Create a shiny app summarising your phenotyping results

shinyDiagnostics(
  result,
  directory,
  minCellCount = 5,
  open = rlang::is_interactive()
)

# \donttest{
library(PhenotypeR)

cdm <- mockPhenotypeR()

result <- phenotypeDiagnostics(cdm$my_cohort)
#> 
#> 
#> Warning: ! cohort_codelist attribute for cohort is empty
#> ℹ Returning an empty summarised result
#> ℹ You can add a codelist to a cohort with `addCodelistAttribute()`.
#> 
#> • Getting cohort attrition
#> • Getting cohort count
#> ℹ summarising data
#> ℹ summarising cohort cohort_1
#> ℹ summarising cohort cohort_2
#> ✔ summariseCharacteristics finished!
#> • 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 2025-04-03 13:05:29.503276
#> ✔ Summary finished, at 2025-04-03 13:05:29.621567
#> • get cohorts and index
#> • cohort summary
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ summarising data
#> ℹ summarising cohort cohort_1
#> ℹ summarising cohort cohort_2
#> ✔ summariseCharacteristics finished!
#> • age density
#> ℹ The following estimates will be computed:
#> • age: density
#> → Start summary of data, at 2025-04-03 13:05:33.937244
#> ✔ Summary finished, at 2025-04-03 13:05:34.219175
#> 
#> • Creating denominator for incidence and prevalence
#> • Sampling person table to 1e+06
#> ℹ Creating denominator cohorts
#> ✔ 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 12 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
#> Returning entry cohort as the size of the cohorts to be sampled is equal or
#> smaller than `n`.
#> • Generating an age and sex matched cohort for cohort_1
#> Starting matching
#> 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
#> ℹ summarising cohort cohort_1_sampled
#> ℹ summarising cohort cohort_1_matched
#> ✔ summariseCharacteristics finished!
#> • Getting age density
#> ℹ The following estimates will be computed:
#> • age: density
#> → Start summary of data, at 2025-04-03 13:06:12.823006
#> ✔ Summary finished, at 2025-04-03 13:06:13.210709
#> • 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)
#> • Sampling cohorts
#> Returning entry cohort as the size of the cohorts to be sampled is equal or
#> smaller than `n`.
#> • Generating an age and sex matched cohort for cohort_2
#> Starting matching
#> 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
#> ℹ summarising cohort cohort_2_sampled
#> ℹ summarising cohort cohort_2_matched
#> ✔ summariseCharacteristics finished!
#> • Getting age density
#> ℹ The following estimates will be computed:
#> • age: density
#> → Start summary of data, at 2025-04-03 13:07:04.375822
#> ✔ Summary finished, at 2025-04-03 13:07:04.754184
#> • 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())
#> ℹ Creating shiny from provided data

CDMConnector::cdmDisconnect(cdm = cdm)
# }