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This course is part of these tracks:

Alison Hill
Alison Hill

Professor and Data Scientist

Alison is an Associate Professor at Oregon Health & Science University (OHSU). She received her PhD in Developmental Psychology & Quantitative Methods from Vanderbilt University. Her research focuses on health-related applications of Natural Language Processing-based methods. She also teaches graduate-level data science courses and leads workshops on statistics, data analysis, and data visualization with R, and she is a co-author of blogdown: Creating Websites with R Markdown. Follow her at @apreshill on Twitter, and find more about her research and teaching on her website.

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Collaborator(s)
  • Chester Ismay

    Chester Ismay

  • Yashas Roy

    Yashas Roy

  • Benjamin  Feder

    Benjamin Feder

Course Description

In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."

  1. 1

    Explore your data

    Free

    You will start this course by learning how to read data into R. We'll begin with the readr package, and use it to read in data files organized in rows and columns. In the rest of the chapter, you'll learn how to explore your data using tools to help you view, summarize, and count values effectively. You'll see how each of these steps gives you more insights into your data.

  2. Tame your data

    In this chapter, you will learn some basics of data taming, like how to tame your variable types, names, and values.

  3. Tidy your data

    Now that your data has been tamed, it is time to get tidy. In this chapter, you will get hands-on experience tidying data and combining multiple tidying functions together in a chain using the pipe operator.

  4. Transform your data

    In this chapter, you will learn how to tame specific types of variables that are known to be tricky to work with, such as dates, strings, and factors.

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