From db4bf80793259c35b674177e9f12782e01335057 Mon Sep 17 00:00:00 2001 From: jamesmbaazam Date: Tue, 19 Nov 2024 11:05:50 +0000 Subject: [PATCH] Fixes to plots --- vignettes/benchmarks.Rmd.orig | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/vignettes/benchmarks.Rmd.orig b/vignettes/benchmarks.Rmd.orig index c90f88201..b1c92ebac 100644 --- a/vignettes/benchmarks.Rmd.orig +++ b/vignettes/benchmarks.Rmd.orig @@ -528,7 +528,7 @@ timing_plot <- ggplot(data = runtimes_dt_detailed[, fit_type := ifelse(fitting = caption = "non-stationary Rt = R(t-1) * GP; stationary Rt = R0 * GP; non-mechanistic Rt = no GP prior." ) + theme_minimal() + - facet_wrap(~fit_type, scales = "free_y", nrow = 2) + facet_wrap(~fit_type, scales = "free_y", nrow = 2, strip.position = "left") timing_plot ``` @@ -705,14 +705,14 @@ Let's first see how the models performed over time for the $R_t$ using the CRPS. Let's start by looking at the two broad individual model types (stationary vs non-stationary) ```{r rt_ns_gp_plot,class.source = 'fold-hide'} rt_ns_gp_plot <- ggplot( - data = rt_crps_dt_final[rt_gp_prior == "non_stationary"], - # data = infections_crps_dt_final + data = rt_crps_dt_final[rt_gp_prior == "non_stationary"] ) + geom_line( - aes(x = date, + aes(x = factor(epidemic_phase, levels = c("growth", "peak", "decline")), y = crps, - color = model - ) + color = model_basename, + linetype = fitting + ) ) + scale_colour_brewer("Model", palette = "Dark2") + # scale_y_log10(labels = label_number_auto()) +