2022 23:59:59;8;created-rejected
created;21-03-2022 15:32:05;9;created-accepted
accepted;26-04-2022 23:59:59;9;created-accepted
created;21-03-2022 15:42:39;10;created

我试着try 使用filter_activity、trace_list(来自edeaR包)和其他命令，但我无法理解.我想使用PROCESS_MAP算法/bupar包代码的结果.以使其与图形中的输出相对应.因此，我不想自己手动实现算法来计算轨迹.所以我当然可以实现一个算法来判断每个 case ，并写下状态等等.但不知何故，这已经在bupar eventlog/process_map命令中了，我想使用它.我想深入挖掘细节，看看哪个案件根据图表有具体的痕迹.这就是为什么让它与bupar输出保持一致，而不是用单独的算法对其进行编程的原因.这一信息必须以某种方式已经包括在内，否则该图将不存在.

那么，我如何才能做到这一点呢？

methods(class = "eventlog") # [1] act_collapse activities activity_frequency # [4] activity_instance_id activity_presence add_end_activity # [7] add_start_activity arrange calculate_queuing_times # [10] case_id case_list cases # [13] detect_resource_inconsistencies dotted_chart durations # [16] end_activities events_to_activitylog filter # [19] filter_activity_instance filter_attributes filter_endpoints_condition # [22] filter_infrequent_flows filter_lifecycle filter_lifecycle_presence # [25] filter_precedence_resource filter_time_period filter_trim # [28] filter_trim_lifecycle first_n fix_resource_inconsistencies # [31] group_by group_by_activity group_by_activity_instance # [34] group_by_case group_by_resource group_by_resource_activity # [37] idle_time last_n lifecycle_id # [40] lifecycle_labels lifecycles lined_chart # [43] mapping mutate n_activity_instances # [46] n_events number_of_repetitions number_of_selfloops # [49] process_map process_matrix processing_time # [52] redo_repetitions_referral_matrix redo_selfloops_referral_matrix resource_frequency # [55] resource_id resource_map resource_matrix # [58] resources sample_n select # [61] set_activity_instance_id set_timestamp setdiff # [64] size_of_repetitions size_of_selfloops slice_activities # [67] slice_events standardize_lifecycle start_activities # [70] summarise summary throughput_time # [73] timestamp timestamps to_activitylog # [76] trace_explorer trace_length trace_list # [79] ungroup_eventlog unite

library(bupaR) library(processmapR) library(edeaR) library(dplyr) d <- readr::read_delim( "STATUS;timestamp;CASEID created;16-02-2023 09:46:32;1 revised;13-04-2023 23:58:59;1 accepted;13-04-2023 23:59:59;1 created;16-02-2023 09:46:32;2 accepted;13-04-2023 23:59:59;2 created;14-12-2022 13:17:54;3 revised;02-01-2023 23:59:59;3 accepted;28-02-2023 19:37:01;3 submitted;03-03-2023 23:59:59;3 created;02-01-2023 07:45:43;5 created;24-01-2022 16:05:58;6 accepted;03-02-2022 23:59:59;6 created;24-01-2022 15:52:53;7 accepted;03-02-2022 23:59:59;7 created;15-08-2022 12:54:23;8 rejected;18-08-2022 23:59:59;8 created;21-03-2022 15:32:05;9 accepted;26-04-2022 23:59:59;9 created;21-03-2022 15:42:39;10", delim = ";") d$timestampcolumn <- as.POSIXct(d$timestamp, format="%d-%m-%Y %H:%M:%S") mytest <- simple_eventlog(d, case_id = "CASEID", activity_id = "STATUS", timestamp = "timestampcolumn") process_map(mytest, type = frequency("absolute"))

d %>% inner_join(case_list(mytest) %>% select(CASEID, trace), "CASEID") # # A tibble: 19 × 5 # STATUS timestamp CASEID timestampcolumn trace # <chr> <chr> <dbl> <dttm> <chr> # 1 created 16-02-2023 09:46:32 1 2023-02-16 09:46:32 created,revised,accepted # 2 revised 13-04-2023 23:58:59 1 2023-04-13 23:58:59 created,revised,accepted # 3 accepted 13-04-2023 23:59:59 1 2023-04-13 23:59:59 created,revised,accepted # 4 created 16-02-2023 09:46:32 2 2023-02-16 09:46:32 created,accepted # 5 accepted 13-04-2023 23:59:59 2 2023-04-13 23:59:59 created,accepted # 6 created 14-12-2022 13:17:54 3 2022-12-14 13:17:54 created,revised,accepted,submitted # 7 revised 02-01-2023 23:59:59 3 2023-01-02 23:59:59 created,revised,accepted,submitted # 8 accepted 28-02-2023 19:37:01 3 2023-02-28 19:37:01 created,revised,accepted,submitted # 9 submitted 03-03-2023 23:59:59 3 2023-03-03 23:59:59 created,revised,accepted,submitted # 10 created 02-01-2023 07:45:43 5 2023-01-02 07:45:43 created # 11 created 24-01-2022 16:05:58 6 2022-01-24 16:05:58 created,accepted # 12 accepted 03-02-2022 23:59:59 6 2022-02-03 23:59:59 created,accepted # 13 created 24-01-2022 15:52:53 7 2022-01-24 15:52:53 created,accepted # 14 accepted 03-02-2022 23:59:59 7 2022-02-03 23:59:59 created,accepted # 15 created 15-08-2022 12:54:23 8 2022-08-15 12:54:23 created,rejected # 16 rejected 18-08-2022 23:59:59 8 2022-08-18 23:59:59 created,rejected # 17 created 21-03-2022 15:32:05 9 2022-03-21 15:32:05 created,accepted # 18 accepted 26-04-2022 23:59:59 9 2022-04-26 23:59:59 created,accepted # 19 created 21-03-2022 15:42:39 10 2022-03-21 15:42:39 created

R Bupar：获取每个 case 的踪迹

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