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Introduction to R for Business Intelligence

Book Description

Learn how to leverage the power of R for Business Intelligence

About This Book

  • Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful.

  • This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R.

  • Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide.

  • Who This Book Is For

    This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected.

    What You Will Learn

  • Extract, clean, and transform data

  • Validate the quality of the data and variables in datasets

  • Learn exploratory data analysis

  • Build regression models

  • Implement popular data-mining algorithms

  • Visualize results using popular graphs

  • Publish the results as a dashboard through Interactive Web Application frameworks

  • In Detail

    Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance.

    In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.

    After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.

    Style and approach

    This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions—and all of this with the help of real-life examples.

    Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

    Table of Contents

    1. Introduction to R for Business Intelligence
      1. Introduction to R for Business Intelligence
      2. Credits
      3. About the Author
      4. Acknowledgement
      5. About the Reviewers
      6. www.PacktPub.com
        1. eBooks, discount offers, and more
          1. Why subscribe?
      7. Preface
        1. What this book covers
        2. What you need for this book
        3. Who this book is for
        4. Conventions
        5. Reader feedback
        6. Customer support
          1. Downloading the example code
          2. Downloading the color images of this book
          3. Errata
          4. Piracy
          5. Questions
      8. 1. Extract, Transform, and Load
        1. Understanding big data in BI analytics
        2. Extracting data from sources
          1. Importing CSV and other file formats
          2. Importing data from relational databases
        3. Transforming data to fit analytic needs
          1. Filtering data rows
          2. Selecting data columns
          3. Adding a calculated column from existing data
          4. Aggregating data into groups
        4. Loading data into business systems for analysis
          1. Writing data to a CSV file
          2. Writing data to a tab-delimited text file
        5. Summary
      9. 2. Data Cleaning
        1. Summarizing your data for inspection
          1. Summarizing using the str() function
          2. Inspecting and interpreting your results
        2. Finding and fixing flawed data
          1. Finding flaws in datasets
            1. Missing values
            2. Erroneous values
          2. Fixing flaws in datasets
        3. Converting inputs to data types suitable for analysis
          1. Converting between data types
          2. Date and time conversions
        4. Adapting string variables to a standard
          1. The power of seven, plus or minus two
          2. Data ready for analysis
        5. Summary
      10. 3. Exploratory Data Analysis
        1. Understanding exploratory data analysis
          1. Questions matter
          2. Scales of measurement
          3. R data types
        2. Analyzing a single data variable
          1. Tabular exploration
          2. Graphical exploration
        3. Analyzing two variables together
          1. What does the data look like?
          2. Is there any relationship between two variables?
          3. Is there any correlation between the two?
          4. Is the correlation significant?
        4. Exploring multiple variables simultaneously
          1. Look
          2. Relationships
          3. Correlation
          4. Significance
        5. Summary
      11. 4. Linear Regression for Business
        1. Understanding linear regression
          1. The lm() function
          2. Simple linear regression
          3. Residuals
        2. Checking model assumptions
          1. Linearity
          2. Independence
          3. Normality
          4. Equal variance
          5. Assumption wrap-up
        3. Using a simple linear regression
          1. Interpreting model output
          2. Predicting unknown outputs with an SLR
          3. Working with big data using confidence intervals
        4. Refining data for simple linear regression
          1. Transforming data
          2. Handling outliers and influential points
        5. Introducing multiple linear regression
        6. Summary
      12. 5. Data Mining with Cluster Analysis
        1. Explaining clustering analysis
        2. Partitioning using k-means clustering
          1. Exploring the data
          2. Running the kmeans() function
          3. Interpreting the model output
          4. Developing a business case
        3. Clustering using hierarchical techniques
          1. Cleaning and exploring data
          2. Running the hclust() function
          3. Visualizing the model output
          4. Evaluating the models
          5. Choosing a model
          6. Preparing the results
        4. Summary
      13. 6. Time Series Analysis
        1. Analyzing time series data with linear regression
          1. Linearity, normality, and equal variance
          2. Prediction and confidence intervals
        2. Introducing key elements of time series analysis
          1. The stationary assumption
          2. Differencing techniques
        3. Building ARIMA time series models
          1. Selecting a model to make forecasts
          2. Using advanced functionality for modeling
        4. Summary
      14. 7. Visualizing the Datas Story
        1. Visualizing data
          1. Calling attention to information
          2. Empowering user interpretation
        2. Plotting with ggplot2
        3. Geo-mapping using Leaflet
          1. Learning geo-mapping
          2. Extending geo-mapping functionality
        4. Creating interactive graphics using rCharts
          1. Framing the data story
          2. Learning interactive graphing with JavaScript
        5. Summary
      15. 8. Web Dashboards with Shiny
        1. Creating a basic Shiny app
          1. The ui.R file
          2. The server.R file
        2. Creating a marketing-campaign Shiny app
          1. Using more sophisticated Shiny folder and file structures
            1. The www folder
            2. The global.R file
          2. Designing a user interface
            1. The head tag
            2. Adding a progress wheel
            3. Using a grid layout
            4. UI components of the marketing-campaign app
          3. Designing the server-side logic
            1. Variable scope
            2. Server components of the marketing-campaign app
        3. Deploying your Shiny app
          1. Located on GitHub
          2. Hosted on RStudio
          3. Hosted on a private web server
        4. Summary
      16. A. References
      17. B. Other Helpful R Functions
        1. Chapter 1 - Extract, Transform, and Load
        2. Chapter 2 - Data Cleaning
      18. C. R Packages Used in the Book
      19. D. R Code for Supporting Market Segment Business Case Calculations