Create a shiny app summarising your phenotyping results
shinyDiagnostics.RdA 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()
)Examples
# \donttest{
library(PhenotypeR)
cdm <- mockPhenotypeR()
result <- phenotypeDiagnostics(cdm$my_cohort)
#>
#> Warning: Vocabulary version in cdm_source (NA) doesn't match the one in the vocabulary
#> table (mock)
#>
#> 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()`.
#>
#> • Starting Cohort Diagnostics
#> → 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 2025-06-17 18:42:11.746656
#> ✔ Summary finished, at 2025-06-17 18:42:11.870359
#> → Creating matching cohorts
#> → Sampling cohort `my_cohort`
#> 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
#> → Sampling cohort `my_cohort`
#> 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
#> → Getting cohorts and indexes
#> → Summarising cohort characteristics
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> window names casted to snake_case:
#> • `-365 to -1` -> `365_to_1`
#> ℹ summarising data
#> ℹ summarising cohort cohort_1
#> ℹ summarising cohort cohort_2
#> ℹ summarising cohort cohort_1_sampled
#> ℹ summarising cohort cohort_1_matched
#> ℹ summarising cohort cohort_2_sampled
#> ℹ summarising cohort cohort_2_matched
#> ✔ summariseCharacteristics finished!
#> → Calculating age density
#> ℹ The following estimates will be computed:
#> • age: density
#> → Start summary of data, at 2025-06-17 18:42:36.776259
#> ✔ Summary finished, at 2025-06-17 18:42:37.115428
#> → Run large scale characteristics (including source and standard codes)
#> 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)
#> Formatting result
#> ✔ Summarising large scale characteristics
#> → Run large scale characteristics (including only standard codes)
#> 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)
#> Formatting result
#> ✔ Summarising large scale characteristics
#> → Creating death cohort
#> ✔ Cohort tmp_060_death_cohort created.
#> → Estimating single survival event
#> - Getting survival for target cohort 'cohort_1' and outcome cohort
#> 'death_cohort'
#> Getting overall estimates
#> - Getting survival for target cohort 'cohort_2' and outcome cohort
#> 'death_cohort'
#> Getting overall estimates
#> - Getting survival for target cohort 'cohort_1_sampled' and outcome cohort
#> 'death_cohort'
#> Getting overall estimates
#> - Getting survival for target cohort 'cohort_1_matched' and outcome cohort
#> 'death_cohort'
#> Getting overall estimates
#> - Getting survival for target cohort 'cohort_2_sampled' and outcome cohort
#> 'death_cohort'
#> Getting overall estimates
#> - Getting survival for target cohort 'cohort_2_matched' and outcome cohort
#> 'death_cohort'
#> Getting overall estimates
#> `eventgap`, `outcome_washout`, `censor_on_cohort_exit`, `follow_up_days`, and
#> `minimum_survival_days` casted to character.
#>
#> • 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 14
#> ℹ Getting incidence for analysis 2 of 14
#> ℹ Getting incidence for analysis 3 of 14
#> ℹ Getting incidence for analysis 4 of 14
#> ℹ Getting incidence for analysis 5 of 14
#> ℹ Getting incidence for analysis 6 of 14
#> ℹ Getting incidence for analysis 7 of 14
#> ℹ Getting incidence for analysis 8 of 14
#> ℹ Getting incidence for analysis 9 of 14
#> ℹ Getting incidence for analysis 10 of 14
#> ℹ Getting incidence for analysis 11 of 14
#> ℹ Getting incidence for analysis 12 of 14
#> ℹ Getting incidence for analysis 13 of 14
#> ℹ Getting incidence for analysis 14 of 14
#> ✔ Overall time taken: 0 mins and 13 secs
#> • Estimating prevalence
#> ℹ Getting prevalence for analysis 1 of 14
#> ℹ Getting prevalence for analysis 2 of 14
#> ℹ Getting prevalence for analysis 3 of 14
#> ℹ Getting prevalence for analysis 4 of 14
#> ℹ Getting prevalence for analysis 5 of 14
#> ℹ Getting prevalence for analysis 6 of 14
#> ℹ Getting prevalence for analysis 7 of 14
#> ℹ Getting prevalence for analysis 8 of 14
#> ℹ Getting prevalence for analysis 9 of 14
#> ℹ Getting prevalence for analysis 10 of 14
#> ℹ Getting prevalence for analysis 11 of 14
#> ℹ Getting prevalence for analysis 12 of 14
#> ℹ Getting prevalence for analysis 13 of 14
#> ℹ Getting prevalence for analysis 14 of 14
#> ✔ Time taken: 0 mins and 7 secs
#>
shinyDiagnostics(result, tempdir())
#> ℹ Creating shiny from provided data
CDMConnector::cdmDisconnect(cdm = cdm)
# }