From 0848975aded0af8a6ae8a69b3ee0cfc750b4af12 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 13:58:33 +0100
Subject: [PATCH 01/12] Delete old alias for `avail_forecasts()`

---
 R/available_forecasts.R | 16 ----------------
 1 file changed, 16 deletions(-)

diff --git a/R/available_forecasts.R b/R/available_forecasts.R
index 17418b0a6..e130a7b77 100644
--- a/R/available_forecasts.R
+++ b/R/available_forecasts.R
@@ -74,19 +74,3 @@ available_forecasts <- function(data,
 
   return(out[])
 }
-
-#' @title Count Number of Available Forecasts `r lifecycle::badge("deprecated")`
-#' @details `r lifecycle::badge("deprecated")` Deprecated in 1.2.2. Use
-#' [available_forecasts()] instead.
-#' @inherit available_forecasts
-#' @keywords check-forecasts
-#' @export
-avail_forecasts <- function(data,
-                            by = NULL,
-                            collapse = c("quantile", "sample")) {
-  lifecycle::deprecate_warn(
-    "1.2.2", "avail_forecasts()",
-    "available_forecasts()"
-  )
-  available_forecasts(data, by, collapse)
-}

From 5b7aa12f49ca26d6828eda58d16e51e8d9cd3d40 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 14:00:40 +0100
Subject: [PATCH 02/12] remove `plot_avail_forecasts()`

---
 R/plot.R | 38 --------------------------------------
 1 file changed, 38 deletions(-)

diff --git a/R/plot.R b/R/plot.R
index 0ec4bb8c1..4f644399c 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -1011,44 +1011,6 @@ plot.scoringutils_available_forecasts <- function(x,
 }
 
 
-#' @title Visualise Where Forecasts Are Available `r lifecycle::badge("deprecated")`
-#'
-#' @description
-#' Old version of [plot.scoringutils_available_forecasts()] for compatibility.
-#' @inheritParams plot.scoringutils_available_forecasts
-#' @param available_forecasts an S3 object of class "scoringutils_available_forecasts"
-#' as produced by [available_forecasts()]
-#' @param y character vector of length one that denotes the name of the column
-#' to appear on the y-axis of the plot. Default is "model".
-#' @param x character vector of length one that denotes the name of the column
-#' to appear on the x-axis of the plot. Default is "forecast_date".
-#' @param make_x_factor logical (default is TRUE). Whether or not to convert
-#' the variable on the x-axis to a factor. This has an effect e.g. if dates
-#' are shown on the x-axis.
-#' @export
-plot_avail_forecasts <- function(available_forecasts,
-                                 y = "model",
-                                 x = "forecast_date",
-                                 make_x_factor = TRUE,
-                                 show_numbers = TRUE) {
-
-  lifecycle::deprecate_warn(
-    "1.2.2", "plot_avail_forecasts()",
-    "plot()"
-  )
-
-  plot.scoringutils_available_forecasts(
-    x = available_forecasts,
-    yvar = y,
-    xvar = x,
-    make_xvar_factor = make_x_factor,
-    show_numbers = show_numbers
-  )
-}
-
-
-
-
 #' @title Plot Correlation Between Metrics
 #'
 #' @description

From 8666a521c8147b4cbf7aed08b07e2b3094dc897f Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 14:11:29 +0100
Subject: [PATCH 03/12] Replace `available_forecasts()` with
 `get_forecast_counts()`

---
 NAMESPACE                                     |  6 +--
 R/available_forecasts.R                       |  6 +--
 R/plot.R                                      | 18 +++----
 R/validate.R                                  |  4 +-
 man/avail_forecasts.Rd                        | 47 -------------------
 ...le_forecasts.Rd => get_forecast_counts.Rd} |  8 ++--
 ...e_forecasts.Rd => plot.forecast_counts.Rd} | 12 ++---
 man/plot_avail_forecasts.Rd                   | 34 --------------
 tests/testthat/test-available_forecasts.R     | 14 +++---
 tests/testthat/test-plot_avail_forecasts.R    |  9 ++--
 vignettes/scoringutils.Rmd                    |  8 ++--
 11 files changed, 41 insertions(+), 125 deletions(-)
 delete mode 100644 man/avail_forecasts.Rd
 rename man/{available_forecasts.Rd => get_forecast_counts.Rd} (91%)
 rename man/{plot.scoringutils_available_forecasts.Rd => plot.forecast_counts.Rd} (79%)
 delete mode 100644 man/plot_avail_forecasts.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 97d7f8b63..0bd3337ec 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,scoringutils_available_forecasts)
+S3method(plot,forecast_counts)
 S3method(print,scoringutils_check)
 S3method(quantile_to_interval,data.frame)
 S3method(quantile_to_interval,numeric)
@@ -19,8 +19,6 @@ export(add_coverage)
 export(add_pairwise_comparison)
 export(ae_median_quantile)
 export(ae_median_sample)
-export(avail_forecasts)
-export(available_forecasts)
 export(available_metrics)
 export(bias_quantile)
 export(bias_range)
@@ -31,6 +29,7 @@ export(crps_sample)
 export(dispersion)
 export(dss_sample)
 export(get_duplicate_forecasts)
+export(get_forecast_counts)
 export(get_forecast_type)
 export(get_forecast_unit)
 export(interval_coverage_deviation_quantile)
@@ -49,7 +48,6 @@ export(overprediction)
 export(pairwise_comparison)
 export(pit)
 export(pit_sample)
-export(plot_avail_forecasts)
 export(plot_correlation)
 export(plot_heatmap)
 export(plot_interval_coverage)
diff --git a/R/available_forecasts.R b/R/available_forecasts.R
index e130a7b77..9f933f701 100644
--- a/R/available_forecasts.R
+++ b/R/available_forecasts.R
@@ -31,10 +31,10 @@
 #' @examples
 #' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
 #'
-#' available_forecasts(example_quantile,
+#' get_forecast_counts(example_quantile,
 #'   by = c("model", "target_type")
 #' )
-available_forecasts <- function(data,
+get_forecast_counts <- function(data,
                                 by = NULL,
                                 collapse = c("quantile", "sample_id")) {
 
@@ -70,7 +70,7 @@ available_forecasts <- function(data,
   out <- merge(out, out_empty, by = by, all.y = TRUE)
   out[, count := nafill(count, fill = 0)]
 
