Matt Dancho – DS4B 102-R: Shiny Dashboards

Description

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Matt Dancho – DS4B 102-R: Shiny Dashboards

Shiny Web Applications Part 1
Build a predictive web application using Shiny, Flexdashboard, and XGBoost

Build Web Apps with Machine Learning
The web application you learn how to build uses data science to predict new product prices!

Predictive Web Applications Productionalize Data Science

A data scientist generates organizational value by building web apps that take machine learning models into production.

Here’s an example of a predictive web application that you build in this course.

New Product Prediction Application (created in this course)

This web application empowers business people to make data-driven decisions by more consistently pricing products. The application incorporates:

• Shiny – A web application framework with UI components that are reactive to user input.
• Flexdashboard – A dashboarding framework that is built on top of RMarkdown.
• parsnip and XGBoost – Machine learning models used to predict product prices.

Most importantly, business people can use the application to improve the consistency of new product prices based on an existing product portfolio thanks to the power of Machine Learning!

Your Organization Cares About Branding

So give it to them. Learn how to customize the appearance of your application to match your organizations branding.

Final Project

You will build a Sales Dashboard that:

• Uses XGBoost to Predict Sales Demand by Customers & Product Categories.
• Toggles between Light and Dark Themes – Customized by You and your theme-building skills!
• Controls flow using Reactive Programming
• Will be distributed via Shinyapps.io

Final Project

Dark Theme

This course is designed for…

1. Beginner data scientists that have completed the Data Science for Business Part 1 course and want to build predictive web applications
2. Intermediate data scientists familiar with R but want to learn Shiny and Flexdashboard

3-Step System

Follow a 3-step learning path:

1. Build your knowledge of core concepts with a Sales Dashboard
2. Extend your knowledge of Machine Learning and advanced techniques into Price Prediction Application
3. Customize the end product with theme and logos

Course Roadmap
Experience the innovative 3-Step System!

Step 1: You’ll start by creating a Sales Dashboard
Creating a Sales Dashboard exposes you to reactive programming. You will apply complex rules to control how your application functions when users interact with the app.

You gain experience using:

• Shiny
• Geographic Data
• Time Series Data
• Interactive Plots
• Reactive Programming
• Observing Events & Controlling Flow

Step 2: Next, You Create A Predictive Web Application
You will build a new application that integrates Machine Learning (XGBoost) along with a more complex interactive visualization.

You learn how to:

• Integrate machine learning (parsnip and XGBoost) into a Web App
• Modularize code into functions
• Create advanced interactive charts

Step 3: You finish by Customizing Your Web Application
Your company’s brand appearance is important. Make an app theme that is consistent with the look and feel of your organization’s branding.

Create Your Own Theme Using HTML & CSS

Use Google’s Chrome Inspector

You will:

• Learn to use Google Inspector for inspecting web pages
• Add logos
• Adjust the theme with CSS

Business Objective: Use Data Science to More Consistently Price Products

The Business Problem:

Businesses can lose customer confidence and profitability if products are inconsistently priced.

The Solution:

This web application solves the inconsistent pricing problem by using predictive analytics to generate new product prices based on existing products.

The application is easy to use, and best of all, an app like this generates business value for your organization!

Tools & Frameworks We Provide

We provide you:

• A Complete Learning Path to taking you from basic knowledge of R to being able to build and deploy interactive, machine-learning powered web apps
• A Cohesive Tool Chain that includes shiny, flexdashboard, shinyWidgets, and shinyjs
• Comprehensive resources: You are provided a cheat sheet, code templates, and resources that speed up learning and make referring back to materials simple.

Summary of What You Get!

• Methodical training program that teaches you how to build web applications using Shiny & Flexdashboard
• 2 Web Apps That You Can Productionalize
  • Sales Dashboard – Exposes you to Geographic and Time Series data along with learning reactive programming with Shiny
  • Product Prediction Application – Integrates Machine Learning (XGBoost) and advanced visualizations
• Hundreds of Resources:
  • ULTIMATE R CHEAT SHEET – The New & Improved Version 2.0
  • 100+ Video Coding Lessons
  • 7 Key Resources
  • 2 Challenges

Your Instructor

 Matt Dancho
Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane startups! Matt loves educating data scientists on how to apply powerful tools within their organization to yield ROI. Matt doesn’t rest until he gets results (literally, he doesn’t sleep so don’t be suprised if he responds to your email at 4AM)!

