| Country_data {FPDclustering} | R Documentation |
Unsupervised Learning on Country Data
Description
Ten vables recorded on 167 countries. The goal is to categorize the countries using socio-economic and health indicators that determine the country's overall development. The data set has been donated by the HELP International organization, an international humanitarian NGO that needs to identify the countries that need aid and asked the analysts to categorize the countries.
Usage
data(Country_data)Format
A data frame with 167 observations and 10 variables.
- country
country name
- child_mort
Death of children under 5 years of age per 1000 live births
- exports
Exports of goods and services per capita. Given as %age of the GDP per capita
- health
Total health spending per capita. Given as %age of GDP per capita
- imports
Imports of goods and services per capita. Given as %age of the GDP per capita
- income
Net income per person
- inflation
The measurement of the annual growth rate of the Total GDP
- life_expec
The average number of years a new born child would live if the current mortality patterns are to remain the same
- total_fer
The number of children that would be born to each woman if the current age-fertility rates remain the same.
- gdpp
The GDP per capita. Calculated as the Total GDP divided by the total population.
Source
https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data/metadata?resource=download
References
R. Kokkula. Unsupervised learning on country data. kaggle, 2022. URL https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data/metadata?resource=download
Examples
data(Country_data)
pairs(Country_data[,2:10])