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Wrangling Data in the Tidyverse

4.5 Filled star Filled star Filled star Filled star Half star
Based on 31 ratings
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Carrie Wright, PhD, Shannon Ellis, PhD, Stephanie Hicks, PhD, and Roger D. Peng, PhD

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 course 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.

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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 course 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.

This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team.

In this specialization we assume familiarity 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.

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What's inside

Syllabus

Wrangling Data in the Tidyverse
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.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops tidyverse skills, which most industries that use R value
Taught by professors from Johns Hopkins University, who are known for their work in biostatistics
Covers data wrangling and transformation, which helps learners clean and manipulate data efficiently
Uses the dplyr package, a widely used tool for data manipulation in R
Provides hands-on practice through a project using real-world data
Requires familiarity with R programming, which may be a barrier for beginners

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Reviews summary

Essential tidyverse data wrangling in r

According to students, this course is a highly effective resource for mastering data wrangling in R using the Tidyverse. Learners consistently praise the incredibly clear explanations of concepts, particularly for the dplyr package and achieving tidy data formats. The hands-on activities, including practical case studies and a comprehensive final project, are highlighted as key strengths, reinforcing learning effectively. While most find the course relevant and up-to-date for data professionals, some note the fast pace requires a solid foundational understanding of R programming.
The course content is current and highly applicable to modern data science.
"This course felt very up-to-date and relevant."
"The content is definitely relevant for data professionals."
"Highly practical and concise. The course helped me understand functional programming concepts within the context of data manipulation, which was a new perspective for me."
Students gain confidence and relevant skills for data analysis careers.
"This course is fantastic for solidifying my data wrangling skills in R."
"I came in with a basic R background and left feeling confident in my data wrangling abilities."
"The content is definitely relevant for data professionals."
"I feel much more confident in my data wrangling abilities now."
The course excels at explaining complex Tidyverse concepts clearly.
"The dplyr package explanations are incredibly clear, and the hands-on project at the end was invaluable."
"The instructor explains complex concepts simply. The case studies are practical and really helped tie everything together."
"This course delivers exactly what it promises. The focus on transforming non-tidy data into tidy formats is a game-changer."
Hands-on projects and case studies deeply reinforce data wrangling skills.
"The hands-on project at the end was invaluable. It truly taught me how to transform messy data into tidy formats."
"The case studies are practical and really helped tie everything together."
"Absolutely loved this course! The practical approach to data cleaning and transformation using the Tidyverse is exactly what I needed."
"The labs and demonstrations were well-designed, making learning highly engaging."
Course pace suits learners with a solid R foundation, challenging for some.
"I found some of the earlier parts a bit basic, and then it jumped quickly into more complex areas without enough transition."
"Found this course somewhat challenging. I struggled with the rapid pace. I had some R experience, but perhaps not enough as assumed."
"While the topics are important, I struggled with the rapid pace... making it hard to get help."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Wrangling Data in the Tidyverse with these activities:
Review R Basics
Refresh your understanding of R programming concepts before starting the course.
Browse courses on R Programming
Show steps
  • Review the RStudio cheatsheet
  • Complete the 'Introduction to R' exercises on DataCamp
Watch Tutorials on Data Reshaping
Gain a visual understanding of data reshaping techniques through interactive tutorials.
Browse courses on Data Reshaping
Show steps
  • Watch the 'Reshaping Data with R' tutorial on DataCamp
  • Follow along with the 'tidyr' package tutorial on RStudio
Create a Data Dictionary
Organize and document your data to facilitate efficient and accurate analysis.
Show steps
  • Identify the variables in your dataset
  • Define the name, type, and description of each variable
  • Create a table or spreadsheet to document the data dictionary
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read The Data Science Handbook
Learn the fundamentals of data wrangling and tidy data from a trusted resource.
View The Data Science Handbook: Advice and Insights... on Amazon
Show steps
  • Read chapters 1-4 to understand the basics of data wrangling and tidy data
  • Work through the exercises in the book to practice your skills
Practice Data Manipulation
Refine your data manipulation skills through guided exercises.
Browse courses on Data Manipulation
Show steps
  • Complete the data manipulation exercises on DataCamp
  • Work through the R for Data Science cheat sheet and practice the provided exercises
Attend a Data Science Meetup
Connect with professionals in the field and exchange knowledge and insights.
Browse courses on Networking
Show steps
  • Find a local data science meetup group on Meetup.com
  • Attend a meetup and participate in discussions
Data Analysis Project
Apply your data wrangling and analysis skills to a real-world dataset.
Browse courses on Data Analysis
Show steps
  • Identify a dataset of interest
  • Clean and prepare the data
  • Perform exploratory data analysis
  • Create visualizations and report on your findings

