Overview

This resource page features course content from the Knight Center for Journalism in the America's massive open online course (MOOC), titled "Introduction to R for journalists: How to find great stories in data." The five-week course took place from June 23 to August 26, 2018. We are now making the content free and available to students who took the course and anyone else who is interested in learning how to use the statistical computing and graphics language R to enhance data analysis and reporting process.

The course, which was supported by the Knight Foundation, was taught by Andrew Ba Tran. He created and curated the content for the course, which includes video classes and tutorials, readings, exercises, and more.

Syllabus

Introduction Module: R

In this introductory module, you will learn how to configure your computer to work with R. Before you can use it analyze data, your computer needs the following tools installed:

A command-line interface to interact with your computer
The git version control software and a GitHub account
The latest version of R
The latest version of RStudio
An API key from Census.gov (https://api.census.gov/data/key_signup.html)

Module 1: Programming in R

This week you will be introduced to RStudio and learn how to start a new analysis project. You will learn the basics of how to import and explore data with R.

This module will cover:

A tour of the RStudio IDE
Syntax for coding in R
Creating R scripts
Importing packages
Good habits for workflow and documentation habits
How to import data like CSVs, Excel spreadsheets, XML
Exploring the data’s structure

Module 2: Wrangling data

This week you will learn how to transform and analyze data the tidy way using the dplyr package.

This module will cover:

Filtering, selecting, arranging, mutating, summarizing data
How to join two data sets for more insight
Chaining analyses functions with pipes for efficiency and readability

Module 3: Visualizing data

This week, you’ll learn about the grammar of graphics and how to use the ggplot2 package to make quick exploratory data visualizations.

This module will cover:

The aesthetics of data visualizations
How to create different charts like, bar, box, line, scatterplots
Grouping for charts
How to create facets or small multiples with the data
Labels and titles for visualizations

Module 4: Spatial analysis

This week you will learn how to visualize geographical data and look for neighborhood racial profiling disparities using Census data and traffic stop data from Connecticut.

This module will cover:

Creating interactive maps with the R Leaflet package
How to geolocate addresses in R
Importing and visualizing shapefiles
Points in a polygon analysis that merges location data and boundaries for deeper insights

Module 5: Publishing for reproducibility

This week you will learn how to use RMarkdown to present your analysis in a narrative format. You’ll also learn how to log changes to your project with version-control software and publish your analysis on the Internet.

This module will cover:

The git version control software and its integration with Github
How data journalists use GitHub and RMarkdown and other notebooks to publish their work
How to use the Markdown markup language to annotate RMarkdown
How to create a new git code repository and start tracking code
How to connect the repository to GitHub and publish to Github Pages