-  class(out) <- c("scoringutils_available_forecasts", class(out))
+  class(out) <- c("forecast_counts", class(out))
 
   return(out[])
 }
diff --git a/R/plot.R b/R/plot.R
index 4f644399c..14c5e12b2 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -942,8 +942,8 @@ plot_pit <- function(pit,
 #' @description
 #' Visualise Where Forecasts Are Available
 #' @inheritParams print.scoringutils_check
-#' @param x an S3 object of class "scoringutils_available_forecasts"
-#' as produced by [available_forecasts()]
+#' @param x an S3 object of class "forecast_counts"
+#' as produced by [get_forecast_counts()]
 #' @param yvar character vector of length one that denotes the name of the column
 #' to appear on the y-axis of the plot. Default is "model".
 #' @param xvar character vector of length one that denotes the name of the column
@@ -960,7 +960,7 @@ plot_pit <- function(pit,
 #' @export
 #' @examples
 #' library(ggplot2)
-#' available_forecasts <- available_forecasts(
+#' available_forecasts <- get_forecast_counts(
 #'   example_quantile, by = c("model", "target_type", "target_end_date")
 #' )
 #' plot(
@@ -968,12 +968,12 @@ plot_pit <- function(pit,
 #' ) +
 #'  facet_wrap("target_type")
 
-plot.scoringutils_available_forecasts <- function(x,
-                                                  yvar = "model",
-                                                  xvar = "forecast_date",
-                                                  make_xvar_factor = TRUE,
-                                                  show_numbers = TRUE,
-                                                  ...) {
+plot.forecast_counts <- function(x,
+                                 yvar = "model",
+                                 xvar = "forecast_date",
+                                 make_xvar_factor = TRUE,
+                                 show_numbers = TRUE,
+                                 ...) {
   x <- as.data.table(x)
 
   if (make_xvar_factor) {
diff --git a/R/validate.R b/R/validate.R
index 5cd246fd0..075abaa0b 100644
--- a/R/validate.R
+++ b/R/validate.R
@@ -111,7 +111,7 @@ validate.scoringutils_sample <- function(data, ...) {
 #' - checks there are no duplicate forecasts
 #' - if appropriate, checks the number of samples / quantiles is the same
 #' for all forecasts
-#' @inheritParams available_forecasts
+#' @inheritParams get_forecast_counts
 #' @return returns the input, with a few new attributes that hold additional
 #' information, messages and warnings
 #' @importFrom data.table ':=' is.data.table setattr
@@ -162,7 +162,7 @@ validate_general <- function(data) {
 #' - makes sure that a column called `model` exists and if not creates one
 #' - assigns a class
 #'
-#' @inheritParams available_forecasts
+#' @inheritParams get_forecast_counts
 #' @param classname name of the class to be created
 #' @return An object of the class indicated by `classname`
 #' @export
diff --git a/man/avail_forecasts.Rd b/man/avail_forecasts.Rd
deleted file mode 100644
index 57d47c5cf..000000000
--- a/man/avail_forecasts.Rd
+++ /dev/null
@@ -1,47 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/available_forecasts.R
-\name{avail_forecasts}
-\alias{avail_forecasts}
-\title{Count Number of Available Forecasts \ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}}
-\usage{
-avail_forecasts(data, by = NULL, collapse = c("quantile", "sample"))
-}
-\arguments{
-\item{data}{A data.frame or data.table with predicted and observed values.}
-
-\item{by}{character vector or \code{NULL} (the default) that denotes the
-categories over which the number of forecasts should be counted.
-By default (\code{by = NULL}) this will be the unit of a single forecast (i.e.
-all available columns (apart from a few "protected" columns such as
-'predicted' and 'observed') plus "quantile" or "sample_id" where present).}
-
-\item{collapse}{character vector (default is \verb{c("quantile", "sample_id"})
-with names of categories for which the number of rows should be collapsed to
-one when counting. For example, a single forecast is usually represented by a
-set of several quantiles or samples and collapsing these to one makes sure
-that a single forecast only gets counted once. Setting \code{collapse = c()}
-would mean that all quantiles / samples would be counted as individual
-forecasts.}
-}
-\value{
-A data.table with columns as specified in \code{by} and an additional
-column "count" with the number of forecasts.
-}
-\description{
-Given a data set with forecasts, count the number of available forecasts
-for arbitrary grouping (e.g. the number of forecasts per model, or the
-number of forecasts per model and location).
-This is useful to determine whether there are any missing forecasts.
-}
-\details{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} Deprecated in 1.2.2. Use
-\code{\link[=available_forecasts]{available_forecasts()}} instead.
-}
-\examples{
-data.table::setDTthreads(1) # only needed to avoid issues on CRAN
-
-available_forecasts(example_quantile,
-  by = c("model", "target_type")
-)
-}
-\keyword{check-forecasts}
diff --git a/man/available_forecasts.Rd b/man/get_forecast_counts.Rd
similarity index 91%
rename from man/available_forecasts.Rd
rename to man/get_forecast_counts.Rd
index 3c214a1e3..1ed71aac7 100644
--- a/man/available_forecasts.Rd
+++ b/man/get_forecast_counts.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/available_forecasts.R
-\name{available_forecasts}
-\alias{available_forecasts}
+\name{get_forecast_counts}
+\alias{get_forecast_counts}
 \title{Count Number of Available Forecasts}
 \usage{
-available_forecasts(data, by = NULL, collapse = c("quantile", "sample_id"))
+get_forecast_counts(data, by = NULL, collapse = c("quantile", "sample_id"))
 }
 \arguments{
 \item{data}{A data.frame or data.table with predicted and observed values.}
@@ -36,7 +36,7 @@ This is useful to determine whether there are any missing forecasts.
 \examples{
 data.table::setDTthreads(1) # only needed to avoid issues on CRAN
 
