R及其库实现了各种各样的统计和图形技术,包括线性和非线性建模、经典统计测试、时间序列分析、分类、聚类等。R很容易通过函数和扩展进行扩展,R社区以其在包方面的积极贡献而闻名。许多R的标准函数都是用R本身编写的,这使得用户很容易遵循所做的算法选择。对于计算密集型任务,可以在运行时链接和调用C、C++和Fortran代码。高级用户可以编写C,C++,[18] Java,[19] NET[20] 或者Python代码来直接操作R对象。[21] R是高度可扩展的,通过使用用户提交的包来实现特定的功能或特定的研究领域。由于其S传统,R拥有比大多数统计计算语言更强大的面向对象编程工具。扩展R也因其词法范围规则而变得容易。[22]
R的另一个优势是静态图形,它可以生成出版质量的图形,包括数学符号。 动态和交互式图形可通过附加软件包获得。[23]
R有Rd,它自己的类似LaTeX的文档格式,用于提供多种格式的在线和硬拷贝的综合文档。[24]
R是一种解释语言;用户通常通过命令行解释器来访问它。如果用户在R命令提示符下键入 2+2
,并按enter键,计算机以4进行响应,如下所示:
> 2 + 2
[1] 4
该计算被解释为两个单元素向量的和,从而得到单元素向量。前缀 [1]
表示在同一行上跟随它的元素列表从向量的 第一 个元素开始(当输出延伸到多行时,这是一个有用的特性)。
与其他类似的语言如APL和MATLAB一样,R支持矩阵运算。R的数据结构包括向量、矩阵、数组、数据帧(类似于关系数据库中的表)和列表。[25] 数组按列主顺序存储。[26] R的可扩展对象系统包括回归模型、时间序列和地理空间坐标等。标量数据类型不是R的一种数据结构类型。[27] 一个标量表示为长度为1的向量。[28]
R的许多特性都来源于Scheme。R使用S表达式来表示数据和代码。函数是一流的,可以像数据对象一样进行操作,便于元编程,并允许多次调用。R中的变量是词汇范围的,并且是动态类型的。函数参数是通过值传递的,并且是惰性的——也就是说,它们只有在使用时才进行计算,而不是在调用函数时。
R支持使用函数的过程编程,对于某些函数,还支持带有泛型函数的面向对象编程。泛型函数的作用根据传递给它的参数类别的不同而不同。换句话说,泛型函数分派特定于该类对象的函数(方法)。例如,R有一个泛型 print
函数,它可以用一个简单的 print(objectname)
语法在R中输出几乎所有的对象类。[29]
虽然主要由统计学家和其他需要统计计算和软件开发环境的从业者使用,但R也可以作为通用矩阵计算工具箱运行——性能基准可与GNU Octave或MATLAB相媲美。[30]
R的功能通过用户创建的包来扩展,这些给出专门的统计技术、图形设备、导入/导出功能、报告工具(knitr、Sweave)等。这些包主要是用R开发的,有时用Java、C、C++和Fortran开发。 研究人员还使用R包系统创建概要,以系统的方式组织研究数据、代码和报告文件,以便共享和公共存档。[31]
R的安装包括一组核心软件包,还有15,000多个附加软件包(截至2018年9月)可从R综合存档网络(CRAN),[32] Bioconductor,Omegahat,[33] GitHub和其他存储库中获得。[34]
CRAN网站上的“任务视图”页面(主题列表)[35] 列出了一系列应用了R的任务(在金融、遗传学、高性能计算、机器学习、医学成像、社会科学和空间统计等领域)以及相应的R包。美国食品和药物管理局也认为r适合解释来自临床研究的数据。[36]
其他R包资源包括Crantastic,[37] 一个对所有CRAN包进行评级和审核的社区网站,[38]以及R- forge,一个用于R包、R相关软件和项目的协作开发的中心平台。R-Forge还托管许多未发布的测试包和CRAN包的开发版本。
Bioconductor项目为基因组数据的分析提供了R包。这包括面向对象的数据处理和分析工具,用于来自Affymetrix、基因芯片和下一代高通量测序方法的数据。[39]
在CRAN的各种“新闻”文件中维护了一个R版本的变更列表。[40] 下面列出了几个主要版本的一些亮点。
发行 | 日期 | 描述 |
---|---|---|
0.16 | 这是主要由Ihaka和Gentleman开发的最后一个alpha版本。 “白皮书”(见S历史)中的许多基本功能都已实现。邮寄名单从1997年4月1日开始。 | |
0.49 | 1997-04-23 | 这是目前在CRAN上可用的最早的源版本。[41] CRAN 开始于这个日期,有3个镜像,最初承载12个包。[42] 适用于Microsoft Windows和经典Mac OS的alpha版本的R在本版本之后不久发布。 |
0.60 | 1997-12-05 | R成为GNU项目的正式部分。代码在CVS上托管和维护。 |
0.65.1 | 1999-10-07 | 更新包和安装包函数的第一个版本,用于从CRAN下载和安装软件包。[43] |
1.0 | 2000-02-29 | 开发人员认为它足够稳定,可以用于生产使用。[44] |
1.4 | 2001-12-19 | 引入了S4方法,不久之后Mac OS X的第一个版本就可用了。 |
1.8 | 2003-10-08 | 介绍了一种灵活的状态处理机制,用于对状态对象进行信令和处理。 |
2.0 | 2004-10-04 | 引入了延迟加载,它支持以最小的系统内存开销快速加载数据。 |
2.1 | 2005-04-18 | 支持UTF-8编码,并开始为不同的语言国际化和本地化。 |
