In the era of big data, the ability to analyze and visually represent complex data is crucial. The "Data Visualization" course offered by Ball State University provides a comprehensive understanding of principles, techniques, and tools for visualizing information in large datasets using the open-source software R.
Students will learn the fundamentals of data visualization, including the introduction to R and RStudio, graphics components for data visualization, ggplot2, embedding images and tables in R Markdown files, boxplot and multiple-view layout. The course also emphasizes the importance of data exploration before delving into advanced data mining techniques.
Throughout the course, students will gain hands-on experience in creating a wide range of visualizations in R, and they will learn to evaluate the effectiveness of visualization designs and think critically about decisions such as color choice and visual encoding. The final project will enable students to apply their skills, theory, and experiences gained from the previous sections to develop a data visualization that effectively communicates a compelling story to the audience and readers.
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This course comprises five modules that cover the fundamentals of data visualization, including R programming, graphics components, ggplot2, embedding images and tables in R Markdown files, and boxplot and multiple-view layout.
Welcome to Data Visualization! This module provides an introduction to data visualization and R, establishing the groundwork for the course. Students will learn the fundamentals of R, including data types, structures, and objects, and gain hands-on experience in creating their first R Markdown file.
This module focuses on the graphics components for data visualization. Students will explore the principles and best practices of effective data visualization, including an introduction to the Grammar of Graphics, marks and channels, color models, and exploratory data analysis (EDA).
Students will delve into ggplot2, a powerful data visualization package in R. This module covers the basic usage of ggplot() function, histograms, colors in ggplot(), grouped histograms, and more. Students will gain practical experience in creating various visualization techniques using ggplot2.
In this module, students will learn to embed images and tables in R Markdown files and output them into different formats. They will also explore scatter plots, bubble plots, and create a comprehensive HTML report in R Markdown file with images and tables.
Module 5 covers boxplot and multiple-view layout, including the interpretation of boxplots, arranging multiple plots on a page, using facets in ggplot2, and the grid.arrange() function. Students will gain practical experience in creating multiple-view plots with provided data and detecting outliers using boxplots.
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