Storytelling with Data: A Data Visualization Course

Site course RSU

Welcome to the Storytelling with Data course at RSU. In this course, you'll learn how to transform raw data into compelling visual stories using R, tidyverse, and ggplot2.

Course Overview

This hands-on course will teach you:

• How to explore and understand data through visualization
• Principles of effective data communication
• Technical skills in R and ggplot2
• How to create publication-ready graphics

Prerequisites

• No prior R experience required - we start from the basics
• Bring your own laptop with R, RStudio, and Tidyverse pre-installed
• Curiosity and willingness to learn!

Lecturer

Dr Sergio Uribe (DDS, MSc, PhD)

• Associate Professor, Deparment of Conservative Dentistry and Oral Health, Riga Stradins University
• Visiting Professor, LMU Klinikum, Deparment of Conservative Dentistry, Periodontology and Digital Dentistry, LMU, Munich

Course Modules

Module	Topic
00	Course Setup: Installing R, RStudio, and Packages
01	Introduction to Data Visualization and Storytelling
02	Choosing the Right Visualization
03	Fundamentals of Data Wrangling
04	Simplifying Visuals and Removing Clutter
05	Directing Audience Attention with Design
06	Creating Visual Narratives
07	Reporting and Sharing Visual Stories
08	Advanced Customization in ggplot2
09	Capstone Project: Telling a Data Story

Datasets

Throughout this course, we'll work with several real-world datasets:

• Gapminder - Global development indicators (life expectancy, GDP, population)
• Baby Names Latvia - Naming trends over time
• OKCupid - Online dating profile data for exploratory analysis

Tools & Packages

We use R with the following key packages:

# Core packages
library(tidyverse)    # Data manipulation and visualization (includes ggplot2)

# Additional packages
library(gapminder)    # Gapminder dataset
library(gtsummary)    # Summary tables
library(here)         # Project-relative file paths
library(naniar)       # Missing data visualization

Code Style

Throughout this course, we use the native R pipe (|>) for readable, chainable code:

# Our coding style
data |>
  filter(year == 2007) |>
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

Getting Help

• Ask questions during class
• Use the course materials and exercises
• R documentation: ?function_name
• RStudio Cheatsheets

License

This course material is provided for educational purposes.

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Happy visualizing!