1.因子
变量可分为：名义型变量、有序性变量（数据间有顺序，但不是连续的）、连续性变量
因子：名义型变量、有序性变量.它们的值被称为水平level.由水平值构成的向量称为因子。
因子可以简单看作一个附加了更多信息的向量。因子最基本的特征是”水平“
> y<-c(2,5,8,12)
> yf<-factor(y)
> yf
[1] 2 5 8 12
Levels: 2 5 8 12
> str(yf) ###查看y的内部结构
Factor w/ 4 levels "2","5","8","12": 1 2 3 4
> unclass(yf) ###
[1] 1 2 3 4
attr(,"levels")
[1] "2" "5" "8" "12"创建因子
attach(mtcars)#####加载mtcars数据集到当前窗口，可以不用使用$
detach(mtcars)######退出该数据集
with(mtcars,{hp})###查看变量hp的所有取值
#####因子的创建
f<-factor(c("red","yellow","blue"),ordered = T,levels = c("red","yellow","blue"))
fcyl<-factor(mtcars$cyl)
plot(mtcars$cyl)
plot(fcyl) ####因子分类，绘制频数条形图因子的常用函数
tapply()
tapply()函数的用法：
tapply(x,f,g) 向量x、因子或因子列表f、函数g
###将x分组，每组对应一个因子水平（一组因子水平的组合），得到X的子向量，然后对这些向量应用函数。
> ages<-c(34,25,28,37,43,35)
> affils<-c("R","D","D","R","U","D")
> tapply(ages,affils,mean)
D R U
29.33333 35.50000 43.00000例子：返回以下几个子组的平均收入
> d<-data.frame(list(sex=c("M","M","F","M","F","F"),age=c(47,59,21,32,33,34),income=c(55000,88000,32450,76500,123000,45650)))
> d
sex age income
1 M 47 55000
2 M 59 88000
3 F 21 32450
4 M 32 76500
5 F 33 123000
6 F 34 45650
> d$over25<-ifelse(d$age>25,1,0) ###按照年龄分组
> d
sex age income over25
1 M 47 55000 1
2 M 59 88000 1
3 F 21 32450 0
4 M 32 76500 1
5 F 33 123000 1
6 F 34 45650 1
> tapply(d$income,list(d$over25,d$sex),mean)
F M
0 32450 NA
1 84325 73166.67split()
输出的是列表
> split(d$income,list(d$over25,d$sex))
$`0.F`
[1] 32450
$`1.F`
[1] 123000 45650
$`0.M`
numeric(0)
$`1.M`
[1] 55000 88000 76500by()
> f<-function(m){
+ lm(abamf$Diameter~abamf$Length)###鲍鱼直径对长度的回归
+ }
> by(abamf,abamf$Sex,f) ###按性别做鲍鱼直径对长度的回归
abamf$Sex: 0
Call:
lm(formula = abamf$Diameter ~ abamf$Length)
Coefficients:
(Intercept) abamf$Length
-0.01197 0.80480
-------------------------------------------------------
abamf$Sex: 1
Call:
lm(formula = abamf$Diameter ~ abamf$Length)
Coefficients:
(Intercept) abamf$Length
-0.01197 0.804802.表
> u<-c(22,8,33,6,8,29,-2)
> f1<-c("a","bc","a","a","bc","a","a")
> tapply(u,f1,length) ####求长度，与u的元素具体取值无关
a bc
5 2
> u<-c(22,8,33,6,8,29,-2)
> f1<-list(c(5,12,13,12,13,5,13),c("a","bc","a","a","bc","a","a"))
> f1
[[1]]
[1] 5 12 13 12 13 5 13
[[2]]
[1] "a" "bc" "a" "a" "bc" "a" "a"
> tapply(u,f1,length) ##因为与u的具体取值无关，即求的是水平的长度，而按照水平随c(5,12,13,12,13,5,13)进行分组，即求5、12、13的长度
a bc
5 2 NA
12 1 1
13 2 1上述结果其实是列联表。
