several minor formatting changes

Author: XanderHuggins

R/analysis/3-uncertainty-spatially-uniform.R

@@ -121,6 +121,6 @@ for (i in 1:nrow(s_df)) {
 }
 
 # Save results
-write.csv(s_df, here('Data/montecaro_basin_10percentpert.csv'))
-write.csv(bt_df, here('Data/mc_uniform_basin_threshold.csv'))
-write.csv(bh_df, here('Data/mc_uniform_basin_hotspot.csv'))
+write.csv(s_df, here('Data/mc_basin_all_uniform.csv'))
+write.csv(bt_df, here('Data/mc_basin_threshold_uniform.csv'))
+write.csv(bh_df, here('Data/mc_basin_hotspot_uniform.csv'))

R/analysis/4-uncertainty-spatially-variable.R

@@ -135,6 +135,6 @@ for (i in 1:nrow(s_df)) {
 }
 
 # Save results
-write.csv(s_df, here('Data/montecaro_basin_10percentpert_variable.csv'))
-write.csv(bt_df, here('Data/mc_uniform_basin_threshold_variable.csv'))
-write.csv(bh_df, here('Data/mc_uniform_basin_hotspot_variable.csv'))
+write.csv(s_df, here('Data/mc_basin_all_variable.csv'))
+write.csv(bt_df, here('Data/mc_basin_threshold_variable.csv'))
+write.csv(bh_df, here('Data/mc_basin_hotspot_variable.csv'))

R/plotting+analysis/figure-1-plotting.R

@@ -36,7 +36,7 @@ tm <- tm_shape(basins_data, projection = "+proj=robin") +
   tm_polygons(col = "fwstrs",  style = "cont", 
               palette = scico(20, palette = "bilbao", direction = 1), 
             midpoint = 0.2, breaks = c(0, 0.4), alpha = 1.00,
-            lwd = 0.2) +
+            lwd = 0.2, colorNA = 'grey50') +
   tm_shape(coastlines) +
   tm_borders(lwd = 0.7, col = "black") +
   tm_layout(legend.show = F, earth.boundary = c(-179, -60, 179, 88),

R/plotting+analysis/figure-s8-plotting+stats.R

@@ -9,9 +9,9 @@ invisible(sapply(paste0(here("R/setup"), "/", list.files(here("R/setup"))), sour
 invisible(sapply(paste0(here("R/udf"), "/", list.files(here("R/udf"))), source))
 
 # Import coastlines and basin shapefile with plotting data
-s_df <- read.csv(here('Data/montecaro_basin_10percentpert.csv'))
-bt_df <- read.csv(here('Data/mc_uniform_basin_threshold.csv'))
-bh_df <- read.csv(here('Data/mc_uniform_basin_hotspot.csv'))
+s_df <- read.csv(here('Data/mc_basin_all_uniform.csv'))
+bt_df <- read.csv(here('Data/mc_basin_threshold_uniform.csv'))
+bh_df <- read.csv(here('Data/mc_basin_hotspot_uniform.csv'))
 
 # Select columns of interest to plot
 p_df <- s_df %>% dplyr::select(NhotH, NhotVH, Cptb, Qptb, Tptb, Aptb, Eptb, Vptb)
@@ -25,7 +25,7 @@ sensplot <-
   gather(-hotspots, key = "var", value = "value") %>%
   ggplot(aes(x = value, y = hotspots)) +
   geom_point(alpha = 0.05, size = 1) + 
-  geom_point(data = NULL, aes(x = 0, y = 172), color = "red", size = 1.5) +
+  geom_point(data = NULL, aes(x = 0, y = 168), color = "red", size = 1.5) +
   # geom_smooth(alpha = 0.5) +
   facet_wrap(~ var, nrow = 1) +
   scale_y_continuous(limits = c(0, 300)) +

R/plotting+analysis/figure-s9-plotting+stats.R

@@ -9,10 +9,11 @@ invisible(sapply(paste0(here("R/setup"), "/", list.files(here("R/setup"))), sour
 invisible(sapply(paste0(here("R/udf"), "/", list.files(here("R/udf"))), source))
 
 # Import coastlines and basin shapefile with plotting data
-s_df <- read.csv(here('Data/montecaro_basin_10percentpert_variable.csv'))
-bt_df <- read.csv(here('Data/mc_uniform_basin_threshold_variable.csv'))
-bh_df <- read.csv(here('Data/mc_uniform_basin_hotspot_variable.csv'))
+s_df <- read.csv(here('Data/mc_basin_all_variable.csv'))
+bt_df <- read.csv(here('Data/mc_basin_threshold_variable.csv'))
+bh_df <- read.csv(here('Data/mc_basin_hotspot_variable.csv'))
 
+# Select columns of interest to plot
 p_df <- s_df %>% dplyr::select(NhotH, NhotVH, Cpsd, Qpsd, Tpsd, Apsd, Epsd, Vpsd)
 names(p_df) <- c('NhotH', 'NhotVH', 'a) Consumption', 'b) Runoff', 'c) TWS', 'd) Adaptability', 'e) EFN', 'g) VSI')
 p_df$hotspots <- p_df$NhotH + p_df$NhotVH # Calculate hotspot total
@@ -24,7 +25,7 @@ sensplot <-
   gather(-hotspots, key = "var", value = "value") %>%
   ggplot(aes(x = value, y = hotspots)) +
   geom_point(alpha = 0.05, size = 1) + 
-  geom_point(data = NULL, aes(x = 0, y = 172), color = "red", size = 1.5) +
+  geom_point(data = NULL, aes(x = 0, y = 168), color = "red", size = 1.5) +
   # geom_smooth(alpha = 0.5) +
   facet_wrap(~ var, nrow = 1) +
   scale_y_continuous(limits = c(0, 300)) +
@@ -127,5 +128,4 @@ summary_df %>% filter(Actual == 1) %>% filter(Freq >= 0.50) %>% nrow()
 summary_df %>% filter(Actual == 1) %>% filter(Freq < 0.50) %>% nrow()
 
 # Number of *non* hotspots identified as so in > 50% of perturbations
-summary_df %>% filter(Actual == 0) %>% filter(Freq >= 0.50) %>% nrow()
-
+summary_df %>% filter(Actual == 0) %>% filter(Freq >= 0.50) %>% nrow()

R/preprocessing/1-data-resolution-harmonization.R

@@ -87,6 +87,7 @@ GDP_2015_0d5 <- raster::aggregate(x = GDP_2015, fact = x, fun = sum, expand = F,
                                   filename = here::here("Data", "Dimensions", 
                                                         "GDP_2015_0d5.tif"))
 
+
 # Amphibian species richness ----
 amph <- raster(here::here("Data", "Dimensions", "all_amphibians.tif"))
 extent(amph) = c(-180, 180, -90, 90)