Book Image

Introduction to R for Business Intelligence

By : Jay Gendron
Book Image

Introduction to R for Business Intelligence

By: Jay Gendron

Overview of this book

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.

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Introduction to R for Business Intelligence
Jay Gendron
Aug, 2016 | 228 pages
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Table of Contents (19 chapters)
Introduction to R for Business Intelligence
Introduction to R for Business Intelligence
Credits
Credits
About the Author
About the Author
Acknowledgement
Acknowledgement
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Extract, Transform, and Load
Extract, Transform, and Load
Understanding big data in BI analytics
Extracting data from sources
Transforming data to fit analytic needs
Loading data into business systems for analysis
Summary
2
Data Cleaning
Data Cleaning
Summarizing your data for inspection
Finding and fixing flawed data
Converting inputs to data types suitable for analysis
Adapting string variables to a standard
Summary
3
Exploratory Data Analysis
Exploratory Data Analysis
Understanding exploratory data analysis
Analyzing a single data variable
Analyzing two variables together
Exploring multiple variables simultaneously
Summary
4
Linear Regression for Business
Linear Regression for Business
Understanding linear regression
Checking model assumptions
Using a simple linear regression
Refining data for simple linear regression
Introducing multiple linear regression
Summary
5
Data Mining with Cluster Analysis
Data Mining with Cluster Analysis
Explaining clustering analysis
Partitioning using k-means clustering
Clustering using hierarchical techniques
Summary
6
Time Series Analysis
Time Series Analysis
Analyzing time series data with linear regression
Introducing key elements of time series analysis
Building ARIMA time series models
Summary
7
Visualizing the Datas Story
Visualizing the Datas Story
Visualizing data
Plotting with ggplot2
Geo-mapping using Leaflet
Creating interactive graphics using rCharts
Summary
8
Web Dashboards with Shiny
Web Dashboards with Shiny
Creating a basic Shiny app
Creating a marketing-campaign Shiny app
Deploying your Shiny app
Summary
References
References
Other Helpful R Functions
Other Helpful R Functions
Chapter 1 - Extract, Transform, and Load
Chapter 2 - Data Cleaning
R Packages Used in the Book
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations
R Code for Supporting Market Segment Business Case Calculations

Appendix A. References

The following is a list of references used throughout the book, starting from the Preface and including the chapters:

Preface:

Guerra, P. & Borne, K. (2016, February). Ten Signs of Data Science Maturity. Sebastopol, CA: O'Reilly Media, Inc.

LawrenceStats. (2016, March 22). RStudio Basics: Install & Load an R Package [Video file]. Retrieved from https://www.youtube.com/watch?v=H3EFjKngPr4 .

Peng, R. (2012a). Setting Your Working Directory and Editing R Code (Mac) [Video file]. Retrieved from https://www.youtube.com/watch?v=8xT3hmJQskU .

Peng, R. (2012b). Setting Your Working Directory and Editing R Code (Windows) [Video file]. Retrieved from https://www.youtube.com/watch?v=XBcvH1BpIBo .

Peng, R. (2014a). Base Plotting part 1 [Video file]. Retrieved from https://www.youtube.com/watch?v=AAXh0egb5WM .

Peng, R. (2014b). Base Plotting part 2 [Video file]. Retrieved from https://www.youtube.com/watch?v=bhyb1gCeAVk .

Peng, R. (2014c). Control Structures in R (part 1) [Video file]. Retrieved from https://www.youtube.com/watch?v=8RmwEBo8yy0 .

Peng, R. (2014d). Control Structures in R (part 2) [Video file]. Retrieved from https://www.youtube.com/watch?v=z8V-a6d8JTg .

Peng, R. (2014e). Data Types part 1 [Video file]. Retrieved from https://www.youtube.com/watch?v=vGY5i_J2c-c .

Peng, R. (2014f). Data Types part 2 [Video file]. Retrieved from https://www.youtube.com/watch?v=w8_XdYI3reU .

Peng, R. (2014g). Data Types part 3 [Video file]. Retrieved from https://www.youtube.com/watch?v=NuY6jY4qE7I .

Peng, R. (2014h). Installing RStudio [Video file]. Retrieved from https://youtu.be/bM7Sfz-LADM .

Peng, R. (2014i). Subsetting Basics [Video file]. Retrieved from https://www.youtube.com/watch?v=VfZUZGUgHqg .

Peng, R. (2014, April 9). Subsetting Matrices [Video file]. Retrieved from https://www.youtube.com/watch?v=FzjXesh9tRw .

Peng, R. (2015a). Install R for Mac [Video file]. Retrieved from https://www.youtube.com/watch?v=uxuuWXU-7UQ .

