Fix plot issue

01_exploratory_plots.R

@@ -150,7 +150,7 @@ suitable_diff <- ggplot(data = df_suitable_diff) +
 ggsave(suitable_diff, filename = "output/era_suit_diff.png")
 
 
-## State monthly scPDSI set out in shape of Brazil (using grid_br)  (Figure M1)
+## State monthly scPDSI set out in shape of Brazil (using grid_br)  (Fig A in S1 Text)
 pdsi_heat <- ggplot(data = df_climate_grid, 
                      aes(x = month, y = year, fill = pdsi)) +
   geom_raster() +
@@ -187,7 +187,7 @@ df_wet <- df_year %>%
   st_as_sf()
 
 
-## Difference in months extremely wet (Figure M2)
+## Difference in months extremely wet (Fig B in S1 Text)
 # Convert decade df into wide format (variable per decade)
 df_wet_diff <- spread(st_drop_geometry(df_wet), 
                       decade, months_wet) %>%
@@ -652,7 +652,7 @@ first_outbreak20 <- ggplot(data = df_outbreak) +
   expand_limits(fill = c(2001, 2020)) +
   # Add municipalities without outbreaks to legend
   scale_colour_manual(values = NA) +
-  guides(colour = guide_legend("No outbreaks")) +
+  guides(colour = guide_legend("No \noutbreaks")) +
   theme_void() +
   theme(legend.title = element_text(size = 15),
         legend.text = element_text(size = 10))
@@ -669,7 +669,7 @@ first_outbreak10 <- ggplot(data = df_outbreak) +
   # Ensure legends are on same scale
   expand_limits(fill = c(2001, 2020)) +
   scale_colour_manual(values = NA) +
-  guides(colour = guide_legend("No outbreaks")) +
+  guides(colour = guide_legend("No \noutbreaks")) +
   theme_void() +
   theme(legend.title = element_text(size = 15),
         legend.text = element_text(size = 10))
@@ -691,7 +691,7 @@ first_outbreak_decade <- plot_grid(first_outbreak_decade, outbreak_leg,
 
 
 ggsave(first_outbreak_decade, filename = "output/first_outbreak_decade.png",
-       height = 5, width = 10)
+       height = 5, width = 12)
 
 
 #### Plot 75th percentile outbreak threshold (Figure M) ####

03_model_output.R

@@ -22,7 +22,7 @@ model_aeg <- readRDS("output/model_aeg.rds")
 model_wet <- readRDS("output/model_wet.rds")
 
 
-## Sensitivity analysis - remove each variable in turn
+## Sensitivity analysis - add each variable in turn
 model_temp <- readRDS("output/model_temp.rds")
 model_prior <- readRDS("output/model_prior.rds")
 model_urb <- readRDS("output/model_urb.rds")
@@ -230,8 +230,8 @@ spat_est01.full <- spat_est.full[df_model$year == 2001, ]
 mean_spat.full <- apply(spat_est01.full, 1, mean)
 
 # Create 95% credible interval by taking 2.5th and 97.5th percentiles
-lci_spat.full <- apply(spat_est01.full, 1, quantile, probs=0.025)
-uci_spat.full <- apply(spat_est01.full, 1, quantile, probs=0.975)
+lci_spat.full <- apply(spat_est01.full, 1, quantile, probs=0.025, na.rm = T)
+uci_spat.full <- apply(spat_est01.full, 1, quantile, probs=0.975, na.rm = T)
 
 
 # Join random effects to model df + plot on a map