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Премиум
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    Building Web Applications that Deliver Business Value!
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    Course Roadmap - Building Production-Ready Web Apps Fast!
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    Why Pricing Products Consistently Is Important
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    Course Objective - Product Price Prediction App with Shiny & Flexdashboard
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    Resource #1: The Ultimate R Cheat Sheet - Version 2.0 (File Download)
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    Installing R (Optional)
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    Installing RStudio IDE (Optional)
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    Setting Up The Project (File Download)
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    Installing R Packages (File Download)
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    Transactional Data - What Is It?
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    Orders: The Building Blocks of Transactional Data
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    Data Model: Entity Relationship Diagram
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    Understanding Database Relationships
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    Part 1 - Learning Shiny By Building A Sales Dashboard!
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    What You Build In This Section
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    Resource #2: Flexdashboard Documentation & Key Resources
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    Flexdashboard: Introduction & Layout Basics
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    Orientation: Column vs Row
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    Vertical Layout: Fill vs Scroll
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    Tabsets
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    Multiple Pages
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    Flexdashboard Setup
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    Libraries
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    Database Connection
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    Joining Data Using The SQLite Backend - Part 1
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    Joining The Data Using The Database Backend - Part 2
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    Processing Data: Final Preparations for the Map
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    Adding A Section To The App
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    Making the Plotly Map, Part 1: Plotly Chloropleth Maps
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    Making The Plotly Map, Part 2: Aggregation By State
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    Making The Plotly Map, Part 3: plot_geo()
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    Making the Plotly Map, Part 4: add_trace()
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    Making the Plotly Map, Part 5: layout()
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    What You Build In This Section
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    Setup (File Download)
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    Resources #3: Shiny Cheat Sheet
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    Resource #4: Shiny Widgets Gallery
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    Resource #5: HTML Widgets Showcase
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    Resource #6: shinyjs
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    Shiny Tutorial App - Overview
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    Checkbox - checkboxGroupInput()
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    Checkbox - renderPrint() & textOutput()
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    Date Range - dateRangeInput()
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    Date Range - renderPrint() & textOutput()
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    Slider - sliderInput()
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    Slider - renderPrint() & textOutput()
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    Reactive Filtering - reactive()
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    Data Table - Interactive Tables with DT
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    Reactive Expressions: Adding More Inputs to reactive()
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    Reactive Summarization: DT
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    Reset Button, Part 1: actionButton()
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    Resource #7: Font Awesome
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    Reset Button, Part 2: observeEvent()
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    Sales Dashboard: Setting Up For Shiny
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    shinyWidgets
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    Data Preparation
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    Bike Type Selector - shinyWidgets::checkboxGroupButtons()
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    Bike Type Selector - reactive() & renderPlotly()
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    Bike Family Selector - shinyWidgets::pickerInput()
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    Bike Family Selector - reactive() filter
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    Reset Button: actionButton()
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    Challenge 1 - Add Date Range Input (File Download)
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    Challenge 1 - Solution, Part 1
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    Challenge 1 - Solution, Part 2
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    What You Build In this Section
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    Setup (File Download)
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    Time Series Plot: Game Plan
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    Flexdashboard Layout: "Over Time" Section
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    Data Preparation
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    Making the ggplot Geometries
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    Formatting the ggplot
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    Adding Interactivity: ggplotly()
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    Parameterizing The Time Unit
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    Next Steps: Reactivity
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    Adding Reactivity: Game Plan
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    Adding Reactivity, Part 1: Date Range Input
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    Adding Reactivity, Part 2: renderPlotly()
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    Adding Reactivity, Part 3: Connecting the Category 1 & 2 Inputs
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    Adding Reactivity, Part 4: Date Aggregation with Radio Group Buttons
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    Adding Reactivity, Part 5: Connecting the Date Aggregation Buttons
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    Finishing Touches
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    Next Steps
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    Apply Button: Reactive Programming Overview
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    Adding the Apply Button: actionButton()
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    Reactive Button Click: eventReactive()
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    Loading Plots When Firing Up The Application
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    Welcome to shinyjs
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    Setting Up shinyjs in RMarkdown
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    Reset Button: Update Time Aggregation Buttons
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    Reset Button: Click "Apply" with shinyjs
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    Reset Button: Delay "Apply" Click with shinyjs
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    Recap
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    What You Build
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    Setup & Layout
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    Making Value Boxes: valueBox()
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    Data Summarization, Part 1
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    Data Summarization, Part 2
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    Reactive Data Summarization
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    Healthy Value Box
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    Wealthy Value Box
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    Wise Value Box
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    Part 2 - Making A Predictive Web Application that Helps Your Organization
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    Predictive Analysis Goals
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    Setup (File Download)
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    Preprocessing the Bikes Table
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    Training Data Set: Getting Ready for parsnip + XGBoost
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    Machine Learning Algorithm: parsnip + XGBoost
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    Modularizing the Preprocessing Code, Part 1: Separate Bike Description
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    Modularizing the Preprocessing Code, Part 2: Separate Model Description
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    Making Predictions from User Input
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    Modularizing the Prediction: Generating New Bikes
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    Formatted Table
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    Modularizing the Table Output: format_table()
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    Bike Prediction Plot: Data Preparation
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    Bike Prediction Plot: ggplot, Part 1
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    Bike Prediction Plot: ggplot, Part 2
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    Modularize the Bike Prediction Plot: plot_bike_prediction()
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    What You Build In This Section
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    Setup: dir_create() & flexdashboard creation
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    Setting Up The Flexdashboard Layout
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    Load Libraries
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    Data: Connect to SQLite and Load Bikes Table Into Memory
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    Scripts: Source Our Modular Prediction Functions
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    Machine Learning Model: Loading Our XGBoost Algorithm
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    Generating Bike Predictions
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    The Prediction Table: format_table()
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    The Price Prediction Plot: plot_bike_prediction()
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    Aside - The Difference Between Flexdashboard With & Without runtime: shiny
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    What You Build In This Section
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    Setup (File Download)
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    Adding Shiny To Our App - runtime: shiny
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    Adding A Text Input: textInput()
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    Adding An Apply Button: actionButton()
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    Adding Reactivity To The Apply Button: eventReactive()
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    Rendering The Prediction Plot: renderPlotly()
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    Rendering The Prediction Table: renderTable()
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    Render On Load: Prediction Plot & Table
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    Challenge #2: Reset Button (File Download)
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    Challenge #2 Solution (File Download)
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    What You Build In This Section
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    Setup (File Download)
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    Organizing Our UI & Adding The shinyWidgets Library
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    Getting The Unique Categories: distinct()
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    Adding Bike Family UI: pickerInput()
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    Connecting Bike Family UI To The Apply Button
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    Connecting Bike Family UI To The Reset Button
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    Generate Bike Type From Bike Family
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    Update Bike Family In Bike Prediction
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    Adding Frame Material UI: pickerInput()
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    Connecting Frame Material UI To Apply, Reset, & Generate New Bike Prediction
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    Part 3 - Customizing The App To Your Organizations Brand!
