From 14f18298ca80d15f620a1dd375960d61889681dc Mon Sep 17 00:00:00 2001
From: Zoe Gibney <z.gibney@imperial.ac.uk>
Date: Wed, 29 Apr 2026 11:45:18 +0100
Subject: [PATCH 1/6] Adding pine countries to constants for impact plotting
 key countries

---
 R/constants.R    | 8 ++++++++
 man/constants.Rd | 7 +++++++
 2 files changed, 15 insertions(+)

diff --git a/R/constants.R b/R/constants.R
index e4c3532..90b4858 100644
--- a/R/constants.R
+++ b/R/constants.R
@@ -215,3 +215,11 @@ DEF_TOUCHSTONE_OLD_OLD <- "202110"
 #'
 #' @export
 COLOUR_VIMC <- "#008080"
+
+#' @name constants
+#' 
+#' @examples
+#' pine
+#' 
+#' @export
+pine <- c("PAK", "IND", "NGA", "ETH")
\ No newline at end of file
diff --git a/man/constants.Rd b/man/constants.Rd
index 1d93a1f..9e08393 100644
--- a/man/constants.Rd
+++ b/man/constants.Rd
@@ -22,6 +22,7 @@
 \alias{DEF_TOUCHSTONE_NEW}
 \alias{DEF_TOUCHSTONE_OLD_OLD}
 \alias{COLOUR_VIMC}
+\alias{pine}
 \title{Package constants}
 \format{
 An object of class \code{character} of length 5.
@@ -61,6 +62,8 @@ An object of class \code{character} of length 1.
 An object of class \code{character} of length 1.
 
 An object of class \code{character} of length 1.
+
+An object of class \code{character} of length 4.
 }
 \usage{
 file_dict_colnames
@@ -100,6 +103,8 @@ DEF_TOUCHSTONE_NEW
 DEF_TOUCHSTONE_OLD_OLD
 
 COLOUR_VIMC
+
+pine
 }
 \description{
 Constant values used in \emph{vimcheck}. See the \strong{Examples} section for the
@@ -144,6 +149,8 @@ DEF_TOUCHSTONE_OLD_OLD
 
 COLOUR_VIMC
 
+pine
+
 }
 \keyword{constants}
 \keyword{datasets}

From 0d446cd7b230138409f611b2017f5ad82150b51a Mon Sep 17 00:00:00 2001
From: Zoe Gibney <z.gibney@imperial.ac.uk>
Date: Wed, 29 Apr 2026 11:45:40 +0100
Subject: [PATCH 2/6] Adding plotting functions from rapid-model-run-impact
 report which plots central impact estimates, coverage and fvps. Associated
 documentation also added from attempts at roxygen!

---
 R/fn_plotting_impact.R    | 240 ++++++++++++++++++++++++++++++++++++++
 man/plot_coverage_fvps.Rd |  29 +++++
 man/plot_impact.Rd        |  35 ++++++
 3 files changed, 304 insertions(+)
 create mode 100644 R/fn_plotting_impact.R
 create mode 100644 man/plot_coverage_fvps.Rd
 create mode 100644 man/plot_impact.Rd

