零基础掌握 R 语言

Transcription

1 零基础掌握 R 语言 pan junchang 2015 年 8 月 10 日 R 语言简介 R 初步印象 R 是由新西兰奥克兰大学的 Ross Ihaka 与 Robert Gentleman 共同开发的一个面向对象的编程语言, 因为两人的名字都是以 R 开头, 故命名 R 语言 R 语言的前身是 S 语言 ( 由 AT&T Bell 实验室的 Rick Becker, John Chambers,Allan Wilks 开发 ) 过去一直是数据分析领域里面的标准语言, 但是现在正在逐步被 R 语言替代.R 语言可以按照使用者的要求完成各种计算问题, 定义函数 绘制简单或复杂的图形, 甚至可以写自己的工具包 可以毫不夸张的说 它可以完成任何你能想到的计算问题 下面的这段代码可以画出一个漂亮的正太分布图. curve(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, main = "Den sity of the standard normal distribution") text(-5, 0.3, expression(f(x) == frac(1, sigma ~~sqrt(2*pi)) ~~ e^{-frac((x - mu)^2, 2*sigma^ 2)}), adj = 0)

2 甚至可以绘制现代艺术图 par(mar = c(0, 0, 0, 0)) n = 76 set.seed(711) plot.new() size = c(replicate(n, 1/rbeta(2, 1.5, 4))) center = t(replicate(n, runif(2))) center = center[rep(1:n, each = 2), ] color = apply(replicate(2 * n, sample(c(0:9, LETTERS[1:6]), 8, replace = TRUE)), 2, function(x) sprintf("#%s", paste(x,collapse = ""))) points(center, cex = size, pch = rep(20:21, n), col = color)

3 代码来源 : 谢益辉 再看一个例子 数据 基金业绩排名 中是 581 家基金 年收入统计表, 现在希望从中挑选出连续 3 年排名前 25 名的基金 library(xlconnect) Loading required package: XLConnectJars XLConnect by Mirai Solutions GmbH [aut], Martin Studer [cre], The Apache Software Foundation [ctb, cph] (Apache POI, Apache Comm ons Codec), Stephen Colebourne [ctb, cph] (Joda-Time Java library) library(dplyr) Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base':

4 intersect, setdiff, setequal, union mdt<-readworksheetfromfile("mydata/ 基金业绩排名.xlsx", sheet = "Wind 资讯 ",header=f,startrow=2,endcol=5) names(mdt)<-c("code"," 名称 ","y2014","y2013","y2012") tmp<-na.omit(mdt) tmp<-tbl_df(tmp) tmp%>%select(code, 名称 )->name k<-25 tmp%>%select(code,y2014)%>%arrange(desc(y2014))%>%head(.,k)->tmp1 tmp%>%select(code,y2013)%>%arrange(desc(y2013))%>%head(.,k)->tmp2 tmp%>%select(code,y2012)%>%arrange(desc(y2012))%>%head(.,k)->tmp3 inner_join(name,tmp1,by="code")%>%inner_join(.,tmp2,by="code")%>%inner_ join(., tmp3,by=" code") Source: local data frame [3 x 5] code 名称 y2014 y2013 y2012 (chr) (chr) (dbl) (dbl) (dbl) OF 兴全全球视野 OF 华安上证 180ETF OF 光大核心 目前由 R 核心开发小组 (R Development Core Team 以后用 R DCT 表示 ) 维护, 他们完全自愿 工作努力负责, 并将全球优秀的统计应用软件打包提供给我们 我们可以通过 R 计划的网站 ( 了解有关 R 的最新信息和使用说明, 得到最新版本的 R 软件和基于 R 的应用统计软件包. R 语言的特点与优势 R 软件是一个完全免费的统计软件. 一个开源软件项目, 得到了庞大用户社区的广泛采取. 站在巨人的肩膀上前进. R 是一个强大的脚本语言, 语言简练高效. R 能够很好的与 LaTex 文档发布系统整合在一起, 这意味着来自于 R 的统计输出和图形可以嵌入到可出版级的文档中. R 安装与工作环境 安装 单击下面的网址, 选择适合自己电脑系统的版本进行安装

