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Arimoro Olayinka Imisioluwa
As data enthusiasts and professionals, our work often requires dealing with data in different forms. In particular, messy data can be a big challenge because the quality of your analysis largely depends on the quality of the data. This project-based course, "Tidy Messy Data using tidyr in R," is intended for beginner and intermediate R users with related experiences who are willing to advance their knowledge and skills.
Read more
As data enthusiasts and professionals, our work often requires dealing with data in different forms. In particular, messy data can be a big challenge because the quality of your analysis largely depends on the quality of the data. This project-based course, "Tidy Messy Data using tidyr in R," is intended for beginner and intermediate R users with related experiences who are willing to advance their knowledge and skills.
Read more
As data enthusiasts and professionals, our work often requires dealing with data in different forms. In particular, messy data can be a big challenge because the quality of your analysis largely depends on the quality of the data. This project-based course, "Tidy Messy Data using tidyr in R," is intended for beginner and intermediate R users with related experiences who are willing to advance their knowledge and skills.
In this course, you will learn practical ways for data cleaning, reshaping, and transformation using R. You will learn how to use different tidyr functions like pivot_longer(), pivot_wider(), separate_rows(), separate(), and others to achieve the tidy data principles. By the end of this 2-hour-long project, you will get hands-on massaging data to put in the proper format. By extension, you will learn to create plots using ggplot().
This project-based course is a beginner to an intermediate-level course in R. Therefore, to get the most out of this project, it is essential to have a basic understanding of using R. Specifically, you should be able to load data into R and understand how the pipe function works. It will be helpful to complete my previous project titled "Data Manipulation with dplyr in R."
Register for this course and see more details by visiting:
OpenCourser.com/course/0az7w9/tidy
What's inside
Syllabus
Project Overview
As data enthusiasts and professionals, our work often requires dealing with data in different forms. In particular, messy data can be a big challenge because the quality of your analysis largely depends on the quality of the data. This project-based course, "Tidy Messy Data using tidyr in R," is intended for beginner and intermediate R users with related experiences who are willing to advance their knowledge and skills.
In this course, you will learn practical ways for data cleaning, reshaping, and transformation using R. You will learn how to use different tidyr functions like pivot_longer(), pivot_wider(), separate_rows(), separate(), and others to achieve the tidy data principles. By the end of this 2-hour-long project, you will get hands-on massaging data to put in the proper format. By extension, you will learn to create plots using ggplot().
This project-based course is a beginner to an intermediate-level course in R. Therefore, to get the most out of this project, it is essential to have a basic understanding of using R. Specifically, you should be able to load data into R and understand how the pipe function works. It will be helpful to complete my previous project titled "Data Manipulation with dplyr in R."
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on organizing data to fit the tidy data principles, which is crucial for further analysis and data visualization
Well-suited for both beginner and intermediate R users who want to enhance their skills in data manipulation
Provides hands-on exercises with real-world data, allowing learners to immediately apply the concepts
Covers a range of tidyr functions, ensuring learners are equipped with various techniques for data cleaning and reshaping
Requires familiarity with basic R functions and the pipe function
Save this course
Save Tidy Messy Data using tidyr in R to your list so you can find it easily later:
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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 Tidy Messy Data using tidyr in R with these
activities:
Review basic data manipulation concepts in R
Show steps
Revisit fundamental data manipulation techniques in R to strengthen your understanding before delving into tidy data principles.
Browse courses on
Data Manipulation
Show steps
-
Review the use of basic data structures like vectors, matrices, and data frames.
-
Practice data manipulation operations like subsetting, filtering, and sorting.
Join a study group or online forum for tidyr users
Show steps
Engage with a community of learners to share knowledge, ask questions, and collaborate on tidyr-related projects.
Browse courses on
tidyr
Show steps
-
Search for study groups or online forums dedicated to tidyr.
-
Join the group or forum and actively participate in discussions.
-
Ask questions, share your experiences, and learn from others.
Follow tutorials on using tidyr for data reshaping
Show steps
Enhance your understanding of tidyr's capabilities by following guided tutorials that demonstrate practical applications.
Browse courses on
tidyr
Show steps
-
Search for tutorials on reshaping data using tidyr packages.
-
Follow the steps in the tutorials to practice reshaping techniques.
-
Experiment with different tidyr functions to explore their functionalities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Tidy Data: Principles and Practices with R
Show steps
Learn the fundamentals of tidy data principles and how to manipulate, reshape, and transform data using tidyverse packages like tidyr.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read the book's introduction and first chapter to grasp the core concepts of tidy data.
-
Complete the exercises in the book to apply your understanding of tidyr functions like pivot_longer() and separate().
Practice data cleaning and reshaping exercises
Show steps
Solidify your skills by completing practice drills that challenge you to clean and reshape messy data efficiently.
