Exercise 8 - Unsupervised Learning (Solution)

Exercise 8.1 – Principal Component Analysis

In today’s assignment, we are going to look at a set of variables describing the economic characteristics of English parliamentary constituencies around 2017-2019 (the dates of the source data vary a bit in terms of year).

You can directly load the data file into R from the course website with the following command:

econ_vars <- read.csv(url("https://raw.githubusercontent.com/lse-me314/lse-me314.github.io/master/data/const-econ-vars.csv"))

This data file has 8 variables

• ONSConstID - Office for National Statistics Parliamentary Constituency ID
• ConstituencyName - Constituency Name
• HouseWageRatio - Ratio of House Prices to Wages
• UnempConstRate - Unemployment Rate
• UnempConstRateChange - Unemployment Rate Change since 2010
• WageMedianConst - Median Wage
• social_mobility_score - Social Mobility Index
• deprivation_index_score - Social Deprivation Index

Q1. Use the cor() and pairs() functions to assess the correlations between the six economic variables in the data set. Which two economic variables are most highly correlated with one another at the constituency level? Which variable is least correlated with the others at the constituency level?

pairs(econ_vars[,3:8])