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84 lines
1.6 KiB
84 lines
1.6 KiB
6 years ago
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rm(list = ls())
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# set the enverionment
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path ='~/Dropbox/MachineLearningAlgorithms/python_code/data/Heart.csv'
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rawdata = read.csv(path)
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# summary of the data
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summary(rawdata)
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# plot of the summary
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plot(rawdata)
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dim(rawdata)
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head(rawdata)
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tail(rawdata)
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colnames(rawdata)
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attach(rawdata)
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# get numerical data and remove NAN
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numdata=na.omit(rawdata[,c(1:2,4:12)])
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cor(numdata)
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cov(numdata)
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dev.off()
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# laod cocorrelation Matrix plot lib
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library(corrplot)
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M <- cor(numdata)
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#par(mfrow =c (1,2))
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#corrplot(M, method = "square")
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corrplot.mixed(M)
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nrow=nrow(rawdata)
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ncol=ncol(rawdata)
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c(nrow, ncol)
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Nvars=ncol(numdata)
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# checking data format
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typeof(rawdata)
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install.packages("mlbench")
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library(mlbench)
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sapply(rawdata, class)
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dev.off()
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name=colnames(numdata)
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Nvars=ncol(numdata)
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# boxplot
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par(mfrow =c (4,3))
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for (i in 1:Nvars)
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{
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#boxplot(numdata[,i]~numdata[,Nvars],data=data,main=name[i])
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boxplot(numdata[,i],data=numdata,main=name[i])
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}
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# Histogram with normal curve plot
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dev.off()
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Nvars=ncol(numdata)
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name=colnames(numdata)
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par(mfrow =c (3,5))
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for (i in 1:Nvars)
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{
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x<- numdata[,i]
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h<-hist(x, breaks=10, freq=TRUE, col="blue", xlab=name[i],main=" ",
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font.lab=1)
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axis(1, tck=1, col.ticks="light gray")
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axis(1, tck=-0.015, col.ticks="black")
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axis(2, tck=1, col.ticks="light gray", lwd.ticks="1")
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axis(2, tck=-0.015)
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xfit<-seq(min(x),max(x),length=40)
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yfit<-dnorm(xfit,mean=mean(x),sd=sd(x))
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yfit <- yfit*diff(h$mids[1:2])*length(x)
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lines(xfit, yfit, col="blue", lwd=2)
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}
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library(reshape2)
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library(ggplot2)
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d <- melt(diamonds[,-c(2:4)])
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ggplot(d,aes(x = value)) +
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facet_wrap(~variable,scales = "free_x") +
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geom_histogram()
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