Browse courses on
Data Cleaning
Show steps
-
Find online exercises or create your own data cleaning and reshaping scenarios.
-
Use tidyr functions to solve the exercises and improve your proficiency.
-
Analyze the results to identify areas for improvement.
Create a data visualization project using tidyverse
Show steps
Apply your knowledge of tidyverse and ggplot2 to create meaningful visualizations that effectively communicate data insights.
Browse courses on
Data Visualization
Show steps
-
Gather and clean data relevant to a specific topic or research question.
-
Use tidyr to reshape and transform the data into a tidy format.
-
Create visualizations using ggplot2 to explore and present the data.
-
Write a brief report to summarize your findings and insights.
Develop a personal data cleaning and reshaping project
Show steps
Challenge yourself to apply your acquired skills in a practical setting by starting a personal project that involves data cleaning and reshaping.
Browse courses on
Data Cleaning
Show steps
-
Identify a real-world dataset that requires cleaning and reshaping.
-
Use tidyr functions to clean and reshape the data into a tidy format.
-
Explore and analyze the data to uncover insights and patterns.
-
Write a report or create a presentation to showcase your findings.
Organize and review course notes and resources
Show steps
Consolidate your learning by organizing and reviewing notes, assignments, and other resources from the course.
Show steps
-
Gather all course materials, including notes, slides, and assignments.
-
Review the materials to reinforce your understanding of concepts.
-
Identify areas where you need further clarification or practice.
Review basic data manipulation concepts in R
Show steps
Revisit fundamental data manipulation techniques in R to strengthen your understanding before delving into tidy data principles.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Review the use of basic data structures like vectors, matrices, and data frames.
- Practice data manipulation operations like subsetting, filtering, and sorting.
Join a study group or online forum for tidyr users
Show steps
Engage with a community of learners to share knowledge, ask questions, and collaborate on tidyr-related projects.
Browse courses on
tidyr
Show steps
Show steps
Show steps
- Search for study groups or online forums dedicated to tidyr.
- Join the group or forum and actively participate in discussions.
- Ask questions, share your experiences, and learn from others.
Follow tutorials on using tidyr for data reshaping
Show steps
Enhance your understanding of tidyr's capabilities by following guided tutorials that demonstrate practical applications.
Browse courses on
tidyr
Show steps
Show steps
Show steps
- Search for tutorials on reshaping data using tidyr packages.
- Follow the steps in the tutorials to practice reshaping techniques.
- Experiment with different tidyr functions to explore their functionalities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Tidy Data: Principles and Practices with R
Show steps
Learn the fundamentals of tidy data principles and how to manipulate, reshape, and transform data using tidyverse packages like tidyr.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter to grasp the core concepts of tidy data.
- Complete the exercises in the book to apply your understanding of tidyr functions like pivot_longer() and separate().
Practice data cleaning and reshaping exercises
Show steps
Solidify your skills by completing practice drills that challenge you to clean and reshape messy data efficiently.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find online exercises or create your own data cleaning and reshaping scenarios.
- Use tidyr functions to solve the exercises and improve your proficiency.
- Analyze the results to identify areas for improvement.
Create a data visualization project using tidyverse
Show steps
Apply your knowledge of tidyverse and ggplot2 to create meaningful visualizations that effectively communicate data insights.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather and clean data relevant to a specific topic or research question.
- Use tidyr to reshape and transform the data into a tidy format.
- Create visualizations using ggplot2 to explore and present the data.
- Write a brief report to summarize your findings and insights.
Develop a personal data cleaning and reshaping project
Show steps
Challenge yourself to apply your acquired skills in a practical setting by starting a personal project that involves data cleaning and reshaping.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Identify a real-world dataset that requires cleaning and reshaping.
- Use tidyr functions to clean and reshape the data into a tidy format.
- Explore and analyze the data to uncover insights and patterns.
- Write a report or create a presentation to showcase your findings.
Organize and review course notes and resources
Show steps
Consolidate your learning by organizing and reviewing notes, assignments, and other resources from the course.
Show steps
Show steps
Show steps
- Gather all course materials, including notes, slides, and assignments.
- Review the materials to reinforce your understanding of concepts.
- Identify areas where you need further clarification or practice.
Career center
Learners who complete Tidy Messy Data using tidyr in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course, "Tidy Messy Data using tidyr in R," provides a solid foundation in data cleaning and transformation, which are essential skills for Data Analysts. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively reshape and manipulate data to prepare it for analysis.
Data Scientist
Data Scientist
Data Scientists use their expertise in data analysis, machine learning, and statistics to solve business problems. This course provides a strong foundation in data cleaning and transformation, which are crucial steps in the data science workflow. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for modeling and analysis.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course provides a foundation in data cleaning and transformation, which are essential skills for Statisticians. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively reshape and manipulate data to prepare it for statistical analysis.
