install.packages("mlbench")

library(mlbench)

update.packages(ask=FALSE, checkBuilt=TRUE)

dataset <- read.csv("C:\\R\\bin\\iris.csv", header=FALSE)

head(dataset,n=10)

    V1  V2  V3  V4          V5
1  5.1 3.5 1.4 0.2 Iris-setosa
2  4.9 3.0 1.4 0.2 Iris-setosa
3  4.7 3.2 1.3 0.2 Iris-setosa
4  4.6 3.1 1.5 0.2 Iris-setosa
5  5.0 3.6 1.4 0.2 Iris-setosa
6  5.4 3.9 1.7 0.4 Iris-setosa
7  4.6 3.4 1.4 0.3 Iris-setosa
8  5.0 3.4 1.5 0.2 Iris-setosa
9  4.4 2.9 1.4 0.2 Iris-setosa
10 4.9 3.1 1.5 0.1 Iris-setosa

library(mlbench)
data(PimaIndiansDiabetes)
y <- PimaIndiansDiabetes$diabetes
cbind(freq=table(y), percentage=prop.table(table(y))*100)

    freq percentage
neg  500   65.10417
pos  268   34.89583

summary(dataset)

      V1              V2              V3              V4       
Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
Median :5.800   Median :3.000   Median :4.350   Median :1.300  
Mean   :5.843   Mean   :3.054   Mean   :3.759   Mean   :1.199  
3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
     V5           
Length:150        
Class :character  
Mode  :character  

# 加载数据集
data(iris)
# 为每个属性创建单独的框线图
par(mfrow=c(1,4))
for(i in 1:4) {
boxplot(iris[,i], main=names(iris)[i])
}

data(iris)
par(mfrow=c(1,4))
for(i in 1:4) {
hist(iris[,i], main=names(iris)[i])
}

library(lattice)
data(iris)
par(mfrow=c(1,4))
for(i in 1:4) {
plot(density(iris[,i]), main=names(iris)[i])
}

library(mlbench)
data(BreastCancer)
# 为每个分类属性创建条形图
par(mfrow=c(2,4))
for(i in 2:9) {
counts <- table(BreastCancer[,i])
name <- names(BreastCancer)[i]
barplot(counts, main=name)
}

# 加载程序包
library(corrplot)
# 加载数据
data(iris)
# 计算相关性
correlations <- cor(iris[,1:4])
# 创建相关图
corrplot(correlations, method="circle")

# 加载数据
data(iris)
# 按类别着色的成对散点图
pairs(Species~., data=iris, col=iris$Species)

# 加载程序包
library(caret)
# 加载数据
data(iris)
# 按类值为每个属性绘制密度图
x <- iris[,1:4]
y <- iris[,5]
scales <- list(x=list(relation="free"), y=list(relation="free"))
featurePlot(x=x, y=y, plot="density", scales=scales)