通过镜像网站下载R语言的程序包
install.packages("包名称",repos="https://mirrors.tuna.tsinghua.edu.cn/CRAN/") ## 换成清华镜像
基础图形
条形图（Bar Plot）
需要导入vcd包
install.packages("vcd",repos="https://mirrors.tuna.tsinghua.edu.cn/CRAN/")
> library(vcd)
载入需要的程辑包：grid
> counts <- table(Arthritis$Improved)
> counts
None Some Marked
42 14 28
> par(mfrow=c(2,2))
> barplot(counts,
+ main="Simple Bar Plot",
+ xlab="Improvement", ylab="Frequency")
> barplot(counts,
+ main="Horizontal Bar Plot",
+ xlab="Frequency", ylab="Improvement",
+ horiz=TRUE)
> counts <- table(Arthritis$Improved, Arthritis$Treatment)
> counts
Placebo Treated
None 29 13
Some 7 7
Marked 7 21
> barplot(counts,
+ main="Stacked Bar Plot",
+ xlab="Treatment", ylab="Frequency",
+ col=c("red", "yellow","green"),
+ legend=rownames(counts))
> barplot(counts,
+ main="Grouped Bar Plot",
+ xlab="Treatment", ylab="Frequency",
+ col=c("red", "yellow", "green"),
+ legend=rownames(counts), beside=TRUE)
Pie Chart（饼图）
> library(plotrix)
> par(mfrow=c(2,2))
> slices <- c(10, 12,4, 16, 8)
> lbls <- c("US", "UK", "Australia", "Germany", "France")
> pie(slices, labels = lbls,main="Simple Pie Chart",edges=300,radius=1)
> pct <- round(slices/sum(slices)*100)
> lbls2 <- paste(lbls, " ", pct, "%", sep="")
> pie(slices, labels=lbls2, col=rainbow(length(lbls2)),
+ main="Pie Chart with Percentages",edges=300,radius=1)
> pie3D(slices, labels=lbls,explode=0.1,
+ main="3D Pie Chart ",edges=300,radius=1)
> mytable <- table(state.region)
> lbls3 <- paste(names(mytable), "\n", mytable, sep="")
> pie(mytable,labels=lbls3,
+ main="Pie Chart from a Table\n(with sample size)",
+ edges=300,radius=1)
Fan Plot（扇形图）
> slices <- c(10, 12,4, 16, 8)
> lbls <- c("US", "UK", "Australia", "Germany", "France")
> fan.plot(slices, labels = lbls, main="Fan Plot")
Dot Plot（点图）
> dotchart(mtcars$mpg,
+ labels=row.names(mtcars),cex=0.7,
+ main="Gas Mileage for Car Models",
+ xlab="Miles Per Gallon")
基础统计
描述统计
head(x)查看数据集,展示x的前几行（默认前6行），可以通过head(x,n=10)来显示前10行
> head(mtcars)
mpg cyl disp hp drat
Mazda RX4 21.0 6 160 110 3.90
Mazda RX4 Wag 21.0 6 160 110 3.90
Datsun 710 22.8 4 108 93 3.85
Hornet 4 Drive 21.4 6 258 110 3.08
Hornet Sportabout 18.7 8 360 175 3.15
Valiant 18.1 6 225 105 2.76
wt qsec vs am gear
Mazda RX4 2.620 16.46 0 1 4
Mazda RX4 Wag 2.875 17.02 0 1 4
Datsun 710 2.320 18.61 1 1 4
Hornet 4 Drive 3.215 19.44 1 0 3
Hornet Sportabout 3.440 17.02 0 0 3
Valiant 3.460 20.22 1 0 3
carb
Mazda RX4 4
Mazda RX4 Wag 4
Datsun 710 1
Hornet 4 Drive 1
Hornet Sportabout 2
Valiant 1
summary()获取描述性统计量，可以提供最小值、最大值、四分位数和数值型变量的均值，以及因子向量和逻辑型向量的频数统计等
> summary(mtcars)
mpg cyl
Min. :10.40 Min. :4.000
1st Qu.:15.43 1st Qu.:4.000
Median :19.20 Median :6.000
Mean :20.09 Mean :6.188
3rd Qu.:22.80 3rd Qu.:8.000
Max. :33.90 Max. :8.000
disp hp
Min. : 71.1 Min. : 52.0
1st Qu.:120.8 1st Qu.: 96.5
Median :196.3 Median :123.0
Mean :230.7 Mean :146.7
3rd Qu.:326.0 3rd Qu.:180.0
Max. :472.0 Max. :335.0
drat wt
Min. :2.760 Min. :1.513
1st Qu.:3.080 1st Qu.:2.581
Median :3.695 Median :3.325
Mean :3.597 Mean :3.217
3rd Qu.:3.920 3rd Qu.:3.610
Max. :4.930 Max. :5.424
qsec vs
Min. :14.50 Min. :0.0000
1st Qu.:16.89 1st Qu.:0.0000
Median :17.71 Median :0.0000
Mean :17.85 Mean :0.4375
3rd Qu.:18.90 3rd Qu.:1.0000
Max. :22.90 Max. :1.0000
am gear
Min. :0.0000 Min. :3.000
1st Qu.:0.0000 1st Qu.:3.000
Median :0.0000 Median :4.000
Mean :0.4062 Mean :3.688
3rd Qu.:1.0000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000
carb
Min. :1.000
1st Qu.:2.000
Median :2.000
Mean :2.812
3rd Qu.:4.000
Max. :8.000
频率和列联表
table()可以展现变量的频率
> attach(mtcars)
> table(cyl)
cyl
4 6 8
11 7 14
#cy1中4有11个，6有7个，8有14个
> summary(mpg)
Min. 1st Qu. Median Mean 3rd Qu.
