My dataset was retrieved from Open Data for Africa, under the Sudan data portal. The data itself was collected and compiled by organizations, including the United Nations Environment Program (UNEP) and Esri, through its Living Status indicators. The dataset includes information related to health, education, and economic conditions in Sudanese states. For my analysis, I will utilize multiple indicators, including hospitals per capita, infant mortality rate, consumer price index, and literacy rate. I aim to investigate which states have the highest versus the lowest quality of life indicators and determine whether these differences are attributed to historical factors or more recent changes and events. I also want to examine how economic and social conditions vary between regions in Sudan. I chose this topic and dataset because I am Sudanese and have spent only a small part of my life in Sudan, so I do not have extensive knowledge about its geopolitical and socioeconomic conditions. This project will help me gain a deeper understanding of the country, its regional differences, and the diverse living conditions people experience across different states.
Variable
Description
infant_mort_per_1000_live
Infant mortality rate per 1000 live births
hospitals_per_100000_pop
Hospitals per 100000 people
inflation_rate
Inflation rate by year
literacy_category
Literacy categorized as “high” or “low” by a 70% threshold
Loading the Dataset
library(tidyverse)
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library(leaflet)
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library(plotly)
Attaching package: 'plotly'
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last_plot
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filter
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layout
Rows: 17 Columns: 22
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chr (4): States, RegionId, Capital, Governor
dbl (18): population, area_sq_km, pop_dens_per_sq_km, literacy_rate, child_m...
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Filtering Data
sudan1 <- sudan_data |>filter(if_all(everything(), ~!is.na(.)))#removing rows with all NAsnames(sudan1) <-tolower(names(sudan1))#standardizing to lowercase names
Call:
lm(formula = infant_mortality_rate_per_1000_live ~ literacy_rate +
hospitals_per_100000_pop + beds_per_100000_populations, data = sudan2)
Residuals:
Min 1Q Median 3Q Max
-16.968 -6.473 3.376 5.317 11.407
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 109.4883 12.3102 8.894 2.35e-06 ***
literacy_rate -1.0786 0.3245 -3.324 0.00679 **
hospitals_per_100000_pop -10.4272 6.5876 -1.583 0.14176
beds_per_100000_populations 0.2900 0.1570 1.848 0.09168 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.173 on 11 degrees of freedom
Multiple R-squared: 0.6158, Adjusted R-squared: 0.511
F-statistic: 5.877 on 3 and 11 DF, p-value: 0.01202
Linear Equation
y = 109.4883 − 1.0786(literacy rate) − 10.4272(hospitals per 100k) + 0.2900(beds per 100k)
y = = predicted infant mortality rate per 1,000 live births
The adjusted R-squared is 0.511 and the p-value is 0.01202 which is less than 0.05, suggesting genuine statistical significance. This correlation does not directly suggest causation but it does represent a relationship exists between the predictors and the predicted infant mortality rate.
Map for Standard of Living Indicators
pal <-colorFactor(palette =c( "red", "black"),levels=c("High Literacy","Low Literacy"))sol_popup <-paste0("<b>Standard of Living Indicators: </b>", "<br>","<b>State: </b>", sudan2$states, "<br>","<b>Governor: </b>", sudan2$governor, "<br>","<b>Population: </b>", sudan2$population, "<br>","<b>Literacy Rate (%): </b>", sudan2$literacy_rate, "<br>","<b>Infant Mortaliy Rate (%): </b>", sudan2$infant_mortality_percent, "<br>","<b>Hospitals per 100k: </b>", sudan2$hospitals_per_100000_pop, "<br>","<b>Inflation Rate (%): </b>", sudan2$inflation_rate, "<br>")#creating legend for color and popup
This map shows different standard of living indicators across states in Sudan, with each circle representing a state based on its geographic location. The colors separate states into high and low literacy categories, which helps show differences in education levels across the country. The size of each circle is based on literacy rate, so you can visually compare how literacy varies between states. When you click on a state, the popup gives more details like population, literacy rate, infant mortality percentage, number of hospitals per 100,000 people, and inflation rate. Overall, this map helps show how living conditions are not the same across Sudan, and how things like education, healthcare access, and economic conditions seem to vary together across different regions. Lat, Long values (Claude AI 2026).
Inflation Plot
sudan_inflation <-ggplot(sudan2,aes(x =reorder(states, inflation_rate),y = inflation_rate,fill = inflation_rate)) +geom_col(position ="dodge", width =0.65) +scale_fill_gradient(low ="#fde2e4",high ="#2596be",name ="Inflation Rate") +#bar plot to show inflation ratesgeom_hline(yintercept =mean(sudan2$inflation_rate, na.rm =TRUE),color ="#f4a261",linetype ="dashed",linewidth =1) +annotate("text",x =4.6,y =13.9,label ="Overall Average",color ="#f4a261",size =3.5) +#text to describe linecoord_flip() +labs(title ="Inflation Rate Across States in Sudan",subtitle ="Gradient shows low to high inflation",y ="Inflation Rate (%)",x ="States",caption ="Source: Open Data for Africa – Sudan Data Portal" ) +theme_minimal(base_size =13, base_family ="serif") +theme(legend.position ="right")sudan_inflation
Inflation Plot Description
This plot shows the inflation rate across different states in Sudan, with each bar representing a state and its corresponding inflation level. The colors use a gradient scale where lighter shades represent lower inflation and darker shades represent higher inflation, making it easy to visually compare economic conditions across regions. The states are ordered by inflation rate, so you can quickly see which areas are experiencing the highest and lowest price increases. The dashed horizontal line represents the overall average inflation rate across all states, which helps provide a benchmark for comparison. Overall, this graph highlights clear differences in economic stability between states in Sudan and shows how inflation is not evenly distributed across the country. Although my graph shows inflation variability across the Sudan, if I wanted to go more in depth, I could add more standard of living indicators or more specific data on the overall economics of Sudan and its states. For instance, GDP may have been incorporated or added as a time series, as that would reveal that in the early 2000s, Sudan experienced multiple recessions. GDP experienced growth from 2000 to 2008, then in 2009 GDP suddenly dropped by 2.8% and GDP per capita dived by 17.6%(Country Economy). These economic declines were in part due to the global great recession from around 2007 to 2009 and the drop in oil prices that decreased Sudan’s revenue. Sudan was also facing internal conflicts in Darfur, which weakened the infrastructure. The recession was also caused by limited economic diversity and investment uncertainty. The GDP decline in 2009 contributed to inflation in Sudan by reducing export revenue and weakening the currency, which made imports more expensive and increased pressure on government finances. These conditions created inflationary pressure even during a period of slower economic growth.
References
Home - Sudan Data Portal. (n.d.). https://sudan.opendataforafrica.org/
Hughes, L. (2024, March 6). World Heritage: The pyramids of Meroe, Sudan. Wanderlust. https://www.wanderlustmagazine.com/inspiration/unesco-world-heritage-sites-meroe-pyramids-sudan/
Anthropic. (2026). Claude (May 2026 version) [Large language model]
Sudan GDP - gross domestic product 2009. countryeconomy.com. (2017, October 20). https://countryeconomy.com/gdp/sudan?year=2009
Tian, F. D., & Almosharaf, H. A. (2014). The causes of Sudan’s recent economic decline. IOSR Journal of Economics and Finance, 2(4), 26–40. https://doi.org/10.9790/5933-0242640
