separate thousands

R/descriptive.R

@@ -38,7 +38,7 @@ table_confint <- function(scores, group_var = NULL) {
 
   table <- scores |>
     summarise(
-      n_forecasts = n(),
+      n_forecasts = format(n(), big.mark = ","),
       p_forecasts = round(n() / total_forecasts * 100, 1),
       n_models = n_distinct(Model),
       p_models = round(n_models / total_models * 100, 1),

report/results.Rmd

@@ -54,7 +54,7 @@ per_week <- scores |>
 per_week <- summary(per_week$n_models)
 ```
 
-We evaluated a total `r n_forecasts` forecast predictions from  `r n_models` forecasting models, contributed by `r nrow(n_teams)` separate modelling teams to the European COVID-19 Forecast Hub (Table \@ref(tab:table-scores)). `r sum(n_teams$n>1)` teams contributed more than one model. Participating models varied over time as forecasting teams joined or left the Hub and contributed predictions for varying combinations of forecast targets. Between `r round(per_week[["Min."]])` and `r round(per_week[["Max."]])` models contributed in any one week, forecasting for any combination of `r 2*4*32` possible weekly forecast targets (32 countries, 4 horizons, and 2 target outcomes). On average each model contributed `r round(model_forecasts[["Mean"]])` forecasts, with the median model contributing `r model_forecasts[["Median"]]` forecasts. 
+We evaluated a total `r format(n_forecasts, big.mark = ",")` forecast predictions from  `r n_models` forecasting models, contributed by `r nrow(n_teams)` separate modelling teams to the European COVID-19 Forecast Hub (Table \@ref(tab:table-scores)). `r sum(n_teams$n>1)` teams contributed more than one model. Participating models varied over time as forecasting teams joined or left the Hub and contributed predictions for varying combinations of forecast targets. Between `r round(per_week[["Min."]])` and `r round(per_week[["Max."]])` models contributed in any one week, forecasting for any combination of `r 2*4*32` possible weekly forecast targets (32 countries, 4 horizons, and 2 target outcomes). On average each model contributed `r format(round(model_forecasts[["Mean"]]), big.mark = ",")` forecasts, with the median model contributing `r model_forecasts[["Median"]]` forecasts. 
 
 ```{r table-scores}
 print_table1(scores)