What exactly is data science? With this book, you’ll gain a clear understanding of this discipline for discovering natural laws in the structure of data. Along the way, you’ll learn how to use the versatile R programming language for data analysis.
Whenever you measure the same thing twice, you get two results—as long as you measure precisely enough. This phenomenon creates uncertainty and opportunity. Author Garrett Grolemund, Master Instructor at RStudio, shows you how data science can help you work with the uncertainty and capture the opportunities. You’ll learn about:
Data Wrangling—how to manipulate datasets to reveal new information
Data Visualization—how to create graphs and other visualizations
Exploratory Data Analysis—how to find evidence of relationships in your measurements
Modelling—how to derive insights and predictions from your data
Inference—how to avoid being fooled by data analyses that cannot provide foolproof results
Through the course of the book, you’ll also learn about the statistical worldview, a way of seeing the world that permits understanding in the face of uncertainty, and simplicity in the face of complexity.
Table of Contents
Part I. Explore
Chapter 1. Data Visualization with ggplot2
Chapter 2. Workflow: Basics
Chapter 3. Data Transformation with dplyr
Chapter 4. Workflow: Scripts
Chapter 5. Exploratory Data Analysis
Chapter 6. Workflow: Projects
Part II. Wrangle
Chapter 7. Tibbles with tibble
Chapter 8. Data Import with readr
Chapter 9. Tidy Data with tidyr
Chapter 10. Relational Data with dplyr
Chapter 11. Strings with stringr
Chapter 12. Factors with forcats
Chapter 13. Dates and Times with lubridate
Part III. Program
Chapter 14. Pipes with magrittr
Chapter 15. Functions
Chapter 16. Vectors
Chapter 17. Iteration with purrr
Part IV. Model
Chapter 18. Model Basics with modelr
Chapter 19. Model Building
Chapter 20. Many Models with purrr and broom
Part V. Communicate
Chapter 21. R Markdown
Chapter 22. Graphics for Communication with ggplot2
Chapter 23. R Markdown Formats
Chapter 24. R Markdown Workflow
