R for Data Science Visualize Model Transform Tidy and Import Data.pdf

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 c apture 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 apture 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