Exploratory Data Analysis and K-Means Clustering
A. RITHIK SANJAY
2026-03-10
Introduction
This report presents Exploratory Data Analysis and K-Means Clustering using World Bank data.
Summary of Income Groups:
summary(s1)## Country Code Country Name GDP per capita Life expectancy
## ABW : 1 Andorra : 1 Min. : 12752 Min. :62.11
## AND : 1 Antigua and Barbuda: 1 1st Qu.: 25584 1st Qu.:78.11
## ARE : 1 Aruba : 1 Median : 38232 Median :81.41
## ATG : 1 Australia : 1 Mean : 51700 Mean :80.29
## AUS : 1 Austria : 1 3rd Qu.: 59880 3rd Qu.:83.07
## AUT : 1 Bahamas, The : 1 Max. :256800 Max. :86.37
## (Other):74 (Other) :74 NA's :7
## Population Urban population Region
## Min. : 11875 Min. : 14.68 East Asia & Pacific :13
## 1st Qu.: 116670 1st Qu.: 66.47 Europe & Central Asia :38
## Median : 2763808 Median : 82.80 Latin America & Caribbean :17
## Mean : 15356838 Mean : 77.98 Middle East & North Africa: 8
## 3rd Qu.: 10426451 3rd Qu.: 94.16 North America : 3
## Max. :336806231 Max. :100.00 South Asia : 0
## Sub-Saharan Africa : 1
## Income group
## High income :80
## Low income : 0
## Lower middle income: 0
## Upper middle income: 0
##
##
## summary(s2)## Country Code Country Name GDP per capita Life expectancy
## AFG : 1 Afghanistan : 1 Min. : 250.6 Min. :55.07
## BDI : 1 Burkina Faso : 1 1st Qu.: 627.6 1st Qu.:61.16
## BFA : 1 Burundi : 1 Median : 799.2 Median :63.64
## CAF : 1 Central African Republic: 1 Mean : 823.4 Mean :64.10
## COD : 1 Chad : 1 3rd Qu.:1014.7 3rd Qu.:67.32
## ERI : 1 Congo, Dem. Rep. : 1 Max. :1555.5 Max. :73.64
## (Other):22 (Other) :22 NA's :5
## Population Urban population Region
## Min. : 2153339 Min. :16.97 East Asia & Pacific : 1
## 1st Qu.: 10938615 1st Qu.:27.42 Europe & Central Asia : 0
## Median : 20914224 Median :35.26 Latin America & Caribbean : 0
## Mean : 27556994 Mean :37.83 Middle East & North Africa: 2
## 3rd Qu.: 31805739 3rd Qu.:44.86 North America : 0
## Max. :128691692 Max. :72.17 South Asia : 1
## Sub-Saharan Africa :24
## Income group
## High income : 0
## Low income :28
## Lower middle income: 0
## Upper middle income: 0
##
##
## summary(s3)## Country Code Country Name GDP per capita Life expectancy
## AGO : 1 Algeria : 1 Min. : 916.3 Min. :54.46
## BEN : 1 Angola : 1 1st Qu.:1973.7 1st Qu.:66.02
## BGD : 1 Bangladesh: 1 Median :2744.2 Median :69.78
## BOL : 1 Benin : 1 Mean :2964.6 Mean :69.29
## BTN : 1 Bhutan : 1 3rd Qu.:3811.3 3rd Qu.:72.35
## CIV : 1 Bolivia : 1 Max. :5838.6 Max. :77.82
## (Other):48 (Other) :48
## Population Urban population Region
## Min. :1.126e+05 Min. :15.06 East Asia & Pacific :14
## 1st Qu.:3.866e+06 1st Qu.:34.80 Europe & Central Asia : 4
## Median :1.222e+07 Median :53.93 Latin America & Caribbean : 5
## Mean :6.479e+07 Mean :50.37 Middle East & North Africa: 8
## 3rd Qu.:3.773e+07 3rd Qu.:65.82 North America : 0
## Max. :1.438e+09 Max. :90.45 South Asia : 6
## Sub-Saharan Africa :17
## Income group
## High income : 0
## Low income : 0
## Lower middle income:54
## Upper middle income: 0
##
##
## summary(s4)## Country Code Country Name GDP per capita Life expectancy
## ALB : 1 Albania : 1 Min. : 4188 Min. :63.71
## ARG : 1 American Samoa: 1 1st Qu.: 6595 1st Qu.:71.21
## ARM : 1 Argentina : 1 Median : 8028 Median :74.01
## ASM : 1 Armenia : 1 Mean : 9413 Mean :73.75
## AZE : 1 Azerbaijan : 1 3rd Qu.:12265 3rd Qu.:77.33
## BGR : 1 Belarus : 1 Max. :20474 Max. :81.04
## (Other):48 (Other) :48 NA's :2
## Population Urban population Region
## Min. :9.816e+03 Min. :21.15 East Asia & Pacific : 9
## 1st Qu.:8.508e+05 1st Qu.:56.26 Europe & Central Asia :16
## Median :3.450e+06 Median :65.50 Latin America & Caribbean :19
## Mean :4.655e+07 Mean :64.44 Middle East & North Africa: 3
## 3rd Qu.:1.809e+07 3rd Qu.:77.99 North America : 0
## Max. :1.411e+09 Max. :92.68 South Asia : 1
## Sub-Saharan Africa : 6
## Income group
## High income : 0
## Low income : 0
## Lower middle income: 0
## Upper middle income:54
##
##
## ##Histogram of GDP per Capita by Income Group
histogram(~`GDP per capita` | `Income group`, data = final_data, breaks = 60, main="Histogram of GDP per Capita by Income Group")
##Histogram of Population by Income Group
histogram(~Population | `Income group`,
data = final_data,
breaks = 60, main="Histogram of Population by Income Group")
##Histogram of Urban Population by Income Group
histogram(~`Urban population` | `Income group`,
data = final_data,
breaks = 60, main = "Histogram of Urban Population by Income Group")
boxplot(Population ~ Region, data = final_data, main = "Boxplot of Population by Region")
boxplot(`GDP per capita` ~ Region, data = final_data, main = "Boxplot of GDP per capita by Region")
xyplot(`GDP per capita`~`Life expectancy`,
data = final_data,
groups=Region,
auto.key = TRUE,
main = "GDP per Capita vs Life Expectancy")
mosaicplot(table(final_data$Region, final_data$`Income group`),
main = "Region vs Income Group",
xlab = "Region",
ylab = "Income Group",
color = TRUE)
plot(density(final_data$Population),
main = "Density Plot of Population",
xlab = "Population",
col = "blue",
lwd = 2)
numeric_data <- final_data[, sapply(final_data, is.numeric)]
numeric_data <- na.omit(numeric_data)
df <- scale(numeric_data)
k1 <- kmeans(df, centers = 5, iter.max = 10, nstart = 1)
k1## K-means clustering with 5 clusters of sizes 42, 46, 39, 73, 2
##
## Cluster means:
## GDP per capita Life expectancy Population Urban population
## 1 1.4710350 1.2052312 -0.09705528 0.9828387
## 2 -0.5157673 -0.6084399 -0.05960753 -1.3178322
## 3 -0.5711102 -1.2922100 -0.06358461 -0.1741253
## 4 -0.2044860 0.3755374 -0.13057417 0.3719036
## 5 -0.4286952 0.1752424 9.41499104 -0.5085108
##
## Clustering vector:
## 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21
## 4 2 3 4 1 1 4 4 2 1 