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    Setup (File Download)
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    Resource #8: Chrome DevTools - Browser Web Development Tools
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    CSS Setup & Coloring the Navbar
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    Coloring the Sidebar
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    Adding a Logo
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    Resource #9: Google Fonts
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    Navbar - Montserrat
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    Headers - Montserrat
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    Body - Roboto
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    Coloring the Buttons
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    Challenge #3: Cannondale Challenge! (File Downloads)
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    Challenge #3 Solution, Part 1 (File Download)
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    Challenge #3 Solution, Part 2
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    Displaying Product Images in Your Shiny App
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    Setup (File Downloads)
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    Connect the Script & Model Paths
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    Image Placeholder
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    Extracting the Model Base
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    Generate New Bike: To Speed Up Debugging
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    Detecting & Retrieving the Image Path
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    Expose the Image Path to the App
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    Rendering Images: renderImage()
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    Handling Bike Models with Numbers in their Names
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    Part 4 - Adding Demand Forecasting & Customizing Your Sales Dashboard
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    App Setup (File Download)
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    Forecast Analysis: Setup & Overview
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    Data Processing
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    Time Series Aggregation: aggregate_time_series()
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    Time Series Plot: plot_time_series()
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    Time Series ML vs ARIMA: Why We Are Using ML For Our App
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    timetk, Part 1: A toolkit for time series ML prep
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    timetk Part 2: A Toolkit for Time Series ML Prep
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    Making Training & Future Data
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    XGBoost Forecast Model (File Download)
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    Making Predictions & Outputting in the Format for Our New Forecast Plot
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    Modularizing the XGBoost Forecast: generate_forecast()
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    Interactive Forecast Plot
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    plot_forecast()
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    Visual Forecast Inspection
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    Preparing for the Model Change
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    Add Logic: Separating the Yearly Data from the Other Time Aggregations
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    Making a Linear Regression Model for Yearly Data
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    Updating the Plot Forecast Function, Part 1
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    Updating the Plot Forecast Function, Part 2
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    Saving Our Functions
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    Setup (File Download)
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    Trend Evaluation
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    Experiment #1: Exploring Trend w/ XGBoost Forecast
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    Experiment #2: Comparing XGBoost & GLMNet
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    Overview
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    Setup
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    Toggle Switch: switchInput() [with CSS Copy-Paste]
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    Showing & Hiding the Forecast Horizon: conditionalPanel()
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    Link the Forecast Toggle Switch to Reset & Update Buttons
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    Integrating the Forecast
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    Producing the Forecast Plot
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    Connecting the Forecast to the Time Unit Radio Buttons: observeEvent()
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    Connecting the Forecast to the Forecast Mode Toggle Switch: observeEvent()
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    Value Boxes, Part 1: Update Colors with Changing Data Filters
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    Value Boxes, Part 2: Update Colors with Changing Data Filters
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    Customer Selection: pickerInput()
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    Connecting the Customer Selection to the Data Filter
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    Recap
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    Creating a CSS Theme for Our Shiny App
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    Setup (File Download)
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    Connecting Our CSS & Logo
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    Adjusting the Sidebar Width
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    Coloring the Forecast Mode Toggle Switch
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    Coloring the Time Series Radio Buttons
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    Coloring the Value Boxes
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    Updating the CSS to Make Responsive for Mobile Devices
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    Enabling Scroll Capability on the UI Sidebar for Mobile Devices
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    Making a Dark Theme!
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    Adding A View Mode Toggle Switch
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    Creating The Dark Theme
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    Switching Themes with includeCSS() & renderUI()
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    Adjusting the Theme for Mobile Devices
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    Final Shiny Sales Dashboard with Forecasting & Dark Theme!
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    Publishing Your Shiny Web Application
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    Publishing Options
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    Setup: Shinyapps.io & App Files
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    Connecting To Shinyapps.io & Publishing
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    Troubleshooting Error #1: Files are Outside of App Directory
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    Troubleshooting Error #2: xgboost package is not being installed
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    Shiny App Deployed!!!
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    Challenge #4: Create & Deploy Your Own App
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    You Did It! I'm So Happy For You!!