IGAD, a prominent economic consortium in East Africa, spans across 5.2 million square kilometers, covering Djibouti, Eritrea, Ethiopia, Kenya, Somalia, South Sudan, Sudan, and Uganda.

We’ll visualize some key profiles of the countries within IGAD, including their population, population density (people per square kilometer), and the percentage of urban population.

Data for African countries, including population, density, urban population percentage, and other classifications, can be accessed from this site. The corresponding CSV file provided in 2024, though containing data from 2023, is available here.

library(plotly)
library(tidyr)
library(tidyverse)
population<-read.csv(
  "https://raw.githubusercontent.com/dawit3000/Data/main/population_africa_2024.csv")
IGAD<-filter(population, population[,2] %in% c("Djibouti", "Eritrea", 
     "Ethiopia", "Kenya", "Somalia", "South Sudan", "Sudan", "Uganda") )
IGAD<-IGAD[, c(2,3,6,11)]

show(IGAD)

##       Country  Population Density Urban
## 1    Ethiopia 126,527,060     127   22%
## 2       Kenya  55,100,586      97   31%
## 3       Sudan  48,109,006      27   35%
## 4      Uganda  48,582,334     243   29%
## 5     Somalia  18,143,378      29   46%
## 6 South Sudan  11,088,796      18   28%
## 7     Eritrea   3,748,901      37   67%
## 8    Djibouti   1,136,455      49   72%

We will create new variables for the following

• the percentages of their population within IGAD (PCT_Population),
• the percentage of their density within their border (PCT_Density) and
• the percentage of urban population within their border (PCT_Urban),

IGAD$Population<-as.numeric(gsub(",", "", IGAD$Population))
IGAD$PCT_Population<-round((IGAD$Population)*(100/sum(IGAD$Population)),0)

IGAD$Density<-as.numeric(gsub(",", "", IGAD$Density))
IGAD$PCT_Density<-round((IGAD$Density)*(100/sum(IGAD$Density)),0)

IGAD$PCT_Urban<-as.numeric(gsub("[\\%,]","", IGAD$Urban))
IGAD<-IGAD[, c(-4)]
show(IGAD)

##       Country Population Density PCT_Population PCT_Density PCT_Urban
## 1    Ethiopia  126527060     127             40          20        22
## 2       Kenya   55100586      97             18          15        31
## 3       Sudan   48109006      27             15           4        35
## 4      Uganda   48582334     243             16          39        29
## 5     Somalia   18143378      29              6           5        46
## 6 South Sudan   11088796      18              4           3        28
## 7     Eritrea    3748901      37              1           6        67
## 8    Djibouti    1136455      49              0           8        72

We use the three new variables and pass it to “plot_ly” to plot bar-graph.

myplot<-plot_ly(IGAD, x = ~Country, y = ~PCT_Population,
        type = "bar", name = "% Population within IGAD") %>% 
  add_trace(y = ~PCT_Density, type = "bar", 
            name = "% Density withing border") %>% 
  add_trace(y = ~PCT_Urban, type = "bar",  
            name = "% Urban pop. within border") %>% 
  layout(title = "IGAD: Key Profiles", 
         xaxis=list(title = "IGAD Country", tickangle = -45), 
         yaxis =list(title = "Percentage"),  margin= list(b=100),    
         barmode = "group")

myplot

1. While Djibouti and Eritrea boast the smallest populations within IGAD, their urban population percentages are among the highest in comparison to other IGAD countries. Is this pattern common across Africa, Asia, and globally?
2. Can a similar question be posed regarding population density?
3. What relationships do you observe among population, density, and urban population percentage?
4. What additional variables do you consider essential for a comprehensive analysis of IGAD’s key profiles?