Career center

Learners who complete Wrangling Data in the Tidyverse will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts gather and analyze data to identify trends and patterns. They use this information to help businesses make better decisions. The Wrangling Data in the Tidyverse course can help you develop valuable skills for Data Analysts, such as data cleaning, manipulation, and analysis. With these skills, you can help businesses understand their data and make better use of it.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists use data to solve business problems. They develop and implement models to predict outcomes and make recommendations. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Data Scientist, such as data wrangling, analysis, and visualization. With these skills, you can help businesses make better decisions and improve their bottom line.
See salaries and explore the career path for Data Scientist
Data Engineer
Data Engineers build and maintain the systems that store and process data. They ensure that data is accurate, accessible, and secure. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Data Engineer, such as data wrangling, management, and analysis. With these skills, you can help businesses build and maintain the data systems they need to succeed.
See salaries and explore the career path for Data Engineer
Statistician
Statisticians collect, analyze, and interpret data to draw conclusions. They use their findings to help businesses make better decisions. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Statistician, such as data wrangling, analysis, and visualization. With these skills, you can help businesses understand their data and make better use of it.
See salaries and explore the career path for Statistician
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning models. They use these models to automate tasks and make predictions. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Machine Learning Engineer, such as data wrangling, analysis, and visualization. With these skills, you can help businesses automate tasks and make better decisions.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their skills to solve business problems and improve efficiency. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Software Engineer, such as data wrangling, analysis, and visualization. With these skills, you can help businesses build and maintain the software systems they need to succeed.
See salaries and explore the career path for Software Engineer
Business Analyst
Business Analysts help businesses understand their data and make better decisions. They use their skills to identify trends, patterns, and opportunities. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Business Analyst, such as data wrangling, analysis, and visualization. With these skills, you can help businesses understand their data and make better use of it.
See salaries and explore the career path for Business Analyst
Consultant
Consultants help businesses improve their performance. They use their expertise to identify problems and develop solutions. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Consultant, such as data wrangling, analysis, and visualization. With these skills, you can help businesses understand their data and make better use of it.
See salaries and explore the career path for Consultant
Researcher
Researchers conduct studies to answer questions and solve problems. They use their findings to improve our understanding of the world. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Researcher, such as data wrangling, analysis, and visualization. With these skills, you can help conduct studies and solve problems.
See salaries and explore the career path for Researcher
Teacher
Teachers help students learn and grow. They use their knowledge and skills to create a positive learning environment. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Teacher, such as data wrangling, analysis, and visualization. With these skills, you can help students understand data and make better use of it.
See salaries and explore the career path for Teacher
Journalist
Journalists report on news and current events. They use their skills to inform the public and hold those in power accountable. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Journalist, such as data wrangling, analysis, and visualization. With these skills, you can help the public understand data and make better use of it.
See salaries and explore the career path for Journalist
Librarian
Librarians help people find and use information. They use their skills to organize and manage libraries and archives. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Librarian, such as data wrangling, analysis, and visualization. With these skills, you can help people find and use data more effectively.
See salaries and explore the career path for Librarian
Museum curator
Museum Curators manage and care for museum collections. They use their skills to preserve and interpret cultural artifacts. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Museum Curator, such as data wrangling, analysis, and visualization. With these skills, you can help preserve and interpret cultural artifacts for the public.
See salaries and explore the career path for Museum curator
Archivist
Archivists manage and care for historical records. They use their skills to preserve and interpret these records for future generations. The Wrangling Data in the Tidyverse course can help you develop the skills you need to be a successful Archivist, such as data wrangling, analysis, and visualization. With these skills, you can help preserve and interpret historical records for the public.
See salaries and explore the career path for Archivist
Historian
Historians study the past to understand the present. They use their skills to research and write about historical events. The Wrangling Data in the Tidyverse course may help you develop some skills that are useful for Historians, such as data wrangling, analysis, and visualization. With these skills, you can help research and write about historical events more effectively.
See salaries and explore the career path for Historian