-available_forecasts(example_quantile,
+get_forecast_counts(example_quantile,
   by = c("model", "target_type")
 )
 }
diff --git a/man/plot.scoringutils_available_forecasts.Rd b/man/plot.forecast_counts.Rd
similarity index 79%
rename from man/plot.scoringutils_available_forecasts.Rd
rename to man/plot.forecast_counts.Rd
index f11e0dc5d..8ace053b4 100644
--- a/man/plot.scoringutils_available_forecasts.Rd
+++ b/man/plot.forecast_counts.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/plot.R
-\name{plot.scoringutils_available_forecasts}
-\alias{plot.scoringutils_available_forecasts}
+\name{plot.forecast_counts}
+\alias{plot.forecast_counts}
 \title{Visualise Where Forecasts Are Available}
 \usage{
-\method{plot}{scoringutils_available_forecasts}(
+\method{plot}{forecast_counts}(
   x,
   yvar = "model",
   xvar = "forecast_date",
@@ -14,8 +14,8 @@
 )
 }
 \arguments{
-\item{x}{an S3 object of class "scoringutils_available_forecasts"
-as produced by \code{\link[=available_forecasts]{available_forecasts()}}}
+\item{x}{an S3 object of class "forecast_counts"
+as produced by \code{\link[=get_forecast_counts]{get_forecast_counts()}}}
 
 \item{yvar}{character vector of length one that denotes the name of the column
 to appear on the y-axis of the plot. Default is "model".}
@@ -40,7 +40,7 @@ Visualise Where Forecasts Are Available
 }
 \examples{
 library(ggplot2)
-available_forecasts <- available_forecasts(
+available_forecasts <- get_forecast_counts(
   example_quantile, by = c("model", "target_type", "target_end_date")
 )
 plot(
diff --git a/man/plot_avail_forecasts.Rd b/man/plot_avail_forecasts.Rd
deleted file mode 100644
index c10035012..000000000
--- a/man/plot_avail_forecasts.Rd
+++ /dev/null
@@ -1,34 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plot.R
-\name{plot_avail_forecasts}
-\alias{plot_avail_forecasts}
-\title{Visualise Where Forecasts Are Available \ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}}
-\usage{
-plot_avail_forecasts(
-  available_forecasts,
-  y = "model",
-  x = "forecast_date",
-  make_x_factor = TRUE,
-  show_numbers = TRUE
-)
-}
-\arguments{
-\item{available_forecasts}{an S3 object of class "scoringutils_available_forecasts"
-as produced by \code{\link[=available_forecasts]{available_forecasts()}}}
-
-\item{y}{character vector of length one that denotes the name of the column
-to appear on the y-axis of the plot. Default is "model".}
-
-\item{x}{character vector of length one that denotes the name of the column
-to appear on the x-axis of the plot. Default is "forecast_date".}
-
-\item{make_x_factor}{logical (default is TRUE). Whether or not to convert
-the variable on the x-axis to a factor. This has an effect e.g. if dates
-are shown on the x-axis.}
-
-\item{show_numbers}{logical (default is \code{TRUE}) that indicates whether
-or not to show the actual count numbers on the plot}
-}
-\description{
-Old version of \code{\link[=plot.scoringutils_available_forecasts]{plot.scoringutils_available_forecasts()}} for compatibility.
-}
diff --git a/tests/testthat/test-available_forecasts.R b/tests/testthat/test-available_forecasts.R
index f8e0ad0c7..ffa9e4f9e 100644
--- a/tests/testthat/test-available_forecasts.R
+++ b/tests/testthat/test-available_forecasts.R
@@ -1,6 +1,6 @@
-test_that("available_forecasts() works as expected", {
+test_that("get_forecast_counts() works as expected", {
   af <- suppressMessages(
-    available_forecasts(example_quantile,
+    get_forecast_counts(example_quantile,
       by = c("model", "target_type", "target_end_date")
     )
   )
@@ -8,12 +8,12 @@ test_that("available_forecasts() works as expected", {
   expect_type(af$target_type, "character")
   expect_type(af$`count`, "integer")
   expect_equal(nrow(af[is.na(`count`)]), 0)
-  af <- available_forecasts(example_quantile, by = "model")
+  af <- get_forecast_counts(example_quantile, by = "model")
   expect_equal(nrow(af), 4)
   expect_equal(af$`count`, c(256, 256, 128, 247))
 
   # Setting `collapse = c()` means that all quantiles and samples are counted
-  af <- available_forecasts(
+  af <- get_forecast_counts(
     example_quantile,
      by = "model", collapse = c()
   )
@@ -21,13 +21,13 @@ test_that("available_forecasts() works as expected", {
   expect_equal(af$`count`, c(5888, 5888, 2944, 5681))
 
   # setting by = NULL, the default, results in by equal to forecast unit
-  af <- available_forecasts(example_quantile)
+  af <- get_forecast_counts(example_quantile)
   expect_equal(nrow(af), 50688)
 
   # check whether collapsing also works for model-based forecasts
-  af <- available_forecasts(example_integer, by = "model")
+  af <- get_forecast_counts(example_integer, by = "model")
   expect_equal(nrow(af), 4)
 
-  af <- available_forecasts(example_integer, by = "model", collapse = c())
+  af <- get_forecast_counts(example_integer, by = "model", collapse = c())
   expect_equal(af$count, c(10240, 10240, 5120, 9880))
 })
diff --git a/tests/testthat/test-plot_avail_forecasts.R b/tests/testthat/test-plot_avail_forecasts.R
index 9f2efea6b..a246987c1 100644
--- a/tests/testthat/test-plot_avail_forecasts.R
+++ b/tests/testthat/test-plot_avail_forecasts.R
@@ -1,8 +1,7 @@
-test_that("plot_available_forecasts() works as expected", {
-  available_forecasts <- suppressMessages(
-    available_forecasts(example_quantile,
-      by = c("model", "target_type", "target_end_date")
-    )
+test_that("plot.forecast_counts() works as expected", {
+  available_forecasts <- get_forecast_counts(
+    example_quantile,
+    by = c("model", "target_type", "target_end_date")
   )
   p <- plot(available_forecasts,
     xvar = "target_end_date", show_numbers = FALSE
diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd
index 4ae09b2ac..e4b4e4c61 100644
--- a/vignettes/scoringutils.Rmd
+++ b/vignettes/scoringutils.Rmd
@@ -79,10 +79,10 @@ The output of `validate()` can later be directly used as input to `score()` (oth
 
 ## Showing available forecasts
 
-The function `available_forecasts()` may also be helpful to determine where forecasts are available. Using the `by` argument you can specify the level of summary. For example, to see how many forecasts there are per model and target_type, we can run
+The function `get_forecast_counts()` may also be helpful to determine where forecasts are available. Using the `by` argument you can specify the level of summary. For example, to see how many forecasts there are per model and target_type, we can run
 
 ```{r}
-available_forecasts(example_quantile, by = c("model", "target_type"))
+get_forecast_counts(example_quantile, by = c("model", "target_type"))
 ```
 