1 4 2 1 3 2 2 4 1 4
## 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
## 4 4 2 1 3 4 4 4 2 3 3 1 1 1 4 5 3 3 3 3
## 42 43 44 45 47 48 49 50 51 52 53 54 55 56 57 58 60 61 62 63
## 4 2 4 4 4 1 4 4 1 3 4 1 4 4 4 2 1 4 2 1
## 64 65 66 67 68 69 70 71 73 74 75 76 77 78 79 80 82 83 84 85
## 3 1 1 2 4 1 4 3 3 3 3 3 4 2 1 4 2 1 4 4
## 86 87 88 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106
## 3 4 3 5 1 4 4 1 1 1 4 4 1 4 2 2 2 3 2 1
## 107 108 109 110 111 112 113 114 115 116 117 118 119 121 122 123 124 125 126 127
## 1 2 4 3 4 2 1 2 3 4 1 4 1 4 1 2 2 4 4 3
## 128 129 130 131 132 133 135 136 137 138 139 140 141 142 143 144 145 146 147 148
## 4 2 1 2 4 4 2 3 2 2 4 3 4 2 3 4 1 1 3 3
## 149 150 151 152 153 154 155 156 157 158 160 161 162 163 164 165 166 167 168 169
## 1 4 2 4 4 3 4 2 4 1 4 4 3 4 1 4 4 2 4 2
## 170 171 172 173 174 175 176 177 179 180 181 182 183 184 185 186 188 189 190 191
## 3 1 2 3 4 1 3 4 3 4 4 4 1 2 1 2 4 3 3 4
## 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 210 211 212 213 215
## 2 2 2 2 4 4 4 3 2 2 4 4 1 2 2 2 2 2 4 3
## 216 217
## 3 3
##
## Within cluster sum of squares by cluster:
## [1] 110.562438 29.325156 32.896680 45.648439 1.287738
## (between_SS / total_SS = 72.7 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"k1$center## GDP per capita Life expectancy Population Urban population
## 1 1.4710350 1.2052312 -0.09705528 0.9828387
## 2 -0.5157673 -0.6084399 -0.05960753 -1.3178322
## 3 -0.5711102 -1.2922100 -0.06358461 -0.1741253
## 4 -0.2044860 0.3755374 -0.13057417 0.3719036
## 5 -0.4286952 0.1752424 9.41499104 -0.5085108k1$cluster## 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21
## 4 2 3 4 1 1 4 4 2 1 1 4 2 1 3 2 2 4 1 4
## 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
## 4 4 2 1 3 4 4 4 2 3 3 1 1 1 4 5 3 3 3 3
## 42 43 44 45 47 48 49 50 51 52 53 54 55 56 57 58 60 61 62 63
## 4 2 4 4 4 1 4 4 1 3 4 1 4 4 4 2 1 4 2 1
## 64 65 66 67 68 69 70 71 73 74 75 76 77 78 79 80 82 83 84 85
## 3 1 1 2 4 1 4 3 3 3 3 3 4 2 1 4 2 1 4 4
## 86 87 88 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106
## 3 4 3 5 1 4 4 1 1 1 4 4 1 4 2 2 2 3 2 1
## 107 108 109 110 111 112 113 114 115 116 117 118 119 121 122 123 124 125 126 127
## 1 2 4 3 4 2 1 2 3 4 1 4 1 4 1 2 2 4 4 3
## 128 129 130 131 132 133 135 136 137 138 139 140 141 142 143 144 145 146 147 148
## 4 2 1 2 4 4 2 3 2 2 4 3 4 2 3 4 1 1 3 3
## 149 150 151 152 153 154 155 156 157 158 160 161 162 163 164 165 166 167 168 169
## 1 4 2 4 4 3 4 2 4 1 4 4 3 4 1 4 4 2 4 2
## 170 171 172 173 174 175 176 177 179 180 181 182 183 184 185 186 188 189 190 191
## 3 1 2 3 4 1 3 4 3 4 4 4 1 2 1 2 4 3 3 4
## 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 210 211 212 213 215
## 2 2 2 2 4 4 4 3 2 2 4 4 1 2 2 2 2 2 4 3
## 216 217
## 3 3 library(cluster)
clusplot(df,k1$cluster,color = TRUE,shade = TRUE,lables=TRUE,main="K-Means Cluster Plot")