Course Curriculum

Welcome to Shiny Web Application Development (Level 1)
Building Web Applications that Deliver Business Value! (2:15)
Course Roadmap – Building Production-Ready Web Apps Fast! (1:54)
Video Subtitles (Captions)
Would You Like To Become An Affiliate (And Earn 20% On Your Sales)?

Prerequisites
Prerequisites

Getting Help
Getting Help (IMPORTANT!!!)

1.0 Getting Started
Overview

1.1 Business Case & Course Roadmap
Why Pricing Products Consistently Is Important (0:57)
Course Objective – Product Price Prediction App with Shiny & Flexdashboard (1:17)

1.2 Tools In Our Toolbox
Resource #1: The Ultimate R Cheat Sheet – Version 2.0 (File Download) (2:51)

1.3 Data Science Project Setup
Installing R (Optional) (3:06)
Installing RStudio IDE (Optional) (3:03)
Setting Up The Project (File Download) (2:34)
Installing R Packages (File Download) (3:03)

1.4 Transactional Data Introduction – Bike Sales (Recap from 101)
Transactional Data – What Is It? (1:41)
Orders: The Building Blocks of Transactional Data (3:53)
Data Model: Entity Relationship Diagram (2:14)
Understanding Database Relationships (6:18)

Part 1 – Sales Dashboard
Part 1 – Learning Shiny By Building A Sales Dashboard! (2:02)

2.0 Making A Sales Dashboard with Flexdashboard
What You Build In This Section (0:54)

2.1 Flexdashboard Primer
Resource #2: Flexdashboard Documentation & Key Resources (6:30)
Flexdashboard: Introduction & Layout Basics (3:05)
Orientation: Column vs Row (1:24)
Vertical Layout: Fill vs Scroll (3:49)
Tabsets (2:50)
Multiple Pages (4:01)
Code Checkpoint

2.2 Sales Dashboard – Integrating a Plotly Chloropleth Map
Flexdashboard Setup (1:35)
Libraries (1:23)
Database Connection (4:02)
Joining Data Using The SQLite Backend – Part 1 (5:00)
Joining The Data Using The Database Backend – Part 2 (4:12)
Processing Data: Final Preparations for the Map (2:20)
Adding A Section To The App (1:59)
Making the Plotly Map, Part 1: Plotly Chloropleth Maps (1:39)
Making The Plotly Map, Part 2: Aggregation By State (3:01)
Making The Plotly Map, Part 3: plot_geo() (2:37)
Making the Plotly Map, Part 4: add_trace() (3:27)
Making the Plotly Map, Part 5: layout() (3:14)
Code Checkpoint

3.0 Adding Shiny Reactive Components to the Sales Dashboard
What You Build In This Section (0:41)
Setup (File Download) (1:47)

3.1 Shiny Tutorial
Resources #3: Shiny Cheat Sheet (8:18)
Resource #4: Shiny Widgets Gallery (1:40)
Resource #5: HTML Widgets Showcase (4:47)
Resource #6: shinyjs (2:05)
Shiny Tutorial App – Overview (5:18)
Checkbox – checkboxGroupInput() (5:37)
Checkbox – renderPrint() & textOutput() (7:19)
Date Range – dateRangeInput() (5:12)
Date Range – renderPrint() & textOutput() (2:41)
Slider – sliderInput() (3:49)
Slider – renderPrint() & textOutput() (2:34)
Reactive Filtering – reactive() (5:48)
Data Table – Interactive Tables with DT (5:26)
Reactive Expressions: Adding More Inputs to reactive() (5:27)
Reactive Summarization: DT (5:17)
Reset Button, Part 1: actionButton() (2:29)
Resource #7: Font Awesome (1:16)
Reset Button, Part 2: observeEvent() (7:31)
Code Checkpoint (File Download)

3.2 Integrating Shiny into the Sales Dashboard
Sales Dashboard: Setting Up For Shiny (4:25)
shinyWidgets (2:08)
Data Preparation (7:10)
Bike Type Selector – shinyWidgets::checkboxGroupButtons() (6:26)
Bike Type Selector – reactive() & renderPlotly() (6:34)
Bike Family Selector – shinyWidgets::pickerInput() (7:03)
Bike Family Selector – reactive() filter (1:20)
Reset Button: actionButton() (6:22)
Code Checkpoint (File Download)

