Merge pull request #917 from epiforecasts/update-suggests

Update suggests
epiforecasts · Sep 23, 2024 · fb58190 · fb58190
2 parents 9d28b4b + f87fe68
commit fb58190
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diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -42,7 +42,7 @@ jobs:
 
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::rcmdcheck
+          extra-packages: any::rcmdcheck, ggdist=?ignore-before-r=4.1.0
           needs: check
 
       - uses: r-lib/actions/check-r-package@v2

diff --git a/vignettes/Deprecated-visualisations.Rmd b/vignettes/Deprecated-visualisations.Rmd
@@ -26,7 +26,6 @@ The example data used is the data shipped with the `scoringutils` package. It co
 library(scoringutils)
 library(data.table)
 library(ggplot2)
-library(ggdist)
 library(magrittr)
 library(magrittr) #pipe operator
 ```
@@ -35,7 +34,7 @@ library(magrittr) #pipe operator
 
 In previous versions of `scoringutils`, forecasts and observed values could be visualised using the function `plot_predictions()` and its `make_na()` helper function. The following shows the function code first and then an example. 
 
-```{r}
+```{r eval=!require("ggdist", quietly = TRUE)}
 #" @title Plot Predictions vs True Values
 #"
 #" @description
@@ -98,6 +97,8 @@ In previous versions of `scoringutils`, forecasts and observed values could be v
 #"   facet_wrap(~ location + target_type, scales = "free_y") +
 #"   aes(fill = model, color = model)
 
+
+library(ggdist)
 plot_predictions <- function(data,
                              by = NULL,
                              x = "date",
@@ -277,7 +278,7 @@ In the following are a few examples of using the two functions to create a plot
 
 Visualising the median forecasts for the example data. The truth data is restricted to a period between 2021-05-01 and 2021-07-22. The forecast data is a forecast from the model "EuroCOVIDhub-ensemble" made on the "2021-06-07". All other data is set to `NA`, effectively removing it from the plot. 
 
-```{r}
+```{r eval=!require("ggdist", quietly = TRUE)}
 median_forecasts <- example_quantile[quantile_level == 0.5]
 median_forecasts %>%
   make_NA(what = "truth",
@@ -295,7 +296,7 @@ median_forecasts %>%
 
 This is the same plot, but with a variety of prediction intervals shown, instead of just the median. 
 
-```{r}
+```{r eval=!require("ggdist", quiet = TRUE)}
 example_quantile %>%
   make_NA(what = "truth",
           target_end_date <= "2021-05-01",
@@ -313,7 +314,7 @@ example_quantile %>%
 
 And a similar plot, this time based on continuous forecasts. The predictions are automatically converted to a quantile-based forecasts for plotting.
 
-```{r}
+```{r eval=!require("ggdist", quietly = TRUE)}
 example_sample_continuous %>%
   make_NA(what = "truth",
           target_end_date <= "2021-05-01",
@@ -331,7 +332,7 @@ example_sample_continuous %>%
 
 Displaying two forecasts at a time with additional colours: 
 
-```{r}
+```{r eval=!require("ggdist", quietly = TRUE)}
 example_quantile %>%
   make_NA(what = "truth",
           target_end_date > "2021-07-15",