Create a shiny app summarising your phenotyping results

A shiny app that is designed for any diagnostics results from phenotypeR, this includes:

* A diagnostics on the database via `databaseDiagnostics`. * A diagnostics on the cohort_codelist attribute of the cohort via `codelistDiagnostics`. * A diagnostics on the cohort via `cohortDiagnostics`. * A diagnostics on the population via `populationDiagnostics`. * A diagnostics on the matched cohort via `matchedDiagnostics`.

Usage

shinyDiagnostics(result, directory, open = rlang::is_interactive())

Arguments

result: A summarised result
directory: Directory where to save report
open: If TRUE, the shiny app will be launched in a new session. If FALSE, the shiny app will be created but not launched.

Value

A shiny app

Examples

# \donttest{
  cdm_local <- omock::mockCdmReference() |>
    omock::mockPerson(nPerson = 100) |>
    omock::mockObservationPeriod() |>
    omock::mockConditionOccurrence() |>
    omock::mockDrugExposure() |>
    omock::mockObservation() |>
    omock::mockMeasurement() |>
    omock::mockCohort(name = "my_cohort")
  cdm_local$visit_occurrence <- dplyr::tibble(
    person_id = 1L,
    visit_occurrence_id = 1L,
    visit_concept_id = 1L,
    visit_start_date = as.Date("2000-01-01"),
    visit_end_date = as.Date("2000-01-01"),
    visit_type_concept_id = 1L)
  cdm_local$procedure_occurrence <- dplyr::tibble(
    person_id = 1L,
    procedure_occurrence_id = 1L,
    procedure_concept_id = 1L,
    procedure_date = as.Date("2000-01-01"),
    procedure_type_concept_id = 1L)

  db <- DBI::dbConnect(duckdb::duckdb())
  cdm <- CDMConnector::copyCdmTo(con = db,
                                 cdm = cdm_local,
                                 schema ="main",
                                 overwrite = TRUE)
  my_result_cohort_diag <- cdm$my_cohort |> phenotypeDiagnostics()
#> 
#> 
#> • Getting codelists from cohorts
#> Warning: No codelists found for the specified cohorts
#> Warning: Empty cohort_codelist attribute for cohort
#> ℹ Returning an empty summarised result
#> 
#> • Getting cohort summary
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> • Getting age density
#> • Getting cohort attrition
#> 
#> • Creating denominator for incidence and prevalence
#> • Sampling person table to 1e+06
#> ℹ Creating denominator cohorts
#> ✔ Cohorts created in 0 min and 7 sec
#> • Estimating incidence
#> Getting incidence for analysis 1 of 12
#> Getting incidence for analysis 2 of 12
#> Getting incidence for analysis 3 of 12
#> Getting incidence for analysis 4 of 12
#> Getting incidence for analysis 5 of 12
#> Getting incidence for analysis 6 of 12
#> Getting incidence for analysis 7 of 12
#> Getting incidence for analysis 8 of 12
#> Getting incidence for analysis 9 of 12
#> Getting incidence for analysis 10 of 12
#> Getting incidence for analysis 11 of 12
#> Getting incidence for analysis 12 of 12
#> Overall time taken: 0 mins and 9 secs
#> • Estimating prevalence
#> Getting prevalence for analysis 1 of 6
#> Getting prevalence for analysis 2 of 6
#> Getting prevalence for analysis 3 of 6
#> Getting prevalence for analysis 4 of 6
#> Getting prevalence for analysis 5 of 6
#> Getting prevalence for analysis 6 of 6
#> Time taken: 0 mins and 3 secs
#> 
#> • Taking 1000 person sample of cohorts
#> • Generating a age and sex matched cohorts
#> Starting matching
#> Warning: Multiple records per person detected. The matchCohorts() function is designed
#> to operate under the assumption that there is only one record per person within
#> each cohort. If this assumption is not met, each record will be treated
#> independently. As a result, the same individual may be matched multiple times,
#> leading to inconsistent and potentially misleading results.
#> ℹ Creating copy of target cohort.
#> • 1 cohort to be matched.
#> ℹ Creating controls cohorts.
#> ℹ Excluding cases from controls
#> • Matching by gender_concept_id and year_of_birth
#> • Removing controls that were not in observation at index date
#> • Excluding target records whose pair is not in observation
#> • Adjusting ratio
#> Binding cohorts
#> ✔ Done
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> • Running large scale characterisation
#> ℹ Summarising large scale characteristics 
#> 
#>  - getting characteristics from table condition_occurrence (1 of 6)
#>  - getting characteristics from table visit_occurrence (2 of 6)
#>  - getting characteristics from table measurement (3 of 6)
#>  - getting characteristics from table procedure_occurrence (4 of 6)
#>  - getting characteristics from table observation (5 of 6)
#>  - getting characteristics from table drug_exposure (6 of 6)
#> 
  shinyDiagnostics(my_result_cohort_diag, tempdir())
# }