Data Tidying and Importing with R
Master R for data science: Learn tidy data principles, data manipulation, and web scraping using tidyverse and dplyr.
Master R for data science: Learn tidy data principles, data manipulation, and web scraping using tidyverse and dplyr.
This course teaches advanced data manipulation and transformation skills using R, focusing on tidyverse principles. Learn to use dplyr and tidyr for data cleaning, joining datasets, and converting between wide and long formats. Explore web scraping techniques and data importing strategies. Gain practical experience in preparing data for visualization and modeling, essential for data science and statistical analysis.
Instructors:
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What you'll learn
Apply tidy data principles to manipulate and restructure data
Develop code to join datasets and perform basic web scraping
Use wide and long data formats, converting between them as needed
Implement data tidying techniques using dplyr and tidyr packages
Understand different data types and classes in R
Import data from various sources into R
Skills you'll gain
This course includes:
3 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, part of the Data Science with R Specialization, focuses on data tidying and importing using R. Students will learn to apply tidy data principles to manipulate and restructure data using the tidyverse ecosystem, particularly dplyr and tidyr packages. The curriculum covers data transformation, joining datasets, and converting between wide and long formats. Participants will also explore web scraping techniques and considerations for ethical data collection. The course emphasizes practical skills in preparing data for visualization and future modeling, providing a strong foundation for advanced data science tasks. By the end of the course, students will be proficient in using R for efficient data manipulation and preparation, essential skills for any data science project.
Tidy Data
Module 1 · 4 Hours to complete
Importing + Recoding Data
Module 2 · 4 Hours to complete
Web Scraping and Programming
Module 3 · 3 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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