The Grammar and Graphics of Data Science
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Data science is the process of turning data into understanding and actionable insight. Two key data science tools are data manipulation and visualization. Learn how you can easily munge data with dplyr (even if it's still in a database), and create interactive visualizations with ggvis.
- dplyr: a grammar of data manipulation – Hadley Wickham
- ggvis: Interactive graphics in R - Winston Chang
The statistical programming language R is fast becoming the preferred environment for data analysis worldwide in science, industry, and education.
RStudio provides the premiere open source and enterprise-ready professional software for data scientists moving to R from less flexible, proprietary, and expensive analytic platforms. Shiny, ggvis, dplyr, knitr, R Markdown, and packrat are recent R packages from RStudio that every data scientist will want to enhance the value, reproducibility, and appearance of their work.
The Essential Tools for Data Science with R webinar series is the perfect place to learn more about the power of these R packages from the authors themselves.
Yihui Xie
RStudio Software Engineer
Hadley Wickham
RStudio Chief Scientist
Winston Chang
RStudio Software Engineer
J.J. Allaire
RStudio Founder
Joe Cheng
RStudio Software Engineer
Garrett Grolemund
RStudio Data Scientist
Webinar Series: Essential Tools for Data Science with R
Reproducible Reporting
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It doesn't matter how great your analysis is unless you can share it with others - easily. R Markdown and knitr make it easy to intermingle code and text to generate compelling reports and presentations that are never out of date. Combine R Markdown with packrat to ensure that your reports are reproducible day in and day out, no matter what other R packages you have installed.
- The Next Generation of R Markdown – Jeff Allen
- Knitr Ninja – Yihui Xie
- Packrat – A Dependency Management System for R – J.J. Allaire & Kevin Ushey
Interactive Reporting
Live! Wednesday, September 3rd at 11am Eastern Time US
In a static report, you answer known questions. With a dynamic report, you give the reader the tools to answer their own questions. Get started by learning how to make your R Markdown documents interactive, and then unleash the full flexibility of analytic app development with shiny.
- Embedding Shiny Apps in R Markdown documents – Garrett Grolemund
- Shiny: R made interactive – Joe Cheng
Kevin Ushey
RStudio Software Engineer
Jeff Allen
RStudio Software Engineer