Detect missing values

Function detecting missing values at different levels of aggregation

• overview: presents an overview of the absolute and relative number of missing values for each column

• column: presents an overview of the absolute and relative number of missing values for a particular column

• activity: presents an overview of the absolute and relative number of missing values for each column, aggregated by activity

detect_missing_values(
  activitylog,
  level_of_aggregation,
  column,
  details,
  filter_condition
)

Arguments

activitylog	The activity log
level_of_aggregation	Level of aggregation at which missing values are identified (either "overview", "column" or "activity)
column	Column name of the column that needs to be analyzed when the level of aggregation is "column"
details	Boolean indicating wheter details of the results need to be shown
filter_condition	Condition that is used to extract a subset of the activity log prior to the application of the function

Value

activitylog containing the rows of the original activity log which contain a missing value

Examples

# \donttest{
data("hospital_actlog")
detect_missing_values(activitylog = hospital_actlog)
#> Selected level of aggregation:overview
#> *** OUTPUT ***
#> Absolute number of missing values per column:
#>                   
#> patient_visit_nr 0
#> activity         0
#> originator       2
#> start            1
#> complete         0
#> triagecode       1
#> specialization   0
#> Relative number of missing values per column (expressed as percentage):
#>                          
#> patient_visit_nr 0.000000
#> activity         0.000000
#> originator       3.773585
#> start            1.886792
#> complete         0.000000
#> triagecode       1.886792
#> specialization   0.000000
#> Overview of activity log rows which are incomplete:
#> # A tibble: 4 x 7
#>   patient_visit_nr activity originator start               complete           
#>              <dbl> <chr>    <chr>      <dttm>              <dttm>             
#> 1              510 Clinica~ Doctor 7   2017-11-20 11:35:01 2017-11-20 11:36:09
#> 2              533 0        NA         2017-11-22 18:35:00 2017-11-22 18:37:00
#> 3              534 Registr~ NA         2017-11-22 18:35:00 2017-11-22 18:37:00
#> 4              512 Clinica~ Doctor 7   NA                  2017-11-20 11:33:57
#> # ... with 2 more variables: triagecode <dbl>, specialization <chr>
detect_missing_values(activitylog = hospital_actlog, level_of_aggregation = "activity")
#> Selected level of aggregation:activity
#> *** OUTPUT ***
#> Absolute number of missing values per column (per activity):
#> # A tibble: 9 x 7
#>   activity  patient_visit_nr originator start complete triagecode specialization
#>   <chr>                <int>      <int> <int>    <int>      <int>          <int>
#> 1 0                        0          1     0        0          0              0
#> 2 Clinical~                0          0     1        0          1              0
#> 3 registra~                0          0     0        0          0              0
#> 4 Registra~                0          1     0        0          0              0
#> 5 Trage                    0          0     0        0          0              0
#> 6 Treatment                0          0     0        0          0              0
#> 7 Treatmen~                0          0     0        0          0              0
#> 8 Triaga                   0          0     0        0          0              0
#> 9 Triage                   0          0     0        0          0              0
#> Relative number of missing values per column (per activity, expressed as percentage):
#> # A tibble: 9 x 7
#>   activity  patient_visit_nr originator start complete triagecode specialization
#>   <chr>                <dbl>      <dbl> <dbl>    <dbl>      <dbl>          <dbl>
#> 1 0                        0     1      0            0      0                  0
#> 2 Clinical~                0     0      0.111        0      0.111              0
#> 3 registra~                0     0      0            0      0                  0
#> 4 Registra~                0     0.0714 0            0      0                  0
#> 5 Trage                    0     0      0            0      0                  0
#> 6 Treatment                0     0      0            0      0                  0
#> 7 Treatmen~                0     0      0            0      0                  0
#> 8 Triaga                   0     0      0            0      0                  0
#> 9 Triage                   0     0      0            0      0                  0
#> Overview of activity log rows which are incomplete:
#> # A tibble: 4 x 7
#>   patient_visit_nr activity originator start               complete           
#>              <dbl> <chr>    <chr>      <dttm>              <dttm>             
#> 1              510 Clinica~ Doctor 7   2017-11-20 11:35:01 2017-11-20 11:36:09
#> 2              533 0        NA         2017-11-22 18:35:00 2017-11-22 18:37:00
#> 3              534 Registr~ NA         2017-11-22 18:35:00 2017-11-22 18:37:00
#> 4              512 Clinica~ Doctor 7   NA                  2017-11-20 11:33:57
#> # ... with 2 more variables: triagecode <dbl>, specialization <chr>
detect_missing_values(activitylog = hospital_actlog, level_of_aggregation = "column",
     column = "triagecode")
#> Selected level of aggregation:column
#> *** OUTPUT ***
#> Absolute number of missing values in columntriagecode:1
#> Relative number of missing values in columntriagecode(expressed as percentage):1.88679245283019
#> 
#> Overview of activity log rows in whichtriagecodeis missing:
#> # A tibble: 1 x 7
#>   patient_visit_nr activity originator start               complete           
#>              <dbl> <chr>    <chr>      <dttm>              <dttm>             
#> 1              510 Clinica~ Doctor 7   2017-11-20 11:35:01 2017-11-20 11:36:09
#> # ... with 2 more variables: triagecode <dbl>, specialization <chr>
# }