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Tidy Messy Data using tidyr in R
Arimoro Olayinka Imisioluwa
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
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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.
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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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