Business Intelligence with R
Business Intelligence with R
From Acquiring Data to Pattern Exploration
About the Book
This is now coming up on 2 years old, and lots has changed in the R ecosystem in those 2 years, so I am dropping the suggested price to free. I don't have any current plans to update it, though if someone wanted to partner with me, I'd love to join forces!
The growth of R has been phenomenal over the past several years, reaching out past academia and research labs into the daily activities of business and industry. More and more line-level analysts are supplementing their SQL or Excel skills with R, and more and more businesses are hiring analysts with R skills to bridge the increasing gap between business needs and on-hand analytic resources.
While there are already many books that explore the use of R in statistics and data science, many of them are set as a series of case studies or are written predominantly for academic audiences as textbooks. There are also many R cookbooks, but they tend to cover the breadth of R's capabilities and options. This book aims to provide a cookbook of R recipes that are specifically of use in the daily workflow of data scientists and analysts in business and industry.
Business Intelligence with R is a practical, hands-on overview of many of the major BI/analytic tasks that can be accomplished with R. It is not meant to be exhaustive--there is always more than one way to accomplish a given task in R, so this book aims to provide the simplest and/or most robust approaches to meet daily workflow needs. It can serve as the go-to desk reference for the professional analyst who needs to get things done in R.
From setting up a project under version control to creating an interactive dashboard of a data product, this book will provide you with the pieces to put it all together.
License
About the Author
Table of Contents
-
-
About
-
- About the Author
- Session Info
- Version Info
- Code and Data
- Cover Image
- Proceeds
- Contact
-
- install.packages
-
Introduction
- Overview
- Conventions
- What you need
- Acknowledgments
- Website/Code
- Happy coding!
-
Chapter 1: An entire project in a few lines of code
- The analytics problem
- Set up
- Acquire data
- Wrangling data
-
Analytics
- Explore the data
- Run a forecasting model
-
Reporting
- Create an interactive HTML plot
- Documenting the project
- Summary
-
Chapter 2: Getting Data
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Working with files
- Reading flat files from disk or the web
- Reading big files with data.table
- Unzipping files within R
- Reading Excel files
- Creating a dataframe from the clipboard or direct entry
- Reading XML files
- Reading JSON files
-
Working with databases
- Connecting to a database
- Creating data frames from a database
- Disconnecting from a database
- Creating a SQLite database inside R
- Creating a dataframe from a SQLite database
-
Getting data from the web
- Working through a proxy
- Scraping data from a web table
- Working with APIs
- Creating fake data to test code
- Writing files to disk
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Working with files
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Chapter 3: Cleaning and Preparing Data
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Understanding your data
- Identifying data types and overall structure
- Identifying (and ordering) factor levels
- Identifying unique values or duplicates
- Sorting
-
Cleaning up
- Converting between data types
- Cleaning up a web-scraped table: Regular expressions and more
- Missing data: Converting dummy NA values
- Rounding
- Concatenation
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Merging dataframes
- Joins
- Unions and bindings
- Subsetting: filter and select from a dataframe
- Creating a derived column
-
Peeking at the outcome with
dplyr -
Reshaping a dataframe between wide and long
- Reshaping: melt and cast
- Reshaping: Crossing variables with ~ and +
- Summarizing while reshaping
- Piping with %>%: Stringing it together in dplyr
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Understanding your data
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Chapter 4: Know Thy Data—Exploratory Data Analysis
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Creating summary plots
- Everything at once: ggpairs
- Create histograms of all numeric variables in one plot
- A better “pairs” plot
- Mosaic plot matrix: “Scatterplot” matrix for categorical data
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Plotting univariate distributions
- Histograms and density plots
- Bar and dot plots
- Plotting multiple univariate distributions with faceting
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Plotting bivariate and comparative distributions
- Double density plots
- Boxplots
- Beanplots
- Scatterplots and marginal distributions
- Mosaic plots
- Multiple bivariate comparisons with faceting
- Pareto charts
- Plotting survey data
- Obtaining summary and conditional statistics
- Finding the mode or local maxima/minima
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Inference on summary statistics
- Confidence intervals
- Tolerance intervals
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Dealing with missing data
