1 Intro to R and RStudio

1.1 Why Learn R for Data Journalism?

Data journalism requires tools that combine statistical rigor with storytelling capabilities. R provides:

Reproducible analysis through script-based workflows
Advanced visualization for impactful storytelling
Open-source community with continuous innovation
Professional-grade tools used by NYT, BBC, and Reuters etc.

1.2 Installing the Tools

Step 1: Install R

Visit CRAN Mirror
Select your operating system (Windows/Mac/Linux)
Download the latest version (≥4.3.0 recommended)
Run the installer with default settings

Step 2: Install RStudio

Go to posit.co/download
Choose Download RStudio Desktop
Install using default options

Important

Installation Order Matters! Always install R before RStudio - the IDE needs the base R engine to function.

1.3 First-Time Setup

Organizing Your Workspace

Create a project folder:
- Windows: C:/Users/[YourName]/Documents/djr
- Mac: /Users/[YourName]/Documents/djr
Use lowercase letters only (avoid spaces/special characters)

# Create folder from R Console (alternative method)
dir.create("~/Documents/djr")

Configuring RStudio

Optimize your setup through:

Tools > Global Options > General
- Uncheck everything above the “Other” panel
- Set “Save workspace” to Never
Tools > Global Options > Code
- Enable “Soft-wrap R source files”
- Check “Use native pipe operator |>”

1.4 Understanding the RStudio Interface

Position	Panel	Purpose	Key Features
Top-Left	Source	Code editing	Scripts, R Markdown, notebooks
Bottom-Left	Console	Direct code execution	Immediate feedback, debugging
Top-Right	Environment	Active data objects	Variable inspection
Bottom-Right	Files/Help	Navigation & documentation	Package help, file browsing

Download full RStudio IDE cheatsheets

1.5 Essential Packages Installation

Run this in the Console:

install.packages(c(
  "tidyverse",   # Data wrangling & visualization
  "rmarkdown",   # Dynamic reporting
  "quarto",      # Modern publishing
  "knitr",       # Document processing
  "janitor"      # Data cleaning
))

1.6 Your First Project

Create an R Project

File > New Project > New Directory
Name: firstname-week1 (e.g., bin-week1)
Browse to your djr folder
Click Create Project

Create R Markdown File

File > New File > R Markdown
Title: “My First R Markdown”
Author: Your name
Default HTML output

Note

Why R Markdown? - Combine narrative and code - Export to multiple formats: HTML, PDF, Word - Track changes with version control

1.7 Writing Your First RMD Document

Once you have created a new R Markdown file, you can start writing in it. R Markdown files use a combination of Markdown and R code to create dynamic documents. You can include text, code chunks, and output in your R Markdown file.

Metadata

On top right of the R Markdown file, you will see a YAML front matter section, which is used to define metadata for the document. This metadata is enclosed within triple-dashed lines.

You can specify metadata such as the title, author, date, and output format in the YAML front matter section. Here is an example of metadata in a R Markdown file:

---
title: "Data Journalism"
subtitle: "Week 1"
author: "Bin Chen"
date: "Jan 20, 2025"
format: html
---

Text Content

You can write narrative text in your R Markdown file using Markdown syntax. Markdown is a lightweight markup language that allows you to format text using simple and intuitive syntax. You can use Markdown to create headings, lists, links, images, and more.

Here is an example of Markdown text in a R Markdown file: - Heading: # Heading 1 - Bold: **bold text** - List: - item 1 - Link: [link text](https://www.example.com) - Image: ![](path/to/image.png) - Code: `code`

Code Chunks

You can include R code in your R Markdown file using code chunks. Code chunks are enclosed within three backticks and curly braces, indicating the language of the code (in this case, R).

You can insert a code chunk by clicking the green “+” icon in the top right corner of the R Markdown file and selecting “R”, or you can user the keyboard shortcut “Ctrl + Alt + I” on Windows or “Cmd + Option + I” on Mac.

Here is an example of a code chunk in a R Markdown file:

1 + 1

Download full RMarkdown cheatsheets

Render the R Markdown file

Once you have written your R Markdown file, you can render it to create a dynamic document. To render the R Markdown file, click the “knit” button in the top right corner of the R Markdown file. This will run the R code chunks and generate the output in the specified format.

2 Practice

Now, lets practice creating a new R project and a new R Markdown file in RStudio.

Create a R project named “yourfirstname-week1” in your “djr” folder.
Create a new R Markdown file named “yourfirstname-week1” in the R project you just created.
Write a short bio about you (about 50 words) in the R Markdown file.
Install the packages “praise” by running install.packages("praise") in console. (NOT IN R Markdown)
Insert a code chunk in the R Markdown file and run library(praise) to load the package.
Insert another code chunk and run praise() to generate a random compliment.
Render the R Markdown file to html format.

Warning

Don’t run install.packages("praise") in the R Markdown file, as this would prevent your R Markdown file from rendering properly. Always run package installation in the console.

3 Key Functions and Shortcuts

Functionality	Command / Shortcut	Example Use
Insert code chunk (Windows)	`Ctrl + Alt + I`	-
Insert code chunk (Mac)	`Cmd + Option + I`	-
Install R packages	`install.packages("package_name")`	`install.packages("ggplot2")`
Load R packages	`library(package_name)`	`library(ggplot2)`
Create dynamic documents	`rmarkdown`	`rmarkdown::render("report.Rmd")`
Tidyverse for data science	`tidyverse`	`library(tidyverse)`
Dynamic report generation	`knitr`	`knitr::kable(head(mtcars))`
Convert R Markdown to various formats	`quarto`	`quarto render document.qmd`
Generate random compliments	`praise`	`praise::praise()`