template-plot-forecasts.Rmd

```{r prediction-plots, echo = FALSE, results='asis', fig.width = 10, fig.height=10}

for (forecast_date in forecast_dates) {
  cat(paste0("\n\n## ", forecast_date, "{.tabset} \n\n"))
  #, "{.tabset}\n\n")
  # plot_df <- dplyr::left_join(tidyr::expand(data = data, target_type, true_value, location_name, model), 
  #                             data)
  
  for (location in locations) {
    cat("\n\n###", location, "{.tabset} \n\n")
  
    
    for (target_type in target_types) {
      cat("\n\n####", target_type, "\n\n")
      
      filter_both <- list(paste0("target_type %in% '", target_type, "'"), 
                          paste0("location_name %in% '", location, "'"))
      filter_truth <- list(paste0("target_end_date > '", as.Date(forecast_date) - 7 * 10, "'"), 
                           paste0("target_end_date <= '", as.Date(forecast_date) + 7 * 4, "'"))
      filter_forecasts <- list(paste0("forecast_date == '", as.Date(forecast_date), "'"))
      
      
      plot <- scoringutils::plot_predictions(data,
                                             x = "target_end_date",
                                             filter_both = filter_both,
                                             filter_truth = filter_truth,
                                             filter_forecasts = filter_forecasts,
                                             facet_formula = ~ model,
                                             # facet_formula = model ~ target_type + location_name,
                                             # facet_wrap_or_grid = "facet",
                                             allow_truth_without_pred = FALSE,
                                             scales = "free") + 
        # ggplot2::ggtitle(paste0("Predictions for incident ", target_type,  "s")) + 
        ggplot2::theme(legend.position = "bottom", 
                       strip.placement = "outside")
      
      print(plot)
    }
  }
}



```