DS4B 102-R – Shiny Web Applications (Intermediate)

What You’ll Discover in DS4B 102-R Shiny Web Applications (Intermediate)

DS4B 102-R – Shiny Web Applications (Intermediate)

Construction Web Apps that use Machine Learning

Learn how to create a web application using data science to predict future product prices

Predictive Web Applications Productionalize Data Science

Data scientists create organizational value by creating web apps that use machine learning models to produce.

Here’s an example for a predictive website application you can create in this course.

This course introduces a new product prediction application.

This web application allows business people to access data-Ensure that products are priced consistently to drive decisions This application includes:

Shiny – A web framework that reacts to user input.
Flexdashboard – A dashboarding tool that’s built on top of RMarkdown.
parsnip and XGBoost – Machine-learning models used to predict prices for products.

The application can be used by business people to improve consistency in new product prices based off an existing product range thanks to Machine Learning.

Download immediately DS4B 102-R – Shiny Web Applications (Intermediate)

Branding is important to your organization

Don’t hesitate to give it. Find out how to personalize your application to match your organization’s branding.

Final Project

A Sales Dashboard will be created by you.

Uses XGBoost to Predict Sales Demand by Customers & Product Categories.
Choose from Light or Dark Themes. You can customize them by you and your theme-building skills!
Reactive Programming controls flow
Shinyapps.io will distribute the information

Final Project

Dark Theme

This course was designed for…

Beginning data scientists who have completed the DS4B 101-You are interested in building predictive web applications.
Intermediate data scientists who are familiar with R, but still want to learn Shiny Flexdashboard

Production is built-Ready applications
Get ed now!

Paid Course

$399

3 Low Monthly Payments

3X Monthly
3 monthly payments of $159

3-Step System

Follow the 3-Step-by-step learning path

A Sales Dashboard will help you to improve your understanding of core concepts.
Learn Machine Learning and advance techniques in Price Prediction Application
You can personalize the final product by adding logos and themes

Course Roadmap

Enjoy the 3 innovative features-Step System

Step 1: Start by creating a Sales Dashboard

Reactive programming is possible when you create a Sales Dashboard. To control the behavior of your app when users interact with it, you will need to create complex rules.

Use these tools to gain valuable experience:

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 create a new app that combines Machine Learning (XGBoost), along with a more complicated interactive visualization.

Learn how to:

Incorporate machine learning (parsnip, XGBoost), into a Web App
Modularize the code into functions
Advanced interactive charts

Step 3: Customize Your Own Web Application

It is crucial to establish a consistent brand image for your company. Create an app theme consistent with your branding.
Create Your Own Theme Using HTML & CSS

Google Chrome Inspector

You will:

Google Inspector can be used to inspect web pages
Add logos
CSS can be used to adjust the theme

Business Objective: Make your products more consistent by using data science

The Business Problem

Inconsistent pricing can cause customers to lose confidence and reduce profitability.

The Solution:

This web application addresses the issue of inconsistent pricing by using predictive analytics to generate new product price based on existing products.

The app is very easy to use. And, the best part, it can generate business value for your company!

Tools & Frameworks We Provide

We provide the following services:

This Complete Learning Path will take you from the basics of R to the ability to create and deploy interactive machines.-Apps for learning-powered web apps
Cohesive Tool Chain with shiny, flexdashboard and shinyWidgets
Comprehensive resources: This includes a cheat sheet and code templates. It also contains resources that will help you speed up your learning and make it easier to refer back to the materials.
Full Life-Time Access: After you have purchased the course, you get lifetime access to it-Access to the content at any time, including updates.
Access to our Private Slack community where you can reach Matt (our course instructor) as well as network with other students.

Download it immediately DS4B 102-R – Shiny Web Applications (Intermediate)

Summary of What You Get

A methodical training program that shows you how to create web applications using HTML Shiny & Flexdashboard
2 Web Apps that You Can Produce ($5000 value).
Sales Dashboard – Provides you with data from Time Series and Geographical sources, as well as learning reactive programming. Shiny
Product Prediction Application – Integrates Machine Learning (XGBoost), and advanced visualizations
Hundreds of Resources ($1000 Value)
ULTIMATE R CHEAT SHEET – The New & Improved Version 2.0
100+ Video Coding Lessons
7 Key Resources
2 Challenges

It adds up to $6,000

Get your order today for only $399

*Price excludes local taxes & VAT
Get ed now!

