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Roger D. Peng, PhD
and
Brooke Anderson
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/55q1x5/the
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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.
Read more
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.
Read more
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
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
Save this course
Save The R Programming Environment to your list so you can find it easily later:
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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'
Show steps
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
Show steps
-
Read the book from cover to cover.
-
Work through the exercises in the book.
Practice visualizing and modeling data in R
Show steps
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.
Show steps
-
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
Show steps
Online coding challenges can be a great way to practice your skills and learn new techniques.
Show steps
-
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
Show steps
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
Show steps
-
Install the tidyverse package.
-
Learn the basics of tidy data.
-
Use tidyverse functions to manipulate your data.
Join a study group
Show steps
Study groups can be a great way to learn from your peers and reinforce what you're learning in class.
Show steps
-
Find a study group to join.
-
Attend study group meetings regularly.
-
Participate in discussions and ask questions.
Build a data science project
Show steps
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
Show steps
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.
Read 'R for Data Science'
Show steps
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
Show steps
Show steps
Show steps
- Read the book from cover to cover.
- Work through the exercises in the book.
Practice visualizing and modeling data in R
Show steps
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.
Show steps
Show steps
Show steps
- 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
Show steps
Online coding challenges can be a great way to practice your skills and learn new techniques.
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- Install the tidyverse package.
- Learn the basics of tidy data.
- Use tidyverse functions to manipulate your data.
Join a study group
Show steps
Study groups can be a great way to learn from your peers and reinforce what you're learning in class.
Show steps
Show steps
Show steps
- Find a study group to join.
- Attend study group meetings regularly.
- Participate in discussions and ask questions.
Build a data science project
Show steps
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
Show steps
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
Show steps
Hackathons are a great way to apply your skills to a real-world problem and learn from other data scientists.
Show steps
Show steps
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 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.
Data Analyst
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.
Software Engineer
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.
Data 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.
Statistician
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.
Business Analyst
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.
Machine Learning Engineer
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.
Data Journalist
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.
Financial Analyst
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.
Web Developer
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.
Quantitative Researcher
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.
Data Visualization Specialist
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.
Actuary
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.
Risk Manager
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.
Market Researcher
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.
For more career information including salaries, visit:
OpenCourser.com/course/55q1x5/the
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.
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.
Provides a practical guide to programming in R, with a focus on solving real-world problems. It covers a wide range of topics, including data manipulation, visualization, modeling, and machine learning. It is particularly useful as a reference for specific R functions and techniques.
Save
Provides a collection of recipes for solving common problems in R. It covers a wide range of topics, including data manipulation, visualization, modeling, and machine learning. It useful supplement to the course, providing practical solutions for common tasks.
Provides a comprehensive guide to data manipulation in R. It covers a wide range of topics, including data cleaning, transformation, and merging. It is most useful as a supplement to the course, providing more in-depth coverage of data manipulation techniques.
Save
Provides a practical guide to text mining in R. It covers a wide range of topics, including text cleaning, preprocessing, and analysis. It is particularly useful for anyone interested in working with text data in R.
Provides a comprehensive guide to machine learning in R. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation. It useful supplement to the course, providing more in-depth coverage of machine learning techniques.
Provides the official documentation for the R programming language. It comprehensive reference for all aspects of the language, including syntax, functions, and packages. It useful reference for anyone who wants to learn more about R.
Save
Provides a collection of recipes for creating visualizations in R. It covers a wide range of topics, including basic plots, advanced plots, and interactive visualizations. It useful supplement to the course, providing practical solutions for common visualization tasks.
Save
Provides a comprehensive guide to R packages. It covers a wide range of topics, including package development, package installation, and package management. It useful supplement to the course, providing more in-depth coverage of R package development.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation. It useful supplement to the course, providing more in-depth coverage of statistical learning techniques.
Provides a comprehensive guide to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation. It useful supplement to the course, providing more in-depth coverage of statistical learning techniques.
Save
Provides a practical guide to predictive modeling. It covers a wide range of topics, including data preparation, model selection, and model evaluation. It useful supplement to the course, providing more in-depth coverage of predictive modeling techniques.
Provides a practical guide to data science for business applications. It covers a wide range of topics, including data collection, data analysis, and data visualization. It useful supplement to the course, providing more in-depth coverage of data science techniques for business applications.
Save
Provides a comprehensive guide to deep learning. It covers a wide range of topics, including deep learning architectures, deep learning algorithms, and deep learning applications. It useful supplement to the course, providing more in-depth coverage of deep learning techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/55q1x5/the
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Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Brooke Anderson
Language
English
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This course belongs to a
collection
called
Mastering Software Development in R. It's comprised of five courses:
- The R Programming Environment (viewing)
- Building R Packages
- Mastering Software Development in R Capstone
- Building Data Visualization Tools
- Advanced R Programming
Good to know
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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