 We see that 'epiforecasts-EpiNow2' has some missing forecasts for the deaths forecast target and that UMass-MechBayes has no case forecasts. 
@@ -91,7 +91,7 @@ This information can also be visualised using `plot()`:
 
 ```{r, fig.width=11, fig.height=6}
 example_quantile %>%
-  available_forecasts(by = c("model", "forecast_date", "target_type")) %>%
+  get_forecast_counts(by = c("model", "forecast_date", "target_type")) %>%
   plot() +
   facet_wrap(~ target_type)
 ```
@@ -107,7 +107,7 @@ example_quantile %>%
   ) %>%
   make_NA(
     what = "forecast",
-    model != "EuroCOVIDhub-ensemble",
+    model != "EuroCOVIDhub-ense mble",
     forecast_date != "2021-06-28"
   ) %>%
   plot_predictions(

From 1626fce249c871ba9ffbb8769961a91718aa76fd Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 14:11:43 +0100
Subject: [PATCH 04/12] Update NEWS file

---
 NEWS.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 0faf9eaf2..3ba310daf 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,10 +25,10 @@ The update introduces breaking changes. If you want to keep using the older vers
 - `add_coverage()` was reworked completely. It's new purpose is now to add coverage information to the raw forecast data (essentially fulfilling some of the functionality that was previously covered by `score_quantile()`)
 - The function `find_duplicates()` was renamed to `get_duplicate_forecasts()`
 - Changes to `avail_forecasts()` and `plot_avail_forecasts()`:
-  - The function `avail_forecasts()` was renamed to `available_forecasts()` for consistency with `available_metrics()`. The old function, `avail_forecasts()` is still available as an alias, but will be removed in the future.
-  - For clarity, the output column in `avail_forecasts()` was renamed from "Number forecasts" to "count".
-  - `available_forecasts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows.
-  - `plot_avail_forecasts()` has been deprecated in favour of an S3 method for `plot()`. An alias is still available, but will be removed in the future.
+  - The function `avail_forecasts()` was renamed to `get_forecast_counts()`. This represents a change in the naming convention where we aim to name functions that provide the user with addtional useful information about the data with a prefix "get_".
+  - For clarity, the output column in `get_forecast_counts()` was renamed from "Number forecasts" to "count".
+  - `get_forecast_counts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows.
+  - `plot_avail_forecasts()` has been deprecated in favour of an S3 method for `plot()`.
 - The deprecated `..density..` was replaced with `after_stat(density)` in ggplot calls.
 - Files ending in ".Rda" were renamed to ".rds" where appropriate when used together with `saveRDS()` or `readRDS()`.
 - added documentation for the return value of `summarise_scores()`. 

From 4ff6d58cca60b48b7ea9bd6437ec77b8cff53a5c Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 14:12:43 +0100
Subject: [PATCH 05/12] Small correction in a previous news item

---
 NEWS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 3ba310daf..7c9f284ef 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -188,7 +188,7 @@ to a function `summarise_scores()`
 - New function `check_forecasts()` to analyse input data before scoring
 - New function `correlation()` to compute correlations between different metrics
 - New function `add_coverage()` to add coverage for specific central prediction intervals.
-- New function `available_forecasts()` allows to visualise the number of available forecasts.
+- New function `avail_forecasts()` allows to visualise the number of available forecasts.
 - New function `find_duplicates()` to find duplicate forecasts which cause an error.
 - All plotting functions were renamed to begin with `plot_`. Arguments were
 simplified.

From 0b8a23d70af0c071720ae5484d5cc76ff302fd4a Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 14:20:22 +0100
Subject: [PATCH 06/12] Update News item

---
 NEWS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 7c9f284ef..57b0c07e2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,7 +25,7 @@ The update introduces breaking changes. If you want to keep using the older vers
 - `add_coverage()` was reworked completely. It's new purpose is now to add coverage information to the raw forecast data (essentially fulfilling some of the functionality that was previously covered by `score_quantile()`)
 - The function `find_duplicates()` was renamed to `get_duplicate_forecasts()`
 - Changes to `avail_forecasts()` and `plot_avail_forecasts()`:
-  - The function `avail_forecasts()` was renamed to `get_forecast_counts()`. This represents a change in the naming convention where we aim to name functions that provide the user with addtional useful information about the data with a prefix "get_".
+  - The function `avail_forecasts()` was renamed to `get_forecast_counts()`. This represents a change in the naming convention where we aim to name functions that provide the user with addtional useful information about the data with a prefix "get_". See Issue #403 and PR #511 by @nikosbosse and reviewed by @seabbs for details. 
   - For clarity, the output column in `get_forecast_counts()` was renamed from "Number forecasts" to "count".
   - `get_forecast_counts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows.
   - `plot_avail_forecasts()` has been deprecated in favour of an S3 method for `plot()`.

From 0cfb741777b21203b02a9207211d345215b81b92 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 28 Nov 2023 14:43:20 +0100
Subject: [PATCH 07/12] fix linting issues

---
 R/available_forecasts.R    | 2 +-
 R/plot.R                   | 2 +-
 vignettes/scoringutils.Rmd | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/available_forecasts.R b/R/available_forecasts.R
index 9f933f701..b6eed078d 100644
--- a/R/available_forecasts.R
+++ b/R/available_forecasts.R
@@ -58,7 +58,7 @@ get_forecast_counts <- function(data,
   data <- data[data[, .I[1], by = collapse_by]$V1]
 
   # count number of rows = number of forecasts
-  out <- data[, .(`count` = .N), by = by]
+  out <- data[, .(count = .N), by = by]
 