2.11 | 2010-04-22 | 支持Windows 64位系统。 |
2.13 | 2011-04-14 | 添加了一个新的编译器函数,它允许通过将函数转换为字节码来加快函数的速度。 |
2.14 | 2011-10-31 | 为包添加了强制命名空间。添加了一个新的并行包。 |
2.15 | 2012-03-30 | 新的负载平衡功能。改进了长向量的序列化速度。 |
3.0 | 2013-04-03 | 在64位系统上支持数字索引值为231或更大。 |
3.4 | 2017-04-21 | 函数和循环到字节码的即时编译(JIT)在默认启用。 |
3.5 | 2018-04-23 | 默认包在安装时进行字节编译。整数序列的紧凑内部表示。添加了新的序列化格式以支持紧凑的内部表示。 |
R最专业的集成开发环境是RStudio。[45] 类似的开发界面是Visual Studio的R Tools。一些泛型的IDEs,比如Eclipse,[46] 还提供了与R一起工作的特性。
图形用户界面更多的是点击式方法,包括Rattle GUI, R Commander, and RKWard。
一些更常见的支持不同级别的R编辑器包括 Emacs (Emacs讲统计),Vim (Nvim-R插件[47]),Neovim (Nvim-R插件[47]),Kate,[48] LyX,[49] Notepad++,[50] Visual Studio Code,WinEdt,[51] 和Tinn-R[52]
R功能可以从几种脚本语言中访问,Python,[53] Perl,[54] Ruby,[55] F#,[56] Julia。[57] 与其他高级编程语言的接口如Java[58], .NET和 C#[59][60] 也是可用的。
主要的R实现是用R、C和Fortran编写的,还有其他几个旨在提高速度或增加可扩展性的实现。一个密切相关的实现是拉德福德·尼尔的pqR(相当快的R),它改进了内存管理并支持自动多线程。Renjin和FastR是在Java虚拟机中使用的R的Java实现。 CXXR、rho和Riposte[61] 是R在C++中的实现。 Renjin, Riposte,和pqR试图通过使用多个处理器内核和某种形式的延迟评估来提高性能。[62] 与R研发核心团队维护的主要实现相比,这些替代实现大多是实验性的和不完整的,用户相对较少。
TIBCO构建了一个名为TERR的运行时引擎,它是Spotfire的一部分。[63]
Microsoft R Open是一个完全兼容的R发行版,对多线程计算进行了修改。[64]
R用户的官方年度会议被称为“useR!”。[69]第一次会议是useR!2004,2004年5月在奥地利维也纳举行。[70] 跳过2005年后,useR!会议每年举行一次,通常在欧洲和北美轮流举行。[71]随后的会议包括:[69]
计划的未来会议如下:[69]
《R Journal》 是统计计算中R项目的开放获取和评审期刊。它的特点是有关于R使用和研发的短到中等长度的文章,包括软件包,编程技巧,CRAN新闻和基础新闻。
虽然R是一个开源项目,由开发它的社区支持,但是一些公司努力为他们的客户提供商业支持和/或扩展。本节给出了一些此类公司的例子。
在2007年,理查德·舒尔茨、马丁·舒尔茨、史蒂夫·韦斯顿和柯克·梅特勒创立了分析力革命(Revolution Analytics)公司为RevolutionR提供商业支持,它是R的分销渠道,其中也包括公司开发的组件。主要的附加组件包括:并行化R(ParallelR)、R生产力环境集成开发环境(R Productivity Environment IDE)、可伸缩性(RevoScaleR)(用于大数据分析)、可调用性(RevoDeployR)、网络服务框架(web services framework)以及以SAS文件格式读写数据的能力。[75]分析力革命(Revolution Analytics)公司还提供了一个设计为符合既定 IQ/OQ/PQ标准的R发行版,使制药行业的客户能够验证他们安装的REvolution R[76] 。在2015年,微软公司完成了对分析力革命(Revolution Analytics)公司的收购。[77] 并已将R编程语言集成到SQL Server 2016、SQL Server 2017、Power BI、Azure SQL数据库、Azure Cortana Intelligence、Microsoft R Server和Visual Studio 2017中。[78]
在2011年10月,甲骨文( Oracle )发布了 大数据设备,它集成了R、Apache Hadoop、Oracle Linux和有 Exadata硬件的NoSQL数据库。[79] 截至2012年,甲骨文R公司[80] 成为“甲骨文高级分析选项”的两个组成部分之一[81] (与甲骨文数据挖掘一起)。
IBM公司提供了R在Hadoop内执行的支持,[82] 并为R中的大规模并行数据库分析提供了编程模型[83]。
其他支持与R连接或集成的主要商业软件系统包括:JMP,[84] Mathematica,[85] MATLAB,[86] Microsoft Power BI,[87] Pentaho,[88]Spotfire,[89] SPSS,[90] Statistica,[91] Platform Symphony,[92] SAS,[93] Tableau Software,[94] Esri ArcGIS,[95] Dundas[96] 和Statgraphics。[97]