diff --git a/R/fn_plotting_impact.R b/R/fn_plotting_impact.R
new file mode 100644
index 0000000..c66027a
--- /dev/null
+++ b/R/fn_plotting_impact.R
@@ -0,0 +1,240 @@
+#' Plot central impact estimates by cohort and year. 
+#' TODO: need to add importFrom ... to avoid package issues with testing? 
+#' 
+#' Produces faceted plots of central impact estimates for priority countries,
+#' stratified either by birth cohort or by year of vaccination.
+#' Impact metrics include cases, deaths, DALYs, and YLLs.
+#' 
+#' @param data A tibble containing impact estimates.
+#' @param burden_type Burden metric used to evaluate impact. burden_type can be: cases, deaths, dalys, yll.
+#' @param title Title of the plot to be rendered
+#' @param view Charactar scalar. The way impact is assigned, either by birth cohort ("cohort")  or by year of vaccination ("year").
+#'  
+#' @return ggplot object showing central impact estimates 
+#'  
+#' @examples
+#' plot_impact(
+#'   data = impact_data,
+#'   burden_type = "cases",
+#'   title = "Cases averted",
+#'   view = "year"
+#' )
+#'  
+#' @export
+plot_impact <- function(
+    data, 
+    burden_type, 
+    title, 
+    view
+){
+  checkmate::assert_tibble(data, min.rows = 1L, min.cols = 1L)
+  checkmate::assert_character(burden_type, len = 1)
+  checkmate::assert_character(title, len = 1)
+  
+  checkmate::assert_choice(
+    burden_type,
+    choices = c("cases", "deaths", "dalys", "yll")
+  )
+  
+  checkmate::assert_choice(
+    view,
+    choices = c("cohort", "year")
+  )
+  
+  Impact <- 
+    data %>% 
+    dplyr::filter(.data$country %in% pine) %>%
+    dplyr::filter(
+      .data$burden_outcome == burden_type & .data$impact != 0) #%>%
+  if(nrow(Impact) > 0){
+# ---- Cohort view ----
+    if(view == "cohort"){
+      Impact <- Impact %>% dplyr::rename(cohort = .data$birth_cohort) %>%
+        dplyr::select(
+          .data$country, 
+          .data$cohort, 
+          .data$impact, 
+          .data$short_name
+          )
+      p <- ggplot(
+        Impact,
+        aes(
+          x = .data$cohort, 
+          y = .data$impact, 
+          ymin = .data$impact, 
+          ymax = .data$impact, 
+          fill = as.character(.data$short_name)
+          ) 
+        ) +
+        ggplot::geom_ribbon(alpha = 0.3) +
+        ggplot::geom_line(aes(colour = .data$short_name), size = 0.5)+
+        ggplot::geom_point(aes(colour = .data$short_name), size = 0.5)+
+        theme_vimc() + #TODO: to check where the theme definition is saved as may not be right for this plot
+        facet_wrap(country~., scales = "free_y") +
+        labs(
+          x = "Birth cohort",
+          y = paste(burden_type, "averted"),
+          title = title
+          ) +
+        theme(
+          legend.position="bottom", 
+          legend.key.size= unit(0.5, 'cm'),
+          legend.key.width = unit(0.3, 'cm')
+        )
+ 
+    } else { # ---- Year (non-cohort) view ----
+      Impact <- Impact %>%
+        dplyr::select(
+          .data$country,
+          .data$year, 
+          .data$impact, 
+          .data$short_name
+          )
+      
+      p <- ggplot (
+        Impact,
+        aes(
+          x = .data$year, 
+          y = .data$impact, 
+          ymin = .data$impact, 
+          ymax = .data$impact, 
+          fill = .data$short_name
+          )
+        ) +
+        ggplot::geom_ribbon(alpha = 0.3)+
+        ggplot::geom_line(aes(colour = .data$short_name), size = 0.5)+
+        ggplot::geom_point(aes(colour = .data$short_name), size = 0.5)+
+        theme_vimc() + #TODO: same note as above re theme definition
+        facet_wrap(country~., scales = "free_y")+
+        labs(
+          x = "Year", 
+          y = paste(burden_type, "averted"),
+          title = title
+          ) +
+        theme(
+          legend.position="bottom", 
+          legend.key.size= unit(0.5, 'cm'),
+          legend.key.width = unit(0.3, 'cm')
+        )
+    }
+  } else {
+    p <- "No estimates in the data." #TODO: both here and in the below plot returning p may be an issue? Can you think of a better way?
+  }
+  return(p)
+  
+}
+
+#' Plot coverage and fully vaccinated persons (FVPs)
+#' 
+#' Generates plots of routine vaccine coverage and fully vaccinated
+#' persons (FVPs) over time for selected countries.
+#' 
+#' @param fvps A tibble showing the number of fvps (fully vaccinated persons) 
+#'  by country, year and scenario/activity type.
+#'  
+#' @return A named list with two ggplot objects:
+#'   \describe{
+#'     \item{coverage}{A plot of routine vaccine coverage over time.}
+#'     \item{fvps}{A plot of fully vaccinated persons over time.}
+#'   }
+#' @examples
+#' plots <- plot_coverage_fvps(fvps)
+#' plots$coverage
