Building Web Applications with Shiny in R
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
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Course Description
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.
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Get Started with Shiny
FreeTo 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.
Introduction to Shiny50 xpClient vs. Server50 xpWhen to build a web-app?100 xpBuild a "Hello, world" Shiny app50 xpBuild a "Hello, world" Shiny app (2)100 xp"Hello, World" app input (UI)100 xp"Hello, World" app output (UI/Server)100 xpBuild a babynames explorer Shiny app50 xpAdd input (UI)100 xpAdd output (UI/Server)100 xpUpdate layout (UI)100 xpUpdate output (server)100 xp - 2
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.
Inputs50 xpSelecting an input100 xpAdd a select input100 xpAdd a slider input to select year100 xpOutputs50 xpAdd a table output100 xpAdd an interactive table output100 xpAdd interactive plot output100 xpLayouts and themes50 xpSidebar layouts100 xpTab layouts100 xpThemes100 xpBuilding apps50 xpApp 1: Multilingual Greeting100 xpApp 2: Popular Baby Names100 xpApp 3: Popular Baby Names Redux100 xp - 3
Reactive Programming
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.
Reactivity 10150 xpSource vs. Conductor vs. Endpoint100 xpAdd a reactive expression100 xpUnderstanding reactive expressions50 xpObservers vs. reactives50 xpAdd another reactive expression100 xpDoes this have a side effect?50 xpAdd an observer to display notifications100 xpStop - delay - trigger50 xpStop reactions with isolate()100 xpDelay reactions with eventReactive()100 xpTrigger reactions with observeEvent()100 xpControlling action triggers50 xpApplying reactivity concepts50 xpReactivity concepts: observe & reactive100 xpConvert height from inches to centimeters100 xp - 4
Build 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.
Build an Alien Sightings Dashboard50 xpAlien sightings: add inputs100 xpAlien sightings: add outputs100 xpAlien sightings: tab layout100 xpExploring the 2014 Mental Health in Tech Survey50 xpThe shinyWidgets gallery50 xpExplore the Mental Health in Tech 2014 Survey100 xpValidate that a user made a selection100 xpExplore cuisines50 xpExplore cuisines: top ingredients100 xpExplore cuisines: top ingredients redux100 xpExplore cuisines: wordclouds100 xpMass shootings50 xpMass shootings: add inputs100 xpMass shootings: modify output100 xpMass shootings: display help100 xpWrap up video50 xp
In the following tracks
Shiny Fundamentals
Maggie Matsui
Anneleen Beckers
kaelen medeiros
Data Scientist
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.
Ramnath Vaidyanathan
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.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Louis Maiden
Harvard Business School
DataCamp is by far my favorite website to learn from.
Ronald Bowers
Decision Science Analytics, USAA
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