变量可归结为名义型，有序型和连续型变量。名义型变量没有顺序之分。如糖尿病类型diabetes（type1，type2）就是名义型变量。有序型变量如status（poor，improved，excellent）。连续型变量如age（25，34，28，52）
类别型变量和有序型变量在R中被称为因子。这些分类变量的可能值被称为一个水平，level，有这些水平值构成的向量被称为因子。
因子在R统计学分析中有很大的作用，计算频数，独立性检验，相关性检验，方差分析，主成分分析，因子分析……
调用mtcars数据集
> mtcars
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2> mtcars$cyl
[1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
> table(mtcars$cyl)
4 6 8
11 7 14 这里并不是说4，6，8就是因子，而是它可以作为因子使用
因子刻印用factor（）函数创建。
> f <- factor(c('red','red','green','blue','green','blue','blue'))
> f
[1] red red green blue green blue blue
Levels: blue green red这是类别型变量的因子创建
以下为有序型变量的因子创建
> week <- factor(c('mon','fri','thu','wed','fri','sun','mon'))
> week
[1] mon fri thu wed fri sun mon
Levels: fri mon sun thu wed可见，因子水平并没有按照我们想象中的那样排列，而是按照字母顺序排列的，所以要人为输入水平及排列方式。
> week <- factor(c('mon','fri','thu','wed','fri','sun','mon'),ordered = T,levels = c('mon','tue','wed','thu','fri','sat','sun'))
> week
[1] mon fri thu wed fri sun mon
Levels: mon < tue < wed < thu < fri < sat < sun
> R中还有一个cut()函数，可以将一个连续型变量进行有规律的分组
> num <- c(1:100)
> a<-cut(num,seq(0,100,10))
> a
[1] (0,10] (0,10] (0,10] (0,10] (0,10] (0,10] (0,10] (0,10] (0,10]
[10] (0,10] (10,20] (10,20] (10,20] (10,20] (10,20] (10,20] (10,20] (10,20]
[19] (10,20] (10,20] (20,30] (20,30] (20,30] (20,30] (20,30] (20,30] (20,30]
[28] (20,30] (20,30] (20,30] (30,40] (30,40] (30,40] (30,40] (30,40] (30,40]
[37] (30,40] (30,40] (30,40] (30,40] (40,50] (40,50] (40,50] (40,50] (40,50]
[46] (40,50] (40,50] (40,50] (40,50] (40,50] (50,60] (50,60] (50,60] (50,60]
[55] (50,60] (50,60] (50,60] (50,60] (50,60] (50,60] (60,70] (60,70] (60,70]
[64] (60,70] (60,70] (60,70] (60,70] (60,70] (60,70] (60,70] (70,80] (70,80]
[73] (70,80] (70,80] (70,80] (70,80] (70,80] (70,80] (70,80] (70,80] (80,90]
[82] (80,90] (80,90] (80,90] (80,90] (80,90] (80,90] (80,90] (80,90] (80,90]
[91] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100] (90,100]
[100] (90,100]
10 Levels: (0,10] (10,20] (20,30] (30,40] (40,50] (50,60] (60,70] (70,80] ... (90,100]