   # make sure that all combinations in "by" are included in the output (with
   # count = 0). To achieve that, take the unique values in data and expand grid
diff --git a/R/plot.R b/R/plot.R
index 14c5e12b2..8fd17e285 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -470,7 +470,7 @@ plot_predictions <- function(data,
   # it separately here to deal with the case when only the median is provided
   # (in which case ggdist::geom_lineribbon() will fail)
   if (0 %in% range) {
-    select_median <- (forecasts$range %in% 0 & forecasts$boundary == "lower")
+    select_median <- (forecasts$range == 0 & forecasts$boundary == "lower")
     median <- forecasts[select_median]
 
     if (nrow(median) > 0) {
diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd
index e4b4e4c61..da2898fb6 100644
--- a/vignettes/scoringutils.Rmd
+++ b/vignettes/scoringutils.Rmd
@@ -35,7 +35,7 @@ Most of the time, the `score()` function will be able to do the entire evaluatio
 
 ```{r, echo=FALSE}
 requirements <- data.table(
-  "Format" = c(
+  Format = c(
     "quantile-based", "sample-based", "binary", "pairwise-comparisons"
   ),
   `Required columns` = c(

From c48d6dd5a7ecdebcf62878d3374036aa6b3517cb Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Wed, 29 Nov 2023 11:58:15 +0100
Subject: [PATCH 08/12] Rename class "forecast_count" to "prediction_count"

---
 NAMESPACE                                          |  2 +-
 R/available_forecasts.R                            |  2 +-
 R/plot.R                                           | 14 +++++++-------
 ...orecast_counts.Rd => plot.prediction_counts.Rd} |  8 ++++----
 4 files changed, 13 insertions(+), 13 deletions(-)
 rename man/{plot.forecast_counts.Rd => plot.prediction_counts.Rd} (90%)

diff --git a/NAMESPACE b/NAMESPACE
index 0bd3337ec..ad4f49cba 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,forecast_counts)
+S3method(plot,prediction_counts)
 S3method(print,scoringutils_check)
 S3method(quantile_to_interval,data.frame)
 S3method(quantile_to_interval,numeric)
diff --git a/R/available_forecasts.R b/R/available_forecasts.R
index b6eed078d..946cbc519 100644
--- a/R/available_forecasts.R
+++ b/R/available_forecasts.R
@@ -70,7 +70,7 @@ get_forecast_counts <- function(data,
   out <- merge(out, out_empty, by = by, all.y = TRUE)
   out[, count := nafill(count, fill = 0)]
 
-  class(out) <- c("forecast_counts", class(out))
+  class(out) <- c("prediction_counts", class(out))
 
   return(out[])
 }
diff --git a/R/plot.R b/R/plot.R
index 8fd17e285..a8244207c 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -942,7 +942,7 @@ plot_pit <- function(pit,
 #' @description
 #' Visualise Where Forecasts Are Available
 #' @inheritParams print.scoringutils_check
-#' @param x an S3 object of class "forecast_counts"
+#' @param x an S3 object of class "prediction_counts"
 #' as produced by [get_forecast_counts()]
 #' @param yvar character vector of length one that denotes the name of the column
 #' to appear on the y-axis of the plot. Default is "model".
@@ -968,12 +968,12 @@ plot_pit <- function(pit,
 #' ) +
 #'  facet_wrap("target_type")
 
-plot.forecast_counts <- function(x,
-                                 yvar = "model",
-                                 xvar = "forecast_date",
-                                 make_xvar_factor = TRUE,
-                                 show_numbers = TRUE,
-                                 ...) {
+plot.prediction_counts <- function(x,
+                                   yvar = "model",
+                                   xvar = "forecast_date",
+                                   make_xvar_factor = TRUE,
+                                   show_numbers = TRUE,
+                                   ...) {
   x <- as.data.table(x)
 
   if (make_xvar_factor) {
diff --git a/man/plot.forecast_counts.Rd b/man/plot.prediction_counts.Rd
similarity index 90%
rename from man/plot.forecast_counts.Rd
rename to man/plot.prediction_counts.Rd
index 8ace053b4..d3343d659 100644
--- a/man/plot.forecast_counts.Rd
+++ b/man/plot.prediction_counts.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/plot.R
-\name{plot.forecast_counts}
-\alias{plot.forecast_counts}
+\name{plot.prediction_counts}
+\alias{plot.prediction_counts}
 \title{Visualise Where Forecasts Are Available}
 \usage{
-\method{plot}{forecast_counts}(
+\method{plot}{prediction_counts}(
   x,
   yvar = "model",
   xvar = "forecast_date",
@@ -14,7 +14,7 @@
 )
 }
 \arguments{
-\item{x}{an S3 object of class "forecast_counts"
+\item{x}{an S3 object of class "prediction_counts"
 as produced by \code{\link[=get_forecast_counts]{get_forecast_counts()}}}
 
 \item{yvar}{character vector of length one that denotes the name of the column

From 18f42a80d335d6048a2132714c1ada5a9d2d5e51 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 5 Dec 2023 13:03:29 +0100
Subject: [PATCH 09/12] Create `plot_forecast_counts()` to replace previous S3
 method

---
 NAMESPACE                                     |  2 +-
 R/available_forecasts.R                       |  2 -
 R/plot.R                                      | 45 +++++++++----------
 ...tion_counts.Rd => plot_forecast_counts.Rd} | 33 +++++++-------
 tests/testthat/test-plot_avail_forecasts.R    |  4 +-
 vignettes/scoringutils.Rmd                    |  2 +-
 6 files changed, 40 insertions(+), 48 deletions(-)
 rename man/{plot.prediction_counts.Rd => plot_forecast_counts.Rd} (51%)

diff --git a/NAMESPACE b/NAMESPACE
index ad4f49cba..d44c528b8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,prediction_counts)
 S3method(print,scoringutils_check)
 S3method(quantile_to_interval,data.frame)
 S3method(quantile_to_interval,numeric)
@@ -49,6 +48,7 @@ export(pairwise_comparison)
 export(pit)
 export(pit_sample)
 export(plot_correlation)
+export(plot_forecast_counts)
 export(plot_heatmap)
 export(plot_interval_coverage)
 export(plot_pairwise_comparison)
diff --git a/R/available_forecasts.R b/R/available_forecasts.R
index 946cbc519..8225620d3 100644
--- a/R/available_forecasts.R
+++ b/R/available_forecasts.R
@@ -70,7 +70,5 @@ get_forecast_counts <- function(data,
   out <- merge(out, out_empty, by = by, all.y = TRUE)
   out[, count := nafill(count, fill = 0)]
 
-  class(out) <- c("prediction_counts", class(out))
-
   return(out[])
 }
diff --git a/R/plot.R b/R/plot.R
index a8244207c..4d8d602e3 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -942,13 +942,13 @@ plot_pit <- function(pit,
 #' @description
 #' Visualise Where Forecasts Are Available
 #' @inheritParams print.scoringutils_check
-#' @param x an S3 object of class "prediction_counts"
+#' @param forecast_counts a data.table (or similar) with forecast counts
 #' as produced by [get_forecast_counts()]
-#' @param yvar character vector of length one that denotes the name of the column
+#' @param y character vector of length one that denotes the name of the column
 #' to appear on the y-axis of the plot. Default is "model".
-#' @param xvar character vector of length one that denotes the name of the column
-#' to appear on the x-axis of the plot. Default is "forecast_date".
-#' @param make_xvar_factor logical (default is TRUE). Whether or not to convert
+#' @param x character vector of length one that denotes the name of the column
+#' to appear on the x-axis of the plot.
+#' @param make_x_factor logical (default is TRUE). Whether or not to convert
 #' the variable on the x-axis to a factor. This has an effect e.g. if dates
 #' are shown on the x-axis.
 #' @param show_numbers logical (default is `TRUE`) that indicates whether