- Visualizing missing data
- Imputation for missing values
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Creating summary plots
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Chapter 5: Effect Sizes
- Overview
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Effect sizes: Measuring differences between groups
- Basic differences
- Standardized differences
- Determining the probability of a difference
-
Effect sizes: Measuring similarities between groups
- Correlation
- Bootstrapping BCa CIs for non-parametric correlation
- Determining the probability of a correlation
- Partial correlations
- Polychoric and polyserial correlation for ordinal data
- Associations between categorical variables
- Cohen’s kappa for comparisons of agreement
- Regression coefficient
- R: Proportion of variance explained
-
Chapter 6: Trends and Time
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Describing trends in non-temporal data
- Smoothed trends
- Quantile trends
- Simple linear trends
- Segmented linear trends
- The many flavors of regression
- Plotting regression coefficients
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Working with temporal data
- Calculate a mean or correlation in circular time (clock time)
- Plotting time-series data
- Detecting autocorrelation
- Plotting monthly and seasonal patterns
- Plotting seasonal adjustment on the fly
- Decomposing time series into components
- Using spectral analysis to identify periodicity
- Plotting survival curves
- Evaluating quality with control charts
- Identifying possible breakpoints in a time series
- Exploring relationships between time series: cross-correlation
- Basic forecasting
-
Describing trends in non-temporal data
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Chapter 7: A Dog’s Breakfast of Dataviz
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Plotting multivariate distributions
- Heatmaps
- Creating calendar heatmaps
- Parallel coordinates plots
- Peeking at multivariate data with dplyr and a bubblechart
- Plotting a table
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Interactive dataviz
- Basic interactive plots
- Scatterplot matrix
- Motionchart: a moving bubblechart
- Interactive parallel coordinates
- Interactive tables with DT
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Making maps in R
- Basic point maps
- Chloropleth maps
- Chloropleth mapping with the American Community Survey
- Using shapefiles and raw data
- Using ggmap for point data and heatmaps
- Interactive maps with leaflet
- Why map with R?
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Plotting multivariate distributions
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Chapter 8: Pattern Discovery and Dimension Reduction
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Mapping multivariate relationships
- Non-metric multidimensional scaling (nMDS)
- Diagnostics for nMDS results
- Vector mapping influential variables over the nMDS plot
- Contour mapping influential variables over the nMDS plot
- Principal Components Analysis (PCA)
- nMDS for Categories: Correspondence Analysis
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Cluster analysis
- Grouping observations with hierarchical clustering
- Plotting a cluster dendrogram with ggplot
- Exploring hierarchical clustering of nMDS results
- How to partition the results of a hierarchical cluster analysis
- Identifying and describing group membership with kMeans and PAM
- How to choose an optimal number of clusters with bootstrapping
- Determining optimal numbers of clusters with model-based clustering
- Identifying group membership with irregular clusters
- Variable selection in cluster analysis
- Error-checking cluster results with known outcomes
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Exploring outliers
- Identifying outliers with distance functions
- Identifying outliers with the local outlier factor
- Anomaly detection
- Extreme value analysis
- Finding associations in shopping carts
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Mapping multivariate relationships
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Chapter 9: Reporting and Dashboarding
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- Output formats for .Rmd documents
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Dashboards
- Simple dashboarding with R Markdown
- Dashboarding and reporting with flexdashboard
- Reports and technical memos
- Slide decks
- purl: scraping the raw code from your .Rmd files
- Shiny apps
- Tweaking the YAML headers
-
-
Appendix 1: Setting up projects and using “Make” files
-
Setting up a project with RStudio and Git
- Committing your changes
- Rolling back to a previous state
- Packaging a project
- “Make” files in R
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Setting up a project with RStudio and Git
- Appendix 2: .Rmd File for the Chapter 1 final report example
- Appendix 3: .R file for Chapter 9 Shiny app example
- Appendix 4: .Rmd file for the Chapter 9 flexdashboard example
- Appendix 5: R Markdown Quick Reference
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About
- Notes
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