3.3 Challenge 1 – Add Date Range Input
Challenge 1 – Add Date Range Input (File Download) (1:37)
Challenge 1 – Solution, Part 1 (5:32)
Challenge 1 – Solution, Part 2 (7:50)
Code Checkpoint (File Download)

Course Survey
Quick Course Survey

4.0 Extending The Sales Dashboard with Time Series & shinyjs
What You Build In this Section (1:02)
Setup (File Download) (1:38)

4.1 Time Series Plot
Time Series Plot: Game Plan (1:03)
Flexdashboard Layout: “Over Time” Section (1:35)
Data Preparation (6:56)
Making the ggplot Geometries (5:45)
Formatting the ggplot (2:53)
Adding Interactivity: ggplotly() (1:26)
Parameterizing The Time Unit (2:02)
Next Steps: Reactivity (0:55)
Code Checkpoint (File Download)

4.2 Adding Reactivity to the Time Series Plot
Adding Reactivity: Game Plan (1:32)
Adding Reactivity, Part 1: Date Range Input (5:55)
Adding Reactivity, Part 2: renderPlotly() (2:04)
Adding Reactivity, Part 3: Connecting the Category 1 & 2 Inputs (4:46)
Adding Reactivity, Part 4: Date Aggregation with Radio Group Buttons (8:40)
Adding Reactivity, Part 5: Connecting the Date Aggregation Buttons (0:40)
Finishing Touches (2:24)
Next Steps (0:45)
Code Checkpoint (File Download)

4.3 Integrating an Apply Button & shinyjs
Apply Button: Reactive Programming Overview (2:09)
Adding the Apply Button: actionButton() (1:56)
Reactive Button Click: eventReactive() (5:19)
Loading Plots When Firing Up The Application (3:07)
Welcome to shinyjs (0:59)
Setting Up shinyjs in RMarkdown (2:25)
Reset Button: Click “Apply” with shinyjs (4:05)
Reset Button: Delay “Apply” Click with shinyjs (2:30)
Recap (1:12)
Code Checkpoint (File Download)

4.4 BONUS – Adding Value Boxes to Your Dashboard
What You Build (0:53)
Setup & Layout (3:30)
Making Value Boxes: valueBox() (3:33)
Data Summarization, Part 1 (7:24)
Data Summarization, Part 2 (4:20)
Reactive Data Summarization (2:23)
Healthy Value Box (4:35)
Wealthy Value Box (2:59)
Wise Value Box (3:00)
Code Checkpoint (File Download)

Part 2 – Product Pricing Prediction App
Part 2 – Making A Predictive Web Application that Helps Your Organization (2:10)

5.0 Predictive Analysis – XGBoost + Parsnip
Predictive Analysis Goals (4:10)
Setup (File Download) (3:32)
Preprocessing the Bikes Table (7:06)
Training Data Set: Getting Ready for parsnip + XGBoost (3:12)
Machine Learning Algorithm: parsnip + XGBoost (6:36)
Code Checkpoint #1 (File Download)
Modularizing the Preprocessing Code, Part 1: Separate Bike Description (9:06)
Modularizing the Preprocessing Code, Part 2: Separate Model Description (5:03)
Code Checkpoint #2 (File Download)
Making Predictions from User Input (6:02)
Modularizing the Prediction: Generating New Bikes (8:12)
Code Checkpoint #3 (File Download)
Formatted Table (5:18)
Modularizing the Table Output: format_table() (1:08)
Start
Bike Prediction Plot: Data Preparation (7:03)
Bike Prediction Plot: ggplot, Part 1 (7:16)
Bike Prediction Plot: ggplot, Part 2 (4:47)
Modularize the Bike Prediction Plot: plot_bike_prediction() (5:05)
Code Checkpoint #4 (File Download)

