Article directory
R data objects
Type division of R objects
Partitioning R objects from a storage perspective
(1) Numerical
type Integer type and real number type; Integer type (Integer) is the storage form of integers, usually requiring 2 bytes or 4 bytes of storage space. The real number type is used to store numerical data including decimal places, and usually requires 4 bytes or 8 bytes of storage space. Numeric data in R defaults to double-precision numbers.
(2) Character type
, such as name and place of origin, is a sequence of characters enclosed in English double quotation marks, referred to as string.
(3) Logical type
TRUE; FALSE
Divide R objects from the perspective of data organization structure
(1) Vector
Vector is the basic unit of R data organization. From a statistical point of view, a vector corresponds to a variable, storing multiple variable values with the same storage type. Unless otherwise specified, vectors are column vectors.
Factor (Factor) is a special kind of vector.
(2) Matrix
A matrix is a two-dimensional form used to organize multiple variables with the same storage type. The columns of the matrix are usually variables, behavior observations.
(3) Array
An array is a collection of multiple two-dimensional tables, generally used to organize panel data in statistics, etc.
(4) Data frame
A data frame is also a two-dimensional table, which is similar to a matrix, but it is used to organize and store different types multiple variables. Among them, the columns of the data frame are usually variables, behavior observations.
(5) List
A collection of multiple vectors, matrices, arrays, data frames, and lists is a list. It is mostly used for "packaging" integration of relevant statistical analysis results.
Create and manage R objects
create R object
对象名<-R常量或R函数
Access to R objects
print(对象名)
View the structure of an R object
str(对象名)
Manage R objects
ls()当前工作空间中的对象名列表将显示在R的控制台窗口中
rm(对象名或对象名列表)对象名列表中包含多个对象名，各个对象之间用英文逗号分隔
remove(对象名)删除当前工作空间中的指定对象
The basic way of R data organization
R vectors and their creation and access
is.vector(对象名)#判断对象是否为向量
Simplest R vector
> #创建包含一个元素的向量
> V1<-100 #创建整数形式的数值型向量V1，存储类型默认为双精度型
> V1 #显示V1的对象值
[1] 100
> V2<-123.5 #创建实数形式的数值型向量V2，存储类型为双精度型
> V2
[1] 123.5
> V3<-"abcd" #创建字符串型向量V3
> print(V3) #显示V3对象值
[1] "abcd"
> (V4<-TRUE) #创建逻辑型向量V4，并直接显示对象值
[1] TRUE
> is.vector(V1) #判断对象V1是否为向量
[1] TRUE
> is.logical(V4) #判断对象V4的存储类型是否为逻辑型
[1] TRUE
①If the assignment statement is placed in parentheses, it means to create an object and directly display the object value
②When displaying object values, each line will automatically start with square brackets, such as [1], the number in square brackets indicates the first element is the number of elements in the R vector object.
