Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution.

Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks.

Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you'll make your code reproducible with LaTeX, RMarkdown, and Shiny.

By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most.

Coverage includes:

- Explore R, RStudio, and R packages
- Use R for math: variable types, vectors, calling functions, and more
- Exploit data structures, including data.frames, matrices, and lists
- Read many different types of data
- Create attractive, intuitive statistical graphics
- Write user-defined functions
- Control program flow with if, ifelse, and complex checks
- Improve program efficiency with group manipulations
- Combine and reshape multiple datasets
- Manipulate strings using R™s facilities and regular expressions
- Create normal, binomial, and Poisson probability distributions
- Build linear, generalized linear, and nonlinear models
- Program basic statistics: mean, standard deviation, and t-tests
- Train machine learning models
- Assess the quality of models and variable selection
- Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods
- Analyze univariate and multivariate time series data
- Group data via K-means and hierarchical clustering
- Prepare reports, slideshows, and web pages with knitr
- Display interactive data with RMarkdown and htmlwidgets
- Implement dashboards with Shiny

- Build reusable R packages with devtools and Rcpp

使用开源的R语言，你可以建立强大的统计模型来回答许多最具挑战性的问题。传统上，R语言对于非统计学家来说是很难学习的，而且大多数R语言书籍都假设了太多的知识，因此无法提供帮助。R for Everyone，第二版，是一个解决方案。

专业数据科学家Jared P. Lander凭借其无与伦比的新用户教学经验，为任何初学统计编程和建模的人编写了完美的教程。本指南的组织方式使学习变得简单而直观，它侧重于你完成80%的现代数据任务所需的20%的R功能。

Lander的独立章节从绝对的基础知识开始，提供了大量的动手练习和示例代码。你将下载和安装R；浏览和使用R环境；掌握基本的程序控制、数据导入、操作和可视化；并通过几个基本测试。然后，在此基础上，你将构建几个完整的模型，包括线性和非线性的，并使用一些数据挖掘技术。在这一切之后，你将用LaTeX、Rmarkdown和Shiny使你的代码具有可重复性。

当你完成后，你将不仅仅知道如何编写R程序，你将准备好解决你最关心的统计问题。

覆盖范围包括。

- 探索R、RStudio和R软件包

- 使用R进行数学运算：变量类型、向量、调用函数等

- 利用数据结构，包括数据框架、矩阵和列表

- 读取许多不同类型的数据

- 创建有吸引力的、直观的统计图形

- 编写用户定义的函数

- 用if、ifelse和复杂的检查来控制程序流程

- 通过分组操作提高程序效率

- 合并和重塑多个数据集

- 使用R™的设施和正则表达式来处理字符串

- 创建正态、二项和泊松概率分布

- 建立线性、广义线性和非线性模型

- 为基本统计学编程：平均值、标准差和t检验

- 训练机器学习模型

- 评估模型的质量和变量选择

- 使用弹性网和贝叶斯方法，防止过度拟合并进行变量选择

- 分析单变量和多变量的时间序列数据

- 通过K-means和分层聚类对数据进行分组

- 用knitr准备报告、幻灯片和网页

- 用RMarkdown和htmlwidgets显示交互式数据

- 用Shiny实现仪表盘

- 用devtools和Rcpp构建可重用的R包