Handling Missing Values in R using tidyr from Coursera

Missing data can be a “serious” headache for data analysts and scientists. This project-based course Handling Missing Values in R using tidyr is for people who are learning R and who seek useful ways for data cleaning and manipulation in R. In this project-based course, we will not only talk about missing values, but we will spend a great deal of our time here hands-on on how to handle missing value cases using the tidyr package. Be rest assured that you will learn a ton of good work here.

By the end of this 2-hour-long project, you will calculate the proportion of missing values in the data and select columns that have missing values. Also, you will be able to use the drop_na(), replace_na(), and fill() function in the tidyr package to handle missing values. By extension, we will learn how to chain all the operations using the pipe function.

This project-based course is an intermediate level course in R. Therefore, to complete this project, it is required that you have prior experience with using R. I recommend that you should complete the projects titled: “Getting Started with R” and “Data Manipulation with dplyr in R“ before you take this current project. These introductory projects in using R will provide every necessary foundation to complete this current project. However, if you are comfortable with using R, please join me on this wonderful ride! Let’s get our hands dirty!

What's inside

Syllabus

Project Overview

Missing data can be a "serious" headache for data analysts and scientists. This project-based course, "Handling Missing Values in R using tidyr" is for R users willing to advance their knowledge and skills. In this course, you will learn practical ways for data cleaning and manipulation in R. We will talk about missing values and spend a great deal of our time here hands-on on handling missing value cases using the tidyr package. Be rest assured that you will learn a ton of good work here. By the end of this 2-hour-long project, you will calculate the proportion of missing values in the data and select columns that have missing values. Also, you will be able to use the drop_na(), replace_na(), and fill() function in the tidyr package to handle missing values. By extension, we will learn how to chain all the operations using the pipe function. This project-based course is an intermediate level course in R. Therefore, to complete this project, it is essential to have prior experience with using R. I recommend that you should complete the projects titled: "Getting Started with R" and "Data Manipulation with dplyr in R "before you take this current project. These introductory projects in using R will provide every necessary foundation to complete this current project. However, if you are comfortable with using R, please join me on this beautiful ride! Let's get our hands dirty!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches intermediate-level data analysis skills using the R programming language, which is a foundational tool for data analysis

Provides hands-on exercises and interactive materials for practical learning

Focuses on using the tidyr package in R, which is specifically designed for data manipulation and wrangling

Suitable for learners with prior experience in using R, which is a common requirement for data analysis courses

Emphasizes practical skills and techniques for handling missing values in data, which is a common challenge in data analysis

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 Handling Missing Values in R using tidyr with these activities:

Read 'R for Data Science'

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This book provides a comprehensive overview of R and will help you refresh your understanding of the language.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Read the first few chapters of the book.
Work through the exercises in the book.

Review Data Cleaning Concepts

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Reviewing data cleaning concepts will provide a strong foundation for understanding the techniques covered in this course.

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Review data cleaning principles and best practices.
Go through examples of common data cleaning tasks.

Follow Data Cleaning Tutorials

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Following data cleaning tutorials will help you learn the basics of data cleaning.

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Find a set of data cleaning tutorials online.
Follow the tutorials.
Practice the techniques you learn.

Five other activities

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Complete Tidyverse Exercises

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Completing these exercises will give you hands-on practice with the tidyverse.

Browse courses on Tidyverse

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Find a set of Tidyverse exercises online.
Work through the exercises.
Check your answers against the solutions.

Develop a Data Cleaning Plan

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Developing a data cleaning plan will help you think through the steps involved in cleaning a dataset.

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Identify the goals of the data cleaning process.
Identify the types of data cleaning that need to be performed.
Develop a workflow for the data cleaning process.

Join a Data Cleaning Study Group

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Joining a data cleaning study group will give you the opportunity to discuss and learn from other students.

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Find a data cleaning study group online or at your local library.
Attend the study group meetings.
Participate in the discussions.

Build a Data Cleaning Pipeline

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Building a data cleaning pipeline will help you apply the techniques you learn in this course to a real-world dataset.

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Choose a dataset to clean.
Write a data cleaning script using the tidyverse.
Test your script and make sure it works correctly.

Contribute to Tidyverse

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Contributing to Tidyverse will give you a deeper understanding of the tidyverse.

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Find a Tidyverse package to contribute to.
Make a small contribution to the package.
Submit a pull request.

Career center

Learners who complete Handling Missing Values in R using tidyr will develop knowledge and skills that may be useful to these careers:

Project Manager

Project managers use data to plan and execute projects. This course may be useful for project managers because it teaches them how to deal with missing data, which is a common problem in project management.

See salaries and explore the career path for Project Manager

Data Scientist

Data scientists use data to build models and solve problems. This course may be useful for data scientists because it teaches them how to deal with missing data, which can be a problem when building models. Additionally, this course teaches how to use the tidyr package, which is a powerful tool for data manipulation.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers build and maintain machine learning models. This course may be useful for machine learning engineers because it teaches them how to deal with missing data, which can be a problem when building machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business analysts use data to help businesses make decisions. This course may be useful for business analysts because it teaches them how to deal with missing data, which is a common problem in business analysis.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians collect, analyze, and interpret data to help businesses and organizations make decisions. This course may be useful for statisticians because it teaches them how to deal with missing data, which is a common problem in statistics.

See salaries and explore the career path for Statistician

Data Architect

Data architects design and build data architectures. This course may be useful for data architects because it teaches them how to deal with missing data, which is a common problem in data architecture.

See salaries and explore the career path for Data Architect

Software Engineer

Software engineers design, build, and maintain software applications. This course may be useful for software engineers because it teaches them how to deal with missing data, which is a common problem in software development.

See salaries and explore the career path for Software Engineer

Data Analyst

Data analysts summarize and analyze data to provide businesses with insights and help them make decisions. This course, Handling Missing Values in R using tidyr, may be useful for data analysts because it teaches them how to deal with missing data, which is a common problem in data analysis.

See salaries and explore the career path for Data Analyst

Database Administrator

Database administrators design, build, and maintain databases. This course may be useful for database administrators because it teaches them how to deal with missing data, which can be a problem when managing databases.

See salaries and explore the career path for Database Administrator

Web Developer

Web developers design, build, and maintain websites. This course may be useful for web developers because it teaches them how to deal with missing data, which can be a problem when developing websites.

See salaries and explore the career path for Web Developer

Financial Analyst

Financial analysts use data to evaluate investments and make recommendations to clients. This course may be useful for financial analysts because it teaches them how to deal with missing data, which is a common problem in financial analysis.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing analysts use data to evaluate marketing campaigns and make recommendations to clients. This course may be useful for marketing analysts because it teaches them how to deal with missing data, which is a common problem in marketing analysis.

See salaries and explore the career path for Marketing Analyst

Product Manager

Product managers use data to develop and improve products. This course may be useful for product managers because it teaches them how to deal with missing data, which is a common problem in product development.

See salaries and explore the career path for Product Manager

Data Engineer

Data engineers design, build, and maintain data pipelines. This course may be useful for data engineers because it teaches them how to deal with missing data, which is a common problem in data engineering.

See salaries and explore the career path for Data Engineer

Data Governance Analyst

Data governance analysts develop and implement data governance policies and procedures. This course may be useful for data governance analysts because it teaches them how to deal with missing data, which is a common problem in data governance.

See salaries and explore the career path for Data Governance Analyst