Business Analyst
Business Analyst
Business Analysts use data to understand and improve business processes. This course provides a foundation in data cleaning and transformation, which are essential skills for Business Analysts. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for analysis and visualization.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course provides a foundation in data cleaning and transformation, which are essential skills for Data Engineers. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively prepare data for storage and processing.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course provides a foundation in data cleaning and transformation, which are essential skills for Machine Learning Engineers. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for model training and evaluation.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a foundation in data cleaning and transformation, which are essential skills for Quantitative Analysts. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively prepare data for analysis and modeling.
Research Analyst
Research Analyst
Research Analysts conduct research and provide insights to support decision-making. This course provides a foundation in data cleaning and transformation, which are essential skills for Research Analysts. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for analysis and reporting.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and inform the public. This course provides a foundation in data cleaning and transformation, which are essential skills for Data Journalists. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively prepare data for analysis and visualization.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in data cleaning and transformation, which are essential skills for Software Engineers working with data-intensive applications. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for processing and analysis.
Product Manager
Product Manager
Product Managers are responsible for the development and success of products. This course provides a foundation in data cleaning and transformation, which are essential skills for Product Managers who need to understand and analyze user data. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively prepare data for analysis and decision-making.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand and target customers. This course provides a foundation in data cleaning and transformation, which are essential skills for Marketing Analysts. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for analysis and visualization.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial performance and make investment recommendations. This course provides a foundation in data cleaning and transformation, which are essential skills for Financial Analysts. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively prepare data for analysis and modeling.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course provides a foundation in data cleaning and transformation, which are essential skills for Operations Research Analysts. By learning how to use tidyverse functions like separate_rows() and separate(), learners can effectively prepare data for analysis and modeling.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course provides a foundation in data cleaning and transformation, which are essential skills for Actuaries. By learning how to use tidyverse functions like pivot_longer() and pivot_wider(), learners can effectively prepare data for analysis and modeling.
For more career information including salaries, visit:
OpenCourser.com/course/0az7w9/tidy
Reading list
We've selected 14 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
Tidy Messy Data using tidyr in R.
Save
Provides a solid foundation in R for data science. It covers data manipulation, visualization, and modeling, and focuses on the tidyverse, a collection of R packages for data science. It commonly used textbook at academic institutions and by industry professionals, and it is helpful in providing both background and more in-depth knowledge for this course.
Save
Provides a conceptual framework for working with data in a tidy format, which is the focus of this course. It explains the principles of tidy data and how to apply them to real-world data. It useful reference tool and can be helpful in providing background knowledge for this course.
Save
Covers advanced topics in R, including programming, object-oriented programming, and high-performance computing. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on data cleaning and transformation.
Covers data visualization using the ggplot2 library, which is used in this course for creating plots. It provides a comprehensive guide to creating various types of plots and customizing their appearance. It useful reference tool and can be helpful in expanding your knowledge of data visualization.
Provides a comprehensive introduction to R, covering a wide range of topics. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on data cleaning and transformation.
Focuses on data manipulation using the dplyr package, which is not covered in this course. However, it provides a good foundation for understanding data manipulation concepts and can be helpful as additional reading.
Save
Provides a collection of recipes for solving common problems in R. It can be a useful reference tool for quickly finding solutions to specific problems, but it does not provide a structured learning path.
Provides a practical introduction to R for data analysis. It covers a wide range of topics, including data manipulation, visualization, and modeling. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on tidyverse functions.
Covers statistical modeling techniques, including linear and logistic regression, decision trees, and support vector machines. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on data cleaning and transformation.
Provides a practical introduction to data science for business professionals. It covers topics such as data mining, machine learning, and data visualization. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on tidyverse functions.
Covers machine learning techniques using R. It provides a practical introduction to supervised and unsupervised learning algorithms. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on data cleaning and transformation.
Covers deep learning techniques using R. It provides a practical introduction to neural networks and deep learning models. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on data cleaning and transformation.
Provides a comprehensive introduction to data analysis using R. It covers a wide range of topics, including data manipulation, visualization, and modeling. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on tidyverse functions.
Provides a practical introduction to R for data science. It covers a wide range of topics, including data manipulation, visualization, and modeling. It is more valuable as additional reading than it is as a current reference for this course, as it does not focus on tidyverse functions.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0az7w9/tidy
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Good to know
Focuses on organizing data to fit the tidy data principles, which is crucial for further analysis and data visualization
Well-suited for both beginner and intermediate R users who want to enhance their skills in data manipulation
Provides hands-on exercises with real-world data, allowing learners to immediately apply the concepts
Covers a range of tidyr functions, ensuring learners are equipped with various techniques for data cleaning and reshaping
Requires familiarity with basic R functions and the pipe function
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