Keep zero estimates in impact plots

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: vimcheck
 Title: Diagnostics for Vaccine Impact Modelling Consortium Burden and
     Impact Estimates
-Version: 0.0.6
+Version: 0.0.7
 Authors@R: c(
     person("Pratik", "Gupte", , "p.gupte24@imperial.ac.uk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-5294-7819")),

NEWS.md

@@ -1,3 +1,9 @@
+# vimcheck 0.0.7
+
+- Fixed `plot_impact()` to retain zero estimates in year and birth-cohort
+  trajectories instead of drawing a line across them. Inputs with no nonzero
+  estimates still raise an error.
+
 # vimcheck 0.0.6
 
 - Fixed `basic_burden_sanity()` to return failed-check messages instead of

R/fn_plotting_impact.R

@@ -67,10 +67,10 @@
     data,
     .data$country %in% country,
     .data$burden_outcome == burden_type,
-    .data$impact != 0 # can this be safely written as impact > 0?
+    !is.na(.data$impact)
   )
 
-  if (nrow(impact) > 0) {
+  if (any(impact$impact != 0)) {
     if (view == "cohort") {
       checkmate::assert_names(names(data), must.include = "birth_cohort")
       x_var <- "birth_cohort"
@@ -93,9 +93,9 @@
     ) +
       ggplot2::geom_ribbon(alpha = 0.3) +
       ggplot2::geom_line(aes(colour = .data$short_name), linewidth = 0.5) +
       ggplot2::geom_point(aes(colour = .data$short_name), size = 0.5) +
       theme_vimc() +
       facet_wrap(ggplot2::vars(.data$country), scales = "free_y") +
       labs(
         x = x_lab,
         y = glue::glue("{burden_type} averted"),
@@ -252,7 +252,7 @@
     geom_point(aes(colour = .data$vaccine_delivery), size = 0.5) +
     scale_x_continuous(labels = scales::number_format(1, big.mark = "")) +
     facet_wrap(
       ggplot2::vars(.data$country),
       scales = "free_y"
     ) +
     labs(

tests/testthat/test-plot_impact.R

@@ -77,6 +77,57 @@ test_that("Plotting impact works", {
   )
 })
 
+test_that("Impact trajectories retain zero estimates in both views", {
+  impact_data <- data.frame(
+    country = "A",
+    year = 2020:2023,
+    birth_cohort = 2000:2003,
+    burden_outcome = "cases",
+    impact = c(10, 0, 10, NA),
+    short_name = "routine"
+  )
+
+  for (values in list(c(10, 0, 10, NA), c(-10, 0, 10, NA))) {
+    impact_data$impact <- values
+
+    for (view in c("year", "cohort")) {
+      p <- plot_impact(impact_data, "A", "cases", view)
+      expected_x <- if (view == "year") 2020:2022 else 2000:2002
+      expected_y <- values[1:3]
+
+      expect_equal(p$data$impact, expected_y)
+
+      layers <- ggplot2::ggplot_build(p)$data
+      for (layer in layers[2:3]) {
+        expect_equal(layer$x, expected_x)
+        expect_equal(layer$y, expected_y)
+      }
+    }
+  }
+})
+
+test_that("Impact plots still reject outcomes without nonzero estimates", {
+  impact_data <- data.frame(
+    country = "A",
+    year = 2020:2022,
+    birth_cohort = 2000:2002,
+    burden_outcome = c("cases", "cases", "deaths"),
+    impact = c(0, NA, 10),
+    short_name = "routine"
+  )
+
+  for (view in c("year", "cohort")) {
+    expect_error(
+      plot_impact(impact_data, "A", "cases", view),
+      "No estimates remaining in the data"
+    )
+    expect_error(
+      plot_impact(impact_data, "A", "dalys", view),
+      "No estimates remaining in the data"
+    )
+  }
+})
+
 test_that("Plotting coverage and FVPs works", {
   p <- plot_coverage_fvps(eg_fvps_2, "AGO")
   expect_list(p, "ggplot")