R for Data Science Import, Tidy, Transform, Visualize, and Model Data下载

weixin_39821746 2020-06-22 05:00:26

R for Data Science Import, Tidy, Transform, Visualize, and Model Data
这本是ggplot2作者的作品，原版电子书，带书签目录，超高清，神书。鉴定完毕
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学习Tidyverse最好地书籍资源，Rstudio公司推荐。 This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist lear

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data ISBN: 1491910399 | 2017 | True PDF | 522 pages | 32 MB 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.

R for Data Science Import, Tidy, Transform, Visualize, and Model Data 这本是ggplot2作者的作品，原版电子书，带书签目录，超高清，神书。鉴定完毕

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, and results

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

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