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Introduction to the Tidyverse
  • Level Foundation
  • Course by Johns Hopkins University
  • Offered by

About

This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project. If you are new to data science, the Tidyverse ecosystem of R packages is an excellent way to learn the different aspects of the data science pipeline, from importing the data, tidying the data into a format that is easy to work with, exploring and visualizing the data, and fitting machine learning models. If you are already experienced in data science, the Tidyverse provides a power system for streamlining your workflow in a coherent manner that can easily connect with other data science tools. In this course it is important that you be familiar with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.

Modules

About This Course

    • 1 Readings
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1 Readings

  • About This Course

Tidy Data

    • 5 Readings
    • 2 Assignment
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2 Assignment

  • Principles of tidy data quiz
  • Tidy Data Rules Quiz

5 Readings

  • Data Terminology
  • Principles of Tidy Data
  • Tidy Data Are Rectangular
  • Tidy Data Benefits
  • Rules for Storing Tidy Data

From Non-Tidy –> Tidy

    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Messy Data Quiz

3 Readings

  • Common problems with messy datasets
  • Examples of untidy data
  • Tidying untidy data

The Data Science Life Cycle

    • 1 Readings
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1 Readings

  • The Data Science Life Cycle

Tidyverse Ecosystem

    • 4 Readings
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4 Readings

  • Reading Data into R
  • Data Tidying
  • Data Visualization
  • Data Modeling

Data Science Project Organization

    • 5 Readings
    • 2 Assignment
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2 Assignment

  • File Naming and here Package Quiz
  • Project Organizing Quiz

5 Readings

  • RStudio Projects
  • File Paths
  • The here package
  • File Naming
  • Project Template: Everything In Its Place

Data Science Workflows

    • 1 Readings
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1 Readings

  • Data Science Workflows

Case Study #1: Health Expenditures

    • 1 Readings
    • 1 Lab
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1 Labs

  • Health Expenditures RStudio Lab

1 Readings

  • Case Study #1: Health Expenditures

Case Study #2: Firearms

    • 1 Readings
    • 1 Lab
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1 Labs

  • Firearms Case Study RStudio Lab

1 Readings

  • Case Study #2: Firearms

Project

    • 1 PeerReview
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1 Peer Review

  • Introduction to the Tidyverse Course Project

Auto Summary

Dive into the Tidyverse with this foundational course in Data Science & AI. Explore the transformative Tidyverse R packages, essential for organizing, analyzing, and modeling data. Perfect for both beginners and experienced data scientists, this course covers everything from importing and tidying data to visualization and machine learning. Taught by Coursera, it requires basic R programming knowledge. Available through Starter and Professional subscriptions, this course streamlines your data science workflow efficiently.

Carrie Wright, PhD
Instructors

Carrie Wright, PhD

Shannon Ellis, PhD
Instructors

Shannon Ellis, PhD

Stephanie Hicks, PhD
Instructors

Stephanie Hicks, PhD

Roger D. Peng, PhD
Instructors

Roger D. Peng, PhD