Using R vectors to organize variables
SiteName<-c("东四","天坛","官园","万寿西宫","奥体中心","农展馆","万柳","北部新区","植物园","丰台花园",
"云岗","古城","房山","大兴","亦庄","通州","顺义","昌平","门头沟","平谷","怀柔","密云","延庆","定陵",
"八达岭","密云水库","东高村","永乐店","榆垡","琉璃河","前门","永定门内","西直门北","南三环","东四环")
SiteTypes<-c(rep("城区环境评价点",12),rep("郊区环境评价点",11),rep("对照点及区域点",7),rep("交通污染监控点",5))
> length(SiteName)
[1] 35
> length(SiteTypes)
[1] 35
rep("城区环境评价点",12)Indicates repeated generation of 12 urban environment assessment points
Accessing elements in an R vector
> a<-vector(length=10)#创建包含10个元素的向量a
> a#显示初始值
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
> a[1]<-1#访问第一个元素，并赋值为1
> a[2:4]<-c(2,3,4)#访问第2至第4个元素，并赋值为2,3,4
> a
[1] 1 2 3 4 0 0 0 0 0 0
> b<-seq(from=5,to=9,by=1)#生成一个取值5至9的序列给向量b
> b
[1] 5 6 7 8 9
> a[c(5:9,10)]<-c(b,10)
> a
[1] 1 2 3 4 5 6 7 8 9 10
> b<-(2:4)#创建数值型位置向量b，依次取值为2,3,4
> a[b]#访问a中位置向量b所指位置（即2,3,4）上的元素
[1] 2 3 4
> b<-c(TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE)#创建逻辑型向量b
> a[b]#访问a中位置向量b取值为TRUE位置（即1,4）上的元素
[1] 1 4
> a[-1]#访问除第一个元素以外的元素
[1] 2 3 4 5 6 7 8 9 10
> a[-(2:4)]#访问除第2至第4个元素以外的元素
[1] 1 5 6 7 8 9 10
> a[-c(5:9,10)]#访问除第5至第9以及第10个元素以外的元素
[1] 1 2 3 4
> b<-(2:4)
> a[-b]#访问除位置向量b以外的元素
[1] 1 5 6 7 8 9 10
> ls()#显示当前工作空间中的对象列表
[1] "a" "b"
> rm(a,b)#删除当前工作空间中的对象a和对象b
Special vectors for R: factors
> (a<-c("Poor","Improved","Excellent","Poor"))#创建包含4个元素的字符型向量a
[1] "Poor" "Improved" "Excellent" "Poor"
> is.vector(a)#判断a是否为向量
[1] TRUE
> (b<-as.factor(a))#将字符型向量a转换为因子b并显示b
[1] Poor Improved Excellent Poor
Levels: Excellent Improved Poor
> is.factor(b)#判断b是否为因子
[1] TRUE
> levels(b)#按因子水平值升序显示所对应的类别值
[1] "Excellent" "Improved" "Poor"
> typeof(b)#显示因子b的存储类型名
[1] "integer"
> (a<-c("Poor","Improved","Excellent","Poor"))#创建字符型向量a
[1] "Poor" "Improved" "Excellent" "Poor"
> (b<-factor(a,order=FALSE,levels=c("Poor","Improved","Excellent")))#指定类别值和水平值的对应关系
[1] Poor Improved Excellent Poor
Levels: Poor Improved Excellent
> (b<-factor(a,order=TRUE, levels=c("Poor","Improved","Excellent")))
[1] Poor Improved Excellent Poor
Levels: Poor < Improved < Excellent
> (a<-c("Poor","Improved","Excellent","Poor"))
[1] "Poor" "Improved" "Excellent" "Poor"
> (b<-factor(a,levels=c("Poor","Improved","Excellent")))
[1] Poor Improved Excellent Poor
Levels: Poor Improved Excellent
> (b<-factor(a,levels=c("Poor","Improved","Excellent"),labels=c("C","B","A")))
[1] C B A C
Levels: C B A
levels is the original category value; labels is the new category value
R matrices and arrays and their creation and access
Combine multiple vectors into an R matrix
> Site<-cbind(SiteName,SiteTypes)
> is.matrix(Site)#判断对象是否为矩阵
[1] TRUE
> dim(Site)#获得矩阵的行数和列数
[1] 35 2
Convert vectors to R matrices
> (a<-c("Poor","Improved","Excellent","Poor"))
[1] "Poor" "Improved" "Excellent" "Poor"
