R语言实战 - 基本统计分析- 描述性统计分析

Posted 2020-10-09 你的踏板车要滑向哪里

> vars <- c("mpg", "hp", "wt")
> head(mtcars[vars])
                   mpg  hp    wt
Mazda RX4         21.0 110 2.620
Mazda RX4 Wag     21.0 110 2.875
Datsun 710        22.8  93 2.320
Hornet 4 Drive    21.4 110 3.215
Hornet Sportabout 18.7 175 3.440
Valiant           18.1 105 3.460
> 

1. 方法云集

> summary(mtcars[vars])
      mpg              hp              wt       
 Min.   :10.40   Min.   : 52.0   Min.   :1.513  
 1st Qu.:15.43   1st Qu.: 96.5   1st Qu.:2.581  
 Median :19.20   Median :123.0   Median :3.325  
 Mean   :20.09   Mean   :146.7   Mean   :3.217  
 3rd Qu.:22.80   3rd Qu.:180.0   3rd Qu.:3.610  
 Max.   :33.90   Max.   :335.0   Max.   :5.424 

> mystats <- function(x, na.omit=FALSE){
+     if (na.omit)
+         x <- x[!is.na(x)]
+     m <- mean(x)
+     n <- length(x)
+     s <- sd(x)
+     skew <- sum((x-m)^3/s^3)/n
+     kurt <- sum((x-m)^4/s^4)/n-3
+     return(c(n=n, mean=m, stdev=s, skew=skew, kurtosis=kurt))
+ }
> sapply(mtcars[vars], mystats)
               mpg          hp          wt
n        32.000000  32.0000000 32.00000000
mean     20.090625 146.6875000  3.21725000
stdev     6.026948  68.5628685  0.97845744
skew      0.610655   0.7260237  0.42314646
kurtosis -0.372766  -0.1355511 -0.02271075
> 

mpg平均值20.1，标准偏差6.0. 分布呈现右偏（偏度0.6），较正态分布稍平（峰度-0.37）

 

Hmisc 包安装失败

1）通过Hmisc包中的describe()函数计算描述性统计量

2）通过pastecs包中的stat.desc()函数计算描述性统计量

> vars <- c("mpg", "hp", "wt")
> library(pastecs)
> stat.desc(mtcars[vars])
                     mpg           hp          wt
nbr.val       32.0000000   32.0000000  32.0000000
nbr.null       0.0000000    0.0000000   0.0000000
nbr.na         0.0000000    0.0000000   0.0000000
min           10.4000000   52.0000000   1.5130000
max           33.9000000  335.0000000   5.4240000
range         23.5000000  283.0000000   3.9110000
sum          642.9000000 4694.0000000 102.9520000
median        19.2000000  123.0000000   3.3250000
mean          20.0906250  146.6875000   3.2172500
SE.mean        1.0654240   12.1203173   0.1729685
CI.mean.0.95   2.1729465   24.7195501   0.3527715
var           36.3241028 4700.8669355   0.9573790
std.dev        6.0269481   68.5628685   0.9784574
coef.var       0.2999881    0.4674077   0.3041285

psych包中describe()函数计算 非缺失值的数量、平均数、标准差、中位数、截尾均值、绝对中位差、最小值、最大值、值域、偏度、峰度和平均值的标准误。

3）通过psych包中的describe()函数计算描述性统计量

> library(psych)
> describe(mtcars[vars])
    vars  n   mean    sd median trimmed   mad   min    max  range skew kurtosis    se
mpg    1 32  20.09  6.03  19.20   19.70  5.41 10.40  33.90  23.50 0.61    -0.37  1.07
hp     2 32 146.69 68.56 123.00  141.19 77.10 52.00 335.00 283.00 0.73    -0.14 12.12
wt     3 32   3.22  0.98   3.33    3.15  0.77  1.51   5.42   3.91 0.42    -0.02  0.17

 

 

2. 分组计算描述性统计量

> aggregate(mtcars[vars], by=list(am=mtcars$am), mean)
  am      mpg       hp       wt
1  0 17.14737 160.2632 3.768895
2  1 24.39231 126.8462 2.411000
> aggregate(mtcars[vars], by=list(am=mtcars$am), sd)
  am      mpg       hp        wt
1  0 3.833966 53.90820 0.7774001
2  1 6.166504 84.06232 0.6169816
> 

　　

使用by()分组计算描述性统计量（失败）

doBy包安装失败

 

使用psych包中的describe.by()分组计算概述统计量

> library(psych)
> describe.by(mtcars[vars], mtcars$am)

 Descriptive statistics by group 
group: 0
    vars  n   mean    sd median trimmed   mad   min    max  range  skew kurtosis    se
mpg    1 19  17.15  3.83  17.30   17.12  3.11 10.40  24.40  14.00  0.01    -0.80  0.88
hp     2 19 160.26 53.91 175.00  161.06 77.10 62.00 245.00 183.00 -0.01    -1.21 12.37
wt     3 19   3.77  0.78   3.52    3.75  0.45  2.46   5.42   2.96  0.98     0.14  0.18
-------------------------------------------------------------------- 
group: 1
    vars  n   mean    sd median trimmed   mad   min    max  range skew kurtosis    se
mpg    1 13  24.39  6.17  22.80   24.38  6.67 15.00  33.90  18.90 0.05    -1.46  1.71
hp     2 13 126.85 84.06 109.00  114.73 63.75 52.00 335.00 283.00 1.36     0.56 23.31
wt     3 13   2.41  0.62   2.32    2.39  0.68  1.51   3.57   2.06 0.21    -1.17  0.17
Warning message:
describe.by is deprecated.  Please use the describeBy function 
> 

　

通过reshape包分组计算概述统计量

> library(reshape)
> dstats <- function(x)(c(n=length(x), mean=mean(x), sd=sd(x)))
> dfm <- melt(mtcars, measure.vars=("mpg","hp","wt"), id.vars=c("am","cyl"))
Error: unexpected ‘,‘ in "dfm <- melt(mtcars, measure.vars=("mpg","
> dfm <- melt(mtcars, measure.vars=c("mpg","hp","wt"), id.vars=c("am","cyl"))
> cast(dfm, am+cyl+variable~.,dstats)
   am cyl variable  n       mean         sd
1   0   4      mpg  3  22.900000  1.4525839
2   0   4       hp  3  84.666667 19.6553640
3   0   4       wt  3   2.935000  0.4075230
4   0   6      mpg  4  19.125000  1.6317169
5   0   6       hp  4 115.250000  9.1787799
6   0   6       wt  4   3.388750  0.1162164
7   0   8      mpg 12  15.050000  2.7743959
8   0   8       hp 12 194.166667 33.3598379
9   0   8       wt 12   4.104083  0.7683069
10  1   4      mpg  8  28.075000  4.4838599
11  1   4       hp  8  81.875000 22.6554156
12  1   4       wt  8   2.042250  0.4093485
13  1   6      mpg  3  20.566667  0.7505553
14  1   6       hp  3 131.666667 37.5277675
15  1   6       wt  3   2.755000  0.1281601
16  1   8      mpg  2  15.400000  0.5656854
17  1   8       hp  2 299.500000 50.2045815
18  1   8       wt  2   3.370000  0.2828427

　　

3. 结果的可视化