@@ -960,35 +960,34 @@ plot_pit <- function(pit,
 #' @export
 #' @examples
 #' library(ggplot2)
-#' available_forecasts <- get_forecast_counts(
+#' forecast_counts <- get_forecast_counts(
 #'   example_quantile, by = c("model", "target_type", "target_end_date")
 #' )
-#' plot(
-#'  available_forecasts, xvar = "target_end_date", show_numbers = FALSE
+#' plot_forecast_counts(
+#'  forecast_counts, x = "target_end_date", show_numbers = FALSE
 #' ) +
 #'  facet_wrap("target_type")
 
-plot.prediction_counts <- function(x,
-                                   yvar = "model",
-                                   xvar = "forecast_date",
-                                   make_xvar_factor = TRUE,
-                                   show_numbers = TRUE,
-                                   ...) {
-  x <- as.data.table(x)
+plot_forecast_counts <- function(forecast_counts,
+                                 y = "model",
+                                 x,
+                                 make_x_factor = TRUE,
+                                 show_numbers = TRUE) {
 
-  if (make_xvar_factor) {
-    x[, eval(xvar) := as.factor(get(xvar))]
+  forecast_counts <- ensure_data.table(forecast_counts)
+
+  if (make_x_factor) {
+    forecast_counts[, eval(x) := as.factor(get(x))]
   }
 
-  setnames(x, old = "count", new = "Count")
+  setnames(forecast_counts, old = "count", new = "Count")
 
   plot <- ggplot(
-    x,
-    aes(y = .data[[yvar]], x = .data[[xvar]])
+    forecast_counts,
+    aes(y = .data[[y]], x = .data[[x]])
   ) +
     geom_tile(aes(fill = `Count`),
-      width = 0.97, height = 0.97
-    ) +
+              width = 0.97, height = 0.97) +
     scale_fill_gradient(
       low = "grey95", high = "steelblue",
       na.value = "lightgrey"
@@ -1001,12 +1000,10 @@ plot.prediction_counts <- function(x,
       )
     ) +
     theme(panel.spacing = unit(2, "lines"))
-
   if (show_numbers) {
     plot <- plot +
       geom_text(aes(label = `Count`))
   }
-
   return(plot)
 }
 
diff --git a/man/plot.prediction_counts.Rd b/man/plot_forecast_counts.Rd
similarity index 51%
rename from man/plot.prediction_counts.Rd
rename to man/plot_forecast_counts.Rd
index d3343d659..c4f4cade9 100644
--- a/man/plot.prediction_counts.Rd
+++ b/man/plot_forecast_counts.Rd
@@ -1,36 +1,33 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/plot.R
-\name{plot.prediction_counts}
-\alias{plot.prediction_counts}
+\name{plot_forecast_counts}
+\alias{plot_forecast_counts}
 \title{Visualise Where Forecasts Are Available}
 \usage{
-\method{plot}{prediction_counts}(
+plot_forecast_counts(
+  forecast_counts,
+  y = "model",
   x,
-  yvar = "model",
-  xvar = "forecast_date",
-  make_xvar_factor = TRUE,
-  show_numbers = TRUE,
-  ...
+  make_x_factor = TRUE,
+  show_numbers = TRUE
 )
 }
 \arguments{
-\item{x}{an S3 object of class "prediction_counts"
+\item{forecast_counts}{a data.table (or similar) with forecast counts
 as produced by \code{\link[=get_forecast_counts]{get_forecast_counts()}}}
 
-\item{yvar}{character vector of length one that denotes the name of the column
+\item{y}{character vector of length one that denotes the name of the column
 to appear on the y-axis of the plot. Default is "model".}
 
-\item{xvar}{character vector of length one that denotes the name of the column
-to appear on the x-axis of the plot. Default is "forecast_date".}
+\item{x}{character vector of length one that denotes the name of the column
+to appear on the x-axis of the plot.}
 
-\item{make_xvar_factor}{logical (default is TRUE). Whether or not to convert
+\item{make_x_factor}{logical (default is TRUE). Whether or not to convert
 the variable on the x-axis to a factor. This has an effect e.g. if dates
 are shown on the x-axis.}
 
 \item{show_numbers}{logical (default is \code{TRUE}) that indicates whether
 or not to show the actual count numbers on the plot}
-
-\item{...}{additional arguments (not used here)}
 }
 \value{
 ggplot object with a plot of interval coverage
@@ -40,11 +37,11 @@ Visualise Where Forecasts Are Available
 }
 \examples{
 library(ggplot2)
-available_forecasts <- get_forecast_counts(
+forecast_counts <- get_forecast_counts(
   example_quantile, by = c("model", "target_type", "target_end_date")
 )
-plot(
- available_forecasts, xvar = "target_end_date", show_numbers = FALSE
+plot_forecast_counts(
+ forecast_counts, x = "target_end_date", show_numbers = FALSE
 ) +
  facet_wrap("target_type")
 }
diff --git a/tests/testthat/test-plot_avail_forecasts.R b/tests/testthat/test-plot_avail_forecasts.R
index a246987c1..d34caec51 100644
--- a/tests/testthat/test-plot_avail_forecasts.R
+++ b/tests/testthat/test-plot_avail_forecasts.R
@@ -3,8 +3,8 @@ test_that("plot.forecast_counts() works as expected", {
     example_quantile,
     by = c("model", "target_type", "target_end_date")
   )
-  p <- plot(available_forecasts,
-    xvar = "target_end_date", show_numbers = FALSE
+  p <- plot_forecast_counts(available_forecasts,
+    x = "target_end_date", show_numbers = FALSE
   ) +
     facet_wrap("target_type")
   expect_s3_class(p, "ggplot")
diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd
index da2898fb6..92e532335 100644
--- a/vignettes/scoringutils.Rmd
+++ b/vignettes/scoringutils.Rmd
@@ -92,7 +92,7 @@ This information can also be visualised using `plot()`:
 ```{r, fig.width=11, fig.height=6}
 example_quantile %>%
   get_forecast_counts(by = c("model", "forecast_date", "target_type")) %>%
-  plot() +
+  plot_forecast_counts(x = "forecast_date") +
   facet_wrap(~ target_type)
 ```
 

From 8c65056e4085f515a9f87d1eb275681ac7ebcb69 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Tue, 5 Dec 2023 13:21:57 +0100
Subject: [PATCH 10/12] Update NEWS file

---
 NEWS.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/NEWS.md b/NEWS.md
index 57b0c07e2..4067c6d9f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,10 +25,10 @@ The update introduces breaking changes. If you want to keep using the older vers
 - `add_coverage()` was reworked completely. It's new purpose is now to add coverage information to the raw forecast data (essentially fulfilling some of the functionality that was previously covered by `score_quantile()`)
 - The function `find_duplicates()` was renamed to `get_duplicate_forecasts()`
 - Changes to `avail_forecasts()` and `plot_avail_forecasts()`:
-  - The function `avail_forecasts()` was renamed to `get_forecast_counts()`. This represents a change in the naming convention where we aim to name functions that provide the user with addtional useful information about the data with a prefix "get_". See Issue #403 and PR #511 by @nikosbosse and reviewed by @seabbs for details. 
+  - The function `avail_forecasts()` was renamed to `get_forecast_counts()`. This represents a change in the naming convention where we aim to name functions that provide the user with additional useful information about the data with a prefix "get_". Sees Issue #403 and #521 and PR #511 by @nikosbosse and reviewed by @seabbs for details. 
   - For clarity, the output column in `get_forecast_counts()` was renamed from "Number forecasts" to "count".
   - `get_forecast_counts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows.
-  - `plot_avail_forecasts()` has been deprecated in favour of an S3 method for `plot()`.
+  - `plot_avail_forecasts()` was renamed `plot_forecast_counts()` in line with the change in the function name. The `x` argument no longer has a default value, as the value will depend on the data provided by the user.
