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,
  minCellCount = 5,
  open = rlang::is_interactive()
)

Arguments

result

A summarised result

directory

Directory where to save report

minCellCount

Minimum cell count for suppression when exporting results.

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{
library(PhenotypeR)

cdm <- mockPhenotypeR()

result <- phenotypeDiagnostics(cdm$my_cohort)
#> 
#> 
#> • Getting codelists from cohorts
#> Warning: No codelists found for the specified cohorts
#> Warning: No codelists found for the specified cohorts
#> Warning: Empty cohort_codelist attribute for cohort
#> ℹ Returning an empty summarised result
#> 
#> • Index cohort table
#> • Getting cohort summary
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> • Getting age density
#> • Getting cohort attrition
#> • Getting cohort overlap
#> • Getting cohort timing
#> ℹ The following estimates will be computed:
#> • days_between_cohort_entries: median, q25, q75, min, max, density
#> ! Table is collected to memory as not all requested estimates are supported on
#>   the database side
#> → Start summary of data, at 2025-01-30 22:14:55.223879
#> ✔ Summary finished, at 2025-01-30 22:14:55.320461
#> 
#> • Creating denominator for incidence and prevalence
#> • Sampling person table to 1e+06
#> ℹ Creating denominator cohorts
#> ! cohort columns will be reordered to match the expected order:
#>   cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
#> ✔ Cohorts created in 0 min and 5 sec
#> • Estimating incidence
#> ℹ Getting incidence for analysis 1 of 12
#> ℹ Getting incidence for analysis 2 of 12
#> ℹ Getting incidence for analysis 3 of 12
#> ℹ Getting incidence for analysis 4 of 12
#> ℹ Getting incidence for analysis 5 of 12
#> ℹ Getting incidence for analysis 6 of 12
#> ℹ Getting incidence for analysis 7 of 12
#> ℹ Getting incidence for analysis 8 of 12
#> ℹ Getting incidence for analysis 9 of 12
#> ℹ Getting incidence for analysis 10 of 12
#> ℹ Getting incidence for analysis 11 of 12
#> ℹ Getting incidence for analysis 12 of 12
#> ✔ Overall time taken: 0 mins and 13 secs
#> • Estimating prevalence
#> ℹ Getting prevalence for analysis 1 of 12
#> ℹ Getting prevalence for analysis 2 of 12
#> ℹ Getting prevalence for analysis 3 of 12
#> ℹ Getting prevalence for analysis 4 of 12
#> ℹ Getting prevalence for analysis 5 of 12
#> ℹ Getting prevalence for analysis 6 of 12
#> ℹ Getting prevalence for analysis 7 of 12
#> ℹ Getting prevalence for analysis 8 of 12
#> ℹ Getting prevalence for analysis 9 of 12
#> ℹ Getting prevalence for analysis 10 of 12
#> ℹ Getting prevalence for analysis 11 of 12
#> ℹ Getting prevalence for analysis 12 of 12
#> ✔ Time taken: 0 mins and 7 secs
#> 
#> • Sampling cohorts
#> • Generating a age and sex matched cohorts
#> Starting matching
#> ℹ Creating copy of target cohort.
#> • 2 cohorts 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
#> • Index matched cohort table
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> • Getting age density
#> • 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(result, tempdir())

CDMConnector::cdmDisconnect(cdm = cdm)
# }