Introduction to R for business intelligence : learn how to leverage the power of R for business intelligence

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...

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Bibliographic Details
Main Author Gendron, Jay (Author)
Format Electronic eBook
LanguageEnglish
Published Birmingham : Packt Publishing, 2016.
SeriesCommunity experience distilled.
Subjects
Business planning > Data processing.
R (Computer program language)
elektronické knihy
electronic books
Online AccessFull text
ISBN9781785286513
178528651X
9781785280252
1785280252
1523106204
9781523106202
Physical Description1 online resource : illustrations.

Cover

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

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