 - The deprecated `..density..` was replaced with `after_stat(density)` in ggplot calls.
 - Files ending in ".Rda" were renamed to ".rds" where appropriate when used together with `saveRDS()` or `readRDS()`.
 - added documentation for the return value of `summarise_scores()`. 

From 14632210043daef1bd762ad6ba172e09aebba8e4 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Wed, 6 Dec 2023 14:33:41 +0100
Subject: [PATCH 11/12] Add input checks to `plot_forecast_counts()`

---
 NAMESPACE | 1 +
 R/plot.R  | 5 +++++
 2 files changed, 6 insertions(+)

diff --git a/NAMESPACE b/NAMESPACE
index d44c528b8..d05f4258d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -86,6 +86,7 @@ importFrom(checkmate,assert_list)
 importFrom(checkmate,assert_logical)
 importFrom(checkmate,assert_number)
 importFrom(checkmate,assert_numeric)
+importFrom(checkmate,assert_string)
 importFrom(checkmate,assert_vector)
 importFrom(checkmate,check_atomic_vector)
 importFrom(checkmate,check_data_frame)
diff --git a/R/plot.R b/R/plot.R
index 4d8d602e3..23894bafb 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -957,6 +957,7 @@ plot_pit <- function(pit,
 #' @importFrom ggplot2 ggplot scale_colour_manual scale_fill_manual
 #' geom_tile scale_fill_gradient .data
 #' @importFrom data.table dcast .I .N
+#' @importFrom checkmate assert_string assert_logical
 #' @export
 #' @examples
 #' library(ggplot2)
@@ -975,6 +976,10 @@ plot_forecast_counts <- function(forecast_counts,
                                  show_numbers = TRUE) {
 
   forecast_counts <- ensure_data.table(forecast_counts)
+  assert_string(y)
+  assert_string(x)
+  assert_logical(make_x_factor)
+  assert_logical(show_numbers)
 
   if (make_x_factor) {
     forecast_counts[, eval(x) := as.factor(get(x))]

From edeeae782e797fd133551e72e2adb06400e8af64 Mon Sep 17 00:00:00 2001
From: nikosbosse <nikosbosse@gmail.com>
Date: Wed, 6 Dec 2023 16:35:49 +0100
Subject: [PATCH 12/12] Update arguments for `plot_forecast_counts()`

---
 R/plot.R                                      | 32 +++++++++----------
 .../R/00-standalone-Figure-replication.R      |  2 +-
 inst/manuscript/manuscript.Rmd                |  2 +-
 man/plot_forecast_counts.Rd                   | 22 ++++++-------
 tests/testthat/test-plot_avail_forecasts.R    |  2 +-
 vignettes/scoringutils.Rmd                    |  2 +-
 6 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/R/plot.R b/R/plot.R
index 23894bafb..1527ef8e6 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -941,23 +941,22 @@ plot_pit <- function(pit,
 #'
 #' @description
 #' Visualise Where Forecasts Are Available
-#' @inheritParams print.scoringutils_check
-#' @param forecast_counts a data.table (or similar) with forecast counts
-#' as produced by [get_forecast_counts()]
-#' @param y character vector of length one that denotes the name of the column
-#' to appear on the y-axis of the plot. Default is "model".
+#' @param forecast_counts a data.table (or similar) with a column `count`
+#' holding forecast counts, as produced by [get_forecast_counts()]
 #' @param x character vector of length one that denotes the name of the column
 #' to appear on the x-axis of the plot.
-#' @param make_x_factor logical (default is TRUE). Whether or not to convert
+#' @param y character vector of length one that denotes the name of the column
+#' to appear on the y-axis of the plot. Default is "model".
+#' @param x_as_factor logical (default is TRUE). Whether or not to convert
 #' the variable on the x-axis to a factor. This has an effect e.g. if dates
 #' are shown on the x-axis.
-#' @param show_numbers logical (default is `TRUE`) that indicates whether
+#' @param show_counts logical (default is `TRUE`) that indicates whether
 #' or not to show the actual count numbers on the plot
 #' @return ggplot object with a plot of interval coverage
 #' @importFrom ggplot2 ggplot scale_colour_manual scale_fill_manual
 #' geom_tile scale_fill_gradient .data
 #' @importFrom data.table dcast .I .N
-#' @importFrom checkmate assert_string assert_logical
+#' @importFrom checkmate assert_string assert_logical assert
 #' @export
 #' @examples
 #' library(ggplot2)
@@ -965,23 +964,24 @@ plot_pit <- function(pit,
 #'   example_quantile, by = c("model", "target_type", "target_end_date")
 #' )
 #' plot_forecast_counts(
-#'  forecast_counts, x = "target_end_date", show_numbers = FALSE
+#'  forecast_counts, x = "target_end_date", show_counts = FALSE
 #' ) +
 #'  facet_wrap("target_type")
 
 plot_forecast_counts <- function(forecast_counts,
-                                 y = "model",
                                  x,
-                                 make_x_factor = TRUE,
-                                 show_numbers = TRUE) {
+                                 y = "model",
+                                 x_as_factor = TRUE,
+                                 show_counts = TRUE) {
 
   forecast_counts <- ensure_data.table(forecast_counts)
   assert_string(y)
   assert_string(x)
-  assert_logical(make_x_factor)
-  assert_logical(show_numbers)
+  assert(check_columns_present(forecast_counts, c(y, x)))
+  assert_logical(x_as_factor)
+  assert_logical(show_counts)
 
-  if (make_x_factor) {
+  if (x_as_factor) {
     forecast_counts[, eval(x) := as.factor(get(x))]
   }
 
@@ -1005,7 +1005,7 @@ plot_forecast_counts <- function(forecast_counts,
       )
     ) +
     theme(panel.spacing = unit(2, "lines"))
-  if (show_numbers) {
+  if (show_counts) {
     plot <- plot +
       geom_text(aes(label = `Count`))
   }
diff --git a/inst/manuscript/R/00-standalone-Figure-replication.R b/inst/manuscript/R/00-standalone-Figure-replication.R
index 3b1b66b9c..373f25ec4 100644
--- a/inst/manuscript/R/00-standalone-Figure-replication.R
+++ b/inst/manuscript/R/00-standalone-Figure-replication.R
@@ -537,7 +537,7 @@ p2 + p1 + p_true +
 available_forecasts(data = example_integer,
                 by = c("model", "target_type", "forecast_date")) |>
   plot_available_forecasts(x = "forecast_date",
-                       show_numbers = FALSE) +
+                       show_counts = FALSE) +
   facet_wrap(~ target_type) +
   labs(y = "Model", x = "Forecast date")
 
diff --git a/inst/manuscript/manuscript.Rmd b/inst/manuscript/manuscript.Rmd
index e907defac..782dc468d 100644
--- a/inst/manuscript/manuscript.Rmd
+++ b/inst/manuscript/manuscript.Rmd
@@ -432,7 +432,7 @@ library("ggplot2")
 available_forecasts(data = example_integer, 
                 by = c("model", "target_type", "forecast_date")) |>
   plot_available_forecasts(x = "forecast_date", 
-                       show_numbers = FALSE) + 
+                       show_counts = FALSE) + 
   facet_wrap(~ target_type) + 
   labs(y = "Model", x = "Forecast date") 
 ```
diff --git a/man/plot_forecast_counts.Rd b/man/plot_forecast_counts.Rd
index c4f4cade9..c5285c9dd 100644
--- a/man/plot_forecast_counts.Rd
+++ b/man/plot_forecast_counts.Rd
@@ -6,27 +6,27 @@
 \usage{
 plot_forecast_counts(
   forecast_counts,
-  y = "model",
   x,
-  make_x_factor = TRUE,
-  show_numbers = TRUE
+  y = "model",
+  x_as_factor = TRUE,
+  show_counts = TRUE
 )
 }
 \arguments{
-\item{forecast_counts}{a data.table (or similar) with forecast counts
-as produced by \code{\link[=get_forecast_counts]{get_forecast_counts()}}}
-
-\item{y}{character vector of length one that denotes the name of the column
-to appear on the y-axis of the plot. Default is "model".}
+\item{forecast_counts}{a data.table (or similar) with a column \code{count}
+holding forecast counts, as produced by \code{\link[=get_forecast_counts]{get_forecast_counts()}}}
 
