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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课程信息
3,909 次近期查看
学习Course的学生是
- Biologists
- Data Scientists
- Economists
- Data Analysts
- Researchers
您将获得的技能
Programming ToolGithubContinuous IntegrationR Programming
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 3 小时
Getting Started with R Packages
1 个视频 （总计 2 分钟）, 16 个阅读材料, 1 个测验
1 个视频
16 个阅读材料
Before You Start10分钟
Using Mac OS10分钟
Using Windows10分钟
Using Unix/Linux10分钟
R packages10分钟
Basic Structure of an R Package10分钟
DESCRIPTION File10分钟
NAMESPACE File10分钟
Namespace Function Notation10分钟
Loading and Attaching a Package Namespace10分钟
The R Sub-directory10分钟
The man Sub-directory10分钟
Summary10分钟
The devtools package10分钟
Creating a Package10分钟
Other Functions10分钟
1 个练习
R Package and devtools20分钟
周
2完成时间为 7 小时
Documentation and Testing
14 个阅读材料, 1 个测验
14 个阅读材料
Documentation10分钟
Vignette's and README Files10分钟
Knitr / Markdown30分钟
Common knitr Options10分钟
Help Files and roxygen210分钟
Common roxygen2 Tags10分钟
Overview10分钟
Data for Demos10分钟
Internal Data10分钟
Data Packages10分钟
Summary10分钟
Introduction10分钟
The testthat Package10分钟
Passing CRAN Checks10分钟
周
3完成时间为 5 小时
Licensing, Version Control, and Software Design
25 个阅读材料, 1 个测验
25 个阅读材料
Overview10分钟
The General Public License10分钟
The MIT License10分钟
The CC0 License10分钟
Overview10分钟
Paying it Forward10分钟
Linus’s Law10分钟
Hiring10分钟
Summary10分钟
Introduction10分钟
git10分钟
Initializing a git repository10分钟
Committing10分钟
Browsing History10分钟
Linking local repo to GitHub repo10分钟
Syncing RStudio and GitHub10分钟
Issues10分钟
Pull Request10分钟
Merge Conflicts10分钟
Introduction10分钟
The Unix Philosophy10分钟
Default Values10分钟
Naming Things10分钟
Playing Well With Others10分钟
Summary10分钟
1 个练习
Testing, GitHub, and Open Source20分钟
周
4完成时间为 6 小时
Continuous Integration and Cross Platform Development
13 个阅读材料, 1 个测验
13 个阅读材料
Overview10分钟
Web Services for Continuous Integration10分钟
Using Travis10分钟
Using AppVeyor10分钟
Summary10分钟
Introduction10分钟
Handling Paths10分钟
Saving Files & rappdirs10分钟
rappdirs10分钟
Options and Starting R10分钟
Package Installation10分钟
Environmental Attributes10分钟
Summary10分钟
4.2
44 个审阅25%
完成这些课程后已开始新的职业生涯
17%
通过此课程获得实实在在的工作福利
来自Building R Packages的热门评论
Fantastic course... Unfortunately, not too many people registered, it's tough to get your assignments graded. The program is the great continuation to the 10 course R data science specialization...
Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.
讲师
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brooke Anderson
Assistant Professor, Environmental & Radiological Health SciencesColorado State University
关于 约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
关于 Mastering Software Development in R 专项课程
R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.
This Specialization will give you rigorous training in the R language, including the skills for handling complex data, building R packages, and developing custom data visualizations. You’ll be introduced to indispensable R libraries for data manipulation, like tidyverse, and data visualization and graphics, like ggplot2. You’ll learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.
This Specialization is designed to serve both data analysts, who may want to gain more familiarity with hands-on, fundamental software skills for their everyday work, as well as data mining experts and data scientists, who may want to use R to scale their developing and programming skills, and further their careers as data science experts.
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