R is a programming language and software environment for statistical analysis and graphics. It allows users to analyze data, create visualizations, and perform statistical tests. Common R commands include functions to get and set the working directory, list objects in the workspace, remove objects, view and set options, save and load the command history, and save and load the entire workspace. R supports various data structures like vectors, arrays, matrices, data frames, and lists to store and manipulate different types of data. Data can be input into R from files, databases, and Excel spreadsheets. Graphs and visualizations created in R can be exported to file formats like PNG, JPEG, PDF and others.

1. R Language
note

2. Data analysis and graphics with R
• R is a language and environment for statistical
computing and graphics, similar to the S
language originally developed at Bell Labs

3. Common commands
• getwd() 读工作目录.
• setwd(“c:/”) 改变工作目录
• ls() 显示当前workspace的变量
• rm(objectlist) Remove (delete) one or more objects.
• options() View or set current options.
• savehistory("myfile") Save the commands history to myfile ( default =
• .Rhistory).
• loadhistory("myfile") Reload a command's history (default = .Rhistory).
• save.image("myfile") Save the workspace to myfile (default = .RData).
• save(objectlist,file="myfile") Save specific objects to a file.
• load("myfile") Load a workspace into the current session (default =
• .RData).

4. R Data Structures
•

5. Vector
• Vector
– c (2,3,1,6,5,2)
– X = c (1:9)
– X*2 向量乘
• 2,4,6,8,10,12,14,16,18

6. Array
• array(c(1:9), dim=c(3,3))
– [,1] [,2] [,3]
– [1,] 1 4 7
– [2,] 2 5 8
– [3,] 3 6 9

7. Matrix
myymatrix <- matrix(
vector,
nrow=number_of_rows,
ncol=number_of_columns,
byrow=logical_value,
dimnames=list(
char_vector_rownames,
char_vector_colnames
)
)
M = matrix(c(1:10), nrow=2, ncol=5, byrow=T)
byrow=T横向填充 byrow=F竖向填充
[,1] [,2] [,3] [,4] [,5] [,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 4 5 [1,] 1 3 5 7 9
[2,] 6 7 8 9 10 [2,] 2 4 6 8 10

8. Matrix
a=matrix(c(1:10), nrow=2, ncol=5, byrow=T,
dimnames=list(c('R1','R2'),c('a','b','c','d','e')))
abcd e
R1 1 2 3 4 5
R2 6 7 8 9 10
• 选择数据：
– a[1,2] 结果：2
– a[2,c(3:5)] 结果：8,9,10

9. Array
• n1=c('r1','r2')
• n2=c('col1','col2','col3')
• n3=c('p1','p2','p3','p4')
•array(c(1:24),c(2,3,4),dimnames=list(n1,n2,n3)
)
三维 数组

10. data frame
• name=c('jack','tom','joe','linda')
• age=c('21','23','20','19')
• city=c('bj','sh','sh','bj')
• d = data.frame(name,age,city)
• Query:
– d['age']
– d[c('age','city')]
– d[1:2]
– d$age
• Edit:
– d = edit(d)

11. data frame
• table(d$city,d$name)
– jack joe linda tom
– bj 1 0 1 0
– sh 0 1 0 1
attach(d) with(d,{
summary(name) summary(name)
… …
detach(d) })

12. Lists
• Lists are the most complex of the R data types
• g <- "My First List"
• h <- c(25, 26, 18, 39)
• j <- matrix(1:10, nrow=5)
• k <- c("one", "two", "three")
• mylist <- list(title=g, ages=h, j, k)

13. data input
•

14. data input
• mydata <-
data.frame(age=numeric(0), gender=char
acter(0), weight=numeric(0))
mydata <- edit(mydata)
• mydataframe =
read.table("c:/a.txt", header=FALSE, sep="t",
row.names="name")
– header:是否把第一行作为column name
– row.names:标识

15. data input
• read excel:
– library(RODBC)
– channel <- odbcConnectExcel("c:/t.xlsx")
– mydataframe <- sqlFetch(channel, "Sheet1")
– odbcClose(channel)
• excel2007:
– library(xlsx)
– workbook <- "c:/myworkbook.xlsx"
– mydataframe <- read.xlsx(workbook, 1)

16. data input
• library(RODBC)
• myconn <-odbcConnect("mydsn", uid="Rob",
pwd="aardvark")
• crimedat <- sqlFetch(myconn, Crime)
• pundat <- sqlQuery(myconn, "select * from
Punishment")
• close(myconn)

17. data export
• png(), jpeg(),
• bmp(), tiff(), xfig()
• pdf("mygraph.pdf")
• …….
• dev.off()