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:
Introduction I’ve been doing some work lately on social networks that exist between organisations or institutions. This is nice as it builds on some of my dissertation work, and I generally find it quite interesting. Networks that form between organisations are often quite powerful, in that they can illustrate where strong areas of like-minded work exist or where new connections might be useful in strengthing one organisation’s influence.
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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.
Introduction This is the initial Deltanomics blog post. So, in this post, I’ll cover a few different approaches to analysis and data visualisation rather quickly that provides a good overview of the types of things covered in this blog.
Let’s start with loading the packages we’ll use. Also, let’s create a ggplot theme that allows us to easily make changes when we want.
## Libraries used in analysis library(tidyverse) library(magrittr) library(scales) library(RColorBrewer) library(janitor) library(ggraph) library(tidygraph) library(graphlayouts) library(flextable) ## a congruent theme throughout for plots post_theme <- function(.