fixes tests; adds tests to cover all of plotting, scenario_inputs and…

… data_loading

NAMESPACE

@@ -112,5 +112,4 @@ importFrom(utils,download.file)
 importFrom(utils,head)
 importFrom(utils,read.csv)
 importFrom(utils,stack)
-importFrom(utils,tail)
 importFrom(utils,write.csv)

R/fct_plotting.R

@@ -165,7 +165,8 @@ plot_trust_icb_proportions <- function(ics_code) {
       nudge_x = 1,
       color = "black",
       show.legend = FALSE,
-      size = 4
+      size = 4,
+      seed = 123
     ) +
     scale_fill_brewer(palette = "Set1") +
     labs(
@@ -178,6 +179,7 @@ plot_trust_icb_proportions <- function(ics_code) {
 }
 
 #' @importFrom rlang sym
+#' @import ggplot2
 plot_hold_message <- function() {
   p <- tibble::tibble(
     x = 1,
@@ -209,7 +211,7 @@ plot_hold_message <- function() {
       panel.border = element_rect(
         colour = "black",
         fill = NA,
-        size = 1
+        linewidth = 1
       )
     )
 

R/fct_scenario_inputs.R

@@ -378,8 +378,13 @@ update_predictions_and_plot_r <- function(prediction_custom_scenario, model_outp
       r$performance_plot <- plot_hold_message()
     }
   }
+
+  invisible(r)
 }
 
+#' @importFrom tidyr pivot_wider
+#' @importFrom purrr lmap
+#' @importFrom dplyr mutate select filter bind_rows
 #' @importFrom stats setNames
 update_observed_time_period_predictions <- function(model_outputs, r) {
   # all available data in long format
@@ -512,7 +517,8 @@ important_variables <- function(model_permutation_importance,
   return(important_metrics)
 }
 
-#' @importFrom utils head tail
+#' @importFrom dplyr filter mutate arrange
+#' @importFrom rlang sym
 update_custom_tables <- function(input_table, model_permutation_importance, performance_metrics, r) {
 
   # character vector of the most important variables based on the selected
@@ -531,6 +537,8 @@ update_custom_tables <- function(input_table, model_permutation_importance, perf
       )
     ) |>
     arrange(!!sym("metric"))
+
+  invisible(r)
 }
 
 

tests/testthat/_snaps/fct_scenario_inputs.md

@@ -0,0 +1,23 @@
+# update_prediction_and_plot_r
+
+    Code
+      update_predictions_and_plot_r(prediction_custom_scenario = "testing",
+        model_outputs = load_model_object("wf"), scenario_name = "incorrect_name",
+        performance_metrics = "Proportion of attended GP appointments (over 4 weeks wait time)",
+        r = list(ics_cd = ics_code, ics_data = all_ics_data, scenario_data = scenario_data))
+    Condition
+      Error in `match.arg()`:
+      ! 'arg' should be one of "last_known", "percent", "linear", "custom"
+
+---
+
+    Code
+      custom_test <- update_predictions_and_plot_r(prediction_custom_scenario = "testing",
+        model_outputs = load_model_object("wf"), scenario_name = scenario,
+        performance_metrics = "Proportion of attended GP appointments (over 4 weeks wait time)",
+        r = list(ics_cd = ics_code, ics_data = all_ics_data, scenario_data = scenario_data,
+          predictions = predictions))
+    Condition
+      Error in `session$sendModal()`:
+      ! attempt to apply non-function
+

tests/testthat/test-fct_data_loading.R

@@ -15,3 +15,62 @@ test_that("dimensions of ics_data function", {
     label = "more than one row of data for BNSSG"
   )
 })
+
+test_that("narrow age bands are replaced by broad age bands using replace_narrow_age_bands", {
+
+  narrow_age_bands <- c("0-9",
+                        "10-19",
+                        "20-29",
+                        "30-39",
+                        "40-49",
+                        "50-59",
+                        "60-69",
+                        "70-79",
+                        "80-89",
+                        "90+")
+
+  broad_age_bands <- c("0-29", "30-59", "60+")
+
+  demand_timeseries <- tibble(
+    metric = paste(
+      "Proportion of population in age band ",
+      narrow_age_bands
+    ),
+    numerator = sample(1:10, 10, replace = TRUE),
+    denominator = sample(50:70, 10, replace = TRUE)
+  )
+  narrow_demand_timeseries <- demand_timeseries |>
+    replace_narrow_age_bands()
+
+  expect_true(
+    all(
+      purrr::map_lgl(
+        broad_age_bands,
+        ~ any(grepl(.x, unique(narrow_demand_timeseries$metric)))
+      )
+    ),
+    info = "each of the age bands are in the broad age band version of population data"
+  )
+
+  expect_true(
+    !all(
+      purrr::map_lgl(
+        narrow_age_bands,
+        ~ any(grepl(.x, unique(narrow_demand_timeseries$metric)))
+      )
+    ),
+    info = "none of the age bands are in the narrow age band version of population data"
+  )
+
+
+})
+
+
+
+test_that("testing errors", {
+  expect_error(
+    ics_data("QUY", domain_type = "performance"),
+    "domain_type needs to be one of 'Performance', 'Demand' or 'Capacity'"
+  )
+})
+

tests/testthat/test-fct_plotting.R

@@ -16,3 +16,18 @@ test_that("ggplot images are consistent", {
     p
   )
 })
+
+test_that("pie chart works", {
+
+  vdiffr::expect_doppelganger(
+    "Acute to trust proportions pie chart is consistent",
+    plot_trust_icb_proportions("QR1")
+  )
+})
+
+test_that("hold message image works", {
+  vdiffr::expect_doppelganger(
+    "ggplot hold message",
+    plot_hold_message()
+  )
+})