Johns Hopkins University

Introduction to the Tidyverse

This course is part of Tidyverse Skills for Data Science in R Specialization

Taught in English

Some content may not be translated

Carrie Wright, PhD
Shannon Ellis, PhD
Stephanie Hicks, PhD

Instructors: Carrie Wright, PhD

4,301 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(41 reviews)

Beginner level

Recommended experience

7 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Distinguish between tidy and non-tidy data

  • Describe how non-tidy data can be transformed into tidy data

  • Describe the Tidyverse ecosystem of packages

  • Organize and initialize a data science project

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(41 reviews)

Beginner level

Recommended experience

7 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the Tidyverse Skills for Data Science in R Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 6 modules in this course

Before we can discuss all the ways in which R makes it easy to work with tidy data, we have to first be sure we know what tidy data are. Tidy datasets, by design, are easier to manipulate, model, and visualize because the tidy data principles that we’ll discuss in this course impose a general framework and a consistent set of rules on data. In fact, a well-known quote from Hadley Wickham is that “tidy datasets are all alike but every messy dataset is messy in its own way.” Utilizing a consistent tidy data format allows for tools to be built that work well within this framework, ultimately simplifying the data wrangling, visualization, and analysis processes. By starting with data that are already in a tidy format or by spending the time at the beginning of a project to get data into a tidy format, the remaining steps of your data science project will be easier.

What's included

6 readings2 quizzes

The reason it’s important to discuss what tidy data are an what they look like is because out in the world, most data are untidy. If you are not the one entering the data but are instead handed the data from someone else to do a project, more often than not, those data will be untidy. Untidy data are often referred to simply as messy data. In order to work with these data easily, you’ll have to get them into a tidy data format. This means you’ll have to fully recognize untidy data and understand how to get data into a tidy format. The following common problems seen in messy datasets again come from Hadley Wickham’s paper on tidy data (http://vita.had.co.nz/papers/tidy-data.pdf). After briefly reviewing what each common problem is, we will then take a look at a few messy datasets. We’ll finally touch on the concepts of tidying untidy data, but we won’t actually do any practice yet. That’s coming soon!

What's included

3 readings1 quiz

With a solid understanding of tidy data and how tidy data fit into the data science life cycle, we’ll take a bit of time to introduce you to the tidyverse and tidyverse-adjacent packages that we’ll be teaching and using throughout this specialization. Taken together, these packages make up what we’re referring to as the tidyverse ecosystem. The purpose for the rest of this course is not for you to understand how to use each of these packages (that’s coming soon!), but rather to help you familiarize yourself with which packages fit into which part of the data science life cycle. Note that the official tidyverse packages below are bold. All other packages are tidyverse-adjacent, meaning they follow the same conventions as the official tidyverse packages and work well within the tidy framework and structure of data analysis.

What's included

5 readings

Data science projects vary quite a lot so it can be difficult to give universal rules for how they should be organized. However, there are a few ways to organize projects that are commonly useful. In particular, almost all projects have to deal with files of various sorts—data files, code files, output files, etc. This section talks about how files work and how projects can be organized and customized.

What's included

6 readings2 quizzes

Throughout this specialization, we’re going to make use of a number of case studies from Open Case Studies to demonstrate the concepts introduced in the course. We’ll generally make use of the same case studies throughout the specialization, providing continuity to allow you to focus on the concepts and skills being taught (rather than the context) while working with interesting data. These case studies aim to address a public-health question and all of them use real data.

What's included

2 readings2 ungraded labs

This project will allow you to create a new project and organize the files that will be needed to engage in a future data analysis

What's included

1 peer review

Instructors

Instructor ratings
4.3 (15 ratings)
Carrie Wright, PhD
Johns Hopkins University
7 Courses7,397 learners
Shannon Ellis, PhD
Johns Hopkins University
5 Courses5,755 learners
Stephanie Hicks, PhD
Johns Hopkins University
5 Courses5,755 learners

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