Data scientists who work with R look to Shiny as the web framework of choice for moving analytical power into the hands of their bosses, clients, and the public at large. The reason? Shiny apps let the non-coders of the world control the visualization of complex data sets so they can explore, analyze and model on their own. Taught by RStudio master instructor Garrett Grolemund, this video details how Shiny combines the computational power of R and the interactivity of the web to produce highly interactive reports and visualizations. Part one offers a detailed description of Shiny and how to use it build an app. Part two covers reactive programming and why it differs from functional programming, the paradigm that guides most of R. Part three outlines the Shiny UI and the toolsets it offers to customize the appearance of a Shiny app. This video is optimized for the intermediate level R coder.
Learn to build, test, and deploy Shiny web apps from start to finish
Explore the RStudio IDE, the Shiny file structure, and the three must-have lines of Shiny code
Discover how Shiny apps instantly and automatically respond to user inputs
Master the fundamentals of reactive programming, the coding paradigm that makes Shiny possible
Understand render*() functions, reactive expressions, observers, plots, and more
Explore the tools R coders with or without HTML skills use to modify the look of Shiny apps
Learn to host your Shiny app over any network
Garrett Grolemund is all about the R. He is a Data Scientist with RStudio, one of the largest contributors of content and software related to the open source R language. He is Editor-in-Chief of the Shiny development center at shiny.rstudio.com. He wrote the popular lubridate R package; the R focused O'Reilly Media titles Hands-On Programming with R, R for Data Science (co-author), and Expert Data Wrangling with R; and has three Rs in his name.
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 (shiny.rstudio.com), the official resource for learning to use the shiny package to make interactive web apps with R.