Modern data science with R

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
About the Authors
Preface
I. Part I: Introduction to Data Science
	1. Prologue: Why data science?
		1.1. What is data science?
		1.2. Case study: The evolution of sabermetrics
		1.3. Datasets
		1.4. Further resources
	2. Data visualization
		2.1. The 2012 federal election cycle
		2.2. Composing data graphics
		2.3. Importance of data graphics: Challenger
		2.4. Creating effective presentations
		2.5. The wider world of data visualization
		2.6. Further resources
		2.7. Exercises
		2.8. Supplementary exercises
	3. A grammar for graphics
		3.1. A grammar for data graphics
		3.2. Canonical data graphics in R
		3.3. Extended example: Historical baby names
		3.4. Further resources
		3.5. Exercises
		3.6. Supplementary exercises
	4. Data wrangling on one table
		4.1. A grammar for data wrangling
		4.2. Extended example: Ben’s time with the Mets
		4.3. Further resources
		4.4. Exercises
		4.5. Supplementary exercises
	5. Data wrangling on multiple tables
		5.1. inner_join()
		5.2. left_join()
		5.3. Extended example: Manny Ramirez
		5.4. Further resources
		5.5. Exercises
		5.6. Supplementary exercises
	6. Tidy data
		6.1. Tidy data
		6.2. Reshaping data
		6.3. Naming conventions
		6.4. Data intake
		6.5. Further resources
		6.6. Exercises
		6.7. Supplementary exercises
	7. Iteration
		7.1. Vectorized operations
		7.2. Using across() with dplyr functions
		7.3. The map() family of functions
		7.4. Iterating over a one-dimensional vector
		7.5. Iteration over subgroups
		7.6. Simulation
		7.7. Extended example: Factors associated with BMI
		7.8. Further resources
		7.9. Exercises
		7.10. Supplementary exercises
	8. Data science ethics
		8.1. Introduction
		8.2. Truthful falsehoods
		8.3. Role of data science in society
		8.4. Some settings for professional ethics
		8.5. Some principles to guide ethical action
		8.6. Algorithmic bias
		8.7. Data and disclosure
		8.8. Reproducibility
		8.9. Ethics, collectively
		8.10. Professional guidelines for ethical conduct
		8.11. Further resources
		8.12. Exercises
		8.13. Supplementary exercises
II. Part II: Statistics and Modeling
	9. Statistical foundations
		9.1. Samples and populations
		9.2. Sample statistics
		9.3. The bootstrap
		9.4. Outliers
		9.5. Statistical models: Explaining variation
		9.6. Confounding and accounting for other factors
		9.7. The perils of p-values
		9.8. Further resources
		9.9. Exercises
		9.10. Supplementary exercises
	10. Predictive modeling
		10.1. Predictive modeling
		10.2. Simple classification models
		10.3. Evaluating models
		10.4. Extended example: Who has diabetes?
		10.5. Further resources
		10.6. Exercises
		10.7. Supplementary exercises
	11. Supervised learning
		11.1. Non-regression classifiers
		11.2. Parameter tuning
		11.3. Example: Evaluation of income models redux
		11.4. Extended example: Who has diabetes this time?
		11.5. Regularization
		11.6. Further resources
		11.7. Exercises
		11.8. Supplementary exercises
	12. Unsupervised learning
		12.1. Clustering
		12.2. Dimension reduction
		12.3. Further resources
		12.4. Exercises
		12.5. Supplementary exercises
	13. Simulation
		13.1. Reasoning in reverse
		13.2. Extended example: Grouping cancers
		13.3. Randomizing functions
		13.4. Simulating variability
		13.5. Random networks
		13.6. Key principles of simulation
		13.7. Further resources
		13.8. Exercises
		13.9. Supplementary exercises
III. Part III: Topics in Data Science
	14. Dynamic and customized data graphics
		14.1. Rich Web content using D3.js and htmlwidgets
		14.2. Animation
		14.3. Flexdashboard
		14.4. Interactive web apps with Shiny
		14.5. Customization of ggplot2 graphics
		14.6. Extended example: Hot dog eating
		14.7. Further resources
		14.8. Exercises
		14.9. Supplementary exercises
	15. Database querying using SQL
		15.1. From dplyr to SQL
		15.2. Flat-file databases
		15.3. The SQL universe
		15.4. The SQL data manipulation language
		15.5. Extended example: FiveThirtyEight flights
		15.6. SQL vs. R
		15.7. Further resources
		15.8. Exercises
		15.9. Supplementary exercises
	16. Database administration
		16.1. Constructing efficient SQL databases
		16.2. Changing SQL data
		16.3. Extended example: Building a database
		16.4. Scalability
		16.5. Further resources
		16.6. Exercises
		16.7. Supplementary exercises
	17. Working with geospatial data
		17.1. Motivation: What’s so great about geospatial data?
		17.2. Spatial data structures
		17.3. Making maps
		17.4. Extended example: Congressional districts
		17.5. Effective maps: How (not) to lie
		17.6. Projecting polygons
		17.7. Playing well with others
		17.8. Further resources
		17.9. Exercises
		17.10. Supplementary exercises
	18. Geospatial computations
		18.1. Geospatial operations
		18.2. Geospatial aggregation
		18.3. Geospatial joins
		18.4. Extended example: Trail elevations at MacLeish
		18.5. Further resources
		18.6. Exercises
		18.7. Supplementary exercises
	19. Text as data
		19.1. Regular expressions using Macbeth
		19.2. Extended example: Analyzing textual data from arXiv.org
		19.3. Ingesting text
		19.4. Further resources
		19.5. Exercises
		19.6. Supplementary exercises
	20. Network science
		20.1. Introduction to network science
		20.2. Extended example: Six degrees of Kristen Stewart
		20.3. PageRank
		20.4. Extended example: 1996 men’s college basketball
		20.5. Further resources
		20.6. Exercises
		20.7. Supplementary exercises
	21. Epilogue: Towards “big data”
		21.1. Notions of big data
		21.2. Tools for bigger data
		21.3. Alternatives to R
		21.4. Closing thoughts
		21.5. Further resources
IV. Part IV: Appendices
	A. Packages used in this book
		A.1. The mdsr package
		A.2. Other packages
		A.3. Further resources
	B. Introduction to R and RStudio
		B.1. Installation
		B.2. Learning R
		B.3. Fundamental structures and objects
		B.4. Add-ons: Packages
		B.5. Further resources
		B.6. Exercises
		B.7. Supplementary exercises
	C. Algorithmic thinking
		C.1. Introduction
		C.2. Simple example
		C.3. Extended example: Law of large numbers
		C.4. Non-standard evaluation
		C.5. Debugging and defensive coding
		C.6. Further resources
		C.7. Exercises
		C.8. Supplementary exercises
	D. Reproducible analysis and workflow
		D.1. Scriptable statistical computing
		D.2. Reproducible analysis with R Markdown
		D.3. Projects and version control
		D.4. Further resources
		D.5. Exercises
		D.6. Supplementary exercises
	E. Regression modeling
		E.1. Simple linear regression
		E.2. Multiple regression
		E.3. Inference for regression
		E.4. Assumptions underlying regression
		E.5. Logistic regression
		E.6. Further resources
		E.7. Exercises
		E.8. Supplementary exercises
	F. Setting up a database server
		F.1. SQLite
		F.2. MySQL
		F.3. PostgreSQL
		F.4. Connecting to SQL
Bibliography
Indices
	Subject index
	R index