R for Everyone: Advanced Analytics and Graphics
R for Everyone: Advanced Analytics and Graphics
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R 适合所有人：高级分析和图形
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发表时间：
2013
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通讯作者：
Jared P. Lander
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作者：
Jared P. Lander
程序员、科学家、定量分析师、Excel用户和其他专业人员的统计计算使用开源R语言，您可以构建强大的统计模型来回答许多最具挑战性的问题。传统上，R对于非统计学家来说是很难学习的，而且大多数R书籍都假定了太多的知识而没有帮助。R代表每个人是解决方案。专业数据科学家Jared P. Lander凭借其教授新用户的无与伦比的经验，为任何新接触统计编程和建模的人编写了完美的教程。为了使学习变得简单和直观，本指南侧重于完成80%的现代数据任务所需的20%的R功能。兰德斯独立的章节从绝对的基础开始，提供广泛的实践和示例代码。下载并安装R；导航和使用R环境；掌握基本的程序控制、数据导入、操作；并通过几个基本测试。然后，在此基础上，您将构建几个完整的模型，包括线性和非线性模型，并使用一些数据挖掘技术。当你完成的时候，你不仅知道如何编写R程序，你还准备好处理你最关心的统计问题。涵盖范围包括探索R， RStudio和R软件包使用R进行数学：变量类型，向量，调用函数等利用数据结构，包括数据框架，矩阵和列表创建有吸引力的，直观的统计图形编写用户定义的函数使用if， ifelse和复杂的检查控制程序流程通过组操作提高程序效率组合和重塑多个数据集使用Rs工具和正则表达式操作字符串创建正常，二项，和泊松概率分布；均值，标准差和t检验建立线性，广义线性和非线性模型评估模型质量和变量选择使用Elastic Net和Bayesian方法防止过拟合分析单变量和多变量时间序列数据通过K-means和分层聚类对数据进行分组使用knitr编写报告，幻灯片和网页使用devtools和Rcpp构建可重用的R包参与R全球社区
Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals 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 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 youll need to accomplish 80 percent of modern data tasks. Landers self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. Youll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, youll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time youre done, you wont just know how to write R programs, youll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES Exploring R, RStudio, and R packages Using R for math: variable types, vectors, calling functions, and more Exploiting data structures, including data.frames, matrices, and lists Creating attractive, intuitive statistical graphics Writing user-defined functions Controlling program flow with if, ifelse, and complex checks Improving program efficiency with group manipulations Combining and reshaping multiple datasets Manipulating strings using Rs facilities and regular expressions Creating normal, binomial, and Poisson probability distributions Programming basic statistics: mean, standard deviation, and t-tests Building linear, generalized linear, and nonlinear models Assessing the quality of models and variable selection Preventing overfitting, using the Elastic Net and Bayesian methods Analyzing univariate and multivariate time series data Grouping data via K-means and hierarchical clustering Preparing reports, slideshows, and web pages with knitr Building reusable R packages with devtools and Rcpp Getting involved with the R global community