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Create a static shiny app

In this vignette we show the basis to create shiny apps. To do so we will use the mock results provided by the package:

library(OmopViewer)
omopViewerResults
#> # A tibble: 39,264 × 13
#>    result_id cdm_name            group_name group_level strata_name strata_level
#>        <int> <chr>               <chr>      <chr>       <chr>       <chr>       
#>  1         1 synthea-covid19-20… overall    overall     overall     overall     
#>  2         1 synthea-covid19-20… overall    overall     overall     overall     
#>  3         1 synthea-covid19-20… overall    overall     overall     overall     
#>  4         1 synthea-covid19-20… overall    overall     overall     overall     
#>  5         1 synthea-covid19-20… overall    overall     overall     overall     
#>  6         1 synthea-covid19-20… overall    overall     overall     overall     
#>  7         1 synthea-covid19-20… overall    overall     overall     overall     
#>  8         1 synthea-covid19-20… overall    overall     overall     overall     
#>  9         1 synthea-covid19-20… overall    overall     overall     overall     
#> 10         1 synthea-covid19-20… overall    overall     overall     overall     
#> # ℹ 39,254 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> #   estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> #   additional_name <chr>, additional_level <chr>
summary(omopViewerResults)
#> A summarised_result object with 39264 rows, 95 different result_id, 1 different
#> cdm names, and 44 settings.
#> CDM names: synthea-covid19-200k.
#> Settings: result_type, package_name, package_version, group, strata,
#> additional, min_cell_count, analysis, analysis_censor_cohort_name,
#> analysis_complete_database_intervals, analysis_full_contribution,
#> analysis_outcome_washout, analysis_repeated_events, analysis_type, censor_date,
#> cohort_definition_id, cohort_table_name, denominator_age_group, …, type, and
#> unknown_indication_table.

Let’s use a subset of the default result data set:

result <- omopViewerResults |>
  omopgenerics::filterSettings(
    result_type %in% c("summarise_omop_snapshot", "summarise_characteristics", "incidence")
  )

Using the default parameters you only have to provide a directory and a object.

exportStaticApp(result = result, directory = tempdir())
#> ℹ Processing data
#> ✔ Data processed: 3 panels idenfied: `summarise_omop_snapshot`,
#>   `summarise_characteristics`, and `incidence`.
#> ℹ Creating shiny from provided data
#> `analysis`, `analysis_full_contribution`, `analysis_type`, `censor_date`,
#> `cohort_definition_id`, `cohort_table_name`, `gap_era`, `incident`,
#> `index_date`, `indication_cohort_name`, `interval`, `mutually_exclusive`,
#> `overlap_by`, `prior_drug_observation`, `prior_use_washout`,
#> `restrict_incident`, `restrict_to_first_discontinuation`,
#> `restrict_to_first_entry`, …, `type`, and `unknown_indication_table` eliminated
#> from settings as all elements are NA.
#> ✔ Shiny created in:
#>   /var/folders/pl/k11lm9710hlgl02nvzx4z9wr0000gp/T//RtmpRMcqhi/shiny

The panels that will be created are defined by the panelDetails argument. By default, a tab is created by each result_type of the result object. The default tab getPanel("default") is used if no tab is defined for that result_type in omopViewerPanels. Let’s see the default panelDetails:

panelDetails <- panelDetailsFromResult(result)
panelDetails
#> $summarise_omop_snapshot
#> Snapshot (OmopViewer panel)
#> • icon: clipboard-list
#> • data: result_type: <summarise_omop_snapshot>
#> • filters: 1 filters + 1 automatic filters
#> • content: Tidy (DT); Table Snapshot (gt)
#> 
#> $summarise_characteristics
#> Cohort Characteristics (OmopViewer panel)
#> • icon: users-gear
#> • data: result_type: <summarise_characteristics>
#> • filters: 1 filters + 4 automatic filters
#> • content: Tidy (DT); Table Characteristics (gt); Plot Characteristics (plot)
#> 
#> $incidence
#> Incidence (OmopViewer panel)
#> • icon: chart-line
#> • data: result_type: <incidence>
#> • filters: 1 filters + 6 automatic filters
#> • content: Tidy (DT); Table Incidence (gt); Plot Incidence (plot)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.