Create Interactive Web Applications with the R Shiny Package
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Create Interactive Web Applications with the R Shiny Package

Learn to create your own sophisticated Shiny applications by practicing with dozens of detailed Shiny Examples !
4.0 (19 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
382 students enrolled
Last updated 1/2017
English
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Includes:
  • 8.5 hours on-demand video
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Build and deploy interactive web applications with no knowledge of HTML, CCS, or JavaScript.
  • Use the R Shiny package to create amazing interactive visual applications for your target web user audience
  • Understand how to effectively use "reactivity" in both the user interface and the server-side logic of online Shiny applications.
View Curriculum
Requirements
  • Students should have some knowledge and use of R and RStudio
Description

Create Interactive Web Applications with the R Shiny Package  is a 5-section course that highlights the features and functionality of the unique Shiny package created by RStudio. No matter what you do with R, Shiny will transform your R world by making it easy for you to turn your R analyses into interactive web applications without the necessity to program in HTML or JavaScript. These web applications are accessible over the Internet by anyone you choose and allow users to enter input parameters with user-friendly and familiar interactive controls such as sliders, drop-down menus, and text fields. It is easy to incorporate any number of outputs including downloadable plots, tables, formatted summaries and reports. Deploy your R data mining analyses as interactive web applications accessible by your students, colleagues or anyone in your organization, with the ability to generate and download summary tables and reports as required.

This particular course begins with a section introducing you to the Shiny package and template, describes how the fluidPage() function creates a user interface to your Shiny application, how to create inputs and outputs, and what the Server function does. Then introductory Shiny examples are presented. The second course section describes the unique Shiny property of "reactivity" in detail. The third section explains, with many examples, the basic Shiny layout elements including HTML 5 Shiny tags, sliders, tabsets, and numerous Shiny widgets. The fourth section goes into more detail about using HTML, dynamic input and output, how "scoping" works, and concludes with several project examples. Finally, the last course section extends all of the previous discussion with detailed analyses of several more extended examples of complete R Shiny projects.

In all cases, all R Shiny code and examples are provided in the course materials for you to download, to practice with, and/or to use as templates in new Shiny apps that you create. The Shiny R code for two dozen complete Shiny examples are provided, including several extended projects. This is a decidedly "hands-on" course and 'brings you up to speed' quickly on how to develop your own sophisticated Shiny applications. This is an intermediate level course, it is useful if you have some prior exposure to R software. You do not have to currently be a professional R programmer, but you should already understand the basics of using R, including basic R data structures and user-defined R functions.

Who is the target audience?
  • Anyone interested in building interactive web applications using Shiny
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Curriculum For This Course
97 Lectures
08:22:09
+
Introduction to Course and to Shiny Applications
17 Lectures 01:19:23



What is Shiny ?
04:24

The Shiny Application Template and a Working Example
05:27

More on the Shiny Application Template
04:33


Creating Inputs
05:27

Creating Outputs
05:08


Server Function (part 2)
03:53

Introduction to Shiny (part 1)
03:33

Introduction to Shiny (part 2)
03:56

Introduction to Shiny (part 3)
06:20

"Hello Shiny" Example
06:33

"Hello Shiny" Example (part 2)
05:24

Shiny Text Example
07:01
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What is "Reactivity" ?
12 Lectures 01:21:45

What is "Reactivity" ? (part 2)
06:52

What is "Reactivity" ? (part 3)
07:37

What is "Reactivity" ? (part 4)
06:32

What is "Reactivity" ? (part 5)
07:27

What is "Reactivity" ? (part 6)
07:38

What is "Reactivity" ? (part 7)
08:32

What is "Reactivity" ? (part 8)
08:09

What is "Reactivity" ? (part 9)
07:53

What is "Reactivity" ? (part 10)
05:50

What is "Reactivity" ? (part 11)
04:58

What is "Reactivity" ? (part 12)
05:26
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User Interface Elements
14 Lectures 01:30:23
More on Formatting Text and Images
01:18


HTML 5 Shiny Tags
01:31


Sliders (part 2)
07:20

Sliders (part 3)
09:27

Sliders (part 4)
09:13

Tabsets (part 1)
08:21

Tabsets (part 2)
08:18


More Widgets (part 2)
06:13

More Widgets (part 3)
05:57

More Widgets (part 4)
07:59

More Widgets (part 5)
07:00
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HTML, Dynamic Output, Scope and Project Examples
17 Lectures 01:23:43

HTML Example (part 2)
05:25


Dynamic Input and Output (part 2)
04:47

Dynamic Input and Output (part 3)
04:49

Dynamic Input and Output (part 4)
04:10

Dynamic Input and Output (part 5)
04:06

Scoping (part 1)
05:24

Scoping (part 2)
05:35

Scoping (part 3)
06:24

Project Example (part 1)
04:42

Project Example (part 2)
05:06

Project Example (part 3)
04:21

Project Example (part 4)
05:35

Project Example (part 5)
05:08

Random Variables Examples (part 1)
03:34

Random Variables Examples (part 2)
05:19
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Shiny Application Examples
37 Lectures 02:46:55
Hello Shiny Example Again (part 1)
03:35

Hello Shiny Example Again (part 2)
04:25


Isolate Demo (part 2)
03:52

Isolate Demo (part 3)
03:23

PLS Example (part 1)
04:05

PLS Example (part 2)
05:22

PLS Example (part 3)
04:58

PLS Example (part 4)
04:41

PLS Example (part 5)
04:29

PLS Example (part 6)
04:05

PLS Example (part 7)
04:17

PLS Example (part 8)
04:40

PLS Example (part 9)
04:45

PLS Example (part 10)
05:54

PLS Example (part 11)
04:53

PLS Example (part 12)
04:54

PLS Example (part 13)
06:02

MLS Closing Price Plots Shiny App (part 1)
04:33

MLS Closing Price Plots Shiny App (part 2)
04:12

MLS Closing Price Plots Shiny App (part 3)
04:04

MLS Closing Price Plots Shiny App (part 4)
04:19

MLS Closing Price Plots Shiny App (part 5)
03:30

MLS Time Series Plots Shiny App (part 1)
04:18

MLS Time Series Plots Shiny App (part 2)
04:05

MLS Time Series Plots Shiny App (part 3)
04:48

MLS Time Series Plots Shiny App (part 4)
04:15

MLS Predictive Closing Price Shiny App (part 1)
03:26

MLS Predictive Closing Price Shiny App (part 2)
04:41

MLS Predictive Closing Price Shiny App (part 3)
04:05

MLS Predictive Closing Price Shiny App (part 4)
04:33

More on PLS-PM Shiny Project (part 1)
04:14

More on PLS-PM Shiny Project (part 2)
05:45

More on PLS-PM Shiny Project (part 3)
04:31

More of PLS-PM Shiny Project (part 4)
05:20

More on PLS-PM Shiny Project (part 5)
04:01

More on PLS-PM Shiny Project (part 6)
06:07
About the Instructor
Geoffrey Hubona, Ph.D.
4.0 Average rating
1,411 Reviews
12,005 Students
28 Courses
Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.