5 为了提高易用性, 推荐安装 Rstudio, 打开下面的网址, 点击 IDE, 下载安装 RStudio Desktop. ( 工作环境 工作空间 (workspace) 就是当前的 R 工作环境, 存储用户定义的所有对象 函数 向量 变量 矩阵 数据框等等都是对象 当你保存 ( 工作空间 ) 时, 所有的对象都将被保存 1. 打开 R 主程序 控制台 (Console) 尝试输入如下代码 : demo(graphics) R 编辑器 点击菜单 文件 -- 新建程序脚本, 将上述代码拷贝到新建脚本文件中, 点击菜单 运行 2. 打开 Rstudio 脚本窗口 Console 窗口, 执行命令的地方 作图 帮助以及扩展包管理窗口 在 Consle 窗口中输入 : demo(graphics) 回车 在脚本窗口输入 : demo(graphics) 点击 run 在脚本窗口输入 : x<- 1:10 data(iris) 点击 run 3. 工作目录 R 读取文件和设置文件的默认目录 函数 功能

6 getwd() setwd() ls() save() load() rm() rm(list=ls()) q() 返回当前工作目录设置默认工作目录列出当前工作空间中的对象保存工作空间加载工作空间删除内存中的对象删除内存中所有的对象退出 注意 : 括号内路径需要用半角引号 4. 扩展包的安装与加载 安装 (a) 方法一, 在 Rstudio 右边中间位置点选 "Package", 点击 Install 就可以安装了 (b) 方法二, 直接在输入 "install.packages('packagename')" 来完成, 在安装过程中如果需要其它扩展报支持,R 会自动安装, 因此, 安装扩展包时, 最好连接网络 加载 load("packagename") 5. 帮助系统 help.start() help(matrix)?matrix??matrix example(sum) 练习 (1) 安装并加载 quantmod 包 ; (2) 安装并加载 xts 和 xtsextra 包

7 R 语言数据类型与识别 R 语言数据类型 Vectors A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Nevertheless, we will just call them members in this site. v1<-c(2,3,4)#creat a vector"v1" containing three numeric values 2, 3 an d 5. v2<-c("true","true","false")#creat a vector"v2" contain character strin gs. v3<-c("aa", "bb", "cc", "dd", "ee") #Creat a vector"v3" contain charact er strings. Matrices All columns in a matrix must have the same mode (numeric, character, etc.) and the same length. we usually creat a matices like this mymatrix<-matix(vector,nrow=r,ncol=c,byrow=false,dimnames=list( char_vector_rownames, char_vector_colnames)) byrow=true indicates that the matrix should be filled by rows. byrow=false indicates that the matrix should be filled by columns (the default). dimnames provides optional labels for the columns and rows. For example cells <- c(1,26,24,68) rnames <- c("r1", "R2") cnames <- c("c1", "C2") mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=true,dimnames=list(rnam es, cnames)) mymatrix C1 C2 R R Data Frames A data frame is more general than a matrix, in that different columns can have different modes (numeric, character, factor, etc.). One example of how to create a data frame is given below: a <- c(1,2,3,4) b <- c(2,4,6,8) levels <- factor(c("a","b","a","b"))

8 mydf<-data.frame(first=a,second=b,f=levels) mydf first second f A B A B Factors The factor stores the nominal values as a vector of integers in the range [ 1... k ] (where k is the number of unique values in the nominal variable), and an internal vector of character strings (the original values) mapped to these integers. For example gender <- c(rep("male",20), rep("female", 30)) gender <- factor(gender) This code generate a vector named "gender" with 20 "male" entries and 30 "female" entries gender [1] male male male male male male male male male male [11] male male male male male male male male male male [21] female female female female female female female female female female [31] female female female female female female female female female female [41] female female female female female female female female female female Levels: female male summary(gender) female male Numeric 数值型数据, 是实数 可以写成整数 (Integers), 小数 (Decimal Fractions), 或者科学技术 (Scientific Notation) Character 字符型数据

9 Dates 日期型数据 日期型数据统称以字符串的形式输入到 R 中, 然后通过 as.date(x,format) 函数进行转化为以数值形式存储的日期变量 符号 含义 %d 数字表示的日期 %m 月份 %y 年份 example dates <- c("02/27/92", "02/27/92", "01/14/92", "02/28/92", "02/01/92") dates<-as.date(dates, "%m/%d/%y") weekdays(dates) [1] " 星期四 " " 星期四 " " 星期二 " " 星期五 " " 星期六 " Sys.Date()-dates Time differences in days [1] list 列表变量 列表变量的元素可以是任何类型的数据 R 语言常见数据类型的识别和转化 查看数据类型 mode(x) class(x) 数据类型识别与转换 类型识别转化 character is.character() as.character() integer is.integer() as.integer() logical is.logical() as.logical() NA is.na() as.na() numeric is.numeric() as.numeric() 数据输入与输出数据 R 语言读取 txt 格式数据 数据输入