Peng, R. (2015b). Install R for Windows [Video file]. Retrieved from https://www.youtube.com/watch?v=Ohnk9hcxf9M .

R Core Team. (2016, June 21). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (Version 3.3.1) [Software]. Available from https://www.R-project.org/ .

Chapter 1 - Extract, Transform, and Load:

Cyberfella, LTD. (2013). Configuring an Excel ODBC Data Source in Windows 7 [Web log]. Retrieved from http://www.cyberfella.co.uk/2012/06/11/windows7-odbc/ .

Kaggle. (2014, May 28). Bike Sharing Demand [Data file]. Retrieved from https://www.kaggle.com/c/bike-sharing-demand/data .

Mulcahy, R. (2007, March 6). Business Intelligence Definition and Solutions [Web log]. Retrieved from http://www.cio.com/article/2439504/business-intelligence/business-intelligence-definition-and-solutions.html .

Press, G. (2013, May 9). A Very Short History of Big Data [Web log]. Retrieved from http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#7508da7255da.

RStudio. (2015, January). Data Wrangling with dplyr and tidyr Cheat Sheet. Retrieved from https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf .

Chapter 2 - Data Cleaning:

Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. New York, NY: Cambridge University Press.

Grolemund, G. & Wickham, H. (2011, April). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3). Retrieved from https://www.jstatsoft.org/article/view/v040i03 .

Jonge, E. de & Loo, M. van der. (2013). Discussion Paper: An introduction to data cleaning with R . Retrieved from https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf .

Levy, J. (2013). Bad Data Lurking in Plain Text. In McCallum, Q. E. (Ed.), Bad Data Handbook (pp. 53-68). Sebastopol, CA: O'Reilly Media, Inc.

Miller, G. A. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review, 63(2), 81-97. doi:10.1037/h0043158.PMID 13310704.

Vries, A. de. (2015, June 24). How many packages are there really on CRAN? [Web log]. Retrieved from http://blog.revolutionanalytics.com/2015/06/how-many-packages-are-there-really-on-cran.html .

Wickham, H. (2015, April 30). Introduction to stringr. Retrieved from https://cran.r-project.org/web/packages/stringr/vignettes/stringr.html .

Chapter 3 - Exploratory Data Analysis:

Anscombe, F. J. (1973). Graphs in Statistical Analysis. American Statistician 27(1): 17-21. Retrieved from http://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf .

Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (1983). Understanding Robust and Exploratory Data Analysis. New York, NY: Wiley.

Johnson. (n.d.). Chapter 15: Descriptive Statistics. Retrieved from http://www.southalabama.edu/coe/bset/johnson/lectures/lec15.htm .

Kahn, S. (2016). Hypothesis testing and p-values [Web log]. Retrieved from https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/hypothesis-testing-and-p-values .

Peng, R. D. (2015, September 3). Exploratory Data Analysis with R. Retrieved from http://leanpub.com/exdata .

Stat Trek (2016). What is Hypothesis Testing?[Web log]. Retrieved from http://stattrek.com/hypothesis-test/hypothesis-testing.aspx .

Stevens, S. S. (1946, June 7). On the Theory of Scales of Measurement. Science, 103(2684), 677-680. doi:10.1126/science.103.2684.677.

Tukey, J. W. (1977, January 1). Exploratory Data Analysis. Reading, PA: Addison-Wesley.

Vigen, T. (2015). Correlation Chart [digital image]. Retrieved from http://tylervigen.com/spurious-correlations .

Wright, K. (2016, July 15). Examples for the corrgram package . Retrieved from https://cran.r-project.org/web/packages/corrgram/vignettes/corrgram_examples.html.

Chapter 4 - Linear Regression for Business:

Kahn, S. (2016). Hypothesis testing and p-values [Web log]. Retrieved from https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/hypothesis-testing-and-p-values .

Rauser J. (2011, September). What is a Career in Big Data? [Video file]. Retrieved from https://www.youtube.com/watch?v=0tuEEnL61HM .

Robinson, D. (2015, January 16). K-means clustering is not a free lunch[Web log]. Retrieved from http://varianceexplained.org/r/kmeans-free-lunch/ .

Sicular, S., (2013, March 27). Gartner's Big Data Definition Consists of Three Parts, Not to Be Confused with Three "V"s [Web log]. Retrieved from http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/#b1728a43bf62.

Stat Trek (2016). What is Hypothesis Testing?[Web log]. Retrieved from http://stattrek.com/hypothesis-test/hypothesis-testing.aspx .

StatSoft. (2016). How To Find Relationship Between Variables, Multiple Regression. Retrieved from http://www.statsoft.com/textbook/multiple-regression .

Chapter 5 - Data Mining with Cluster Analysis:

Berkley University. (2011, March 16). Cluster Analysis. Retrieved from http://www.stat.berkeley.edu/~s133/Cluster2a.html .

Han, J. (2011). Data Mining: Concepts and Techniques. Waltham, MA: Morgan Kaufmann Publishers.

Chapter 6 - Time Series Analysis:

Hyndman, R. J. (2016). Hyndsight[Web log]. Retrieved from http://robjhyndman.com/hyndsight/ .

The Pennsylvania State University. (2016). Welcome to STAT 510 – Applied Time Series Analysis. Retrieved from https://onlinecourses.science.psu.edu/stat510/node/33 .

Chapter 7 - Visualizing the Data's Story:

Bache, S. M. (2014, November). magrittr: Ceci n'est pas un pipe[Web log]. Retrieved from https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html .

Cairo, A. (2013). The Functional Art: An introduction to information graphics and visualization. Berkeley, CA: New Riders.

Community NVD3 (2016, July). NVD3 documentation [Web log]. Retrieved from https://nvd3-community.github.io/nvd3/examples/documentation.html .

Eschelman-Haynes, C., Gendron, G. R., Hall, S., & Hall, B. (2016). Visualizing Big Data: Telling a Better Story. In Proceedings from MODSIM World 2016. Retrieved from http://www.modsimworld.org/papers/2016/Visualizing_Big_Data.pdf .

Leaflet-Extras. (n.d.) Leaflet-providers preview . Retrieved from http://leaflet-extras.github.io/leaflet-providers/preview/ .

RStudio (2015, March). Data Visualization with ggplot2. Retrieved from http://www.rstudio.com/wp-content/uploads/2015/12/ggplot2-cheatsheet-2.0.pdf .

Shneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Proceedings of the IEEE Symposium on Visual Languages, 1996, 336-343. doi: 10.1109/VL.1996.545307.

Vaidyanathan, R. Russell, K., & RStudio. (2015). htmlwidgets for R[Web log]. Retrieved from http://www.htmlwidgets.org/ .

Wickham, H. (2010). A Layered Grammar of Graphics. Journal of Computational and Graphical Statistics , 19(1), 3-28. doi:10.1198/jcgs.2009.07098. Retrieved from http://vita.had.co.nz/papers/layered-grammar.pdf .

Wilkinson, L. (2005). The Grammar of Graphics (statistics and computing) (2nd ed.). New York: Springer.

Chapter 8 - Web Dashboards with Shiny:

Allaire, J. (2014, January 24). Application layout guide [Web log]. Retrieved from http://shiny.rstudio.com/articles/layout-guide.html .

Bootstrap Project (n.d.). Bootstrap [Web log]. Retrieved from http://getbootstrap.com/ .

Grolemund, G. & Cheng, J. (2014, May 6). Style your apps with CSS [Web log]. Retrieved from http://shiny.rstudio.com/articles/css.html .

Heckmann, M. (2013, November 20). Sending data from client to server and back using shiny [Web log]. Retrieved from https://ryouready.wordpress.com/2013/11/20/sending-data-from-client-to-server-and-back-using-shiny/ .

RStudio (n.d.-a). Add control widgets [Web log]. Retrieved from http://shiny.rstudio.com/tutorial/lesson3/ .

RStudio (n.d.-b). DT: An R interface to the DataTables library [Web log]. Retrieved from http://rstudio.github.io/DT/ .

RStudio (n.d.-c). Run a Shiny application from a URL [Web log]. Retrieved from http://shiny.rstudio.com/reference/shiny/latest/runUrl.html .

RStudio (n.d.-d). Share your apps [Web log]. Retrieved from http://shiny.rstudio.com/tutorial/lesson7/ .

RStudio (n.d.-e). Shiny Widgets Gallery [Web log]. Retrieved from http://shiny.rstudio.com/gallery/widget-gallery.html .

RStudio (2014a). Reactivity: An overview [Web log]. Retrieved from http://shiny.rstudio.com/articles/reactivity-overview.html .

RStudio (2014b). Scoping rules for Shiny apps [Web log]. Retrieved from http://shiny.rstudio.com/articles/scoping.html .

RStudio (2016a). Share your Shiny Applications Online [Web log]. Retrieved from https://www.shinyapps.io/.

RStudio (2016b). Teach yourself Shiny [Web log]. Retrieved from http://shiny.rstudio.com/tutorial/.

W3Schools.com (n.d.). CSS Tutorial [Web log]. Retrieved from http://www.w3schools.com/css/.

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