6.0 Prediction App – Getting The Analysis Into The Flexdashboard Layout
What You Build In This Section (0:52)
Setup: dir_create() & flexdashboard creation (2:47)
Setting Up The Flexdashboard Layout (3:25)
Load Libraries (3:35)
Data: Connect to SQLite and Load Bikes Table Into Memory (3:19)
Scripts: Source Our Modular Prediction Functions (3:00)
Machine Learning Model: Loading Our XGBoost Algorithm (1:51)
Generating Bike Predictions (3:33)
The Prediction Table: format_table() (0:40)
The Price Prediction Plot: plot_bike_prediction() (2:26)
Aside – The Difference Between Flexdashboard With & Without runtime: shiny (1:37)
Code Checkpoint (File Download)

7.0 Prediction App – Adding User Input with Shiny!
What You Build In This Section (0:59)
Setup (File Download) (0:47)

7.1 Adding Shiny To The Predictive Web Application
Adding Shiny To Our App – runtime: shiny (1:33)
Adding A Text Input: textInput() (2:09)
Adding An Apply Button: actionButton() (2:09)
Adding Reactivity To The Apply Button: eventReactive() (4:21)
Rendering The Prediction Plot: renderPlotly() (2:31)
Rendering The Prediction Table: renderTable() (1:49)
Render On Load: Prediction Plot & Table (1:26)
Code Checkpoint (File Download)

7.2 Challenge 2 – Reset Button
Challenge #2: Reset Button (File Download) (1:29)
Challenge #2 Solution (File Download) (6:22)

8.0 Completing the Predictive Web App – More Shiny!
What You Build In This Section (0:54)
Setup (File Download) (0:43)
Organizing Our UI & Adding The shinyWidgets Library (2:04)
Getting The Unique Categories: distinct() (3:00)
Adding Bike Family UI: pickerInput() (3:58)
Connecting Bike Family UI To The Apply Button (2:07)
Connecting Bike Family UI To The Reset Button (2:00)
Generate Bike Type From Bike Family (6:22)
Adding Frame Material UI: pickerInput() (2:17)
Connecting Frame Material UI To Apply, Reset, & Generate New Bike Prediction (3:37)
Code Checkpoint (File Download)

Part 3 – Customizing The Application Theme
Part 3 – Customizing The App To Your Organizations Brand! (1:07)

9.1 HTML & CSS Crash Courses
Training for Web Developers – Traversy Media
1 Hour HTML Crash Course for Beginners – Traversy Media
1.5 Hour CSS Crash Course – Traversy Media

9.2 Customizing The Appearance Of Your App
Setup (File Download) (1:44)
Resource #8: Chrome DevTools – Browser Web Development Tools (10:10)
[OPTIONAL / ADVANCED] 1 Hour Chrome DevTools Crash Course – Traversy Media
CSS Setup & Coloring the Navbar (7:07)
Coloring the Sidebar (3:49)
Adding a Logo (4:53)
Resource #9: Google Fonts (8:03)
Navbar – Montserrat (5:01)
Headers – Montserrat (4:31)
Body – Roboto (2:25)
Coloring the Buttons (9:15)
Code Checkpoint (File Download)

9.3 Challenge 3 – Matching Cannondale’s Brand Appearance
Challenge #3: Cannondale Challenge! (File Downloads) (4:18)
Challenge #3 Solution, Part 1 (File Download) (14:10)
Challenge #3 Solution, Part 2 (15:28)

9.4 Bonus – Adding Images To Your Cannondale App
Displaying Product Images in Your Shiny App (1:20)
Setup (File Downloads) (4:52)
Connect the Script & Model Paths (4:09)
Image Placeholder (1:20)
Extracting the Model Base (5:58)
Generate New Bike: To Speed Up Debugging (4:01)
Detecting & Retrieving the Image Path (5:20)
Expose the Image Path to the App (2:36)
Rendering Images: renderImage() (4:58)
Handling Bike Models with Numbers in their Names (5:38)
Code Checkpoint (File Download)

Part 4 – Add Demand Forecasting & Customizing Your Sales Dashboard
Part 4 – Adding Demand Forecasting & Customizing Your Sales Dashboard (0:59)
App Setup (File Download) (4:04)

