Analysis in R
This chapter will show you how to draw insights from data, understanding correlations and visualising trends.
Categorical data in R is called factors. This module will show you how to work with factors by creating a factor variable and re-ordering the levels of a factor. You will create 2-way counts of categorical data by creating contingency tables and proportional contingency tables. You will also learn how to visualise categorical data using the `ggplot` package to plot different types of bar charts.
This chapter will show you techniques for exploratory analysis of numeric variables. You will create several different charts including box plots, scatter plots histograms and density plots. You will also learn how to visualise association between 2 variables, and extend this to more than 2 variables.
This chapter explores data published by the Department of Education on graduate earnings and employment rates. You will use the data wrangling techniques to transform the data and the package
ggplot2 to create plots and draw insights from data.
It can often be insightful to include weather trends when doing analysis and forecasting. This chapter shows you how to get historical weather data from locations across the world using the
riem package in R.
Sarah is a Data scientist with experience of using R and R Shiny to build interactive dashboards in the public sector to provide evidence which informs policy decisions. She has led a project to deliver a dashboard to display and analyse international trade data and various macroeconomic indicators, based on a range of data sources.