10.40 15.43 19.20 20.09 22.80
Max.
33.90
#通过summary可以分析出mpg在[10,34]之间
#cut()可以讲区间划分
> table(cut(mpg,seq(10,34,by=2)))#10到34，2个一组
(10,12] (12,14] (14,16] (16,18] (18,20]
2 1 7 3 5
(20,22] (22,24] (24,26] (26,28] (28,30]
5 2 2 1 0
(30,32] (32,34]
2 2
相关性
- cov(x,y):x和y间的协方差；如果x,y为矩阵或数据框，返回x和y各列的协方差
- var(x):向量x的样本方差；如果x是矩阵或数据框，协方差矩阵将被计算
- cor(x):如果x是矩阵或数据框，相关系数矩阵将被计算
求矩阵或数据框时cov=var
> states = state.x77[,1:6]
> cov(states)
Population Income
Population 19931683.7588 571229.7796
Income 571229.7796 377573.3061
Illiteracy 292.8680 -163.7020
Life Exp -407.8425 280.6632
Murder 5663.5237 -521.8943
HS Grad -3551.5096 3076.7690
Illiteracy Life Exp
Population 292.8679592 -407.8424612
Income -163.7020408 280.6631837
Illiteracy 0.3715306 -0.4815122
Life Exp -0.4815122 1.8020204
Murder 1.5817755 -3.8694804
HS Grad -3.2354694 6.3126849
Murder HS Grad
Population 5663.523714 -3551.509551
Income -521.894286 3076.768980
Illiteracy 1.581776 -3.235469
Life Exp -3.869480 6.312685
Murder 13.627465 -14.549616
HS Grad -14.549616 65.237894
> var(states)
Population Income
Population 19931683.7588 571229.7796
Income 571229.7796 377573.3061
Illiteracy 292.8680 -163.7020
Life Exp -407.8425 280.6632
Murder 5663.5237 -521.8943
HS Grad -3551.5096 3076.7690
Illiteracy Life Exp
Population 292.8679592 -407.8424612
Income -163.7020408 280.6631837
Illiteracy 0.3715306 -0.4815122
Life Exp -0.4815122 1.8020204
Murder 1.5817755 -3.8694804
HS Grad -3.2354694 6.3126849
Murder HS Grad
Population 5663.523714 -3551.509551
Income -521.894286 3076.768980
Illiteracy 1.581776 -3.235469
Life Exp -3.869480 6.312685
Murder 13.627465 -14.549616
HS Grad -14.549616 65.237894
> cor(states)
Population Income
Population 1.00000000 0.2082276
Income 0.20822756 1.0000000
Illiteracy 0.10762237 -0.4370752
Life Exp -0.06805195 0.3402553
Murder 0.34364275 -0.2300776
HS Grad -0.09848975 0.6199323
Illiteracy Life Exp
Population 0.1076224 -0.06805195
Income -0.4370752 0.34025534
Illiteracy 1.0000000 -0.58847793
Life Exp -0.5884779 1.00000000
Murder 0.7029752 -0.78084575
HS Grad -0.6571886 0.58221620
Murder HS Grad
Population 0.3436428 -0.09848975
Income -0.2300776 0.61993232
Illiteracy 0.7029752 -0.65718861
Life Exp -0.7808458 0.58221620
Murder 1.0000000 -0.48797102
HS Grad -0.4879710 1.00000000
T检验
> x = rnorm(100, mean = 10, sd = 1)#平均数为10，标准差为1
> y = rnorm(100, mean = 30, sd = 10)#平均数为30，标准差为10
> t.test(x, y, alt = "two.sided",paired=TRUE)
Paired t-test
data: x and y
t = -19.353, df = 99, p-value <
2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-21.78096 -17.73004
sample estimates:
mean of the differences
-19.7555
群体差异的非参数检验
> wilcox.test(x,y,alt="less")
Wilcoxon rank sum test with
continuity correction
data: x and y
W = 294, p-value < 2.2e-16
alternative hypothesis: true location shift is less than 0