因为5没出现过bc，所以对应位置应该写0，所以用table()函数建立表。table()的第一个参数是因子或者是因子的列表
table()函数计算频数
> f1<-list(c(5,12,13,12,13,5,13),c("a","bc","a","a","bc","a","a"))
> table(f1)
f1.2
f1.1 a bc
5 2 0
12 1 1
13 2 1
> x<-c("Yes","Yes","Yes","No","No" ,"No","Not Sure", "Yes","No","No")
> > xm<-matrix(x,5,2,byrow = T)
> xm
[,1] [,2]
[1,] "Yes" "Yes"
[2,] "Yes" "No"
[3,] "No" "No"
[4,] "Not Sure" "Yes"
[5,] "No" "No"
> xd<-as.data.frame(xm)
> xd
V1 V2
1 Yes Yes
2 Yes No
3 No No
4 Not Sure Yes
5 No No
> colnames(xd)<-c("Vote.x","Vote.X.Last.Time")
> xd
Vote.x Vote.X.Last.Time
1 Yes Yes
2 Yes No
3 No No
4 Not Sure Yes
5 No No
> table(xd) ########生成该投票数据的表
Vote.X.Last.Time
Vote.x No Yes
No 2 0
Not Sure 0 1
Yes 1 1R也可以生成3维表
> v<-list(gender=c("M","M","F","M","F","F"),race=c("W","W","A","O","B","B"),pol=c("L","L","C","L","L","c"))
> vd<-as.data.frame(v)
> vd
gender race pol
1 M W L
2 M W L
3 F A C
4 M O L
5 F B L
6 F B c
> vdt<-table(vd) ###生成3维表
> vdt
, , pol = c
race
gender A B O W
F 0 1 0 0
M 0 0 0 0
, , pol = C
race
gender A B O W
F 1 0 0 0
M 0 0 0 0
, , pol = L
race
gender A B O W
F 0 1 0 0
M 0 0 1 2表中有关矩阵和类似数组的操作
> class(vdt) ###查看vdt的类
[1] "table"
> xdt<-table(xd)
> xdt[1,2] ###访问表中1行2列的元素
[1] 0
> xdt[2,]
No Yes
0 1用标量乘以矩阵，addmargins() ###计算边际值，dimnames() ####获取维度名称和水平值
> xdt/5
Vote.X.Last.Time
Vote.x No Yes
No 0.4 0.0
Not Sure 0.0 0.2
Yes 0.2 0.2
> apply(xdt,1,sum)
No Not Sure Yes
2 1 2
> addmargins(xdt) ###计算边际值
Vote.X.Last.Time
Vote.x No Yes Sum
No 2 0 2
Not Sure 0 1 1
Yes 1 1 2
Sum 3 2 5
> dimnames(xdt) ####获取维度名称和水平值
$Vote.x
[1] "No" "Not Sure" "Yes"
$Vote.X.Last.Time
[1] "No" "Yes"aggregate()
aggregate()
##操作对象是数据框，第2个参数必须是列表，最后一个是应用的函数
> aggregate(aba[,-1],list(aba$Sex),median)
Group.1 Length Diameter Height Whole.weight Shucked.weight Viscera.weight Shell.weight
1 F 0.590 0.465 0.160 1.03850 0.44050 0.2240 0.295
2 I 0.435 0.335 0.110 0.38400 0.16975 0.0805 0.113
3 M 0.580 0.455 0.155 0.97575 0.42175 0.2100 0.276
Rings
1 10
2 8
3 10cut()
cut()是生成因子的常用方法，常用于表的操作
cut(x,c,labels=FALSE)
> num<-c(1,3,4,5,5.5,2.4,8,7.8)
> cut(num,seq(1,10,2)) ####看num中元素落入哪个区域，输出结果是落入的区域
[1] <NA> (1,3] (3,5] (3,5] (5,7] (1,3] (7,9] (7,9]
Levels: (1,3] (3,5] (5,7] (7,9]
> cut(num,seq(0,10,2))
[1] (0,2] (2,4] (2,4] (4,6] (4,6] (2,4] (6,8] (6,8]
Levels: (0,2] (2,4] (4,6] (6,8] (8,10]
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