Building R packages (The Course)
This course includes 1 attempt.
Data science is one of the most exciting and fastest growing careers in the world. The goal of this series is to help people with no background and limited resources transition into data science. The only pre-requisites are a computer with a web browser and the ability to type and follow instructions. We guide you through the rest!
After taking this course you will be able to:
- Explain the structure of an R package.
- Create a complete and well-documented R package
- Release your R package using version control and continuous integration
Things you need to do this course
This course is designed for people with no background with package or software development. It would be helpful if you had already taken our Introduction to R and Version Control courses. This should be a great introduction to R package development for individuals who have not previously developed software.
This course is designed for people with no background in data science and so is a great introduction for high-school students or people looking for a career change into the tech industry. The only requirements are:
- A computer with a web browser and internet connection
- The ability to type and follow instructions.
- Experience writing functions in R
- Familiarity with using R packages in RStudio (or RStudio Cloud)
How you will be graded
The course has a series of short quizzes, one for each chapter. You will get two attempts at each quiz and your best score for each quiz will count toward your final score. If you receive more than 70% of the points across all quizzes you will pass. If you receive more than 90% of the points across all quizzes you will pass with honors. You get two attempts at the class with each class purchase.
How to report an error
If you find a bug, typo, or issue in the material, feel free to contact us using this form.
- 1 Building R Packages
- 2 Overview
- 3 Functions
- 4 Code
- 5 Testing
- 6 Description
- 7 Documentation
- 8 Vignettes
- 9 Data
- 10 Release
- 11 References
- About this Course
- About the Authors
Jeff is a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he has helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and Mortimer Spiegelman Award recipient.
Shannon Ellis is an Associate Teaching Professor in the Cognitive Science Department at UC San Diego.
Frederick Tan is on the Bioinformatics Research Faculty at Carnegie Institution, Department of Embryology, and an Adjunct Assistant Research Scientist at Johns Hopkins University, Department of Biology. His educational activities include the Practical Genomics Workshop, Quantitative Biology Bootcamp, C-MOOR, and AnVIL Outreach.
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