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Building R Packages

Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.

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What's inside

Syllabus

Getting Started with R Packages

Documentation and Testing

Licensing, Version Control, and Software Design

Continuous Integration and Cross Platform Development

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches the industry-standard software R for data science

Covers the core elements of R software and its distribution

Develops skills in R package development and software distribution

Builds a strong foundation for R software and its applications in data science

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Reviews summary

Informative r package building course

Learners say that this course offers a solid overview of how to build R packages. It provides up-to-date materials covering modern approaches to package development. The course features engaging programming exercises to help learners apply their knowledge. However, some students have noted that the lack of dedicated video lectures can be a drawback, and recommend supplementing the course with additional resources.

Learners have found the peer grading system to be helpful.

"But, I heavily made use of the discussion rooms to get my grades done and peer reviewed at least 15 submissions."

Students appreciate the course's practical exercises

"Useful programming exercises to guide learning the basic elements of R packages."

Students have expressed a need for more dedicated support and interactive elements.

"This isn't really a course, consists just of having you read a book."

"There is zero content, zero support and zero walk-troughs."

Some reviewers have noted that some of the course materials need updating.

"This course is in desperate need of updating."

"Outdated materials, like those related to travis and devtools"

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 Building R Packages with these activities:

Identify and connect with experienced R package developers

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Accelerate your learning by seeking guidance and support from experts in the field.

Browse courses on Mentoring

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Attend industry events and meetups focusing on R package development
Reach out to experienced R package developers on LinkedIn or other professional networking platforms
Attend workshops or online courses taught by R package development experts

Review web programming fundamentals

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Solidify your understanding of HTML, CSS, and JavaScript to better grasp the concepts of R package development.

Browse courses on HTML

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Review HTML syntax and semantics
Practice building simple web pages using HTML
Review CSS syntax and selectors
Practice styling web pages using CSS
Review JavaScript syntax and data types

Read 'R Packages' by Hadley Wickham

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Gain a comprehensive understanding of R package development principles and best practices from a renowned expert.

View R Packages on Amazon

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Five other activities

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Follow tutorials on R package development best practices

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Develop a deeper understanding of industry best practices and standards for creating and distributing R packages.

Browse courses on R Packages

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Find online tutorials on R package development best practices
Follow along with the tutorials and implement the practices
Experiment with different package development techniques
Apply best practices to your own R package development projects

Participate in peer review sessions for R package development

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Enhance your R package development skills by receiving feedback and providing constructive criticism to peers.

Browse courses on Peer Review

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Join or create a peer review group for R package development
Submit your R package for review by peers
Provide constructive feedback on other packages
Incorporate feedback into your own package development

Write a blog post or article on a specific aspect of R package development

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Deepen your understanding of a particular topic in R package development and share your knowledge with the community.

Browse courses on Software Development

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Choose a specific topic in R package development that you want to explore
Research and gather information on the topic
Write a well-structured and informative blog post or article
Publish your content on a relevant platform

Participate in R package development competitions

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Challenge yourself and showcase your R package development skills by participating in competitions.

Browse courses on R Packages

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Find and register for R package development competitions
Develop an R package that meets the competition requirements
Submit your package to the competition
Receive feedback and improve your package based on competition results

Create an R package for a real-world problem

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Gain hands-on experience in the entire R package development lifecycle by working on a practical project.

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Identify a real-world problem that can be solved using an R package
Design and implement the R package solution
Write comprehensive documentation and vignettes for the package
Test and debug the package thoroughly
Publish the package on CRAN or GitHub

Career center

Learners who complete Building R Packages will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their expertise in mathematics, statistics, and computer science to solve complex problems and uncover insights from data. They use R for data analysis, modeling, and visualization. This course provides the foundation to build a career in data science.

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Data Analyst

Data Analysts interpret and communicate data, using statistical techniques to uncover trends and insights. This course would help build a foundation in R, a popular programming language for data science.

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Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. They use R for data analysis and model development. This course provides a foundation in R, a key language in the field of machine learning.

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Business Intelligence Analyst

Business Intelligence Analysts provide insights to help businesses grow and make informed decisions. Using statistical techniques, they analyze data, identify trends and patterns, and recommend solutions. This course teaches how to use R, a language often used in data science and business intelligence.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They use R for data analysis and modeling. This course may be useful for Quantitative Analysts looking to enhance their skills in R.

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Health Economist

Health Economists analyze the costs and benefits of healthcare interventions. They use R for data analysis and modeling. This course may be useful for Health Economists looking to enhance their skills in R.

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Statistician

Statisticians collect, analyze, and interpret data to uncover trends and patterns. They use R for data analysis and statistical modeling. This course may be useful for Statisticians looking to enhance their skills in R.

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Data Architect

Data Architects design and manage data systems. They use R for data analysis and modeling. This course may be useful for Data Architects looking to enhance their skills in R.

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Risk Analyst

Risk Analysts identify and assess risks. They use R for data analysis and modeling. This course may be useful for Risk Analysts looking to enhance their skills in R.

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Epidemiologist

Epidemiologists investigate the causes and spread of diseases. They use R for data analysis and statistical modeling. This course may be useful for Epidemiologists looking to enhance their skills in R.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical methods to analyze and interpret data in the field of biology. They use R for data analysis and statistical modeling. This course may be useful for Biostatisticians looking to enhance their skills in R.

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Research Scientist

Research Scientists conduct research in a variety of fields, including science, engineering, and medicine. They use R for data analysis and statistical modeling. This course may be useful for Research Scientists looking to enhance their skills in R.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They use R for data analysis and modeling. This course may be useful for Software Engineers looking to enhance their skills in R.

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Actuary

Actuaries analyze and manage financial risks. They use R for data analysis and statistical modeling. This course may be useful for Actuaries looking to enhance their skills in R.

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Data Engineer

Data Engineers build and maintain pipelines for data collection and processing. The information then can be analyzed by Data Analysts and Business Intelligence professionals. R is a useful tool for performing data manipulation and statistical analysis, and this course may be useful for someone wishing to enter the field of Data Engineering.

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Reading list

We've selected 15 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 Building R Packages.

R Packages

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Comprehensive guide to creating and distributing R packages. It provides thorough explanations of package structure, testing, documentation, and distribution. It is widely used as a textbook at academic institutions and by industry professionals.

Building R Packages

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Informative r package building course

Activities

Career center

Reading list

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