Cut and paste for reporting doesn’t cut it anymore! In this webinar you’ll learn to write reports quickly and effectively with the R Markdown package. Using R Markdown you’ll be able to generate reports straight from your R code, documenting your work — and its results — as an HTML, pdf, slideshow, or Microsoft Word document.
We’ll see how to combine code and text into a single R Markdown file, the perfect document format for automated reporting and reproducible research. The .Rmd file retains all of your code for reproducibility, but lets you set how the code and its results will appear in the final report. Best of all, R Markdown reports are parameterizable. You can apply the same report to multiple data sets.
Hadley Wickham Chief Scientist at RStudio and Adjunct Professor of Statistics at Rice University will discuss broadly an effective framework for thinking about data analysis/data science problems in R. He will touch on the various packages that he thinks you should know about, and talk a little bit about where things are going. He will also give an overview of big data in R, but mainly to explain why he does not believe you should worry a lot about i.
RStudio has recorded a presentation of an overview of RStudio, Shiny, and RMarkdown (including our commercial products) for the ebay R user community. This webinar provides a great opportunity to learn and be inspired about new capabilities for creating compelling analyses of complex datasets.
The webinar focused on the following topics:
- R Markdown is an authoring format that enables easy creation of dynamic documents, presentations and reports from R. It combines the core syntax of markdown with embedded R code chunks that are run so their output can be included in the final document. RMarkdown documents are fully reproducible and can be automatically regenerated whenever underlying R code or data changes.
RStudio's mission is to provide the most widely used open source and enterprise-ready professional software for the R statistical computing environment. These tools help expand the use of R and the field of data science.
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You can't use R for data analysis unless you can get your data into R. Getting your data into R can be a major hassle, so in the last few months Hadley has been working hard to make it easier.
In this webinar Hadley will discuss the places you most often find data (databases, excel, text files, other statistical packages, web apis, and web pages) and the packages (DBI, xml2, jsonlite, haven, readr, exel) that make it easy to get your data into R.
In Part 3, Garrett Grolemund will show you how to customize the appearance of your app. You will learn how to arrange the components of your app into an attractive layout, as well as how to change the appearance of text, images, and other HTML elements in your app.