10 read.table(file = "filename.txt",header = TRUE,sep="",fill=T) example read.table(file="mydata/file1.txt") read.table(file="mydata/file1.txt",header=t) read.table(file="mydata/file2.txt",header=t,sep=",") read.table(file="mydata/file3.txt",header=t,sep=" ") read.table(file="mydata/file3.txt",header=t,sep=" ",fill=t) 同学可以思考下, 同样的数据为什么第 4 句读不出来, 而第 5 句能读出来? 数据输出 write.table(data,file='filename',col.names=f,sep='',quote = FALSE, append = FALSE, na = "NA") write.table(file1,file="mydata/file3.txt",col.names=f,sep=" ",quote=f) example data(iris) write.table(iris,file="mydata/file4.txt",row.names=f,sep=" ",quote=f) R 语言 csv 格式数据 数据输入 read.csv("filename.csv") example read.csv("mydata/file6.csv") x<-read.csv("mydata/file6.csv") 数据输出 write.csv(x, file = "foo.csv",row.names = FALSE) R 语言读取 excel 格式数据 数据输入 library(xlconnect) readworksheetfromfile("filename.xls", sheet = "sheet1",startrow = NULL, endrow = NULL, startcol = NULL, endcol = NULL) 请同学们修改命令中的参数, 练习读入 基金业绩排名.xlsx 数据输出 writeworksheettofile("mydata/file5.xlsx", iris, sheet = "sheet1",header =T)

11 R 语言读取 yahoo 网站证券交易数据 quantmod 包默认是访问 yahoo finance 的数据, 其中包括上证和深证的股票数据, 还有港股数据 上证代码是 ss, 深证代码是 sz, 港股代码是 hk library(quantmod) Loading required package: xts Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.date, as.date.numeric Attaching package: 'xts' The following objects are masked from 'package:dplyr': first, last Loading required package: TTR Version included new data defaults. See?getSymbols. setsymbollookup(wk=list(name=' sz',src='yahoo')) getsymbols("wk",from = " ") As of 0.4-0, 'getsymbols' uses env=parent.frame() and auto.assign=true by default. This behavior will be phased out in when the call will default to use auto.assign=false. getoption("getsymbols.env") and getoptions("getsymbols.auto.assign") are now checked for alternate defaults This message is shown once per session and may be disabled by setti ng options("getsymbols.warning4.0"=false). See?getSymbols for more de tails. Warning in download.file(paste(yahoo.url, "s=", Symbols.name, "&a=", from.m, : downloaded length 5261!= reported length 200 [1] "WK" chartseries(wk,theme='white')

12 addsma(n=10)

13 R 语言获取 wind 数据 library('windr') w.start() w.menu() w.wsd(" sz","sec_name,ipo_date,exch_city,open,high,low,close,volu me, dealnum"," "," ","fill=previous;priceadj=b") w.wss(' sz','sec_name,ipo_date,exch_city,mkt') 获取多只股票 ipo 日期和地点 code<-read.table(file="mydata/stockid.txt",header=f,sep=" ",fill=t) head(code) mdt<-data.frame(id=1, numobs=1)[10,5 ] for(i in 1:10){ tmp<-w.wss(code[i,2],'sec_name,ipo_date,exch_city,mkt')$data mdt<-rbind(mdt,tmp) } mdt transform(mdt,ipo_date=w.asdatetime(mdt$ipo_date,asdate=t)) 结构化网络数据抓取 library(xml) url<-" tables<-readhtmltable(url,stringsasfactors=f,header=t) tables[[6]] 数据整理了解数据常用 R 语言函数 : 函数 功能 head(x, n = 6L,...) 产看前 6 个数据 tail(x, n = 6L,...) 产看后 6 个数据 length(x) 产看一维向量元素的个数 dim(x) 产看二维向量维度 ncol(x) 返回二维向量 x 的列数 nrow(x) 返回二维向量 x 的行数 str(x) 返回对象 x 的结构 summary(x) 描述统计 sample() 随机抽样

14 Examples data(iris) head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa tail(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species virginica virginica virginica virginica virginica virginica length(iris) [1] 5 dim(iris) [1] ncol(iris) [1] 5 nrow(iris) [1] 150 str(iris) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num $ Sepal.Width : num $ Petal.Length: num $ Petal.Width : num $ Species : Factor w/ 3 levels "setosa","versicolor",..: summary(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Min. :4.300 Min. :2.000 Min. :1.000 Min. : st Qu.: st Qu.: st Qu.: st Qu.:0.300 Median :5.800 Median :3.000 Median :4.350 Median :1.300

