Joining Data in R using dplyr from Coursera

In this course, you will learn practical ways for data manipulation in R. We will talk about different join operations and spend a great deal of our time here joining the sales and customers data sets using the dplyr package. By the end of this 2-hour-long project, you will perform inner join, full (outer) join, right join, left join, cross join, semi join, and anti join using the merge() and dplyr functions.

This project-based course is an intermediate-level course in R. Therefore, to get the most of this project, it is essential to have prior experience using R for basic analysis. I recommend that you complete the project titled: "Data Manipulation with dplyr in R" before you take this current project.

What's inside

Syllabus

Project Overview

You will need to join or merge two or more data sets at different points in your work as a data enthusiast. The dplyr package offers very sophisticated functions to help you achieve the join operation you desire. This project-based course, "Joining Data in R using dplyr" is for R users willing to advance their knowledge and skills. In this course, you will learn practical ways for data manipulation in R. We will talk about different join operations and spend a great deal of our time here joining the sales and customers data sets using the dplyr package. By the end of this 2-hour-long project, you will perform inner join, full (outer) join, right join, left join, cross join, semi join, and anti join using the merge() and dplyr functions. This project-based course is an intermediate-level course in R. Therefore, to get the most of this project, it is essential to have prior experience with using R for basic analysis. I recommend that you should complete the project titled: "Data Manipulation with dplyr in R" before you take this current project.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

This intermediate-level course in R is suitable for users wishing to advance their knowledge and skills with the dplyr package for data manipulation in R

Suitable for those wanting to join or merge two or more data sets using the dplyr package

Covers various join operations such as inner join, full (outer) join, right join, left join, cross join, semi join, and anti join

Prior experience using R for basic analysis is recommended to fully benefit from the project

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 Joining Data in R using dplyr with these activities:

Read 'R for Data Science'

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Review the fundamentals of data science and R programming, which will provide a solid foundation for this course.

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

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Read chapters 1-3
Complete the exercises in chapters 1-3
Create a small data science project using R

Organize course materials

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Stay organized and enhance your learning by compiling and reviewing course materials regularly.

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Create a system for organizing notes, assignments, quizzes, and exams
Review materials regularly to reinforce your understanding
Summarize key concepts and make connections between topics

Follow dplyr tutorials

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Expand your knowledge of dplyr by following guided tutorials, enhancing your understanding and skills in data manipulation.

Browse courses on Dplyr

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Find tutorials on dplyr functions and data manipulation
Follow the tutorials step-by-step
Practice using the techniques covered in the tutorials

Four other activities

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Practice dplyr functions

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Reinforce your understanding of dplyr functions and improve your proficiency in data manipulation.

Browse courses on Dplyr

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Use the dplyr package to perform basic data manipulation tasks
Practice using dplyr functions to join data sets
Complete practice exercises using dplyr functions

Join a study group

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Connect with peers, share knowledge, and reinforce your understanding of dplyr and data manipulation techniques.

Browse courses on Dplyr

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Find or create a study group with other students taking this course
Meet regularly to discuss course material and practice dplyr functions
Provide support and encouragement to each other

Create a data visualization using dplyr

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Apply your knowledge of dplyr to create a data visualization, solidifying your understanding of data manipulation and visualization techniques.

Browse courses on Data Visualization

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Choose a dataset to visualize
Use dplyr functions to prepare and manipulate the data
Create a data visualization using a plotting library

Contribute to dplyr package

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Deepen your understanding of dplyr's inner workings and contribute to the community by making improvements or reporting issues.

Browse courses on Dplyr

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Read the dplyr documentation and codebase
Identify areas where you can contribute
Make a pull request or report an issue on GitHub

Career center

Learners who complete Joining Data in R using dplyr will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts collect, analyze, interpret, and present data in order to help companies make informed decisions. This course on Joining Data in R using dplyr can help Data Analysts develop the skills they need to effectively combine and analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to identify and solve business problems. This course on Joining Data in R using dplyr can help Business Analysts develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Business Analyst

Data Scientist

Data Scientists use data to build models and predict future outcomes. This course on Joining Data in R using dplyr can help Data Scientists develop the skills they need to effectively combine and analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use data to draw conclusions about the world around us. This course on Joining Data in R using dplyr can help Statisticians develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use data to improve the efficiency of organizations. This course on Joining Data in R using dplyr can help Operations Research Analysts develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. This course on Joining Data in R using dplyr can help Financial Analysts develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use data to identify and mitigate risks. This course on Joining Data in R using dplyr can help Risk Analysts develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Risk Analyst

Market Researcher

Market Researchers use data to understand consumer behavior. This course on Joining Data in R using dplyr can help Market Researchers develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Market Researcher

Data Visualization Analyst

Data Visualization Analysts create visual representations of data. This course on Joining Data in R using dplyr can help Data Visualization Analysts develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Data Visualization Analyst

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. This course on Joining Data in R using dplyr can help Machine Learning Engineers develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. This course on Joining Data in R using dplyr can help Software Engineers develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Software Engineer

Database Administrator

Database Administrators manage and maintain databases. This course on Joining Data in R using dplyr can help Database Administrators develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Database Administrator

Information Security Analyst

Information Security Analysts protect computer systems and networks from unauthorized access. This course on Joining Data in R using dplyr can help Information Security Analysts develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Information Security Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that is used to store and process data. This course on Joining Data in R using dplyr can help Data Engineers develop the skills they need to effectively combine and analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for Data Engineer

User Experience Researcher

User Experience Researchers study how users interact with products and services. This course on Joining Data in R using dplyr can help User Experience Researchers develop the skills they need to effectively analyze data from multiple sources, which is a key part of their role.

See salaries and explore the career path for User Experience Researcher