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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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4 Hours16 Videos61 Exercises23,567 Learners

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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.
  1. 1

    Get Started with Shiny

    Free

    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.

    Play Chapter Now
    Introduction to Shiny
    50 xp
    Client vs. Server
    50 xp
    When to build a web-app?
    100 xp
    Build a "Hello, world" Shiny app
    50 xp
    Build a "Hello, world" Shiny app (2)
    100 xp
    "Hello, World" app input (UI)
    100 xp
    "Hello, World" app output (UI/Server)
    100 xp
    Build a babynames explorer Shiny app
    50 xp
    Add input (UI)
    100 xp
    Add output (UI/Server)
    100 xp
    Update layout (UI)
    100 xp
    Update output (server)
    100 xp
  2. 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.

    Play Chapter Now
    Inputs
    50 xp
    Selecting an input
    100 xp
    Add a select input
    100 xp
    Add a slider input to select year
    100 xp
    Outputs
    50 xp
    Add a table output
    100 xp
    Add an interactive table output
    100 xp
    Add interactive plot output
    100 xp
    Layouts and themes
    50 xp
    Sidebar layouts
    100 xp
    Tab layouts
    100 xp
    Themes
    100 xp
    Building apps
    50 xp
    App 1: Multilingual Greeting
    100 xp
    App 2: Popular Baby Names
    100 xp
    App 3: Popular Baby Names Redux
    100 xp
  3. 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.

    Play Chapter Now
    Reactivity 101
    50 xp
    Source vs. Conductor vs. Endpoint
    100 xp
    Add a reactive expression
    100 xp
    Understanding reactive expressions
    50 xp
    Observers vs. reactives
    50 xp
    Add another reactive expression
    100 xp
    Does this have a side effect?
    50 xp
    Add an observer to display notifications
    100 xp
    Stop - delay - trigger
    50 xp
    Stop reactions with isolate()
    100 xp
    Delay reactions with eventReactive()
    100 xp
    Trigger reactions with observeEvent()
    100 xp
    Controlling action triggers
    50 xp
    Applying reactivity concepts
    50 xp
    Reactivity concepts: observe & reactive
    100 xp
    Convert height from inches to centimeters
    100 xp
  4. 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.

    Play Chapter Now
    Build an Alien Sightings Dashboard
    50 xp
    Alien sightings: add inputs
    100 xp
    Alien sightings: add outputs
    100 xp
    Alien sightings: tab layout
    100 xp
    Exploring the 2014 Mental Health in Tech Survey
    50 xp
    The shinyWidgets gallery
    50 xp
    Explore the Mental Health in Tech 2014 Survey
    100 xp
    Validate that a user made a selection
    100 xp
    Explore cuisines
    50 xp
    Explore cuisines: top ingredients
    100 xp
    Explore cuisines: top ingredients redux
    100 xp
    Explore cuisines: wordclouds
    100 xp
    Mass shootings
    50 xp
    Mass shootings: add inputs
    100 xp
    Mass shootings: modify output
    100 xp
    Mass shootings: display help
    100 xp
    Wrap up video
    50 xp

In the following tracks

Shiny Fundamentals with R

Datasets

RecipesUFO SightingsMental Health in Tech SurveyShootings

Collaborators

Anneleen Beckers
Maggie Matsui

Prerequisites

Introduction to the TidyverseIntermediate R
kaelen medeiros Headshotkaelen 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.
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Ramnath Vaidyanathan HeadshotRamnath 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.
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