Shiny is an R package that makes it easy to build highly interactive web apps directly in R. Using Shiny, data scientists can create interactive web apps that allow your team to dive in and explore your data as dashboards or visualizations. If you want to bring your data to life, Shiny is the way to go! Using data about baby names, food ingredients, and UFO sightings, you'll build a variety of different Shiny apps that leverage different inputs and outputs. You’ll also learn the basics of reactive expressions. By the end of this course, you’ll have the Shiny skills you need to build your first app in R.
Get Started with ShinyFree
To kick off the course you'll learn what a web app is and when you should build one, plus build a few apps of your own! You'll first learn to make text inputs and outputs in a few ways, including exploring the popularity of certain names over time.
Inputs, Outputs, and Layouts
In this chapter you will learn how to take advantage of different input and output options in shiny. You''ll learn the syntax for taking inputs from users and rendering different kinds of outputs, including text, plots, and tables.
In this chapter, you will learn about reactive programming. You will learn about reactive sources, conductors and endpoints and how they come together to drive the magic behind Shiny. You will also learn how to utilize your understanding of reactivity to build performant Shiny apps.
It’s time to build your own Shiny apps. You’ll make several apps from scratch, including one that allows you to gather insights from the Mental Health in Tech Survey and another that uses recipe ingredients as its input to accurately categorize different cuisines of the world. Along the way, you’ll also learn about more advanced input and output widgets, such as input validation, word clouds, and interactive maps.
In the following tracksShiny Fundamentals
Kaelen is a data scientist and an admin for the R-Ladies Global community. Kaelen received a MS in Biostatistics from Louisiana State University Health Sciences Center, where they worked at the Louisiana Tumor Registry. Before DataCamp, they designed experiments (and more!) for the American College of Surgeons, HERE Technologies, and HealthLabs. If you meet them, you will undoubtedly hear about their cat, Scully, within the first 3 minutes. Other favorite topics include aliens, popcorn, podcasts, and nail polish.
VP of Product Research at DataCamp
Ramnath Vaidyanathan is the VP of Product Research at DataCamp, where he drives product innovation and data-driven development. He has 10+ years experience doing statistical modeling, machine learning, optimization, retail analytics, and interactive visualizations. He brings a unique perspective to product development, having worked in diverse industries like management consulting, academia, and enterprise softwares.
Prior to joining DataCamp, he worked as a data scientist at Alteryx, leading the roadmap for interactive visualizations and dashboards for predictive analytics. Prior to Alteryx, he was an Assistant Professor of Operations Management in the Desautels Faculty of Management at McGill University. His research primarily focused on the application of predictive analytics and optimization methodologies to improve operational decisions in retailing. He got his Ph.D. in Operations Management from the Wharton School.