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.
Este curso faz parte do Programa de cursos integrados Mastering Software Development in R
Building R Packages
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Informações sobre o curso
5,841 visualizações recentes
Habilidades que você terá
Programming ToolGithubContinuous IntegrationR Programming
Programa - O que você aprenderá com este curso
Semana
13 horas para concluir
Getting Started with R Packages
1 vídeo (Total 2 mín.), 16 leituras, 1 teste
1 vídeos
16 leituras
Before You Start10min
Using Mac OS10min
Using Windows10min
Using Unix/Linux10min
R packages10min
Basic Structure of an R Package10min
DESCRIPTION File10min
NAMESPACE File10min
Namespace Function Notation10min
Loading and Attaching a Package Namespace10min
The R Sub-directory10min
The man Sub-directory10min
Summary10min
The devtools package10min
Creating a Package10min
Other Functions10min
1 exercício prático
R Package and devtools20min
Semana
27 horas para concluir
Documentation and Testing
14 leituras, 1 teste
14 leituras
Documentation10min
Vignette's and README Files10min
Knitr / Markdown30min
Common knitr Options10min
Help Files and roxygen210min
Common roxygen2 Tags10min
Overview10min
Data for Demos10min
Internal Data10min
Data Packages10min
Summary10min
Introduction10min
The testthat Package10min
Passing CRAN Checks10min
Semana
35 horas para concluir
Licensing, Version Control, and Software Design
25 leituras, 1 teste
25 leituras
Overview10min
The General Public License10min
The MIT License10min
The CC0 License10min
Overview10min
Paying it Forward10min
Linus’s Law10min
Hiring10min
Summary10min
Introduction10min
git10min
Initializing a git repository10min
Committing10min
Browsing History10min
Linking local repo to GitHub repo10min
Syncing RStudio and GitHub10min
Issues10min
Pull Request10min
Merge Conflicts10min
Introduction10min
The Unix Philosophy10min
Default Values10min
Naming Things10min
Playing Well With Others10min
Summary10min
1 exercício prático
Testing, GitHub, and Open Source20min
Semana
46 horas para concluir
Continuous Integration and Cross Platform Development
13 leituras, 1 teste
13 leituras
Overview10min
Web Services for Continuous Integration10min
Using Travis10min
Using AppVeyor10min
Summary10min
Introduction10min
Handling Paths10min
Saving Files & rappdirs10min
rappdirs10min
Options and Starting R10min
Package Installation10min
Environmental Attributes10min
Summary10min
25%
comecei uma nova carreira após concluir estes cursos
17%
consegui um benefício significativo de carreira com este curso
Principais avaliações do Building R Packages
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...
This is a critical skill and it's barely covered anywhere else. Thanks for making this course!
Instrutores
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brooke Anderson
Assistant Professor, Environmental & Radiological Health SciencesColorado State University
Sobre Universidade Johns Hopkins
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.
Sobre Programa de cursos integrados Mastering Software Development in R
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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