Data Visualization and Transformation with R
Learn data visualization and transformation in R using Tidyverse. Master exploratory data analysis and reproducible research.
Learn data visualization and transformation in R using Tidyverse. Master exploratory data analysis and reproducible research.
This course introduces data science and statistical thinking, focusing on data visualization and transformation for exploratory data analysis using R. Students will gain experience in exploring, visualizing, and analyzing data to understand natural phenomena, investigate patterns, and model outcomes. The course emphasizes reproducible and shareable data analysis techniques using RStudio, Quarto for reporting, and Git/GitHub for version control. Learners will work with packages from the Tidyverse, particularly ggplot2 for visualization and dplyr for data wrangling. Through lectures, live coding videos, and interactive exercises, students will tackle real-world inspired problems and datasets. The skills acquired prepare learners for various data-related careers, including data scientist, analyst, quantitative analyst, and statistician.
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What you'll learn
Transform, visualize, summarize, and analyze data in R using Tidyverse packages
Create effective data visualizations using ggplot2 and the Grammar of Graphics
Perform data wrangling and transformation tasks with dplyr
Conduct exploratory data analysis on various types of data
Use RStudio for efficient R programming and data analysis
Create reproducible reports with Quarto
Skills you'll gain
This course includes:
3.9 Hours PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to data visualization and transformation using R, with a focus on exploratory data analysis. The curriculum is divided into three modules: 1) Introduction to data science concepts and tools (R, RStudio, Quarto, GitHub), 2) Data visualization techniques using ggplot2, and 3) Data transformation and summarization using dplyr. Students will learn to create various types of plots, handle different data types, and explore relationships between variables. The course emphasizes reproducible research practices and introduces version control with Git and GitHub. Throughout the modules, learners will work with real-world datasets and problems, gaining practical experience in data analysis and visualization techniques.
Hello World
Module 1 · 3 Hours to complete
Data and Visualization
Module 2 · 2 Hours to complete
Visualizing, transforming, and summarizing types of data
Module 3 · 6 Hours to complete
Fee Structure
Payment options
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Instructors
Associate Professor of the Practice at Duke University
Dr. Mine Çetinkaya-Rundel is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She earned her Ph.D. in Statistics from the University of California, Los Angeles, and holds a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Çetinkaya-Rundel is dedicated to innovative statistics pedagogy, focusing on developing student-centered learning tools for introductory statistics courses. Her recent work emphasizes teaching computation at the introductory level with a strong commitment to reproducibility and addressing the gender gap in self-efficacy within STEM fields. Additionally, her research interests include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and actively contributes to the OpenIntro project, which aims to create open-licensed educational materials that reduce barriers to education. Dr. Çetinkaya-Rundel also co-edits the Citizen Statistician blog and contributes to the "Taking a Chance in the Classroom" column in Chance Magazine.
Assistant Teaching Professor at Duke University
Dr. Elijah Meyer is an Assistant Teaching Professor in the Department of Statistical Science at Duke University, where he focuses on enhancing the teaching and learning experiences in statistics and data science. He aims to inspire students to discover their passion for working with data through innovative course creation, curriculum development, and instrument development. Dr. Meyer has a keen interest in sports analytics and enjoys playing basketball, tennis, and disc golf in his spare time. He earned both his Master's and Ph.D. in Statistics with a focus on education from Montana State University. Recently, he transitioned to North Carolina State University after completing a postdoctoral position at Duke University, where he continued his work in statistics education and data science pedagogy. For more information about his teaching and research, you can visit his personal website.
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