10.1 Demand Forecast Analysis – Parnsip + XGBoost
Forecast Analysis: Setup & Overview (4:04)
Data Processing (5:16)
Time Series Aggregation: aggregate_time_series() (7:04)
Time Series Plot: plot_time_series() (5:16)
Time Series ML vs ARIMA: Why We Are Using ML For Our App (1:45)
timetk, Part 1: A toolkit for time series ML prep (6:21)
timetk Part 2: A Toolkit for Time Series ML Prep (3:14)
Making Training & Future Data (8:47)
XGBoost Forecast Model (File Download) (7:51)
Making Predictions & Outputting in the Format for Our New Forecast Plot (8:22)
Modularizing the XGBoost Forecast: generate_forecast() (6:02)
Interactive Forecast Plot (6:45)
plot_forecast() (2:30)
Code Checkpoint (File Download)

10.2 Visual Forecast Inspection & Model Adjustment
Visual Forecast Inspection (6:10)
Preparing for the Model Change (4:08)
Add Logic: Separating the Yearly Data from the Other Time Aggregations (2:32)
Making a Linear Regression Model for Yearly Data (5:01)
Saving Our Functions (1:21)
Code Checkpoint (File Download)

10.3 Forecasting – What About Trend? (XGBoost vs GLMNet)
Setup (File Download) (3:23)
Trend Evaluation (3:07)
Experiment #1: Exploring Trend w/ XGBoost Forecast (3:09)
Experiment #2: Comparing XGBoost & GLMNet (13:53)

10.4 Forecast Mode – Integrating the ML Forecast into our App!
Overview (2:02)
Setup (2:24)
Toggle Switch: switchInput() [with CSS Copy-Paste] (5:46)
Showing & Hiding the Forecast Horizon: conditionalPanel() (4:07)
Integrating the Forecast (8:34)
Producing the Forecast Plot (4:20)
Connecting the Forecast to the Time Unit Radio Buttons: observeEvent() (3:25)
Connecting the Forecast to the Forecast Mode Toggle Switch: observeEvent() (3:26)
Customer Selection: pickerInput() (6:00)
Connecting the Customer Selection to the Data Filter (4:50)
Recap (0:58)
Code Checkpoint (File Download)

10.5 Customization – Business Science Light Theme
Creating a CSS Theme for Our Shiny App (1:09)
Setup (File Download) (1:14)
Connecting Our CSS & Logo (2:59)
Adjusting the Sidebar Width (8:34)
Coloring the Forecast Mode Toggle Switch (4:54)
Coloring the Time Series Radio Buttons (10:59)
Coloring the Value Boxes (5:51)
Enabling Scroll Capability on the UI Sidebar for Mobile Devices (3:16)
Code Checkpoint (File Download)

10.6 Customization – Business Science Dark Theme
Making a Dark Theme! (0:45)
Adding A View Mode Toggle Switch (9:19)
Creating The Dark Theme (6:05)
Switching Themes with includeCSS() & renderUI() (5:39)
Adjusting the Theme for Mobile Devices (3:14)
Final Shiny Sales Dashboard with Forecasting & Dark Theme! (1:10)
Code Checkpoint (File Download)

Part 5: Production & Deployment
Publishing Your Shiny Web Application (0:29)
Publishing Options (6:03)

11.1 Publishing Your Shiny Web App!
Setup: Shinyapps.io & App Files (3:40)
Connecting To Shinyapps.io & Publishing (3:04)
Troubleshooting Error #1: Files are Outside of App Directory (5:20)
Troubleshooting Error #2: xgboost package is not being installed (1:54)
Shiny App Deployed!!! (0:26)

11.2 Challenge
Challenge #4: Create & Deploy Your Own App (1:36)

Congratulations!!!
You Did It! I’m So Happy For You!! (1:38)

Frequently Asked Questions:

1. Innovative Business Model:
   • Embrace the reality of a genuine business! Our approach involves forming a group buy, where we collectively share the costs among members. Using these funds, we purchase sought-after courses from sale pages and make them accessible to individuals facing financial constraints. Despite potential reservations from the authors, our customers appreciate the affordability and accessibility we provide.
2. The Legal Landscape: Yes and No:
   • The legality of our operations falls into a gray area. While we lack explicit approval from the course authors for resale, there’s a technicality at play. When procuring the course, the author didn’t specify any restrictions on resale. This legal nuance presents both an opportunity for us and a boon for those seeking budget-friendly access.
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Refund is acceptable:

• Firstly, item is not as explained
• Secondly, Item do not work the way it should.
• Thirdly, and most importantly, support extension can not be used.

Thank you for choosing us! We’re so happy that you feel comfortable enough with us to forward your business here.