Paid Course

$399

3 Monthly Low Payments

3X Monthly
3 monthly payments of $159

Your Instructor

Matt Dancho
Matt Dancho

Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane ups! Matt enjoys teaching data scientists how to use powerful tools within their organizations to increase ROI. Matt is relentless in his pursuit of results and doesn’t stop until he achieves them.

Course Curriculum

You are welcome to Shiny Web Level 1: Application Development

Building Web Applications They Deliver Business Value (2:15)

Course Roadmap – Building Production-Get ready Web Fast Apps (1:54)

Private Slack Channel: How To Join

Instructions for Course Certificate

Prerequisites

Prerequisites

How to Get Help

Getting Help (IMPORTANT !!!)

1.0 Getting ed

Overview

1.1 Business Case & Course Roadmap

It is important to price products consistently (0:57).

Course Objective – Product Prediction App with Shiny & Flexdashboard (1:17)

1.2 Tools in our Toolbox

Resource #1: The Ultimate Cheat Sheet Version 2.0 (File download) (2:51

1.3 Data Science Project Settingup

Installing R (Optional), 3:06

Installing RStudio IDE (Optional) (3:03)

Setup The Project (File download) (2:34).

Installing R Packages (File download) (3:03

1.4 Transactional Information Introduction – Bike Sales (Recap at 101)

What is transactional data? (1:41)

Orders of Transactional Data Building Blocks (3:53).

Data Model: Entity Relationship Diagram (2.14).

Understanding Database Relationships (6.18)

Part 1 – Sales Dashboard

Part 1 – Learning Shiny Build a Sales Dashboard (2:02)

Flexdashboard 2.0 Creating a Sales Dashboard

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)

Part 1 – Joining Data using The SQLite backend – (5:00)

Part 2: Joining Data Using the Database Backend (4:12).

Processing Data: Final Preparations to the Map (2.20).

Adding a Section to the App (1:59).

Making the Plotly Chart, Part 1: Plotly Choloropleth 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 Plotlymap, Part 4 – add_trace() (3:27)

Part 5: Layout of the Plotly Map() (3:14)

Code Checkpoint

3.0 Adding Shiny Reactive Components to Sales Dashboard

This Section (:41): What you Build

Setup (File Upload) (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 Overview of App (5:00)

Checkbox – checkboxGroupInput() (5:37)

Checkbox – renderPrint() & textOutput() (7:19)

Date Range – dateRangeInput() (5:12)

Download it immediately DS4B 102-R – Shiny Web Applications (Intermediate)

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 react() (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 Integration Shiny In 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)

Selector for Bike Types – Reactive() & renderPlotly() (6:34)

Bike Family Selector – shinyWidgets::pickerInput() (7:03)

Bike Family Selectionor – 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

This Section: What Can You Build (1:02).

Setup (File Upload) (1.38)

4.1 Time Series Chart

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:() (1:26)

Parameterizing 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)

Part 1: Date Range Input (5.55)

Part 2: Add Reactivity. renderPlotly() (2:04)

Adding Reactivity, Part 3: Connecting the Category 1 & 2 Inputs (4:46)

Adding Reactivity Part 4: Date Aggregation using Radio Group Buttons (8.40)

Part 5: Connecting 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: Overview of Reactive Programming (2:09).

ActionButton: Add the Apply Button() (1:56)

Reactive button Click: eventReactive() (5:19)

Loading plots during application launch (3:07).

Welcome to Shinyjs (0.59)

Setting up shinyjs at RMarkdown (2.25)

Reset Button: Update time Aggregation Buttons (2.39)

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)

ValueBoxes: Making Value Boxes() (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 Benefits Your Organization (2.10)

5.0 Predictive Analytics – XGBoost+ Parsnip

Predictive analysis goals (4:10)

Setup (File Upload) (3.32)

Preprocessing of the Bikes Table (7.06)

Training Data Set – Getting Ready for Parsnip + XGBoost (3.12)

Machine Learning Algorithm for Parsnip + XGBoost (6.36)

Code Checkpoint #1 (File Download)

Modularizing Preprocessing Code Part 1: Separate Description of Bike (9:06)

Modularizing the Preprocessing Cod, Part 2: Separate Description (5:03).

Code Checkpoint #2 (File Download)

Predicting user input (6:02).

Modularizing Prediction: Generating NEW Bikes (8.12)

Code Checkpoint #3 (File Download)

Formatted Table (5/18)

Modularizing the Table Output: format_table() (1:08)

Bike Prediction Plot Data Preparation (7.03)

Bike Prediction: ggplot Part 1 (7.16)

Bike Prediction Plot – ggplot Part 2 (4.47)

Modularize the Bike Prediction Plan: 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)

Installation of The Flexdashboard Layout (3.25)

Load Libraries (3.35)

Data: Connect to SQLite & Load Bikes Table into Memory (3:19).

