Fix figure in modelling vignette

vignettes/projecting_incidence.Rmd

@@ -18,12 +18,13 @@ editor_options:
 ---
 
 ```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE,
-                      message = FALSE,
-                      warning = FALSE,
-                      collapse = TRUE,
-                      comment = "#>"
-                      )
+knitr::opts_chunk$set(
+  echo = TRUE,
+  message = FALSE,
+  warning = FALSE,
+  collapse = TRUE,
+  comment = "#>"
+)
 
 ```
 
@@ -275,22 +276,11 @@ Now we will aggregate the simulations by day and evaluate the median
 daily cases across all simulations.
 ```{r}
 # Median daily number of cases aggregated across all simulations
-median_daily_cases <- incidence_ts %>%
-  group_by(day) %>%
+median_daily_cases <- incidence_ts_by_date %>%
+  group_by(date) %>%
   summarise(median_cases = median(cases)) %>%
   ungroup() %>%
-  arrange(day)
-
-head(median_daily_cases)
-```
-
-As was done for the individual simulations, we will add a date column in the
-same manner.
-```{r}
-# Add dates
-median_daily_cases <- median_daily_cases %>%
-  mutate(date = index_date + days(seq(0, projection_end_day))) %>%
-  ungroup()
+  arrange(date)
 
 head(median_daily_cases)
 ```
@@ -299,7 +289,7 @@ head(median_daily_cases)
 
 We will now plot the individual simulation results alongside the median
 of the aggregated results.
-```{r viz, fig.cap ="COVID-19 incidence projected over a two week window. The gray lines represent individual simulations, red connected dots represent the median daily cases across all simulations, and the black triangles represent the observed data.", fig.width=6.0, fig.height=6}
+```{r viz, fig.cap ="COVID-19 incidence in South Africa projected over a two week window in 2020. The light gray lines represent the individual simulations, the red line represents the median daily cases across all simulations, the black connected dots represent the observed data, and the dashed vertical line marks the beginning of the projection.", fig.width=6.0, fig.height=6}
 
 ggplot(data = incidence_ts_by_date) +
   geom_line(
@@ -338,19 +328,22 @@ ggplot(data = incidence_ts_by_date) +
       y = cases
     ),
     color = "black",
-    size = 1.75,
-    shape = 21
+    linewidth = 1
   ) +
   scale_x_continuous(
     breaks = seq(
       min(incidence_ts_by_date$date),
       max(incidence_ts_by_date$date),
-      10
+      5
     ),
     labels = seq(
       min(incidence_ts_by_date$date),
       max(incidence_ts_by_date$date),
-      10
+      5
+    ),
+    limits = c(
+      min(incidence_ts_by_date$date),
+      max(incidence_ts_by_date$date) - 4 # for a better visual look
     )
   ) +
   scale_y_continuous(
@@ -365,6 +358,10 @@ ggplot(data = incidence_ts_by_date) +
       250
     )
   ) +
-  labs(x = "Date", y = "Daily cases (median)")
+  geom_vline(
+    mapping = aes(xintercept = max(covid19_sa$date)),
+    linetype = "dashed"
+  ) +
+  labs(x = "Date", y = "Daily cases")
 ```
 ## References