 \item{x}{character vector of length one that denotes the name of the column
 to appear on the x-axis of the plot.}
 
-\item{make_x_factor}{logical (default is TRUE). Whether or not to convert
+\item{y}{character vector of length one that denotes the name of the column
+to appear on the y-axis of the plot. Default is "model".}
+
+\item{x_as_factor}{logical (default is TRUE). Whether or not to convert
 the variable on the x-axis to a factor. This has an effect e.g. if dates
 are shown on the x-axis.}
 
-\item{show_numbers}{logical (default is \code{TRUE}) that indicates whether
+\item{show_counts}{logical (default is \code{TRUE}) that indicates whether
 or not to show the actual count numbers on the plot}
 }
 \value{
@@ -41,7 +41,7 @@ forecast_counts <- get_forecast_counts(
   example_quantile, by = c("model", "target_type", "target_end_date")
 )
 plot_forecast_counts(
- forecast_counts, x = "target_end_date", show_numbers = FALSE
+ forecast_counts, x = "target_end_date", show_counts = FALSE
 ) +
  facet_wrap("target_type")
 }
diff --git a/tests/testthat/test-plot_avail_forecasts.R b/tests/testthat/test-plot_avail_forecasts.R
index d34caec51..f131a6d7a 100644
--- a/tests/testthat/test-plot_avail_forecasts.R
+++ b/tests/testthat/test-plot_avail_forecasts.R
@@ -4,7 +4,7 @@ test_that("plot.forecast_counts() works as expected", {
     by = c("model", "target_type", "target_end_date")
   )
   p <- plot_forecast_counts(available_forecasts,
-    x = "target_end_date", show_numbers = FALSE
+    x = "target_end_date", show_counts = FALSE
   ) +
     facet_wrap("target_type")
   expect_s3_class(p, "ggplot")
diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd
index 92e532335..05ea4b365 100644
--- a/vignettes/scoringutils.Rmd
+++ b/vignettes/scoringutils.Rmd
@@ -107,7 +107,7 @@ example_quantile %>%
   ) %>%
   make_NA(
     what = "forecast",
-    model != "EuroCOVIDhub-ense mble",
+    model != "EuroCOVIDhub-ensemble",
     forecast_date != "2021-06-28"
   ) %>%
   plot_predictions(