15 Mean :5.843 Mean :3.057 Mean :3.758 Mean : rd Qu.: rd Qu.: rd Qu.: rd Qu.:1.800 Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 Species setosa :50 versicolor:50 virginica :50 iris[sample(nrow(iris),10),] Sepal.Length Sepal.Width Petal.Length Petal.Width Species virginica versicolor setosa versicolor versicolor virginica setosa setosa virginica virginica sample(x,size) sample(1:100,10) [1] R 语言选取数据子集 向量元素 (vector) x[n] 返回向量 x 中第 n 个元素 ; 例如 : x<-c(8,7,6,5,4,3,2,1) x[2] [1] 7 x[c(2,4)] [1] 7 5 which 返回满足条件的元素所在位置. 例如 x<-c(8,7,6,5,4,3,2,1) which(x>5) [1] x[which(x>5)]

16 [1] 矩阵元素 (matrix) x.matr<-matrix(1:9,nrow=3) x.matr [,1] [,2] [,3] [1,] [2,] [3,] x.matr[2,1] [1] 2 x.matr[2,2] [1] 5 x.matr[2,] [1] x.matr[,2] [1] 列表元素 (list) a1<-"good morning!" a2<-c(1,2,3,4,5) a3<-matrix(1:9,nrow=3) mylist<-list(a1,a2,a3) mylist [[1]] [1] "good morning!" [[2]] [1] [[3]] [,1] [,2] [,3] [1,] [2,] [3,] mylist[[1]] [1] "good morning!" mylist[[2]]

17 [1] mylist[[3]] [,1] [,2] [,3] [1,] [2,] [3,] mylist[[3]][3,2] [1] 6 数据框元素 (data frame) x1<-seq(1,10) x2<-letters[1:10] y<-seq(2,20,by=2) mdt<-data.frame(x1,x2,y) mdt x1 x2 y 1 1 a b c d e f g h i j 20 mdt[,1] [1] mdt[1,] x1 x2 y 1 1 a 2 mdt['x1'] x

18 mdt$x1 [1] subset(mdt,select = c(2,3)) x2 y 1 a 2 2 b 4 3 c 6 4 d 8 5 e 10 6 f 12 7 g 14 8 h 16 9 i j 20 subset(mdt,select=c(2,3),x1>8) x2 y 9 i j 20 R 语言数据合并 函数 c() cbind() rbind() 功能 向量合并 列拼接 行拼接 merge(x,y,by=...,by.x=...,by.y=...) 横向合并两个数据框. Example a<-1:5 a [1] b<-6:10 b [1] c<-11:15 c [1]

19 ab<-c(a,b) ab [1] ab<-data.frame(a,b) ab a b ac<-data.frame(a,c) ac a c abc<-cbind(ab,ac) abc a b a c abc<-merge(ab,ac,by='a') abc a b c R 语言数据排序 mdt[order(mdt$varable,decreasing=t),] Example data("iris") head(iris)

20 Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa iris[order(iris$sepal.length,decreasing = F)[1:10],] Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa R 语言生成数据常用函数 seq(from,to,by/length) 生成等差序列向量 seq(from=1,to=10,by=2) [1] seq(from=1,to=10,length=5) [1] rep(x,times/each) 重复 x 中的元素 time 次或者每个元素 each 次 rep(1:3,times=2) [1] rep(1:3,each=2) [1] 生成零个观察值的数据框 mdt<-data.frame(age=numeric(0),gender=character(0),weight=numeric(0)) mdt [1] age gender weight <0 rows> (or 0-length row.names)

21 R 语言数据编辑 手动编辑 用 edit() 函数可以对数据进行手动编辑 Example a<-head(iris) edit(a) 数值变换 transform() 函数用于变化数据中的对象, 在数据框的数据结构中经常用到此函数 Example data("iris") a<-head(iris) a Sepal.Length Sepal.Width Petal.Length Petal.Width Species setosa setosa setosa setosa setosa setosa a<-transform(a,sepal.length=-sepal.length,sum=sepal.length+sepal.width) a Sepal.Length Sepal.Width Petal.Length Petal.Width Species sum setosa setosa setosa setosa setosa setosa 9.3 修改变量名称 names() 函数返回变量名称 Example names(a) [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" [5] "Species" "sum" names(a)<-letters[1:6] a

