Process Matrix

library(bupaverse)

A process matrix is a two-dimensional matrix showing the flows between activities. Its configuration is exactly the same as that used by process_map(), and can thus be the following:

frequency()
- Absolute frequency of flows
- Relative case frequency of flows
- Relative frequency of flows, for each antecedent
  - I.e. given antecedent A, it is followed x% of the time by Consequent B
- Relative frequency of flows, for each consequent
  - I.e. given consequent B, it is preceded x% of the time by Antecedent A
performance()
- aggregation function x time unite x flow time type

The result of process_matrix() is is a data.frame with antecedent-consequent pairs, which can be visualized using plot().

Frequency

Absolute

traffic_fines %>%
    process_matrix(frequency("absolute"))

## # A tibble: 47 × 3
##    antecedent      consequent                                n
##    <fct>           <fct>                                 <dbl>
##  1 Add penalty     Insert Date Appeal to Prefecture         41
##  2 Add penalty     Notify Result Appeal to Offender          3
##  3 Add penalty     Payment                                1117
##  4 Add penalty     Receive Result Appeal from Prefecture    15
##  5 Add penalty     Send Appeal to Prefecture               171
##  6 Add penalty     Send for Credit Collection             3288
##  7 Appeal to Judge Add penalty                              13
##  8 Appeal to Judge End                                       5
##  9 Appeal to Judge Insert Date Appeal to Prefecture          1
## 10 Create Fine     Payment                                3443
## # ℹ 37 more rows

traffic_fines %>%
    process_matrix(frequency("absolute")) %>%
    plot()

Relative-case

traffic_fines %>%
    process_matrix(frequency("relative-case"))

## # A tibble: 47 × 4
##    antecedent      consequent                            n_cases rel_n_cases
##    <fct>           <fct>                                   <dbl>       <dbl>
##  1 Add penalty     Insert Date Appeal to Prefecture           41      0.0041
##  2 Add penalty     Notify Result Appeal to Offender            3      0.0003
##  3 Add penalty     Payment                                  1117      0.112 
##  4 Add penalty     Receive Result Appeal from Prefecture      15      0.0015
##  5 Add penalty     Send Appeal to Prefecture                 171      0.0171
##  6 Add penalty     Send for Credit Collection               3288      0.329 
##  7 Appeal to Judge Add penalty                                13      0.0013
##  8 Appeal to Judge End                                         5      0.0005
##  9 Appeal to Judge Insert Date Appeal to Prefecture            1      0.0001
## 10 Create Fine     Payment                                  3443      0.344 
## # ℹ 37 more rows

traffic_fines %>%
    process_matrix(frequency("relative-case")) %>%
    plot()

Relative-antecedent

traffic_fines %>%
    process_matrix(frequency("relative-antecedent"))

## # A tibble: 47 × 4
##    antecedent      consequent                                n rel_antecedent
##    <fct>           <fct>                                 <dbl>          <dbl>
##  1 Add penalty     Insert Date Appeal to Prefecture         41       0.00885 
##  2 Add penalty     Notify Result Appeal to Offender          3       0.000647
##  3 Add penalty     Payment                                1117       0.241   
##  4 Add penalty     Receive Result Appeal from Prefecture    15       0.00324 
##  5 Add penalty     Send Appeal to Prefecture               171       0.0369  
##  6 Add penalty     Send for Credit Collection             3288       0.709   
##  7 Appeal to Judge Add penalty                              13       0.684   
##  8 Appeal to Judge End                                       5       0.263   
##  9 Appeal to Judge Insert Date Appeal to Prefecture          1       0.0526  
## 10 Create Fine     Payment                                3443       0.344   
## # ℹ 37 more rows

traffic_fines %>%
    process_matrix(frequency("relative-antecedent")) %>%
    plot()

Relative-consequent

traffic_fines %>%
    process_matrix(frequency("relative-consequent"))

## # A tibble: 47 × 4
##    antecedent      consequent                                n rel_consequent
##    <fct>           <fct>                                 <dbl>          <dbl>
##  1 Add penalty     Insert Date Appeal to Prefecture         41        0.177  
##  2 Add penalty     Notify Result Appeal to Offender          3        0.0556 
##  3 Add penalty     Payment                                1117        0.227  
##  4 Add penalty     Receive Result Appeal from Prefecture    15        0.273  
##  5 Add penalty     Send Appeal to Prefecture               171        0.753  
##  6 Add penalty     Send for Credit Collection             3288        0.971  
##  7 Appeal to Judge Add penalty                              13        0.00280
##  8 Appeal to Judge End                                       5        0.0005 
##  9 Appeal to Judge Insert Date Appeal to Prefecture          1        0.00431
## 10 Create Fine     Payment                                3443        0.701  
## # ℹ 37 more rows

traffic_fines %>%
    process_matrix(frequency("relative-consequent")) %>%
    plot()

Performance

traffic_fines %>%
    process_matrix(performance(FUN = mean, units = "weeks"))

## # A tibble: 47 × 4
##    antecedent      consequent                                n flow_time
##    <fct>           <fct>                                 <dbl>     <dbl>
##  1 Add penalty     Insert Date Appeal to Prefecture         41     9.43 
##  2 Add penalty     Notify Result Appeal to Offender          3    11.1  
##  3 Add penalty     Payment                                1117    25.1  
##  4 Add penalty     Receive Result Appeal from Prefecture    15     6.96 
##  5 Add penalty     Send Appeal to Prefecture               171    36.4  
##  6 Add penalty     Send for Credit Collection             3288    69.7  
##  7 Appeal to Judge Add penalty                              13     4.51 
##  8 Appeal to Judge End                                       5     0    
##  9 Appeal to Judge Insert Date Appeal to Prefecture          1     0.286
## 10 Create Fine     Payment                                3443     1.33 
## # ℹ 37 more rows

traffic_fines %>%
    process_matrix(performance(FUN = mean, units = "weeks"))  %>%
    plot()

bupaR Docs | Process Matrix