+#' plots$fvps
+#' 
+#' @export 
+plot_coverage_fvps <- function(fvps){
+  checkmate::assert_tibble(fvps, min.rows = 1L, min.cols = 1L)
+  
+  fvps <- fvps %>%
+    dplyr::filter(.data$country %in% pine)
+  
+  cov <- fvps %>% 
+    dplyr::filter(.data$activity_type == "routine") %>%
+    dplyr::mutate(
+      vaccine_delivery = paste(.data$scenario_type, .data$vaccine, sep = "_"),
+      coverage_adjusted = round(.data$coverage_adjusted*100, 2)
+      ) %>%
+    dplyr::select(
+      .data$country, 
+      .data$vaccine_delivery, 
+      .data$year, 
+      .data$coverage_adjusted) %>%
+    dplyr::rename(coverage = .data$coverage_adjusted) 
+  
+  fvp <- fvps %>% 
+    dplyr::mutate(
+      vaccine_delivery = paste(.data$scenario_type, .data$activity_type, sep = "_")
+      ) %>%
+    dplyr::select(
+      .data$country, 
+      .data$vaccine_delivery, 
+      .data$year, 
+      .data$fvps
+      ) %>%
+    dplyr::group_by(
+      .data$country, 
+      .data$vaccine_delivery, 
+      .data$year) %>%
+    dplyr::summarise(
+      fvps = round(sum(.data$fvps)/1e6, 2),
+      .groups = "drop"
+      ) 
+  if(nrow(cov) > 0){
+    p <- ggplot(
+      cov, 
+      aes(
+        x = .data$year, 
+        y = .data$coverage, 
+        ymin = 0, 
+        ymax = 1, 
+        fill = .data$vaccine_delivery) 
+      ) +
+      ggplot::geom_line(aes(colour = .data$vaccine_delivery), size = 0.5) +
+      theme_vimc() + #TODO: same note as above
+      facet_wrap(country~., scales = "free_y")+
+      labs(
+        x = "Year", 
+        y = "Coverage (%)",
+        title = "Routine vaccine coverage" 
+        ) +
+      theme(
+        legend.position="bottom", 
+        legend.key.size= unit(0.5, 'cm'),
+        legend.key.width = unit(0.3, 'cm')
+) 
+    
+  } else {
+    p <- "There is no routine coverage in the database."
+  }
+  
+  
+  q <- ggplot(
+    fvp,
+    aes(
+      x = .data$year, 
+      y = .data$fvps, 
+      ymin = .data$fvps, 
+      ymax = .data$fvps, #TODO: min/max both here and above seem to be the same so may be irrelevant to define
+      fill = .data$vaccine_delivery
+    )
+      ) +
+    geom_point(aes(colour = .data$vaccine_delivery), size = 0.5) +
+    theme_vimc()+ #TODO: same note above on theme 
+    facet_wrap(country~., scales = "free_y") +
+    labs(
+      x = "Year", 
+      y = "FVPs (in millions)",
+      title = "FVPs"
+      ) +
+    theme(
+      legend.position="bottom", 
+      legend.key.size = unit(0.5, 'cm'),
+      legend.key.width = unit(0.3, 'cm')
+      )
+  
+  return(list(
+    coverage = p,
+    fvps = q
+    ))
+}
diff --git a/man/plot_coverage_fvps.Rd b/man/plot_coverage_fvps.Rd
new file mode 100644
index 0000000..4e389fc
--- /dev/null
+++ b/man/plot_coverage_fvps.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/fn_plotting_impact.R
+\name{plot_coverage_fvps}
+\alias{plot_coverage_fvps}
+\title{Plot coverage and fully vaccinated persons (FVPs)}
+\usage{
+plot_coverage_fvps(fvps)
+}
+\arguments{
+\item{fvps}{A tibble showing the number of fvps (fully vaccinated persons)
+by country, year and scenario/activity type.}
+}
+\value{
+A named list with two ggplot objects:
+\describe{
+\item{coverage}{A plot of routine vaccine coverage over time.}
+\item{fvps}{A plot of fully vaccinated persons over time.}
+}
+}
+\description{
+Generates plots of routine vaccine coverage and fully vaccinated
+persons (FVPs) over time for selected countries.
+}
+\examples{
+plots <- plot_coverage_fvps(fvps)
+plots$coverage
+plots$fvps
+
+}
diff --git a/man/plot_impact.Rd b/man/plot_impact.Rd
new file mode 100644
index 0000000..ef25df7
--- /dev/null
+++ b/man/plot_impact.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/fn_plotting_impact.R
+\name{plot_impact}
+\alias{plot_impact}
+\title{Plot central impact estimates by cohort and year.
+TODO: need to add importFrom ... to avoid package issues with testing?}
+\usage{
+plot_impact(data, burden_type, title, view)
+}
+\arguments{
+\item{data}{A tibble containing impact estimates.}
+
+\item{burden_type}{Burden metric used to evaluate impact. burden_type can be: cases, deaths, dalys, yll.}
+
+\item{title}{Title of the plot to be rendered}
+
+\item{view}{Charactar scalar. The way impact is assigned, either by birth cohort ("cohort")  or by year of vaccination ("year").}
+}
+\value{
+ggplot object showing central impact estimates
+}
+\description{
+Produces faceted plots of central impact estimates for priority countries,
+stratified either by birth cohort or by year of vaccination.
+Impact metrics include cases, deaths, DALYs, and YLLs.
+}
+\examples{
+plot_impact(
+  data = impact_data,
+  burden_type = "cases",
+  title = "Cases averted",
+  view = "year"
+)
+ 
+}

