## ----include = FALSE----------------------------------------------------------
NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")

knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = NOT_CRAN
)

## ----include = FALSE----------------------------------------------------------
# knitr::opts_chunk$set(
#   collapse = TRUE,
#   comment = "#>", message = FALSE, warning = FALSE,
#   fig.width = 7
# )
# 
# library(CDMConnector)
# if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir())
# if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER"))
# if (!eunomiaIsAvailable()) downloadEunomiaData(datasetName = "synpuf-1k")

## ----message=FALSE, warning=FALSE---------------------------------------------
# library(CDMConnector)
# library(CohortConstructor)
# library(CodelistGenerator)
# library(PatientProfiles)
# library(IncidencePrevalence)
# library(PhenotypeR)
# 
# 
# con <- DBI::dbConnect(duckdb::duckdb(),
#                       CDMConnector::eunomiaDir("synpuf-1k", "5.3"))
# cdm <- CDMConnector::cdmFromCon(con = con,
#                                 cdmName = "Eunomia Synpuf",
#                                 cdmSchema   = "main",
#                                 writeSchema = "main",
#                                 achillesSchema = "main")
# 
# cdm$injuries <- conceptCohort(cdm = cdm,
#   conceptSet = list(
#     "ankle_sprain" = 81151
#   ),
#   name = "injuries")

## -----------------------------------------------------------------------------
# pop_diag <- populationDiagnostics(cdm$injuries)

## -----------------------------------------------------------------------------
# tableIncidence(pop_diag,
#                groupColumn = c("cdm_name", "outcome_cohort_name"),
#                hide = "denominator_cohort_name",
#                settingsColumn = c("denominator_age_group",
#                          "denominator_sex",
#                          "denominator_days_prior_observation",
#                          "outcome_cohort_name"))

## -----------------------------------------------------------------------------
# results <- pop_diag |>
#   omopgenerics::filterSettings(result_type == "incidence") |>
#   visOmopResults::filterAdditional(analysis_interval == "years")
# plotIncidence(results,
#               colour = "denominator_age_group",
#               facet = c("denominator_sex", "denominator_days_prior_observation"))

## -----------------------------------------------------------------------------
# tablePrevalence(pop_diag,
#                groupColumn = c("cdm_name", "outcome_cohort_name"),
#                hide = "denominator_cohort_name",
#                settingsColumn = c("denominator_age_group",
#                          "denominator_sex",
#                          "denominator_days_prior_observation",
#                          "outcome_cohort_name"))

## -----------------------------------------------------------------------------
# results <- pop_diag |>
#   omopgenerics::filterSettings(result_type == "prevalence") |>
#   visOmopResults::filterAdditional(analysis_interval == "years")
# plotPrevalence(results,
#                colour = "denominator_age_group",
#                facet = c("denominator_sex", "denominator_days_prior_observation"))