> data<-(1:30)#生成一个名为data的数值型向量
> data<-matrix(data,nrow=5,ncol=6,byrow=FALSE)#将向量a按列排列放置到5行6列的矩阵中
> data
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 6 11 16 21 26
[2,] 2 7 12 17 22 27
[3,] 3 8 13 18 23 28
[4,] 4 9 14 19 24 29
[5,] 5 10 15 20 25 30
Accessing elements in an R matrix
> data[2,3]#第2行第3列
[1] 12
> data[1:2,2:3]#访问第1行至第2行，第2列至第3列位置上的元素
[,1] [,2]
[1,] 6 11
[2,] 7 12
> data[1:2,c(1,3)]#访问第1行至第2行，第1,3列位置上的元素
[,1] [,2]
[1,] 1 11
[2,] 2 12
> data[2,]#访问第2行上的所有元素
[1] 2 7 12 17 22 27
> data[c(1,3),]#访问第1,3行上的所有元素
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 6 11 16 21 26
[2,] 3 8 13 18 23 28
> a<-c(TRUE,FALSE,TRUE,FALSE,FALSE)
> data[a,]
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 6 11 16 21 26
[2,] 3 8 13 18 23 28
> data[,1:3]#访问1-3列上的所有元素
[,1] [,2] [,3]
[1,] 1 6 11
[2,] 2 7 12
[3,] 3 8 13
[4,] 4 9 14
[5,] 5 10 15
> a<-matrix(nrow=5,ncol=2)#创建一个5x2的矩阵，初始值默认为缺失值
> a
[,1] [,2]
[1,] NA NA
[2,] NA NA
[3,] NA NA
[4,] NA NA
[5,] NA NA
> a[,1]<-seq(from=1,to=10,by=2)#给矩阵第1列赋值
> a[,2]<-seq(from=10,to=1,by=-2)#给矩阵第2列赋值
> a
[,1] [,2]
[1,] 1 10
[2,] 3 8
[3,] 5 6
[4,] 7 4
[5,] 9 2
Create and access R arrays
> a<-(1:60)
> dim1<-c("R1","R2","R3","R4")
> dim2<-c("C1","C2","C3","C4","C5")
> dim3<-c("T1","T2","T3")
> a<-array(a,c(4,5,3),dimnames = list(dim1,dim2,dim3))
> a#数组a由3张行数为4列数为5的二维表组成
> is.array(a)#判断a是否为数组
[1] TRUE
> a[1:3,c(1,3),2]#第2张表1-3行，1，3列
C1 C3
R1 21 29
R2 22 30
R3 23 31
R data frame and its creation and access
Create an R data frame
> #创建数据框
> SiteName<-c("东四","天坛","官园","万寿西宫","奥体中心","农展馆","万柳","北部新区","植物园","丰台花园",
+ "云岗","古城","房山","大兴","亦庄","通州","顺义","昌平","门头沟","平谷","怀柔","密云","延庆","定陵",
+ "八达岭","密云水库","东高村","永乐店","榆垡","琉璃河","前门","永定门内","西直门北","南三环","东四环")
> SiteTypes<-c(rep("城区环境评价点",12),rep("郊区环境评价点",11),rep("对照点及区域点",7),rep("交通污染监控点",5))
> SiteX<-c(116.417,116.407,116.339,116.352,116.397,116.461,116.287,116.174,116.207,116.279,116.146,116.184,
+ 116.136,116.404,116.506,116.663,116.655,116.23,116.106,117.1,116.628,116.832,115.972,
+ 116.22,115.988,116.911,117.12,116.783,116.30,116.00,
+ 116.395,116.394,116.349,116.368,116.483)
> SiteY<-c(39.929,39.886,39.929,39.878,39.982,39.937,39.987,40.09,40.002,39.863,39.824,39.914,
+ 39.742,39.718,39.795,39.886,40.127,40.217,39.937,40.143,40.328,40.37,40.453,
+ 40.292,40.365,40.499,40.10,39.712,39.52,39.58,
+ 39.899,39.876,39.954,39.856,39.939)
> Site<-data.frame(Sitename=SiteName,Sitetypes=SiteTypes,Sitex=SiteX,Sitey=SiteY)
> names(Site)#显示数据框的域名
[1] "Sitename" "Sitetypes" "Sitex" "Sitey"
> str(Site)#显示对象的结构信息
'data.frame': 35 obs. of 4 variables:
$ Sitename : chr "东四" "天坛" "官园" "万寿西宫" ...
$ Sitetypes: chr "城区环境评价点" "城区环境评价点" "城区环境评价点" "城区环境评价点" ...
$ Sitex : num 116 116 116 116 116 ...
$ Sitey : num 39.9 39.9 39.9 39.9 40 ...
