Introduction to R for Business Intelligence

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