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Wrangling Data in the Tidyverse.
Cover image
R for Data Science
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers the basics of data manipulation, visualization, and statistical modeling, and it assumes no prior knowledge of R or programming.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
R Packages
Save
Provides an in-depth look at the R programming language, with a focus on advanced topics such as data manipulation, visualization, and statistical modeling. It assumes some prior knowledge of R and programming.
R Packages
Paperback
Check
R Packages
Kindle Edition
Check
Cover image
Data Manipulation with R
Save
Provides a comprehensive introduction to data manipulation in R. It covers the basics of data import, cleaning, and transformation, and it assumes no prior knowledge of R or programming.
Data Manipulation with R (Use R!)
Paperback
$$$
Data Manipulation with R (Use R!)
Kindle Edition
$$$
Cover image
R Packages
Save
Provides a comprehensive introduction to data visualization in R. It covers the basics of creating different types of plots, and it assumes no prior knowledge of R or programming.
R Packages: Organize, Test, Document, and Share...
Paperback
$$
Cover image
R for Data Science
Save
Provides a comprehensive introduction to exploratory data analysis in R. It covers the basics of data exploration, visualization, and statistical modeling, and it assumes no prior knowledge of R or programming.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Machine Learning with R
Save
Provides a comprehensive introduction to machine learning in R. It covers the basics of supervised and unsupervised learning, and it assumes some prior knowledge of statistics and R.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Deep Learning with R, Second Edition
Save
Provides a comprehensive introduction to deep learning in R. It covers the basics of deep learning models, and it assumes some prior knowledge of machine learning and R.
Deep Learning with R, Second Edition
Paperback
$$$
Deep Learning with R, Second Edition
Kindle Edition
$$
Cover image
Text Mining with R
Save
Provides a comprehensive introduction to natural language processing in R. It covers the basics of NLP tasks, and it assumes some prior knowledge of R.
Text Mining with R
Paperback
Check
Text Mining with R
Kindle Edition
Check
Cover image
Time Series Analysis
Save
Provides a comprehensive introduction to time series analysis in R. It covers the basics of time series models, and it assumes some prior knowledge of statistics and R.
Time Series Analysis: With Applications in R...
Hardcover
$$$
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive introduction to statistical learning. It covers the basics of supervised and unsupervised learning, and it assumes some prior knowledge of statistics and R.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Tidy Modeling with R
Save
Provides a comprehensive introduction to applied predictive modeling. It covers the basics of supervised and unsupervised learning, and it assumes some prior knowledge of statistics and R.
Tidy Modeling with R
Paperback
Check
Tidy Modeling with R
Kindle Edition
Check
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive introduction to statistical learning. It covers the basics of supervised and unsupervised learning, and it assumes some prior knowledge of statistics and R.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Modern Applied Statistics with S-Plus
Save
Provides a comprehensive introduction to applied statistics. It covers the basics of statistical modeling, and it assumes some prior knowledge of statistics and R.
Modern Applied Statistics with S-PLUS (Statistics...
Hardcover
$$
Modern Applied Statistics with S-Plus
Hardcover
$$
Modern Applied Statistics with S-Plus (Statistics...
Paperback
$$

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Rating
4.5 Filled star Filled star Filled star Filled star Half star
Effort
14 hours to complete
Via
Coursera
Institution
Johns Hopkins University
Instructors
Carrie Wright, PhD
Shannon Ellis, PhD
Stephanie Hicks, PhD
Roger D. Peng, PhD
Language
English
This course belongs to a collection called Tidyverse Skills for Data Science in R. It's comprised of five courses:
  1. Modeling Data in the Tidyverse
  2. Wrangling Data in the Tidyverse (viewing)
  3. Importing Data in the Tidyverse
  4. Visualizing Data in the Tidyverse
  5. Introduction to the Tidyverse
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops tidyverse skills, which most industries that use R value
Taught by professors from Johns Hopkins University, who are known for their work in biostatistics
Covers data wrangling and transformation, which helps learners clean and manipulate data efficiently
Uses the dplyr package, a widely used tool for data manipulation in R
Provides hands-on practice through a project using real-world data
Requires familiarity with R programming, which may be a barrier for beginners
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