Modern Data Science with R
by Benjamin S. Baumer, Daniel T. Kaplan, Nicholas Jon Horton
2nd Edition
Publisher: Chapman & Hall
Book Details
| Print ISBN | 9780367191498 |
| eText ISBN | 9780429575396 |
| Publisher | Chapman & Hall |
| Publishing Year | 2021 |
| Edition | 2nd Edition |
| Language | English |
| Pages | 650 |
Modern Data Science with R, 2nd Edition, is an undergraduate textbook that blends computational and statistical reasoning to address real-world questions with data. Rather than treating coding syntax or case studies in isolation, the volume shows how statistical programming in R and RStudio extracts meaningful information from diverse datasets.
Coverage spans foundational data visualization, tidy data wrangling, statistical modeling, machine learning, and advanced applications such as SQL databases and geospatial analysis. The book also dedicates a chapter to data science ethics and reproducibility.
The 2021 edition revised and styled all R code around the tidyverse ecosystem to improve readability and comprehension, supporting student practice with extended examples, exercises, and supplementary exercises.
Table of Contents
Chapter 1: Prologue: Why data science?
- • What is data science?
- • Case study: The evolution of sabermetrics
- • Datasets
- • Further resources
Chapter 2: Data visualization
- • The federal election cycle
- • Composing data graphics
- • Importance of data graphics: Challenger
- • Creating effective presentations
- • The wider world of data visualization
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 3: A grammar for graphics
- • A grammar for data graphics
- • Canonical data graphics in R
- • Extended example: Historical baby names
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 4: Data wrangling on one table
- • A grammar for data wrangling
- • Extended example: Ben’s time with the Mets
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 5: Data wrangling on multiple tables
- • inner_join()
- • left_join()
- • Extended example: Manny Ramirez
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 6: Tidy data
- • Tidy data
- • Reshaping data
- • Naming conventions
- • Data intake
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 7: Iteration
- • Vectorized operations
- • Using across() with dplyr functions
- • The map() family of functions
- • Iterating over a one-dimensional vector
- • Iteration over subgroups
- • Simulation
- • Extended example: Factors associated with BMI
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 8: Data Science Ethics
- • Introduction
- • Truthful falsehoods
- • Role of data science in society
- • Some settings for professional ethics
- • Some principles to guide ethical action
- • Algorithmic bias
- • Data and disclosure
- • Reproducibility
- • Ethics, collectively
- • Professional guidelines for ethical conduct
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 9: Statistical foundations
- • Samples and populations
- • Sample statistics
- • The bootstrap
- • Outliers
- • Statistical models: Explaining variation
- • Confounding and accounting for other factors
- • The perils of p-values
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 10: Predictive modeling
- • Predictive modeling
- • Simple classification models
- • Evaluating models
- • Extended example: Who has diabetes?
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 11: Supervised learning
- • Non-regression classifiers
- • Parameter tuning
- • Example: Evaluation of income models redux
- • Extended example: Who has diabetes this time?
- • Regularization
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 12: Unsupervised learning
- • Clustering
- • Dimension reduction
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 13: Simulation
- • Reasoning in reverse
- • Extended example: Grouping cancers
- • Randomizing functions
- • Simulating variability
- • Random networks
- • Key principles of simulation
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 14: Dynamic and customized data graphics
- • Rich Web content using Djs and htmlwidgets
- • Animation
- • Flexdashboard
- • Interactive Web apps with Shiny
- • Customization of library(ggplot)ggplot graphics
- • Extended example: Hot dog eating
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 15: Database querying using SQL
- • From dplyr to SQL
- • Flat-file databases
- • The SQL universe
- • The SQL data manipulation language
- • Extended example: FiveThirtyEight flights
- • SQL vs R
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 16: Database administration
- • Constructing efficient SQL databases
- • Changing SQL data
- • Extended example: Building a database
- • Scalability
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 17: Working with geospatial data
- • Motivation: What’s so great about geospatial data?
- • Spatial data structures
- • Making maps
- • Extended example: Congressional districts
- • Effective maps: How (not) to lie
- • Projecting polygons
- • Playing well with others
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 18: Geospatial computations
- • Geospatial operations
- • Geospatial aggregation
- • Geospatial joins
- • Extended example: Trail elevations at MacLeish
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 19: Text as data
- • Regular expressions using Macbeth
- • Extended example: Analyzing textual data from arXivorg
- • Ingesting text
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 20: Network science
- • Introduction to network science
- • Extended example: Six degrees of Kristen Stewart
- • PageRank
- • Extended example: men’s college basketball
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter 21: Epilogue: Towards "big data"
- • Notions of big data
- • Tools for bigger data
- • Alternatives to R
- • Closing thoughts
- • Further resources
Chapter A: Packages used in this book
- • The mdsr package
- • Other packages
- • Further resources
Chapter B: Introduction to R and RStudio
- • Installation
- • Learning R
- • Fundamental structures and objects
- • Add-ons: Packages
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter C: Algorithmic thinking
- • Introduction
- • Simple example
- • Extended example: Law of large numbers
- • Non-standard evaluation
- • Debugging and defensive coding
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter D: Reproducible analysis and workflow
- • Scriptable statistical computing
- • Reproducible analysis with R Markdown
- • Projects and version control
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter E: Regression modeling
- • Multiple regression
- • Inference for regression
- • Assumptions underlying regression
- • Logistic regression
- • Further resources
- • Exercises
- • Supplementary exercises
Chapter F: Setting up a database server
- • SQLite
- • MySQL
- • PostgreSQL
- • Connecting to SQL
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