Introduction The US Presidential vote count has nearly finished. Trump is fighting the results in court though appears to have lost, but a clear theme that’s coming out is the contrast of rural and urban voters. The theory seems to go: Trump voters for the most part come from rural areas, and the election is based on a rural vs urban debate. We can test this theory using some data science approaches - mainly, cleaning and merging data from several places into a useful format.
Introduction A considerable issue today related to food and rural population research are food deserts. Food deserts are a complicated issue, but the idea centres on a simple premise: areas, where it’s hard to reach a grocery store or access food, can be thought of as food deserts. If you’re interested in knowing more about the discourse on food deserts, I’d recommend looking into these papers:
First published on 26-April-2020 Last updated on 16-Aug-2020 Introduction Now that we are square in the middle of the Covid-19 pandemic, I thought it might be beneficial to look at some statistics associated with the number of cases. We’ll differentiate our analysis by focusing on cases of Covid-19 in rural areas of the U.S. There are a couple of reasons for this: mainly, rural analytics is my speciality, so while I don’t know much about the virus, I do know some about rural societies and economies; we can easily find pertinent data on rural counties; and, we can utilise some cool built-in R functions to help us along the way.
Why create a new dataset? I’d like to do a series of posts looking at social network analysis using primary data (i.e. data collected by yourself.). There are a lot of different examples of when you might want to use a survey to collect data for use in analysing social networks. But that’s for another time.
The purpose of this post is to create a new dataset that can be used in practising social network analysis in future posts.