> is.data.frame(Site)#判断Site是否为数据框
[1] TRUE
> fix(Site)#显示部分数据内容
> head(Site)#仅显示数据框的前6行内容
Sitename Sitetypes Sitex Sitey
1 东四 城区环境评价点 116.417 39.929
2 天坛 城区环境评价点 116.407 39.886
3 官园 城区环境评价点 116.339 39.929
4 万寿西宫 城区环境评价点 116.352 39.878
5 奥体中心 城区环境评价点 116.397 39.982
6 农展馆 城区环境评价点 116.461 39.937
> head(Site$Sitename)#访问Sitename域且仅显示前6条内容
[1] "东四" "天坛" "官园" "万寿西宫" "奥体中心" "农展馆"
> tail(Site[["Sitename"]])#访问Sitename域且仅显示后6条内容
[1] "琉璃河" "前门" "永定门内" "西直门北" "南三环" "东四环"
> head(Site[1])#访问第一个域且仅显示前6条内容
Sitename
1 东四
2 天坛
3 官园
4 万寿西宫
5 奥体中心
6 农展馆
> tail(Site[c("Sitename","Sitetypes")])#访问Sitename和Sitetypes域且仅显示后6条内容
Sitename Sitetypes
30 琉璃河 对照点及区域点
31 前门 交通污染监控点
32 永定门内 交通污染监控点
33 西直门北 交通污染监控点
34 南三环 交通污染监控点
35 东四环 交通污染监控点
> attach(Site)#绑定Site数据框
> head(Sitename)
[1] "东四" "天坛" "官园" "万寿西宫" "奥体中心" "农展馆"
> detach(Site)#解除Site数据框的绑定
> head(Sitename)
Error in head(Sitename) : object 'Sitename' not found
R lists and their creation and access
> a<-c(1,2,3)#创建向量a
> b<-matrix(nrow=5,ncol=2)#创建矩阵b
> b[,1]=seq(from=1,to=10,by=2)
> b[,2]=seq(from=10,to=1,by=-2)
> c<-array(1:60,c(4,5,3))#创建数组c
> d<-list(L1=a,L2=b,L3=c)#创建列表d
> names(d)#显示列表d各成分名
[1] "L1" "L2" "L3"
> str(d)#显示对象d的存储类型和结构信息
List of 3
$ L1: num [1:3] 1 2 3
$ L2: num [1:5, 1:2] 1 3 5 7 9 10 8 6 4 2
$ L3: int [1:4, 1:5, 1:3] 1 2 3 4 5 6 7 8 9 10 ...
> is.list(d)#判断d是否为列表
[1] TRUE
> d$L1#访问列表d中的成分L1
[1] 1 2 3
> d[[2]]#访问列表d中的第二个成分(L2)
[,1] [,2]
[1,] 1 10
[2,] 3 8
[3,] 5 6
[4,] 7 4
[5,] 9 2
Other issues with R data organization
Storage of R object data
write.table(Site,file="监测点信息.txt",row.names=FALSE,col.names=FALSE)
row.names=FALSE,col.names=FALSE
不将行编号和变量名写入文本文件
Read in data via keyboard
> a<-scan()#R将在控制台窗口等待用户输入数据，每个数据以回车键分隔
1: 10
2: 20
3: 30
4:
Read 3 items
> a
[1] 10 20 30
When the input is closed, just press the Enter key without entering the content
Sharing data packages that come with R
data(), the displayed data set name and description information are as shown in the figure below
data("数据集名")#指定加载使用某个数据集
Data Structures and R Organization for Big Data Cases
Read text file data into R data frame
> getwd()#获取当前工作目录
[1] "D:/Program Files/RStudio/Projects"
> setwd("./课程数据集")#改变工作目录
> MyData<-read.table(file = "空气质量.txt",header = TRUE,sep=" ",stringsAsFactors = FALSE)
> str(MyData)
'data.frame': 12705 obs. of 11 variables:
$ SiteName : chr "奥体中心" "奥体中心" "奥体中心" "奥体中心" ...
$ date : int 20160101 20160626 20160505 20160307 20160907 20160314 20160717 20160122 20161119 20160526 ...
$ PM2.5 : num 165 39.9 48.7 50 40.2 ...
$ AQI : num 154.6 68.1 55.1 120.8 67.9 ...
$ CO : num 3.929 0.454 0.946 0.992 0.607 ...
$ NO2 : num 122.6 35.1 41 30.4 43.7 ...
$ O3 : num 10.7 119.2 82.1 53.3 106.9 ...
$ SO2 : num 45.67 4.88 14.83 14.83 4 ...
$ SiteTypes: chr "城区环境评价点" "城区环境评价点" "城区环境评价点" "城区环境评价点" ...
$ SiteX : num 116 116 116 116 116 ...
$ SiteY : num 40 40 40 40 40 ...