In Module 2, you will develop insight into how functions work as you are introduced to various functions from the tidyverse, which is a collection of eight R packages useful in data science. The lessons will guide you through performing common data wrangling tasks, such as filtering observations of a dataset and joining data from different sources. By the end of the module, you will have used these tools to reproduce the Indicator Table from The Global Findex Database 2017, which estimates account ownership statistics, including gender and income gaps, for all of the surveyed countries.

Module 3 introduces you to R graphical capabilities. You will both learn about different types of plots – including scatterplots, lineplots, barplots, boxplots, and histograms – and how to make them in R. You’ll learn how to create multipanel plots. And you’ll continue to learn good overall R “hygiene” by keeping your code tidy. You’ll put these newly learned skills to work re-creating Figure 1.1 from The Global Findex Database 2017, which shows how account ownership varies by the income level of a country.

Having worked through the first three modules, you’ve (re)produced a table and figure from The Global Findex Database 2017. Now what? In Module 4, you will learn about sharing your work with others: exporting tables and figures from R onto your computer. You’ll be introduced to a means of writing reports in R using RMarkdown. And finally we’ll talk about what happens when you get stuck: how to ask questions and where to get help.