test_that("test plot", { skip_on_cran() person <- dplyr::tibble( person_id = c(1, 2, 3), gender_concept_id = c(8507, 8532, 8532), year_of_birth = c(1985, 2000, 1962), month_of_birth = c(10, 5, 9), day_of_birth = c(30, 10, 24), race_concept_id = 0, ethnicity_concept_id = 0 ) dus_cohort <- dplyr::tibble( cohort_definition_id = c(1, 1, 1, 2), subject_id = c(1, 1, 2, 3), cohort_start_date = as.Date(c( "1990-04-19", "1991-04-19", "2010-11-14", "2000-05-25" )), cohort_end_date = as.Date(c( "1990-04-19", "1991-04-19", "2010-11-14", "2000-05-25" )) ) comorbidities <- dplyr::tibble( cohort_definition_id = c(1, 2, 2, 1), subject_id = c(1, 1, 3, 3), cohort_start_date = as.Date(c( "1990-01-01", "1990-06-01", "2000-01-01", "2000-06-01" )), cohort_end_date = as.Date(c( "1990-01-01", "1990-06-01", "2000-01-01", "2000-06-01" )) ) medication <- dplyr::tibble( cohort_definition_id = c(1, 1, 2, 1), subject_id = c(1, 1, 2, 3), cohort_start_date = as.Date(c( "1990-02-01", "1990-08-01", "2009-01-01", "1995-06-01" )), cohort_end_date = as.Date(c( "1990-02-01", "1990-08-01", "2009-01-01", "1995-06-01" )) ) observation_period <- dplyr::tibble( observation_period_id = c(1, 2, 3), person_id = c(1, 2, 3), observation_period_start_date = as.Date(c( "1985-01-01", "1989-04-29", "1974-12-03" )), observation_period_end_date = as.Date(c( "2011-03-04", "2022-03-14", "2023-07-10" )), period_type_concept_id = 0 ) cdm <- mockCohortCharacteristics( connectionDetails, dus_cohort = dus_cohort, person = person, comorbidities = comorbidities, medication = medication, observation_period = observation_period, cohort1 = emptyCohort, cohort2 = emptyCohort ) cdm$dus_cohort <- omopgenerics::newCohortTable( table = cdm$dus_cohort, cohortSetRef = dplyr::tibble( cohort_definition_id = c(1, 2), cohort_name = c("exposed", "unexposed") ) ) cdm$comorbidities <- omopgenerics::newCohortTable( table = cdm$comorbidities, cohortSetRef = dplyr::tibble( cohort_definition_id = c(1, 2), cohort_name = c("covid", "headache") ) ) cdm$medication <- omopgenerics::newCohortTable( table = cdm$medication, cohortSetRef = dplyr::tibble( cohort_definition_id = c(1, 2, 3), cohort_name = c("acetaminophen", "ibuprophen", "naloxone") ), cohortAttritionRef = NULL ) test_data <- summariseCharacteristics( cdm$dus_cohort, cohortIntersectFlag = list( "Medications" = list( targetCohortTable = "medication", window = c(-365, 0) ), "Comorbidities" = list( targetCohortTable = "comorbidities", window = c(-Inf, 0) ) ) ) # barplot plot <- plotCharacteristics( data = test_data |> dplyr::filter( variable_name == "Medications", estimate_type == "percentage" ), x = "variable_name", plotStyle = "barplot", facet = c("group_level"), colour = c("variable_name", "variable_level") ) expect_true(ggplot2::is.ggplot(plot)) # boxplot plot2 <- plotCharacteristics( data = test_data |> dplyr::filter(variable_name == "Age"), x = "variable_name", plotStyle = "boxplot", facet = "variable_name", colour = c("group_level") ) expect_true(ggplot2::is.ggplot(plot2)) expect_no_error(plotCharacteristics( data = test_data |> dplyr::filter(variable_name == "Age"), x = "variable_name", plotStyle = "boxplot" )) expect_no_error(plotCharacteristics( data = test_data |> dplyr::filter(variable_name == "Age"), x = "variable_name", plotStyle = "barplot" )) expect_no_error(plotCharacteristics( data = test_data |> dplyr::filter(variable_name == "Age"), x = "estimate_value", plotStyle = "barplot" )) })