R語言實戰 - 基本統計分析（1）- 描述性統計分析
阿新 • • 發佈：2017-10-05
4.3 summary eas 方法 func -- 4.4 1.0 6.5
> 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. 方法雲集
> 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，標準偏差6.0. 分布呈現右偏（偏度0.6），較正態分布稍平（峰度-0.37）
Hmisc 包安裝失敗
1）通過Hmisc包中的describe()函數計算描述性統計量
2）通過pastecs包中的stat.desc()函數計算描述性統計量
> 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()函數計算 非缺失值的數量、平均數、標準差、中位數、截尾均值、絕對中位差、最小值、最大值、值域、偏度、峰度和平均值的標準誤。
3）通過psych包中的describe()函數計算描述性統計量
> 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. 分組計算描述性統計量
> 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()分組計算描述性統計量（失敗）
doBy包安裝失敗
使用psych包中的describe.by()分組計算概述統計量
> 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
>
通過reshape包分組計算概述統計量
> 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. 結果的可視化
R語言實戰 - 基本統計分析（1）- 描述性統計分析