Tibco提供了R的运行时版本作为Spotfire的一部分。[98]
Mango为R, ValidR提供了一个验证包,[99][100] 使其符合药品审批机构的要求,如美国食品和药物管理局(FDA)。这些机构允许在提交时使用任何统计软件,只要软件经过供应商或赞助商本身的验证。[101]
以下示例说明了该语言的基本语法和命令行界面的使用方法。
在R中,通常首选[102] 的赋值运算符是由两个字符组成的箭头 <-
,不过通常可以使用 =
代替[103]。
> x <- 1:6 # Create vector.
> y <- x^2 # Create vector by formula.
> print(y) # Print the vector’s contents.
[1] 1 4 9 16 25 36
> mean(y) # Arithmetic mean of vector.
[1] 15.16667
> var(y) # Sample variance of vector.
[1] 178.9667
> model <- lm(y ~ x) # Linear regression model y = A + B * x.
> print(model) # Print the model’s results.
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
-9.333 7.000
> summary(model) # Display an in-depth summary of the model.
Call:
lm(formula = y ~ x)
Residuals:
1 2 3 4 5 6
3.3333 -0.6667 -2.6667 -2.6667 -0.6667 3.3333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.3333 2.8441 -3.282 0.030453 *
x 7.0000 0.7303 9.585 0.000662 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared: 0.9583, Adjusted R-squared: 0.9478
F-statistic: 91.88 on 1 and 4 DF, p-value: 0.000662
> par(mfrow = c(2, 2)) # Create a 2 by 2 layout for figures.
> plot(model) # Output diagnostic plots of the model.
R的优势之一是易于创建新函数。函数体中的对象仍然是函数的本地对象,并且可以返回任何数据类型。[104]下面是一个用户创建的函数示例:
# Declare function “f” with parameters “x”, “y“
# that returns a linear combination of x and y.
f <- function(x, y) {
z <- 3 * x + 4 * y
return(z)
}
> f(1, 2)
[1] 11
> f(c(1,2,3), c(5,3,4))
[1] 23 18 25
> f(1:3, 4)
[1] 19 22 25
通过方程z = z2 + c 的前20次迭代计算曼德勃罗集的短R代码 ,对不同的复常数 c 绘制图形。该示例演示了:
C
, Z
和 X
。install.packages("caTools") # install external package
library(caTools) # external package providing write.gif function
jet.colors <- colorRampPalette(c("red", "blue", "#007FFF", "cyan", "#7FFF7F",
"yellow", "#FF7F00", "red", "#7F0000"))
dx <- 1500 # define width
dy <- 1400 # define height
C <- complex(real = rep(seq(-2.2, 1.0, length.out = dx), each = dy),
imag = rep(seq(-1.2, 1.2, length.out = dy), dx))
C <- matrix(C, dy, dx) # reshape as square matrix of complex numbers
Z <- 0 # initialize Z to zero
X <- array(0, c(dy, dx, 20)) # initialize output 3D array
for (k in 1:20) { # loop with 20 iterations
Z <- Z^2 + C # the central difference equation
X[, , k] <- exp(-abs(Z)) # capture results
}
write.gif(X, "Mandelbrot.gif", col = jet.colors, delay = 100)
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