Proportion of Darfur Civilian Killings by Region

A closer inspection of civilian killings by state in Darfur via the ACLED dataset shows that as UN bases were being withdrawn between 2017 and 2021, the proportion of all such killings was shrinking in Central Darfur, but generally growing in South Darfur and West Darfur. By the end of 2020 51% of civilian killings were concentrated in South Darfur, and by the end of 2021 48% of civilian killings were concentrated in West Darfur.

# Note: All code, insights shared, and methodological notes in this document and other documents in this portfolio ARE NOT open source and MUST be cited.

setwd("C:/Users/rsb84/Desktop/RB/COLUMBIA/QMSS/COURSES/Spring_2021/Data Visualization/End_project")

ACLED_data <- readxl::read_excel("ACLED-DARFUR-VAC-2008-2021 (After Course Ended-for 2023 Portfolio)-UPDATED VERSION-inter1_numbers_replaced_with_actor_names.xlsx", 
     col_types = c("date", "numeric", "text", 
         "text", "text", "text", "text", "text", 
         "text", "text", "text", "text", "text", 
         "numeric", "numeric", "text", "text", 
         "text", "numeric", "numeric"))

library(tidyverse)
library(ggplot2)
library(ggthemes)
library(plotly)

fatalities_by_year_region=ACLED_data %>% group_by(year, admin1) %>% tally(fatalities)

df=fatalities_by_year_region %>% 
  group_by(year) %>% 
  mutate(pct= prop.table(n) * 100) %>%
  mutate(admin1 = factor(admin1, levels = c("Central Darfur", "North Darfur", "South Darfur", "East Darfur", "West Darfur"))) %>% #This controls the order of the Darfur regions on the legend 
  mutate(Percent = round(pct, digits = 2),
         Region = admin1,
         Year = year)

df_2016_2021 = subset(df, df$Year != 2008 & df$Year != 2009 & df$Year != 2010 & df$Year != 2011 & df$Year != 2012 & df$Year != 2013 & df$Year != 2014 & df$Year != 2015)

# Define colors corresponding to each group
colors <- c("Central Darfur" = "maroon1",
            "North Darfur" = "deepskyblue1",
            "South Darfur" = "peru",
            "East Farfur" = "gray65",
            "West Darfur" = "black")

# Specify the custom labels for the y-axis
custom_labels <- c("0", "25", "50", "75", "100")

# Create the plot
gg <- ggplot(df_2016_2021, aes(x = Year, y = Percent, fill = Region)) +
  geom_col(position = "fill", width = 0.8, size = 1, alpha = 0.7) +
  scale_fill_manual(values = colors, 
                    breaks = c("Central Darfur", "North Darfur", "South Darfur", "East Darfur", "West Darfur"),
                    labels = c("Central Darfur", "North Darfur", "South Darfur", "East Darfur", "West Darfur")) +
  scale_y_continuous(breaks = seq(0, 1, by = 0.25), labels = custom_labels) +  # Set custom breaks and labels for y-axis
  scale_x_continuous(breaks = seq(min(df_2016_2021$Year), max(df_2016_2021$Year), by = 1)) +  # Ensure years are displayed without gaps
  theme_wsj() +
  theme(legend.position = "top",
        title = element_text(size = 14.5, color = "steelblue", face = 'bold'),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 14),
        legend.text=element_text(size=14, face = 'bold')) +
  labs(title = "Percentage Breakdown of Darfur Civilian Killings by Region \n(2016 - 2021)",
       subtitle = "",
       caption = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  guides(fill = guide_legend(title = ""))

ggplotly(gg, height = 600, width = 750)

# Get the current page number from the file name
current_page <- as.numeric(str_extract(knitr::current_input(), "\\d+"))

# Set the total number of pages
total_pages <- 9

# Generate the URLs for the previous and next pages
previous_page <- ifelse(current_page > 1, paste0("visual_", current_page - 1, "-darfur_violence-code_included.html"), NA)
next_page <- ifelse(current_page < total_pages, paste0("visual_", current_page + 1, "-darfur_violence-code_included.html"), NA)

Proportion of Darfur Civilian Killings by Region

(2016 - 2021) - Visual 4

Rob Boswell

7-27-2023