22 A B C D E F setosa setosa setosa setosa setosa setosa 9.3 names(a)<-paste("x",1:6,sep="") a x1 x2 x3 x4 x5 x setosa setosa setosa setosa setosa setosa 9.3 删除含有缺失值的行 na.omit() 将会删除数据中有缺失值的观测样本 ( 行 ) Example mt<-read.table(file="mydata/file3.txt",header=t,sep=" ",fill=t) mt X1 name age height 1 2 John Jack Mary DDD NA NA 5 6 Smith mt<-na.omit(mt) mt X1 name age height 1 2 John Jack Mary Smith 将数值型变量根据大小转化成因子变量 cut() 函数将数值变换成因子, 是生产因子的一种常用方法 函数形式为 : cut(x,breaks,include.lowest=f)

23 data(iris) a<-iris[1:50,] cut(a$sepal.length,breaks=4.2,to=5.9,length=5) [1] (5.05,5.42] (4.67,5.05] (4.67,5.05] (4.3,4.67] (4.67,5.05] [6] (5.05,5.42] (4.3,4.67] (4.67,5.05] (4.3,4.67] (4.67,5.05] [11] (5.05,5.42] (4.67,5.05] (4.67,5.05] (4.3,4.67] (5.42,5.8] [16] (5.42,5.8] (5.05,5.42] (5.05,5.42] (5.42,5.8] (5.05,5.42] [21] (5.05,5.42] (5.05,5.42] (4.3,4.67] (5.05,5.42] (4.67,5.05] [26] (4.67,5.05] (4.67,5.05] (5.05,5.42] (5.05,5.42] (4.67,5.05] [31] (4.67,5.05] (5.05,5.42] (5.05,5.42] (5.42,5.8] (4.67,5.05] [36] (4.67,5.05] (5.42,5.8] (4.67,5.05] (4.3,4.67] (5.05,5.42] [41] (4.67,5.05] (4.3,4.67] (4.3,4.67] (4.67,5.05] (5.05,5.42] [46] (4.67,5.05] (5.05,5.42] (4.3,4.67] (5.05,5.42] (4.67,5.05] Levels: (4.3,4.67] (4.67,5.05] (5.05,5.42] (5.42,5.8] b<-cut(a$sepal.length,breaks=4.2,to=5.9,length=5) b [1] (5.05,5.42] (4.67,5.05] (4.67,5.05] (4.3,4.67] (4.67,5.05] [6] (5.05,5.42] (4.3,4.67] (4.67,5.05] (4.3,4.67] (4.67,5.05] [11] (5.05,5.42] (4.67,5.05] (4.67,5.05] (4.3,4.67] (5.42,5.8] [16] (5.42,5.8] (5.05,5.42] (5.05,5.42] (5.42,5.8] (5.05,5.42] [21] (5.05,5.42] (5.05,5.42] (4.3,4.67] (5.05,5.42] (4.67,5.05] [26] (4.67,5.05] (4.67,5.05] (5.05,5.42] (5.05,5.42] (4.67,5.05] [31] (4.67,5.05] (5.05,5.42] (5.05,5.42] (5.42,5.8] (4.67,5.05] [36] (4.67,5.05] (5.42,5.8] (4.67,5.05] (4.3,4.67] (5.05,5.42] [41] (4.67,5.05] (4.3,4.67] (4.3,4.67] (4.67,5.05] (5.05,5.42] [46] (4.67,5.05] (5.05,5.42] (4.3,4.67] (5.05,5.42] (4.67,5.05] Levels: (4.3,4.67] (4.67,5.05] (5.05,5.42] (5.42,5.8] table(b) b (4.3,4.67] (4.67,5.05] (5.05,5.42] (5.42,5.8] b<-factor(b,labels=c(" 小 "," 中 "," 大 "," 特大 ")) table(b) b 小 中 大特大 R 语言常用数学运算 R 语言数值运算 功能 运算符号 加法 +

24 减法 - 乘法 除法 / 相除取整 %/% 乘幂 ^ R 语言逻辑运算 功能 判断是否相等 == 判断是否小于等于 <= 判断是否大于等于 >= 逻辑 与 运算符号 & 逻辑 非!= R 语言常用数学运算 功能 sum(x) cumsum(x) prod(x) cumprod(x) log(x) exp(x) Example1 运算符号 向量 x 之和 向量 x 累积和 向量的积 向量的累积积 计算向量 x 的自然对数 计算自然数的 x 次方 计算万科股票的收益率算术平均值和几何平均值 library(quantmod) setsymbollookup(wk=list(name=' sz',src='yahoo')) getsymbols("wk",from = " ") Warning in download.file(paste(yahoo.url, "s=", Symbols.name, "&a=", from.m, : downloaded length 5261!= reported length 200 [1] "WK" close<-wk[,6] ret<-dailyreturn(wk) n<-length(ret) arithemetic.average<-sum(ret)/n arithemetic.average [1]

25 gemetric.avgerage<-prod(ret+1)^(1/n)-1 gemetric.avgerage [1] Example2 计算累积日回报率 library(quantmod) setsymbollookup(wk=list(name=' sz',src='yahoo')) getsymbols("wk",from = " ") Warning in download.file(paste(yahoo.url, "s=", Symbols.name, "&a=", from.m, : downloaded length 5261!= reported length 200 [1] "WK" close<-wk[,6] ret<-dailyreturn(wk) cumret<-cumsum(ret) devi<-par(mfrow = c(2,1), mar = c(3, 3, 2, 1)) plot(ret,main = " 日回报率 ") plot(cumret,main=" 日累积回报率 ") par(devi)

26 R 语言常用统计函数 函数 描述 mean(x) 平均数 median(x) 中位数 sd(x) 标准差 var(x) 方差 quantile(x) 分位数 range(x) 求取值范围 max(x) 求最大值 min(x) 求最小值 R 语言高级技巧 R 语言的自编函数 R 语言中可以自己写函数, 来完成特定的任务 编写函数的一般语法 : function.name<-function(argument1,argument2,...){ statements return(object) } 各部分内容含义如下 : 项目 function.name argument statements return(object) Example1 含义所建函数的名称函数参数, 运算时参数数值将传递到函数内部参与运算函数体内部的运算过程最终输出的对象 编写一个计算圆面积的函数 myfunc1<-function(x){ res<-pi*x^2 return(res) } myfunc1(2) [1] Example2

27 计算资金流现值 mypvf<-function(cf,r){ n<-length(cf) tmp1<-1:n cf.pv<-cf/(1+r)^tmp1 pv<-sum(cf.pv) return(pv) } cf<-c(rep(10,4),110) r<-0.1 mypvf(cf,r) [1] 100 条件结构 if/else 语句 if (cond) expression 如果条件成立 (condition 为 True) 则执行表达式, 否则跳过 ; if (cond) expression1 else expression2 如果条件成立则执行表达式 1, 否则执行表达式 2 ifelse() 函数 ifelse(condition,expression1,expression2) 如果条件 condition 成立, 则执行表达式 1, 否则执行表达式 2 switch 语句 switch(expression,list) 如果 switch 等于列表分量名, 则返回分量对应的值 js1<-function(x,y,type){ switch(type, jia=x+y, jian=x-y, chu=x/y, cheng=x*y ) } js1(4,2,"jia") [1] 6

28 js1(4,2,"jian") [1] 2 js1(4,2,"cheng") [1] 8 js1(4,2,"chu") [1] 2 如果 switch 在 1 到 length(list) 之间, 最大返回列表相应位置的值 js2<-function(x,y,list){ switch(list, x+y, x-y, x*y, x/y ) } js2(4,2,1) [1] 6 js2(4,2,2) [1] 2 js2(4,2,3) [1] 8 js2(4,2,4) [1] 2 循环结构 for 语句 for( 循环变量 in expression1){ expression2 } expression1 是一个向量表达式, 比如 1:5,2:15,expression2 通常是一组表达式 表示循环重复执行 expression2, 直到循环变量的值不再包含在 expression1( 序列 ) 里为止 Example n<-0 for(i in 1:100){

29 } n n<-n+i [1] 5050 while 语句 while (condition) expression 当条件 (condition) 成立时, 执行 expression, 条件不满足时不执行 x<-0 while(x < 5) { x <- x+1 print(x) } [1] 1 [1] 2 [1] 3 [1] 4 [1] 5 x<-0 while(x < 5) {x <- x+1; if (x == 3) next; print(x);} [1] 1 [1] 2 [1] 4 [1] 5 repeat 语句 repeat{ expression... if(condition){ break } } 重复 expression 直到 break 语句 x<-0 repeat{ x<-x+1 print(x) if(x>5){ break } }

30 [1] 1 [1] 2 [1] 3 [1] 4 [1] 5 [1] 6 dplyr 工具包 dplyr 工具包是一个用 C++ 编写的工具包, 主要用于数据运算 其特点是速度快, 语法简洁 观测值筛选 ( 筛选行 ) 语法 : filter(mdt,condition1,condition2) Example library(dplyr) data("iris") mdt<-tbl_df(iris) mdt Source: local data frame [150 x 5] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa filter(mdt,species=="setosa") Source: local data frame [50 x 5] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) setosa setosa setosa

31 setosa setosa setosa setosa setosa setosa setosa filter(mdt,species=="setosa",petal.width==0.2) Source: local data frame [29 x 5] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa 变量筛选 ( 筛选列 ) 语法 : select(data,columnname1,columnname2) Example library(dplyr) data("iris") mdt<-tbl_df(iris) mdt Source: local data frame [150 x 5] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa setosa setosa

32 setosa setosa setosa select(mdt,species,sepal.length) Source: local data frame [150 x 2] Species Sepal.Length (fctr) (dbl) 1 setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa select(mdt,-sepal.length) Source: local data frame [150 x 4] Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa select(mdt,-sepal.length,-sepal.width) Source: local data frame [150 x 3] Petal.Length Petal.Width Species (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa

33 setosa setosa setosa setosa setosa select(mdt,length=sepal.length,width=sepal.width) Source: local data frame [150 x 2] length width (dbl) (dbl) 数据排序 语法 : arrange(data,colunmname1,colunmname2) Example arrange(mdt,sepal.length) Source: local data frame [150 x 5] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa arrange(mdt,desc(sepal.length),desc(sepal.width ))

34 Source: local data frame [150 x 5] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) virginica virginica virginica virginica virginica virginica virginica virginica virginica virginica 数据拓展 语法 : mutate(data,newvariable=expression) Example mutate(mdt,ration=sepal.length/sepal.width) Source: local data frame [150 x 6] Sepal.Length Sepal.Width Petal.Length Petal.Width Species ratio n (dbl) (dbl) (dbl) (dbl) (fctr) (dbl) setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa

35 生成新数据 语法 : transmute(data,expression) Example transmute(mdt,x1=sepal.length*sepal.width,x2=petal.length*petal.width) Source: local data frame [150 x 2] x1 x2 (dbl) (dbl) 数据分组 语法 : group_by(data,factornames) Example group_by(mdt,species) Source: local data frame [150 x 5] Groups: Species [3] Sepal.Length Sepal.Width Petal.Length Petal.Width Species (dbl) (dbl) (dbl) (dbl) (fctr) setosa setosa setosa setosa setosa setosa setosa setosa

36 setosa setosa 数据汇总计算 语法 : summarise(data,expression) Example summarise(mdt,total=sum(sepal.length,petal.length )) Source: local data frame [1 x 1] total (dbl) groupdt<-group_by(mdt,species) summarise(groupdt,total=sum(sepal.length,petal.length ),mean(sepal.leng th),mean(petal.length)) Source: local data frame [3 x 4] Species total mean(sepal.length) mean(petal.length) (fctr) (dbl) (dbl) (dbl) 1 setosa versicolor virginica 数据连接 语法 : left_join(x,y,by="") inner_join(x,y,by="") semi_join(x,y,by="")# 相当于根据 y 保留 x 中的数据 anti_join(x,y,by="")# 相当于根据 y 剔除 x 中的数据 Example mdt1<-read.table("mydata/inkfish1.txt",header = T) mdt2<-read.table("mydata/inkfish2.txt",header = T) dim(mdt1);dim(mdt2) [1] [1] head(mdt1);head(mdt2)

37 Sample GSI Sample YEAR MONTH Location Sex tail(mdt1);tail(mdt2) Sample GSI Sample YEAR MONTH Location Sex mdt_l<-left_join(mdt1,mdt2,by="sample") dim(mdt_l) [1] head(mdt_l) Sample GSI YEAR MONTH Location Sex NA NA NA NA mdt_i<-inner_join(mdt1,mdt2,by="sample") dim(mdt_i) [1]

38 head(mdt_i) Sample GSI YEAR MONTH Location Sex mdt_s<-semi_join(mdt1,mdt2,by="sample") dim(mdt_s) [1] head(mdt_s) Sample GSI mdt_a<-anti_join(mdt1,mdt2,by="sample") dim(mdt_a) [1] 1 2 head(mdt_a) Sample GSI 管道函数 %>% Example data(iris) iris %>% group_by(species) %>% summarise(sum(sepal.length),mean(sepal.l ength)) Source: local data frame [3 x 3] Species sum(sepal.length) mean(sepal.length) (fctr) (dbl) (dbl) 1 setosa versicolor virginica

39 R 语言绘图 plot() 函数 plot() 函数是 R 中创建图形的最基本的函数, 是泛函数 使用 plot() 时, 其绘图效果依赖于对象所属的类别 基本语法如下 : plot(x, y = NULL, type =, xlim =, ylim =,log = "", main =, sub =, xlab =, ylab =, axes = TRUE, frame.plot =, asp = NA,...) x,y:vector of coordinates type: 绘图类型 参数 p l b c o h s S n 参数 pch cex col main sub col.main cex.main xlab ylab xlim ylim asp axes=t/f 含义 Points, default lines Points with line connection Line connections without points Both overplotted Histogram like vertical lines Stair steps Stair steps, another style No plotting 功能绘制点图时所使用的符号类型绘图符号相对于默认值大小的倍数绘图颜色图形上方图形标题图形下方标题标题颜色标题字体大小 x 轴名称 y 轴名称 x 轴坐标范围 y 轴坐标范围坐标轴 y/x 比例绘制 / 不绘制坐标轴

40 plot.frame=t/f col.axis col.lab col.main cex.axis Example1 是否画框坐标刻度文字颜色坐标轴标签颜色标题颜色坐标轴刻度文字大小 data(cars) de<-par(col.axis="blue",col.main="red",cex.axis=0.9,tck=-0.05) plot(cars,type="o",main="cars",xlab="speed",ylab="stopping distance") par(de) Example2 x<-1:8 plot(x,col=x,pch=19,cex=5,main=" 颜色数值 ",xlab=" ",ylab=" ")

41 abline() 函数 为图形添加线条语法如下 : abline(a = NULL, b = NULL, h = NULL, v = NULL, reg = NULL, coef = NULL, untf = FALSE,...) 参数 功能 a, b the intercept and slope, single values. h v the y-value(s) for horizontal line(s). the x-value(s) for vertical line(s). Example1 plot(dnorm, from = -5, to = 5, col = "slategray", lwd = 3, main = "Dens ity of the standard normal distribution") abline(h=0.2) abline(v=0)

42 Example2 n<-100 x<-rnorm(n) y<-rnorm(n,x) plot(x,y, xlab="explanatory Variable", ylab="outcome Variable") abline(lm(y~x),col="red",lwd=2)

43 legend() 函数 在图形中包含多条线条时, 可以用图例加以区分 legend(location,title,...) 参数 location title x.intersp y.intersp pt.cex lty,lwd bty 功能 Example 参数制定图例的位置, 可以直接给定图例坐标 (x,y), 或者用 locator(1) 指定位置, 或者使用关键字 bottom,"bottomleft","left","top","topright","right","center", 如果使用关键字, 可以同时使用参数 inset= 指定图例向图形内侧移动的大小 ( 以绘图区域大小的分数表示 ) 图例字符串 图例和文字之间的距离 文字之间的行距 图例字体大小 图例线条形状和宽度 图例是否家边框 data(iris) plot(cars,type="o",main="cars",xlab="speed",ylab="stopping distance")

44 abline(lm(cars$dist~cars$speed),lwd=1.5,lty=1,col="red") legend("topleft",inset=0.05,c("linear regression","point sets"),lty=c(1, 1),col=c("red","black"),bty="n",pt.cex=0.8,y.intersp =0.9) 条形图 条形图用垂直或水平的条形来展现类别变量的频数语法 : barplot(height,horiz=,legend.text=,col=,main=,xlab=,ylab=,...) 参数 height horiz legend.text col main xlab ylab Example1 功能 向量或者矩阵 条形图是垂直还是水平 图例标签 图形颜色 标题 x 坐标名称 y 坐标名称 a<-seq(5,20,by=5) barplot(a,col=c("lightblue", "mistyrose", "lightcyan",

45 "lavender"),main="abc",xlab="123",legend.text = c("a", "B","C","D"), args.legend = list(x = "topleft",y.intersp=0.8)) - Example2 b<-matrix(1:9,3) barplot(b,main="abc",xlab="123",col=rainbow(3)) legend("topleft",inset=0.05,c("x","y","z"),pch=c(15,15,15),col=rainbow (3),bty="n",pt.cex=1.8,y.intersp =0.8)

46 饼图 饼图是应用非常广泛的统计图形之一 语法 : pie(x, labels = names(x), edges = 200, radius = 0.8,clockwise = FALSE, init.angle = NULL, col = NULL, main = NULL) 参数 参数 x labels clockwise 功能 为一个数值向量 为标签 画图的方向 init.angle 第一块饼图的起始位置, 可写 0 或 90 col main Example 颜色 标题 pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12) pie.col<- c("purple", "violetred1", "green3", "cornsilk", "cyan", "whit e")

47 pi.names<-c("a","b","c","d","e","f") pie(pie.sales, col = pie.col,labels=pi.names,main=" 饼图示例 ")