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강의 계획 - 이 강좌에서 배울 내용

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1

1

완료하는 데 3시간 필요

Getting Started with R Packages

완료하는 데 3시간 필요
1개 동영상 (총 2분), 16 개의 읽기 자료, 1 개의 테스트
1개의 동영상
16개의 읽기 자료
Before You Start10m
Using Mac OS10m
Using Windows10m
Using Unix/Linux10m
R packages10m
Basic Structure of an R Package10m
DESCRIPTION File10m
NAMESPACE File10m
Namespace Function Notation10m
Loading and Attaching a Package Namespace10m
The R Sub-directory10m
The man Sub-directory10m
Summary10m
The devtools package10m
Creating a Package10m
Other Functions10m
1개 연습문제
R Package and devtools20m
2

2

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Documentation and Testing

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14 개의 읽기 자료
14개의 읽기 자료
Documentation10m
Vignette's and README Files10m
Knitr / Markdown30m
Common knitr Options10m
Help Files and roxygen210m
Common roxygen2 Tags10m
Overview10m
Data for Demos10m
Internal Data10m
Data Packages10m
Summary10m
Introduction10m
The testthat Package10m
Passing CRAN Checks10m
3

3

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Licensing, Version Control, and Software Design

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25 개의 읽기 자료
25개의 읽기 자료
Overview10m
The General Public License10m
The MIT License10m
The CC0 License10m
Overview10m
Paying it Forward10m
Linus’s Law10m
Hiring10m
Summary10m
Introduction10m
git10m
Initializing a git repository10m
Committing10m
Browsing History10m
Linking local repo to GitHub repo10m
Syncing RStudio and GitHub10m
Issues10m
Pull Request10m
Merge Conflicts10m
Introduction10m
The Unix Philosophy10m
Default Values10m
Naming Things10m
Playing Well With Others10m
Summary10m
1개 연습문제
Testing, GitHub, and Open Source20m
4

4

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Continuous Integration and Cross Platform Development

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13 개의 읽기 자료
13개의 읽기 자료
Overview10m
Web Services for Continuous Integration10m
Using Travis10m
Using AppVeyor10m
Summary10m
Introduction10m
Handling Paths10m
Saving Files & rappdirs10m
rappdirs10m
Options and Starting R10m
Package Installation10m
Environmental Attributes10m
Summary10m

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BUILDING R PACKAGES의 최상위 리뷰

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Mastering Software Development in R 특화 과정 정보

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....
Mastering Software Development in R

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