R语言频数统计函数和独立性检验函数

目录

1. 频数统计函数

1.1 数据分组

1.2 频数统计

1.2.1 一维

 1.2.2 二维

2. 独立性检验函数

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1. 频数统计函数

1.1 数据分组

我们要进行频数统计，首先要利用因子对数据进行分组，以mtcars数据集为例，如果要对cyl中的数据进行离散分组

> mtcars$cyl<-as.factor(mtcars$cyl)
> split(mtcars$cyl)
> split(mtcars,mtcars$cyl)
$`4`
                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
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
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
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
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

$`6`
                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
Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
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
Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6

$`8`
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    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
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
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8

 如果我们要将数据分为连续的几段，可以用cut函数

> cut(mtcars$mpg,c(seq(10,50,10)))
 [1] (20,30] (20,30] (20,30] (20,30] (10,20] (10,20] (10,20] (20,30] (20,30]
[10] (10,20] (10,20] (10,20] (10,20] (10,20] (10,20] (10,20] (10,20] (30,40]
[19] (30,40] (30,40] (20,30] (10,20] (10,20] (10,20] (10,20] (20,30] (20,30]
[28] (30,40] (10,20] (10,20] (10,20] (20,30]
Levels: (10,20] (20,30] (30,40] (40,50]

1.2 频数统计

1.2.1 一维

我们使用table函数进行频数统计

> table(mtcars$cyl)

 4  6  8 
11  7 14 

> table(cut(mtcars$mpg,c(seq(10,50,10))))

(10,20] (20,30] (30,40] (40,50] 
     18      10       4       0 

还可以进行频率统计，也就是获取百分比的值

> prop.table(table(mtcars$cyl))

      4       6       8 
0.34375 0.21875 0.43750 

> prop.table(table(cut(mtcars$mpg,c(seq(10,50,10)))))

(10,20] (20,30] (30,40] (40,50] 
 0.5625  0.3125  0.1250  0.0000

 1.2.2 二维

我们使用vcd库中的Arthritis数据集为例

> table(Arthritis$Treatment,Arthritis$Improved)
         
          None Some Marked
  Placebo   29    7      7
  Treated   13    7     21

 获取百分比也是加上prop前缀即可

2. 独立性检验函数

还是以vcd包中的Arthritis数据集为例，使用chisq.test函数进行卡方检验，p-value越小独立性越弱，p-value<0.05时认为变量不独立

> x<-table(Arthritis$Treatment,Arthritis$Improved)
> chisq.test(x)

	Pearson's Chi-squared test

data:  x
X-squared = 13.055, df = 2, p-value = 0.001463