Scripts: Use Our Modular Prediction Functions (3.00)

Machine Learning Model: Loading Our XGBoost Algorithm (1.51)

Generator of Bike Predictions (3.33)

Format_table: The Prediction 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 Shiny!

What you build in this section (0:59).

Setup (File download) (0:47

7.1 Shiny To The Predictive Web Application

Addition Shiny To Our App – runtime: shiny (1:33)

Text Input: textInput() (2:09)

Download it immediately DS4B 102-R – Shiny Web Applications (Intermediate)

ActionButton: Add an Apply button() (2:09)

EventReactive: Add Reactivity to The Apply Button() (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 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)

Get The Unique Categories: Different() (3:00)

PickerInput: Adding Bike Family to UI() (3:58)

Connecting Bike FamilyUI To The Apply Button (2.07)

Connecting Bike FamilyUI To The Reset Button

Generate Bike Type from Bike Family (6.22)

Bike Prediction: Update Bike Family (3:17)

PickerInput: Add Frame Material UI() (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 Web Developers – Traversy Media

1 Hour HTML Course for Beginners – Traversy Media

1.5 Hour CSS Course – Traversy Media

9.2 Personalizing the Appearance of Your App

Setup (File Upload) (1.44)

Resource #8: Chrome DevTools Browser Web Development Tools (10:10)

[OPTIONAL / ADVANCED] 1 Hour Chrome Developer Tools Crash Course – Traversy Media

Resource #9: Google Fonts (8.03)

CSS Setup & Coloring the Navbar (7:07)

Coloring the Sidebar (3.49)

Adding a logo (4:53).

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

Display 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)

Extend the Image Path to the Application (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 Upload) (4.04)

Parnsip +XGBoost 10.1 Demand Forecast Analysis

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 and ARIMA: Why we are using ML for our app (1:45).

Timetk, Part 1 – A toolkit to prepare time series ML Preparation (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 to Change the Model (4:08).

Add Logic: Separating Yearly and Other Time Aggregations (2.32)

A Linear Regression Model for Yearly Information (5:01).

Update of the Plot Forecast function, Part 1 (5.28)

Part 2: Updating the plot forecast function, Part 5 (5:00)

Saving Our Functions (1.21)

Code Checkpoint (File Download)

10.3 Forecasting: What About Trend? (XGBoost vs GLMNet).

Setup (File Upload) (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)

Link the Forecast Toggle Switch to Reset & Update Buttons (2:59)

Incorporating the Forecast (8.34).

Making the Forecast Plot (4.20)

Connecting the Forecast and the Time Unit Radio Buttons with observeEvent() (3:25)

Connecting the Forecast to Forecast Mode Toggle: observeEvent() (3:26)

Value boxes, Part 1: Update colors with changing data filters (6:04).

Value boxes, Part 2: Update colors with changing data filters (6:51).

PickerInput: Customer Selection() (6:00)

Connecting Customer Selection to Data Filter (4.50).

Recap (0.58)

Code Checkpoint (File Download)

10.5 Customization-Business Science Light Theme

Create a CSS theme for our website 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 (54:54)

Coloring the Time Series Radio Buttons (10.59)

Color the Value Boxes (5.51)

2.35 Update of the CSS to Make Responsive on Mobile Devices

Scroll Capability Enabled on the UI Sidebar For Mobile Devices (3.16)

Code Checkpoint (File Download)

10.6 Customization – Business Science Dark Theme

Make a Dark Theme! (0:45)

Adding a View Mode Toggle (09:19)

Creating The Dark Theme (6.05)

Use includeCSS to switch themes() & renderUI() (5:39)

Mobile Devices: Setting the Theme (13:14)

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 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: The xgboost program is not being properly installed (1:54).

Shiny App Deployed!!! (0:26)

11.2 Challenge

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

Congratulations!!!

It was possible, you did it! You are so amazing! (1:38)

Most Frequently Asked Questions

What time does the course and end?
It is a course that never ends. It is self-evident.-Online course that can be paced at your own pace – you choose when you start and when you end.
What time do I have to access the course?
What does lifetime access look like? Once you enroll, you will have unlimited access to this course on all your devices.
What happens if I’m unhappy with the course?
We wouldn’t want to make you unhappy. We will refund your entire purchase if you are not satisfied within the first 30 days.

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