Johns Hopkins University

Wrangling Data in 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

1,938 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.5

(31 reviews)

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

What you'll learn

  • Apply Tidyverse functions to transform non-tidy data to tidy data

  • Conduct basic exploratory data analysis

  • Conduct analyses of text data

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(31 reviews)

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

See how employees at top companies are mastering in-demand skills

Placeholder

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
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 6 modules in this course

Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This module addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data.

What's included

19 readings2 quizzes

In R, categorical data are handled as factors. By definition, categorical data are limited in that they have a set number of possible values they can take. For example, there are 12 months in a calendar year. In a month variable, each observation is limited to taking one of these twelve values. Thus, with a limited number of possible values, month is a categorical variable. Categorical data, which will be referred to as factors for the rest of this lesson, are regularly found in data. Learning how to work with this type of variable effectively will be incredibly helpful.

What's included

14 readings2 quizzes

Working with text data is increasingly common in data science projects. Text manipulation is often needed to clean up messy datasets and to create numerical measurements out of text input. In addition, often the text themselves are the data and this module covers tools to extract information from the text.

What's included

13 readings2 quizzes

The goal of an exploratory analysis is to examine, or explore the data and find relationships that weren’t previously known. Exploratory analyses explore how different measures might be related to each other but do not confirm that relationship as causal, i.e., one variable causing another. You’ve probably heard the phrase “Correlation does not imply causation,” and exploratory analyses lie at the root of this saying. Just because you observe a relationship between two variables during exploratory analysis, it does not mean that one necessarily causes the other.

What's included

2 readings

Now we will demonstrate how to import data using our case study examples. When working through the steps of the case studies, you can use either RStudio on your own computer or Coursera lab spaces provided for each case study.

What's included

11 readings2 ungraded labs

In this project, you will practice data exploration and data wrangling with the tidyverse using consumer complaint data from the Consumer Financial Protection Bureau (CFPB).

What's included

1 reading1 quiz

Instructors

Instructor ratings
4.6 (9 ratings)
Carrie Wright, PhD
Johns Hopkins University
7 Courses7,056 learners
Shannon Ellis, PhD
Johns Hopkins University
5 Courses5,523 learners
Stephanie Hicks, PhD
Johns Hopkins University
5 Courses5,523 learners

Offered by

Recommended if you're interested in Data Analysis

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 31

4.5

31 reviews

  • 5 stars

    68.75%

  • 4 stars

    18.75%

  • 3 stars

    9.37%

  • 2 stars

    3.12%

  • 1 star

    0%

AN
5

Reviewed on Apr 18, 2022

LV
5

Reviewed on Apr 24, 2021

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions