write.csv(x,file="mydata2",row.names=FALSE,col.names=FALSE)#不可以1.首先用getwd()获得当前目录," />
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R语言数据储存与读取

R语言数据储存与读取

检索:write.table write.csv区别不写入行名 https://www.sodocs.net/doc/098650822.html,s 不写入列名 https://www.sodocs.net/doc/098650822.html,s

> write.table(x,file="mydata",https://www.sodocs.net/doc/098650822.html,s=FALSE,https://www.sodocs.net/doc/098650822.html,s=FALSE) #可以

> write.csv(x,file="mydata2",https://www.sodocs.net/doc/098650822.html,s=FALSE,https://www.sodocs.net/doc/098650822.html,s=FALSE) #不可以

1. 首先用getwd() 获得当前目录,用setwd("C:/data")设定当前目录:

2.0 数据保存:创建数据框d:

>d <- data.frame(obs = c(1, 2, 3), treat = c("A", "B", "A"), weight = c(2.3, NA, 9))

2.1 保存为简单文本:

>write.table(d, file = "c:/data/foo.txt", https://www.sodocs.net/doc/098650822.html,s = F, quote = F) 2.2 保存为逗号分割文本:

>write.csv(d, file = "c:/data/foo.csv", https://www.sodocs.net/doc/098650822.html,s = F, quote = F)

2.3 保存为R格式文件:

>save(d, file = "c:/data/foo.Rdata")

2.4 保存工作空间镜像:

>save.image( ) = save(list =ls(all=TRUE), file=".RData")

3.0 数据读取:读取函数主要有:read.table( ), scan( ) ,read.fwf( ). 3.1 用 read.table( ) 读 "c:\data”下houses.dat:

>setwd("C:/data"); HousePrice <- read.table(file="houses.dat")

如果明确数据第一行做表头,则使用header选项:

>HousePrice <- read.table("houses.dat", header=TRUE)

read.table( ) 变形有: aread.csv( ),read.csv2( ), read.delim( ), read.delim2( ).前两读取逗号分割数据,后两个读取其他分割符数据。

3.2 用scan( ) 比read.table( ) 更灵活。但要指定变量类型:如:

C:\data\data.dat:

M 65 168

M 70 172

F 54 156

F 58 163

>mydata <- scan("data.dat", what = list("", 0, 0))

>mydata <- scan("data.dat", what = list(Sex="", Weight=0, Height=0)) 3.3 用read.fwf( )读取文件中一些固定宽度数据:如:C:\data\data.txt: A1.501.2

A1.551.3

B1.601.4

>mydata <- read.fwf("data.txt", widths=c(1, 4,

3), https://www.sodocs.net/doc/098650822.html,s=c("X","Y","Z"))

4.0 excel格式数据读取:

4.1 利用剪切板:选择excel数据,再用(CTRL+C)复制。在R中键入命令:

>mydata <- read.delim("clipboard")

4.2 使用程序包 RODBC.如: c:\data\body.xls

Sex Weight Height

M 65 168

M 70 172

F 54 156

F 58 163

> library(RODBC)

> z <- odbcConnectExcel("c:/data/body.xls") > foo <- sqlFetch(z, "Sheet1")

> close(z)

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