From fce44b85afce75892832f9f69744d5b9d6483159 Mon Sep 17 00:00:00 2001
From: Zoe Gibney <z.gibney@imperial.ac.uk>
Date: Mon, 1 Jun 2026 10:39:42 +0100
Subject: [PATCH 3/6] Fix for enabling package checks

---
 .github/workflows/R-CMD-check.yaml        | 2 +-
 .github/workflows/lint-changed-files.yaml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index e1c58b9..2909c4a 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -4,7 +4,7 @@ on:
   push:
     branches: [main, master]
   pull_request:
-    branches: [main, master]
+    branches: [*]
 
 name: R-CMD-check
 
diff --git a/.github/workflows/lint-changed-files.yaml b/.github/workflows/lint-changed-files.yaml
index cf5c34b..96e2368 100644
--- a/.github/workflows/lint-changed-files.yaml
+++ b/.github/workflows/lint-changed-files.yaml
@@ -3,7 +3,7 @@
 on:
   workflow_dispatch:
   pull_request:
-    branches: [main, master]
+    branches: [*]
     paths:
       - '**.R'
       - '**.Rmd'

From 291e905472516223f9e13cce7db2ffa4b1943f0f Mon Sep 17 00:00:00 2001
From: Zoe Gibney <z.gibney@imperial.ac.uk>
Date: Mon, 1 Jun 2026 12:31:48 +0100
Subject: [PATCH 4/6] Adding more meaningful checks and assertions

---
 R/fn_plotting_impact.R | 138 ++++++++++++++++++++++-------------------
 1 file changed, 75 insertions(+), 63 deletions(-)

diff --git a/R/fn_plotting_impact.R b/R/fn_plotting_impact.R
index c66027a..8f23acd 100644
--- a/R/fn_plotting_impact.R
+++ b/R/fn_plotting_impact.R
@@ -1,5 +1,4 @@
 #' Plot central impact estimates by cohort and year. 
-#' TODO: need to add importFrom ... to avoid package issues with testing? 
 #' 
 #' Produces faceted plots of central impact estimates for priority countries,
 #' stratified either by birth cohort or by year of vaccination.
@@ -23,39 +22,41 @@
 #' @export
 plot_impact <- function(
     data, 
-    burden_type, 
+    burden_type = c("cases", "deaths", "dalys", "yll"),
     title, 
-    view
+    view = c("cohort", "year")
 ){
   checkmate::assert_tibble(data, min.rows = 1L, min.cols = 1L)
-  checkmate::assert_character(burden_type, len = 1)
-  checkmate::assert_character(title, len = 1)
   
-  checkmate::assert_choice(
-    burden_type,
-    choices = c("cases", "deaths", "dalys", "yll")
-  )
+  required_cols <- c("country", "burden_outcome", "impact", "short_name")
   
