diff --git a/NAMESPACE b/NAMESPACE
index 97d7f8b63..d05f4258d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,scoringutils_available_forecasts)
 S3method(print,scoringutils_check)
 S3method(quantile_to_interval,data.frame)
 S3method(quantile_to_interval,numeric)
@@ -19,8 +18,6 @@ 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 +28,7 @@ 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)
@@ -49,8 +47,8 @@ export(overprediction)
 export(pairwise_comparison)
 export(pit)
 export(pit_sample)
-export(plot_avail_forecasts)
 export(plot_correlation)
+export(plot_forecast_counts)
 export(plot_heatmap)
 export(plot_interval_coverage)
 export(plot_pairwise_comparison)
@@ -88,6 +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/NEWS.md b/NEWS.md
index 0faf9eaf2..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 `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 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()` 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()`. 
@@ -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.
diff --git a/R/available_forecasts.R b/R/available_forecasts.R
index 17418b0a6..8225620d3 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")) {
 
@@ -58,7 +58,7 @@ available_forecasts <- 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
@@ -70,23 +70,5 @@ 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))
-
   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)
-}
diff --git a/R/plot.R b/R/plot.R
index 0ec4bb8c1..1527ef8e6 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) {
@@ -941,54 +941,58 @@ 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 yvar character vector of length one that denotes the name of the column
+#' @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 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_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 assert
 #' @export
 #' @examples
 #' library(ggplot2)
-#' available_forecasts <- available_forecasts(
+#' 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_counts = FALSE
 #' ) +
 #'  facet_wrap("target_type")
 
-plot.scoringutils_available_forecasts <- function(x,
-                                                  yvar = "model",
-                                                  xvar = "forecast_date",
-                                                  make_xvar_factor = TRUE,
-                                                  show_numbers = TRUE,
-                                                  ...) {
-  x <- as.data.table(x)
-
-  if (make_xvar_factor) {
-    x[, eval(xvar) := as.factor(get(xvar))]
+plot_forecast_counts <- function(forecast_counts,
+                                 x,
+                                 y = "model",
+                                 x_as_factor = TRUE,
+                                 show_counts = TRUE) {
+
+  forecast_counts <- ensure_data.table(forecast_counts)
+  assert_string(y)
+  assert_string(x)
+  assert(check_columns_present(forecast_counts, c(y, x)))
+  assert_logical(x_as_factor)
+  assert_logical(show_counts)
+
+  if (x_as_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"
@@ -1001,54 +1005,14 @@ plot.scoringutils_available_forecasts <- function(x,
       )
     ) +
     theme(panel.spacing = unit(2, "lines"))
-
-  if (show_numbers) {
+  if (show_counts) {
     plot <- plot +
       geom_text(aes(label = `Count`))
   }
-
   return(plot)
 }
 
 
-#' @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
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/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/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.scoringutils_available_forecasts.Rd
deleted file mode 100644
index f11e0dc5d..000000000
--- a/man/plot.scoringutils_available_forecasts.Rd
+++ /dev/null
@@ -1,50 +0,0 @@
-% 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}
-\title{Visualise Where Forecasts Are Available}
-\usage{
-\method{plot}{scoringutils_available_forecasts}(
-  x,
-  yvar = "model",
-  xvar = "forecast_date",
-  make_xvar_factor = TRUE,
-  show_numbers = TRUE,
-  ...
-)
-}
-\arguments{
-\item{x}{an S3 object of class "scoringutils_available_forecasts"
-as produced by \code{\link[=available_forecasts]{available_forecasts()}}}
-
-\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".}
-
-\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{make_xvar_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
-}
-\description{
-Visualise Where Forecasts Are Available
-}
-\examples{
-library(ggplot2)
-available_forecasts <- available_forecasts(
-  example_quantile, by = c("model", "target_type", "target_end_date")
-)
-plot(
- available_forecasts, xvar = "target_end_date", show_numbers = FALSE
-) +
- facet_wrap("target_type")
-}
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/man/plot_forecast_counts.Rd b/man/plot_forecast_counts.Rd
new file mode 100644
index 000000000..c5285c9dd
--- /dev/null
+++ b/man/plot_forecast_counts.Rd
@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot_forecast_counts}
+\alias{plot_forecast_counts}
+\title{Visualise Where Forecasts Are Available}
+\usage{
+plot_forecast_counts(
+  forecast_counts,
+  x,
+  y = "model",
+  x_as_factor = TRUE,
+  show_counts = TRUE
+)
+}
+\arguments{
+\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{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_counts}{logical (default is \code{TRUE}) that indicates whether
+or not to show the actual count numbers on the plot}
+}
+\value{
+ggplot object with a plot of interval coverage
+}
+\description{
+Visualise Where Forecasts Are Available
+}
+\examples{
+library(ggplot2)
+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_counts = FALSE
+) +
+ facet_wrap("target_type")
+}
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..f131a6d7a 100644
--- a/tests/testthat/test-plot_avail_forecasts.R
+++ b/tests/testthat/test-plot_avail_forecasts.R
@@ -1,11 +1,10 @@
-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
+  p <- plot_forecast_counts(available_forecasts,
+    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 4ae09b2ac..05ea4b365 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(
@@ -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. 
@@ -91,8 +91,8 @@ 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")) %>%
-  plot() +
+  get_forecast_counts(by = c("model", "forecast_date", "target_type")) %>%
+  plot_forecast_counts(x = "forecast_date") +
   facet_wrap(~ target_type)
 ```