ucdp

An R data package for the Uppsala Conflict Data Program, containing the Georeferenced Event Dataset (GED) Global version 20.1. This dataset is UCDP’s most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.

Installation

You can install the development version of ucdp from GitHub with:

# install.packages("devtools")
devtools::install_github("mcgirjau/ucdp")

Usage

Load the package in your R session with:

library(ucdp)

Use the GED dataset as-is, or load it explicitly into your environment using:

data("GED")

Examples

The following are some examples of questions that the Georeferenced Event Dataset may help investigate. Load these additional packages to try out the examples:

library(dplyr)
library(ggplot2)
library(knitr)
library(tidyr)

How does the number of total conflict fatalities vary from 1989 to 2019?

Let’s make a time series plot to visualize how the number of total deaths varies by year.

GED %>%
  group_by(year) %>% 
  summarize(total_deaths = sum(best)) %>% 
  ggplot(aes(x = year, y = total_deaths)) + 
  geom_line(color = "darkred") +
  labs(x = "Year", y = "Total Deaths", title = "Total Deaths by Year")

Notice the very dramatic peak in total fatalities around 1994. Was that a tumultuous year for the entire world, or was there a single event that produced such a large volume of deaths? Let’s add region to our graph in order to find out:

GED %>%
  group_by(year, region) %>% 
  summarize(total_deaths = sum(best)) %>% 
  ggplot(aes(x = year, y = total_deaths, color = region)) + 
  geom_line() +
  labs(x = "Year", y = "Total Deaths", color = "Region", title = "Total Deaths by Year") +
  theme(legend.position = "bottom")

Seems like the 1994 peak in total fatalities was due to some African conflict. By comparison, that same year was relatively non-lethal for all other regions.

Notice how conflicts in Africa used to produce a large volume of deaths until around 2001, after which things began to calm down. Conflicts in Asia, Europe, and the Americas have produced very few deaths from 1989 to 2019, and conflicts in the Middle East region used to follow the same pattern until around 2011, when a sudden surge in fatalities can be observed. That surge peaked around 2014, and conflict fatalities in the region have been decreasing steadily since, almost reaching pre-2011 levels in 2019.

Which 10 countries sustained the most conflict fatalities from 1989 to 2019?

GED %>%
  group_by(country) %>% 
  summarize(total_deaths = sum(best)) %>% 
  arrange(desc(total_deaths)) %>%
  head(10) %>%
  kable(col.names = c("Country", "Total Fatalities"))

Country	Total Fatalities
Rwanda	543176
Syria	359843
Afghanistan	257944
Ethiopia	178169
Iraq	121659
DR Congo (Zaire)	114859
Sudan	93128
Sri Lanka	65628
Nigeria	57032
India	56716

What were the deadliest conflicts in each region from 1989 to 2019?

GED %>%
  group_by(region, conflict_name) %>% 
  summarize(total_deaths = sum(best)) %>% 
  slice_max(order_by = total_deaths, n = 1) %>%
  kable(col.names = c("Region", "Conflict", "Total Fatalities"))

Region	Conflict	Total Fatalities
Africa	Government of Rwanda - Civilians	534213
Americas	Colombia: Government	19954
Asia	Afghanistan: Government	229878
Europe	Russia (Soviet Union): Chechnya	18196
Middle East	Syria: Government	251210

What were the most intentionally-lethal actors (i.e. actors not targetting civilians) on the world stage from 1989 to 2019?

GED %>%
  select(side_a, side_b, deaths_a, deaths_b) %>%
  pivot_longer(cols = c(side_a, side_b), names_to = "side_id", values_to = "side_name") %>%
  mutate(deaths_inflicted = ifelse(side_id == "side_a", deaths_b, deaths_a)) %>%
  group_by(side_name) %>% 
  summarize(total_deaths_inflicted = sum(deaths_inflicted)) %>% 
  arrange(desc(total_deaths_inflicted)) %>%
  head(10) %>%
  kable(col.names = c("Actor", "Total Deaths Inflicted"))

Actor	Total Deaths Inflicted
Government of Afghanistan	136781
Syrian insurgents	102555
Government of Syria	85091
IS	47671
Government of Iraq	45135
EPLF	42900
EPRDF	39893
Government of Sri Lanka	37831
Taleban	34561
Government of Turkey	25461