-  checkmate::assert_choice(
-    view,
-    choices = c("cohort", "year")
+  checkmate::assert_names(
+    names(data),
+    must.include = required_cols
   )
   
-  Impact <- 
-    data %>% 
-    dplyr::filter(.data$country %in% pine) %>%
-    dplyr::filter(
-      .data$burden_outcome == burden_type & .data$impact != 0) #%>%
+  checkmate::assert_character(title, len = 1)
+  
+  burden_type <- rlang::arg_match(burden_type)
+  view <- rlang::arg_match(view)
+  
+  Impact <- dplyr::filter(data, 
+                          .data$country %in% pine,
+                          .data$burden_outcome == burden_type,
+                          .data$impact != 0)
+  
   if(nrow(Impact) > 0){
 # ---- Cohort view ----
     if(view == "cohort"){
-      Impact <- Impact %>% dplyr::rename(cohort = .data$birth_cohort) %>%
-        dplyr::select(
-          .data$country, 
-          .data$cohort, 
-          .data$impact, 
-          .data$short_name
-          )
+      
+      checkmate::assert_names(names(data), must.include = "birth_cohort")
+
+      Impact <- Impact %>% dplyr::rename(Impact, cohort = .data$birth_cohort)
+      
+      cols_to_select <- c("country", "cohort", "impact", "short_name")
+        
+      Impact <- dplyr::select(Impact, all_of(cols_to_select))
+       
       p <- ggplot(
         Impact,
         aes(
@@ -83,13 +84,11 @@ plot_impact <- function(
         )
  
     } else { # ---- Year (non-cohort) view ----
-      Impact <- Impact %>%
-        dplyr::select(
-          .data$country,
-          .data$year, 
-          .data$impact, 
-          .data$short_name
-          )
+      cols_to_select <- c("country", "year", "impact", "short_name")
+      
+      checkmate::assert_names(names(data), must.include = "year")
+      
+      Impact <- dplyr::select(Impact, all_of(cols_to_select))
       
       p <- ggplot (
         Impact,
@@ -146,40 +145,53 @@ plot_impact <- function(
 plot_coverage_fvps <- function(fvps){
   checkmate::assert_tibble(fvps, min.rows = 1L, min.cols = 1L)
   
-  fvps <- fvps %>%
-    dplyr::filter(.data$country %in% pine)
+  required_cols <- c(
+    "country",
+    "activity_type",
+    "scenario_type",
+    "vaccine",
+    "coverage_adjusted",
+    "year",
+    "fvps"
+  )
   
-  cov <- fvps %>% 
-    dplyr::filter(.data$activity_type == "routine") %>%
-    dplyr::mutate(
+  checkmate::assert_names(
+    names(fvps),
+    must.include = required_cols
+  )
+  
+  
+  fvps <- dplyr::filter(fvps, .data$country %in% pine)
+  
+  cov <- dplyr::filter(fvps, .data$activity_type == "routine")
+  
+  cov <- dplyr::mutate(cov,
       vaccine_delivery = paste(.data$scenario_type, .data$vaccine, sep = "_"),
       coverage_adjusted = round(.data$coverage_adjusted*100, 2)
-      ) %>%
-    dplyr::select(
-      .data$country, 
-      .data$vaccine_delivery, 
-      .data$year, 
-      .data$coverage_adjusted) %>%
-    dplyr::rename(coverage = .data$coverage_adjusted) 
-  
-  fvp <- fvps %>% 
-    dplyr::mutate(
-      vaccine_delivery = paste(.data$scenario_type, .data$activity_type, sep = "_")
-      ) %>%
-    dplyr::select(
-      .data$country, 
-      .data$vaccine_delivery, 
-      .data$year, 
-      .data$fvps
-      ) %>%
-    dplyr::group_by(
-      .data$country, 
-      .data$vaccine_delivery, 
-      .data$year) %>%
-    dplyr::summarise(
-      fvps = round(sum(.data$fvps)/1e6, 2),
-      .groups = "drop"
-      ) 
+      )
+  
+  cols_to_select <- c("country", "vaccine_delivery", "year", "coverage_adjusted")
+  
+  cov <- dplyr::select(cov, all_of(cols_to_select))
+  
+  cov <- dplyr::rename(cov, coverage = .data$coverage_adjusted) 
+  
+  fvps <-  dplyr::mutate(fvps,
+                        vaccine_delivery = paste(.data$scenario_type, .data$activity_type, sep = "_")
+                        )
+  cols_to_select <- c("country", "vaccine_delivery", "year", "fvps")
+  
+  fvps <- dplyr::select(fvps, all_of(cols_to_select)) 
+  
+  fvps <- dplyr::group_by(fvps,
+                          .data$country, 
+                          .data$vaccine_delivery, 
+                          .data$year) 
+  
+  fvps <- dplyr::summarise(fvps, 
+                           fvps = round(sum(.data$fvps)/1e6, 2),
+                           .groups = "drop"
+                           ) 
   if(nrow(cov) > 0){
     p <- ggplot(
       cov, 
@@ -210,7 +222,7 @@ plot_coverage_fvps <- function(fvps){
   
