Data Visualization with ggplot2: Histograms and Density Plots

Visualizing the distribution of your data is a fundamental step in any data analysis workflow. This text-based course guides you through the process of conceptualizing, writing, and refining R code to create clear and informative data visualizations. You will move from understanding the foundational theory of continuous data distributions to writing clean, production-ready visualization code. By working through practical written explanations and code examples, you will learn how to reveal the underlying patterns in your datasets, compare groups effectively, and apply modern design principles to your plots. We focus on writing clean, readable R code that adheres to tidyverse best practices, ensuring your visualization workflow is both efficient and reproducible. What you'll learn: - Understand the core principles of the grammar of graphics using ggplot2 syntax - Create and customize histograms by adjusting bin widths and boundary alignments - Build smooth density plots to estimate probability density functions of continuous variables - Map aesthetics to group, fill, and color to compare multiple distributions side-by-side - Apply modern styling, custom color palettes, and clean theme configurations - Practice debugging common plotting errors and formatting labels for clear communication This course begins with essential definitions of data distributions, histograms, and density estimates before introducing the step-by-step syntax of ggplot2. You will then progress to advanced customization techniques, including layering multiple plots and formatting output for reports. This course is designed for beginners who have a basic familiarity with R and want to specialize in data visualization. No prior experience with ggplot2 or advanced statistics is required. Start reading today to transform your raw datasets into clear, professional distribution plots.