查看数据基本情况
data<-read.csv("/Users/xxx/Desktop/whitewine.csv",sep=",",header = TRUE)
head(data)
class(data)
str(data)
library(mice)
library(VIM)
md.pattern(data)
aggr(data,prop=F,numbers=T)
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可以看出缺失两个,那么就要去掉
data<-data[-which(is.na(data[,2])),] aggr(data,prop=F,numbers=T)
单变量分析
由于我们最关注葡萄酒质量分布情况,所以对质量进行单变量分析
ggplot(aes(x=quality),data=data) + geom_bar()+scale_x_continuous(lim=c(3,9),breaks = seq(3,9,1))
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呈正态分布,很好
双变量分析
ggplot(data,aes(x=quality,y=density,group=quality))+geom_boxplot()
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糟糕,看到了离群值,删去
ylim1<-boxplot.stats(data$density)$stats[c(1, 5)]
ggplot(data = data,aes(x=quality,y=density,group=quality))+geom_boxplot()+
coord_cartesian(ylim = ylim1)
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多变量分析
ggplot(aes(x = alcohol, y = density, color = factor(quality)), data = data) +
geom_jitter(alpha = 0.2) +
scale_color_brewer(palette = "Blues") +
geom_smooth(method = "lm", se = FALSE,size=1) +
ylim(0.985, 1.005) +
labs(y = 'Density',x = 'Alcohol') +
ggtitle("density VS alcohol VS quality")
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由于quality是等序变量,所以用不同颜色的渐变表示
ggplot(data,aes(x=alcohol,y=density,colour=factor(quality)))+geom_point()+facet_wrap(~quality)
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想要原始数据,就在下面点个喜欢吧,然后留言就可以了~
6.1日更:最近事多没上简书,忽然发现好多人要数据集,领走吧
链接: https://pan.baidu.com/s/19827tjCK9HyCmvba6MAjeQ 密码: 3vuq
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