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(),
  expectations = NULL
)

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.
expectations: Data frame or tibble with cohort expectations. It must contain the following columns: cohort_name, estimate, value, and source.

Value

A shiny app

Examples

# \donttest{
library(PhenotypeR)
library(dplyr)

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!
#> → Skipping cohort sampling as all cohorts have less than 20000 individuals.
#> → 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 2025-08-20 13:18:50.904332
#> ✔ Summary finished, at 2025-08-20 13:18:51.030443
#> → Creating matching cohorts
#> → Sampling cohort `tmp_033_sampled`
#> 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 `tmp_033_sampled`
#> 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 2025-08-20 13:19:15.40505
#> ✔ Summary finished, at 2025-08-20 13:19:15.743682
#> → 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
#> `cohortSample` and `matchedSample` 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
#> `populationDateStart`, `populationDateEnd`, and `populationSample` casted to
#> character.
#> `populationDateStart` and `populationDateEnd` eliminated from settings as all
#> elements are NA.
#> 
expectations <- tibble("cohort_name" = rep(c("cohort_1", "cohort_2"),3),
                       "value" = c(rep(c("Mean age"),2),
                                   rep("Male percentage",2),
                                   rep("Survival probability after 5y",2)),
                       "estimate" = c("32", "54", "25%", "74%", "95%", "21%"),
                       "source" = rep(c("AlbertAI"),6))

shinyDiagnostics(result, tempdir(), expectations = expectations)
#> ℹ Creating shiny from provided data
#> Warning: codelistDiagnostics not present in the summarised result. Removing tab from the
#> shiny app.
#> Warning: No survival analysis present in cohortDiagnostics. Removing tab from the shiny
#> app.
#> Warning: '/tmp/RtmpgDV0Ug/PhenotypeRShiny/data/raw/expectations' already exists
#> ℹ Shiny app created in /tmp/RtmpgDV0Ug/PhenotypeRShiny

CDMConnector::cdmDisconnect(cdm = cdm)
# }