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The R Programming Environment

This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.

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What's inside

Syllabus

Basic R Language

In this module, you'll learn the basics of R, including syntax, some tidy data principles and processes, and how to read data into R.

Basic R Language: Lesson Choices

Data Manipulation

During this module, you'll learn to summarize, filter, merge, and otherwise manipulate data in R, including working through the challenges of dates and times.

Data Manipulation: Lesson Choices

Text Processing, Regular Expression, & Physical Memory

During this module, you'll learn to use R tools and packages to deal with text and regular expressions. You'll also learn how to manage and get the most from your computer's physical memory when working in R.

Text Processing, Regular Expression, & Physical Memory: Lesson Choices

Choice 1: Get credit while using swirl | Choice 2: Get credit by providing a code from swirl

Large Datasets

In this final module, you'll learn how to overcome the challenges of working with large datasets both in memory and out as well as how to diagnose problems and find help.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches R programming, which is an in demand skill in data science

Introduces fundamental R concepts, ensuring a strong foundation for beginners

Includes hands-on labs, enhancing practical skills and understanding

Explores essential data science tasks, providing valuable insights for learners

Facilitates contributions to data science teams or within developer communities

Taught by seasoned instructors, Roger D. Peng, PhD and Brooke Anderson, ensuring high-quality content

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Reviews summary

Introductory r fundamentals

According to students, introductory concepts are clear in this beginner-friendly R course. The course largely has positive reviews. Learners mention that assignments help solidify learning and that the course provides a strong foundation for future endeavors.

Exercises help learners reinforce R concepts.

Concepts broken down in an easy way.

Course introduces concepts clearly.

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 The R Programming Environment with these activities:

Read 'R for Data Science'

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This book is a comprehensive guide to using R for data science. It covers a wide range of topics, from data cleaning and manipulation to statistical modeling and machine learning.

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

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Read the book from cover to cover.
Work through the exercises in the book.

Practice visualizing and modeling data in R

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R is a powerful tool for visualizing and modeling data. This activity will help you develop your skills in these areas, which are essential for data science.

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Find a dataset to work with.
Visualize the data using R's built-in plotting functions.
Fit a model to the data using R's modeling functions.

Complete online coding challenges

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Online coding challenges can be a great way to practice your skills and learn new techniques.

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Find an online coding challenge website.
Solve coding challenges on the website.
Review your solutions and learn from your mistakes.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Learn how to use tidyverse packages

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Tidyverse packages are a powerful set of tools for data manipulation in R. This activity will help you learn how to use these packages to clean, transform, and analyze your data.

Browse courses on Tidy Data

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Install the tidyverse package.
Learn the basics of tidy data.
Use tidyverse functions to manipulate your data.

Join a study group

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Study groups can be a great way to learn from your peers and reinforce what you're learning in class.

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Find a study group to join.
Attend study group meetings regularly.
Participate in discussions and ask questions.

Build a data science project

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The best way to learn data science is by doing. This activity will help you apply your skills to a real-world problem.

Show steps

Define the problem you want to solve.
Collect and clean the data.
Build a model to solve the problem.
Evaluate the performance of your model.
Deploy your model to production.

Participate in a hackathon

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Hackathons are a great way to apply your skills to a real-world problem and learn from other data scientists.

Show steps

Find a hackathon to participate in.
Form a team and develop a project.
Present your project to the judges.

Career center

Learners who complete The R Programming Environment will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists gather, analyze, and present data in a way that is easy to understand and actionable. They combine statistical concepts, advanced computing, and business knowledge to help companies better understand their data. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the necessary skills to succeed as a Data Scientist and leverage R to extract valuable insights from data.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use data to solve business problems. They collect, clean, and analyze data in order to identify trends and make recommendations. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the necessary skills to succeed as a Data Analyst and leverage R to extract valuable insights from data.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, test, and maintain software systems. They use programming languages to create applications that meet the needs of users. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. While this course focuses on using R for data science, the skills you'll learn can be applied to software development in general.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design, build, and maintain the infrastructure that supports data science and analytics. They ensure that data is available, reliable, and secure. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. While this course focuses on using R for data science, the skills you'll learn can be applied to data engineering as well.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They work in a variety of fields, including healthcare, finance, and marketing. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the necessary skills to use R for statistical analysis and modeling.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts use data to understand business problems and make recommendations. They work with stakeholders to identify needs, analyze data, and develop solutions. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for data-driven decision-making in a business setting.

See salaries and explore the career path for Business Analyst

Machine Learning Engineer

Machine Learning Engineers design, build, and deploy machine learning models. They use a variety of programming languages and tools to automate the process of learning from data. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. While this course does not cover machine learning specifically, the skills you'll learn can be applied to machine learning engineering.

See salaries and explore the career path for Machine Learning Engineer

Data Journalist

Data Journalists use data to tell stories and inform the public. They combine statistical analysis, data visualization, and storytelling skills to create engaging and informative articles and visualizations. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for data-driven storytelling.

See salaries and explore the career path for Data Journalist

Financial Analyst

Financial Analysts use data to evaluate investments and make recommendations to clients. They analyze financial data, build models, and provide insights to help investors make informed decisions. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for financial analysis.

See salaries and explore the career path for Financial Analyst

Web Developer

Web Developers design, build, and maintain websites and web applications. They use a variety of programming languages and tools to create websites that meet the needs of users. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. While this course focuses on using R for data science, the skills you'll learn can be applied to web development as well.

See salaries and explore the career path for Web Developer

Quantitative Researcher

Quantitative Researchers use mathematical and statistical techniques to analyze data and make investment decisions. They work in hedge funds, investment banks, and other financial institutions. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for quantitative research.

See salaries and explore the career path for Quantitative Researcher

Data Visualization Specialist

Data Visualization Specialists design and create visual representations of data. They use a variety of tools and techniques to communicate data in a clear and concise way. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for data visualization.

See salaries and explore the career path for Data Visualization Specialist

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in insurance companies, pension funds, and other financial institutions. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for actuarial work.

See salaries and explore the career path for Actuary

Risk Manager

Risk Managers identify, assess, and mitigate risks. They work in a variety of industries, including finance, healthcare, and manufacturing. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for risk management.

See salaries and explore the career path for Risk Manager

Market Researcher

Market Researchers collect and analyze data about consumer behavior. They use this data to help businesses understand their customers and make informed decisions. This course provides a solid foundation in R, a popular programming language in data science, and covers topics such as data manipulation, text processing, and working with large datasets. By taking this course, you'll gain the skills needed to use R for market research.

See salaries and explore the career path for Market Researcher

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 The R Programming Environment.

R for Data Science

Save

Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers a wide range of topics, including data manipulation, visualization, modeling, and machine learning. It will be helpful as an in-depth reference and for background on data science practices.

The R Programming Environment

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Introductory r fundamentals

Activities

Career center

Reading list

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