   
   q <- ggplot(
-    fvp,
+    fvps,
     aes(
       x = .data$year, 
       y = .data$fvps, 

From 8a1be98d2db5e211effc862216a95bcb607ce4dd Mon Sep 17 00:00:00 2001
From: Zoe Gibney <z.gibney@imperial.ac.uk>
Date: Mon, 1 Jun 2026 12:38:51 +0100
Subject: [PATCH 5/6] Defining example data and fvps dataframes

---
 R/fn_plotting_impact.R | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/R/fn_plotting_impact.R b/R/fn_plotting_impact.R
index 8f23acd..cf1d24a 100644
--- a/R/fn_plotting_impact.R
+++ b/R/fn_plotting_impact.R
@@ -12,6 +12,16 @@
 #' @return ggplot object showing central impact estimates 
 #'  
 #' @examples
+#' 
+#' # Create example data
+#' impact_data <- tibble::tibble(
+#'   country = c("A", "A", "B", "B"),
+#'   year = c(2020, 2021, 2020, 2021),
+#'   birth_cohort = c(2000, 2001, 2000, 2001),
+#'   burden_outcome = c("deaths", "cases", "deaths", "cases"),
+#'   impact = c(15, 5, 14, 8),
+#'   short_name = c("short1", "short2", "short3", "short4")
+#' )
 #' plot_impact(
 #'   data = impact_data,
 #'   burden_type = "cases",
@@ -137,6 +147,18 @@ plot_impact <- function(
 #'     \item{fvps}{A plot of fully vaccinated persons over time.}
 #'   }
 #' @examples
+#' 
+#' # Create example data
+#' fvps <- tibble::tibble(
+  #'   country = c("AGO", "AGO", "BEN", "BEN"),
+  #'   year = c(2020, 2021, 2020, 2021),
+  #'   activity_type = c("routine", "campaign", "routine", "campaign"),
+  #'   scenario_type = c("default", "default", "default", "default"),
+  #'   vaccine = c("measles", "measles", "measles", "measles"),
+  #'   coverage_adjusted = c(0.8, 0.85, 0.4, 0.7),
+  #'   fvps = c(1000000, 1200000, 800000, 900000)
+  #' )
+  
 #' plots <- plot_coverage_fvps(fvps)
 #' plots$coverage
 #' plots$fvps

From 876048ad9f3bdf17d75e7808bc269e9b3a29c8d9 Mon Sep 17 00:00:00 2001
From: Zoe Gibney <z.gibney@imperial.ac.uk>
Date: Mon, 1 Jun 2026 13:20:56 +0100
Subject: [PATCH 6/6] Adding impact plots to pkgdown documentation

---
 _pkgdown.yml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 40a29ee..f73c2cb 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -32,6 +32,12 @@ reference:
   - subtitle: Plot impact estimates
     contents:
     - plot_impact_diagnostics
+  - subtitle: Plot central impact estimates by cohort and year
+    contents: 
+    - plot_impact
+  - subtitle: Plot coverage and fully vaccinated persons (FVPs)
+    contents:
+    - plot_coverage_fvps
 
   - title: Plotting helper functions
     contents: