diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index e1c58b9..c552a83 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: [main, master, develop]
 
 name: R-CMD-check
 
diff --git a/.github/workflows/lint-changed-files.yaml b/.github/workflows/lint-changed-files.yaml
index cf5c34b..c6a9228 100644
--- a/.github/workflows/lint-changed-files.yaml
+++ b/.github/workflows/lint-changed-files.yaml
@@ -3,7 +3,6 @@
 on:
   workflow_dispatch:
   pull_request:
-    branches: [main, master]
     paths:
       - '**.R'
       - '**.Rmd'
diff --git a/.lintr b/.lintr
index e26a154..3866cf5 100644
--- a/.lintr
+++ b/.lintr
@@ -6,6 +6,9 @@ linters: all_linters(
     object_length_linter = NULL # due to length of method names
     )
 exclusions: list(
+    "R/fn_plotting_impact.R" = list(
+      object_overwrite_linter = Inf
+    ),
     "tests/testthat.R" = list(
       unused_import_linter = Inf,
       undesirable_function_linter = Inf
diff --git a/DESCRIPTION b/DESCRIPTION
index d96f7e7..ed0d8c3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: vimcheck
 Title: Diagnostics for Vaccine Impact Modelling Consortium Burden and
     Impact Estimates
-Version: 0.0.4
+Version: 0.0.5
 Authors@R: c(
     person("Pratik", "Gupte", , "p.gupte24@imperial.ac.uk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-5294-7819")),
diff --git a/NAMESPACE b/NAMESPACE
index cb7c3f0..571eae6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -14,6 +14,7 @@ export(MIN_TS_MONTH)
 export(MIN_TS_YEAR)
 export(N_TS_MIN_CHARS)
 export(N_TS_YEAR_CHARS)
+export(PINE)
 export(basic_burden_sanity)
 export(burden_outcome_names)
 export(check_demography_alignment)
@@ -31,12 +32,14 @@ export(gen_national_iqr)
 export(generate_diffs)
 export(plot_age_patterns)
 export(plot_compare_demography)
+export(plot_coverage_fvps)
 export(plot_coverage_set)
 export(plot_cumul)
 export(plot_diff)
 export(plot_fvp)
 export(plot_global_burden)
 export(plot_global_burden_decades)
+export(plot_impact)
 export(plot_modelling_group_variation)
 export(plot_sig_diff)
 export(plot_vaccine_gavi)
diff --git a/NEWS.md b/NEWS.md
index fcaa798..0ee9eac 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,7 @@
+# vimcheck 0.0.5
+
+- Added functions to plot impact and FVPs and coverage.
+
 # vimcheck 0.0.4
 
 - Added impact diagnostics functions in `R/fn_impact_diagnostics.R`.
diff --git a/R/constants.R b/R/constants.R
index e4c3532..c30bdaf 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")
diff --git a/R/example_data.R b/R/example_data.R
index c6a1d7b..dfa0fae 100644
--- a/R/example_data.R
+++ b/R/example_data.R
@@ -115,6 +115,28 @@
 #' @source Prepared by the VIMC secretariat.
 "eg_fvps"
 
+#' A second FVP data example
+#'
+#' Exampled data for fully-vaccinated persons, created manually from arbitrary
+#' data.
+#'
+#' @format ## `eg_fvps_2`
+#' A data frame with 4 rows and 7 columns:
+#' \describe{
+#'   \item{country}{Country name as ISO 3 character code.}
+#'   \item{year}{Year.}
+#'   \item{activity_type}{Vaccination activity identifier.}
+#'   \item{scenario_type}{Scenario type name.}
+#'   \item{vaccine}{Vaccine identifier.}
+#'   \item{coverage_adjusted}{Ratio of adjusted FVPs to adjusted target.}
+#'   \item{fvps}{Count of fully vaccinated persons.}
+#' }
+#'
+#' @keywords data
+#'
+#' @source Example data prepared by the VIMC secretariat.
+"eg_fvps_2"
+
 #' Example of impact data
 #'
 #' Example of vaccine impact data taken from data used to test \pkg{vimpact}.
@@ -139,3 +161,21 @@
 #'
 #' @source Prepared by the VIMC secretariat.
 "eg_impact"
+
+#' Second example of impact data
+#'
+#' @format ## `eg_impact_2`
+#' A data frame with 4 rows and 6 columns:
+#' \describe{
+#'   \item{country}{Example country identifier.}
+#'   \item{year}{Year.}
+#'   \item{birth_cohort}{Numeric for the birth year.}
+#'   \item{burden_outcome}{Outcome identifier.}
+#'   \item{impact}{Numeric for impact.}
+#'   \item{short_name}{Example identifier.}
+#' }
+#'
+#' @keywords data
+#'
+#' @source Prepared manually by the VIMC secretariat.
+"eg_impact_2"
diff --git a/R/fn_plotting_impact.R b/R/fn_plotting_impact.R
new file mode 100644
index 0000000..345617b
--- /dev/null
+++ b/R/fn_plotting_impact.R
@@ -0,0 +1,267 @@
+#' Plot central impact estimates by cohort and year
+#'
+#' 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 country The country names as a character vector. Defaults to PINE
+#' countries.
+#'
+#' @param burden_type Burden metric used to evaluate impact; may be one of:
+#' `"cases", "deaths", "dalys", "s"`.
+#'
+#' @param view A string for the way impact is assigned, either by birth
+#' cohort ("cohort")  or by year of vaccination ("year").
+#'
+#' @param title Title of the plot to be rendered. Defaults to `NULL`.
+#'
+#' @return ggplot object showing central impact estimates
+#'
+#' @examples
+#' impact_data <- eg_impact_2
+#'
+#' plot_impact(
+#'   data = impact_data,
+#'   "A",
+#'   burden_type = "cases",
+#'   title = "Cases averted",
+#'   view = "year"
+#' )
+#'
+#' @export
+plot_impact <- function(
+  data,
+  country = PINE,
+  burden_type = c("cases", "deaths", "dalys", "yll"),
+  view = c("cohort", "year"),
+  title = NULL
+) {
+  required_cols <- c("country", "burden_outcome", "impact", "short_name")
+
+  checkmate::assert_data_frame(
+    data,
+    min.rows = 1L,
+    min.cols = length(required_cols)
+  )
+  checkmate::assert_names(colnames(data), must.include = required_cols)
+
+  checkmate::assert_character(country, any.missing = FALSE)
+
+  burden_type <- rlang::arg_match(burden_type)
+  view <- rlang::arg_match(view)
+
+  checkmate::assert_string(title, null.ok = TRUE)
+
+  # check if country is in data
+  if (!all(country %in% data[["country"]])) {
+    missing_country <- setdiff(country, data[["country"]]) # nolint used in err
+    cli::cli_abort(
+      "Impact data `data` expected to have country {.str {missing_country}} \
+      but it is missing."
+    )
+  }
+
+  impact <- dplyr::filter(
+    data,
+    .data$country %in% country,
+    .data$burden_outcome == burden_type,
+    .data$impact != 0 # can this be safely written as impact > 0?
+  )
+
+  if (nrow(impact) > 0) {
+    if (view == "cohort") {
+      checkmate::assert_names(names(data), must.include = "birth_cohort")
+      x_var <- "birth_cohort"
+      x_lab <- "Birth cohort"
+    } else {
+      checkmate::assert_names(names(data), must.include = view)
+      x_var <- view
+      x_lab <- "Year"
+    }
+
+    ggplot(
+      impact,
+      aes(
+        x = .data[[x_var]],
+        y = .data$impact,
+        ymin = .data$impact,
+        ymax = .data$impact,
+        fill = .data$short_name
+      )
+    ) +
+      ggplot2::geom_ribbon(alpha = 0.3) +
+      ggplot2::geom_line(aes(colour = .data$short_name), linewidth = 0.5) +
+      ggplot2::geom_point(aes(colour = .data$short_name), size = 0.5) +
+      # TODO: theme definition may not be right for this plot
+      theme_vimc() +
+      facet_wrap(ggplot2::vars("country"), scales = "free_y") +
+      labs(
+        x = x_lab,
+        y = glue::glue("{burden_type} averted"),
+        title = title
+      ) +
+      theme(
+        legend.position = "bottom",
+        legend.key.size = ggplot2::unit(0.5, "cm"),
+        legend.key.width = ggplot2::unit(0.3, "cm")
+      )
+  } else {
+    cli::cli_abort(
+      "No estimates remaining in the data after filtering for \\
+      countries: {.str {country}} and impact != 0 for `burden_type`: \\
+      {.str {burden_type}}."
+    )
+  }
+}
+
+#' Plot coverage and fully vaccinated persons (FVPs)
+#'
+#' Generates plots of routine vaccine coverage and fully vaccinated
+#' persons (FVPs) over time for selected countries.
+#'
+#' @param fvps A data.frame (or class extending it) showing the number of
+#' FVPs (fully vaccinated persons) by country, year and scenario/activity type.
+#'
+#' @param country A character vector of country identifiers, with all
+#' identifiers expected to be found in `fvps`. Defaults to PINE countries.
+#'
+#' @return A named list with two ggplot objects:
+#'   \describe{
+#'     \item{coverage}{A plot of routine vaccine coverage over time.}
+#'     \item{fvps}{A plot of fully vaccinated persons over time.}
+#'   }
+#'
+#' If there is no data on routine vaccination in the dataset, the `coverage`
+#' element of the return will be an empty `<ggplot>` object, and a warning is
+#' thrown.
+#'
+#' @examples
+#' fvps <- eg_fvps_2
+#'
+#' plots <- plot_coverage_fvps(fvps, "AGO")
+#' plots$coverage
+#' plots$fvps
+#'
+#' @export
+plot_coverage_fvps <- function(fvps, country = PINE) {
+  required_cols <- c(
+    "country",
+    "activity_type",
+    "scenario_type",
+    "vaccine",
+    "coverage_adjusted",
+    "year",
+    "fvps"
+  )
+
+  checkmate::assert_data_frame(
+    fvps,
+    min.rows = 1L,
+    min.cols = length(required_cols)
+  )
+  checkmate::assert_names(colnames(fvps), must.include = required_cols)
+
+  country <- checkmate::assert_character(country, any.missing = FALSE)
+  if (!all(country %in% fvps[["country"]])) {
+    missing_country <- setdiff(country, fvps[["country"]]) # nolint used in err
+    cli::cli_abort(
+      "Impact data `fvps` expected to have country {.str {missing_country}} \
+      but it is missing."
+    )
+  }
+
+  # handle FVPs plot
+  fvps <- dplyr::filter(fvps, .data$country %in% country)
+  cov <- dplyr::filter(fvps, .data$activity_type == "routine")
+
+  fvps <- dplyr::mutate(
+    fvps,
+    vaccine_delivery = paste(
+      .data$scenario_type,
+      .data$activity_type,
+      sep = "_"
+    )
+  )
+  cols_to_select <- c("country", "vaccine_delivery", "year", "fvps")
+
+  fvps <- dplyr::select(fvps, dplyr::all_of(cols_to_select))
+
+  fvps <- dplyr::group_by(
+    fvps,
+    .data$country,
+    .data$vaccine_delivery,
+    .data$year
+  )
+
+  fvps <- dplyr::summarise(
+    fvps,
+    fvps = round(sum(.data$fvps) / 1e6, 2),
+    .groups = "drop"
+  )
+
+  # handle coverage plot
+  cov <- dplyr::mutate(
+    cov,
+    vaccine_delivery = paste(.data$scenario_type, .data$vaccine, sep = "_"),
+    coverage_adjusted = round(.data$coverage_adjusted * 100, 2)
+  )
+
+  cols_to_select <- c(
+    "country",
+    "vaccine_delivery",
+    "year",
+    "coverage_adjusted"
+  )
+  cov <- dplyr::select(cov, dplyr::all_of(cols_to_select))
+  cov <- dplyr::rename(cov, coverage = "coverage_adjusted")
+
+  if (nrow(cov) > 0) {
+    p <- .plot_cov_fvp(
+      cov,
+      "coverage",
+      "Coverage (%)",
+      "Routine vaccine coverage"
+    )
+  } else {
+    p <- ggplot()
+    cli::cli_warn(
+      "There is no routine coverage in the database after filtering for \
+      country: {.str {country}}"
+    )
+  }
+
+  # assumed FVP data always available
+  q <- .plot_cov_fvp(fvps, "fvps", "FVPs (in millions)", "FVPs")
+
+  list(coverage = p, fvps = q)
+}
+
+#' @keywords internal
+.plot_cov_fvp <- function(data, col, ylab, title) {
+  ggplot(
+    data,
+    aes(
+      x = .data$year,
+      y = .data[[col]],
+      fill = .data$vaccine_delivery
+    )
+  ) +
+    geom_point(aes(colour = .data$vaccine_delivery), size = 0.5) +
+    theme_vimc() + # TODO: same note above on theme
+    facet_wrap(
+      ggplot2::vars("country"),
+      scales = "free_y"
+    ) +
+    labs(
+      x = "Year",
+      y = ylab,
+      title = title
+    ) +
+    theme(
+      legend.position = "bottom",
+      legend.key.size = ggplot2::unit(0.5, "cm"),
+      legend.key.width = ggplot2::unit(0.3, "cm")
+    )
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 40a29ee..fc17cac 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -21,7 +21,7 @@ reference:
     - plot_burden_diagnostics
 
     
-  - title: Impact estimate diagnostics
+  - title: Impact estimates and diagnostics
     
   - subtitle: Check impact estimates
     contents:
@@ -32,6 +32,8 @@ reference:
   - subtitle: Plot impact estimates
     contents:
     - plot_impact_diagnostics
+    - plot_impact
+    - plot_coverage_fvps
 
   - title: Plotting helper functions
     contents:
diff --git a/data-raw/eg_fvps.R b/data-raw/eg_fvps.R
index 2bcc468..4541123 100644
--- a/data-raw/eg_fvps.R
+++ b/data-raw/eg_fvps.R
@@ -14,3 +14,16 @@ eg_fvps$target_adjusted <- eg_fvps$target # assumed same as target
 eg_fvps$coverage_adjusted <- eg_fvps$fvps_adjusted / eg_fvps$target_adjusted
 
 usethis::use_data(eg_fvps, overwrite = TRUE)
+
+## A second example of FVP data
+eg_fvps_2 <- 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)
+)
+
+usethis::use_data(eg_fvps_2, overwrite = TRUE)
diff --git a/data-raw/eg_impact.R b/data-raw/eg_impact.R
index 29e5c27..6eea856 100644
--- a/data-raw/eg_impact.R
+++ b/data-raw/eg_impact.R
@@ -26,3 +26,15 @@ eg_impact <- mutate(
 )
 
 usethis::use_data(eg_impact, overwrite = TRUE)
+
+# second impact dataset
+eg_impact_2 <- 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")
+)
+
+usethis::use_data(eg_impact_2, overwrite = TRUE)
diff --git a/data/eg_fvps_2.rda b/data/eg_fvps_2.rda
new file mode 100644
index 0000000..4c81e66
Binary files /dev/null and b/data/eg_fvps_2.rda differ
diff --git a/data/eg_impact_2.rda b/data/eg_impact_2.rda
new file mode 100644
index 0000000..3e1af86
Binary files /dev/null and b/data/eg_impact_2.rda differ
diff --git a/man/constants.Rd b/man/constants.Rd
index 1d93a1f..7f7e66b 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}
diff --git a/man/eg_fvps_2.Rd b/man/eg_fvps_2.Rd
new file mode 100644
index 0000000..d49fa06
--- /dev/null
+++ b/man/eg_fvps_2.Rd
@@ -0,0 +1,32 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/example_data.R
+\docType{data}
+\name{eg_fvps_2}
+\alias{eg_fvps_2}
+\title{A second FVP data example}
+\format{
+\subsection{\code{eg_fvps_2}}{
+
+A data frame with 4 rows and 7 columns:
+\describe{
+\item{country}{Country name as ISO 3 character code.}
+\item{year}{Year.}
+\item{activity_type}{Vaccination activity identifier.}
+\item{scenario_type}{Scenario type name.}
+\item{vaccine}{Vaccine identifier.}
+\item{coverage_adjusted}{Ratio of adjusted FVPs to adjusted target.}
+\item{fvps}{Count of fully vaccinated persons.}
+}
+}
+}
+\source{
+Example data prepared by the VIMC secretariat.
+}
+\usage{
+eg_fvps_2
+}
+\description{
+Exampled data for fully-vaccinated persons, created manually from arbitrary
+data.
+}
+\keyword{data}
diff --git a/man/eg_impact_2.Rd b/man/eg_impact_2.Rd
new file mode 100644
index 0000000..1f8ef23
--- /dev/null
+++ b/man/eg_impact_2.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/example_data.R
+\docType{data}
+\name{eg_impact_2}
+\alias{eg_impact_2}
+\title{Second example of impact data}
+\format{
+\subsection{\code{eg_impact_2}}{
+
+A data frame with 4 rows and 6 columns:
+\describe{
+\item{country}{Example country identifier.}
+\item{year}{Year.}
+\item{birth_cohort}{Numeric for the birth year.}
+\item{burden_outcome}{Outcome identifier.}
+\item{impact}{Numeric for impact.}
+\item{short_name}{Example identifier.}
+}
+}
+}
+\source{
+Prepared manually by the VIMC secretariat.
+}
+\usage{
+eg_impact_2
+}
+\description{
+Second example of impact data
+}
+\keyword{data}
diff --git a/man/plot_coverage_fvps.Rd b/man/plot_coverage_fvps.Rd
new file mode 100644
index 0000000..17e1ea3
--- /dev/null
+++ b/man/plot_coverage_fvps.Rd
@@ -0,0 +1,38 @@
+% 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, country = PINE)
+}
+\arguments{
+\item{fvps}{A data.frame (or class extending it) showing the number of
+FVPs (fully vaccinated persons) by country, year and scenario/activity type.}
+
+\item{country}{A character vector of country identifiers, with all
+identifiers expected to be found in \code{fvps}. Defaults to PINE countries.}
+}
+\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.}
+}
+
+If there is no data on routine vaccination in the dataset, the \code{coverage}
+element of the return will be an empty \verb{<ggplot>} object, and a warning is
+thrown.
+}
+\description{
+Generates plots of routine vaccine coverage and fully vaccinated
+persons (FVPs) over time for selected countries.
+}
+\examples{
+fvps <- eg_fvps_2
+
+plots <- plot_coverage_fvps(fvps, "AGO")
+plots$coverage
+plots$fvps
+
+}
diff --git a/man/plot_impact.Rd b/man/plot_impact.Rd
new file mode 100644
index 0000000..4ccd8a0
--- /dev/null
+++ b/man/plot_impact.Rd
@@ -0,0 +1,48 @@
+% 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}
+\usage{
+plot_impact(
+  data,
+  country = PINE,
+  burden_type = c("cases", "deaths", "dalys", "yll"),
+  view = c("cohort", "year"),
+  title = NULL
+)
+}
+\arguments{
+\item{data}{A tibble containing impact estimates.}
+
+\item{country}{The country names as a character vector. Defaults to PINE
+countries.}
+
+\item{burden_type}{Burden metric used to evaluate impact; may be one of:
+\verb{"cases", "deaths", "dalys", "s"}.}
+
+\item{view}{A string for the way impact is assigned, either by birth
+cohort ("cohort")  or by year of vaccination ("year").}
+
+\item{title}{Title of the plot to be rendered. Defaults to \code{NULL}.}
+}
+\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{
+impact_data <- eg_impact_2
+
+plot_impact(
+  data = impact_data,
+  "A",
+  burden_type = "cases",
+  title = "Cases averted",
+  view = "year"
+)
+
+}
diff --git a/tests/testthat/test-plot_impact.R b/tests/testthat/test-plot_impact.R
new file mode 100644
index 0000000..dab2b59
--- /dev/null
+++ b/tests/testthat/test-plot_impact.R
@@ -0,0 +1,104 @@
+test_that("Plotting impact works", {
+  p <- plot_impact(
+    eg_impact_2,
+    "A",
+    "cases",
+    "year",
+    "Dummy title"
+  )
+  expect_class(p, "ggplot")
+
+  p <- plot_impact(
+    eg_impact_2,
+    "A",
+    "deaths",
+    "year",
+    "Dummy title"
+  )
+  expect_class(p, "ggplot")
+
+  p <- plot_impact(
+    eg_impact_2,
+    "A",
+    "cases",
+    "cohort",
+    "Dummy title"
+  )
+  expect_class(p, "ggplot")
+
+  # errors
+  expect_error(
+    plot_impact("dummy_data"),
+    "Must be of type 'data.frame'"
+  )
+  expect_error(
+    plot_impact("dummy_data"),
+    "Must be of type 'data.frame'"
+  )
+
+  expect_error(
+    plot_impact(eg_impact_2[-1]),
+    "Names must include"
+  )
+
+  expect_error(
+    plot_impact("dummy_data"),
+    "Must be of type 'data.frame'"
+  )
+
+  expect_error(
+    plot_impact(eg_impact_2),
+    "expected to have country"
+  )
+
+  expect_error(
+    plot_impact(eg_impact_2, "A", "dummy_impact"),
+    "`burden_type` must be one of"
+  )
+
+  expect_error(
+    plot_impact(eg_impact_2, "A", view = "dummy"),
+    "`view` must be one of"
+  )
+
+  expect_error(
+    plot_impact(eg_impact_2, "A", "dummy_impact"),
+    "`burden_type` must be one of"
+  )
+
+  ei2 <- eg_impact_2
+  ei2$impact <- 0
+
+  expect_error(
+    plot_impact(ei2, "A"),
+    "No estimates remaining in the data"
+  )
+})
+
+test_that("Plotting coverage and FVPs works", {
+  p <- plot_coverage_fvps(eg_fvps_2, "AGO")
+  expect_list(p, "ggplot")
+
+  # errors
+  expect_error(
+    plot_coverage_fvps("dummy_data"),
+    "Must be of type 'data.frame'"
+  )
+  expect_error(
+    plot_coverage_fvps(eg_fvps_2[-1]),
+    "Must have at least 7 cols"
+  )
+
+  expect_error(
+    plot_coverage_fvps(eg_fvps_2, "ETH"),
+    "expected to have country"
+  )
+
+  expect_warning(
+    plot_coverage_fvps(
+      eg_fvps_2[eg_fvps_2$activity_type == "campaign", ],
+      "AGO"
+    ),
+    "There is no routine coverage"
+  )
+})
diff --git a/vignettes/pressure_testing.Rmd b/vignettes/pressure_testing.Rmd
index a39330f..d7acfda 100644
--- a/vignettes/pressure_testing.Rmd
+++ b/vignettes/pressure_testing.Rmd
@@ -78,7 +78,8 @@ The excluded diseases are stored as the package constant `EXCLUDED_DISEASES` (`r
 # make a copy and add dummy disease values
 df_copy <- df
 df_copy$disease <- rep(
-  EXCLUDED_DISEASES, each = nrow(df_copy) / length(EXCLUDED_DISEASES)
+  EXCLUDED_DISEASES,
+  each = nrow(df_copy) / length(EXCLUDED_DISEASES)
 )
 
 # pass dummy touchstone to filter out all rows
@@ -331,4 +332,4 @@ Use `plot_cumul()` on data prepared using `prep_plot_cumul()` and `gen_combined_
 gen_combined_df(prev_df, curr_df) |>
   prep_plot_cumul("deaths_averted", "Measles") |>
   plot_cumul()
-```
\ No newline at end of file
+```