Easy, reproducible reports with R
How to use R Markdown to show off everything you learned in Introduction to Data Science with R
Date: This event took place live on August 26 2015
Presented by: Garrett Grolemund
Duration: Approximately 60 minutes.
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The R Markdown package makes it very easy to generate reports straight from your R code. With R Markdown, you combine code and text into a single .Rmd file. You use this document to generate polished reports automatically in a variety of formats (html, pdf, MS Word, and slideshows). 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. This webcast will cover applying the same report to multiple data sets.
About Garrett Grolemund
Garrett maintains shiny.rstudio.com, the development center for the Shiny R package, and is the author of Hands-On Programming with R as well as Data Science with R, a forthcoming book by O'Reilly Media. Garrett is a Data Scientist and Chief Instructor at RStudio, Inc. In his own words: I specialize in teaching people how to use R - and especially Hadley Wickham's R packages - to do insightful, reliable data science. Hadley was my dissertation advisor at Rice University, where I gained a first-hand understanding of his R libraries. While at Rice, I taught (and helped developed) the courses "Statistics 405: Introduction to Data Analysis," and "Visualization in R with ggplot2". Before that, I taught introductory statistics as a Teaching Fellow at Harvard University. I'm very passionate about helping people analyze data better. I have travelled as far as New Zealand, where R was born, to learn new ways to teach data science. I worked alongside some of the original developers of R to hone my programming skills, and I collaborated with the New Zealand government in a nationwide project to improve how New Zealand teaches data analysis to new statisticians. Back in the states, I focused my doctoral research on developing pragmatic principles that guide data science. These principles create a foundation for learning R, which is a bit of a layer cake. R is a set of tools for implementing statistical methods, and statistical methods are themselves a set of tools for learning from data. Like all toolkits, R gives its best results to those who use it wisely. Outside of teaching, I have spent time doing clinical trials research, legal research, and financial analysis. I also develop R software. I co-authored the `lubridate` R package, which provides methods to parse, manipulate, and do arithmetic with date-times, and I wrote the `ggsubplot` package, which extends 'ggplot2'. I'm also the Editor-in-chief of RStudio's Shiny Development Center, the official resource for learning to use the shiny package to make interactive web apps with R.