rm(list = ls())
# set the enverionment
path ='~/Dropbox/MachineLearningAlgorithms/python_code/data/Heart.csv'
rawdata = read.csv(path)

# summary of the data
summary(rawdata)
# plot of the summary
plot(rawdata)

dim(rawdata)
head(rawdata)
tail(rawdata)

colnames(rawdata)
attach(rawdata)

# get numerical data and remove NAN
numdata=na.omit(rawdata[,c(1:2,4:12)])

cor(numdata)
cov(numdata)

dev.off()
# laod cocorrelation Matrix plot lib
library(corrplot)
M <- cor(numdata)
#par(mfrow =c (1,2))
#corrplot(M, method = "square")
corrplot.mixed(M)


nrow=nrow(rawdata)
ncol=ncol(rawdata)
c(nrow, ncol)



Nvars=ncol(numdata)
# checking data format
typeof(rawdata)
install.packages("mlbench")
library(mlbench)
sapply(rawdata, class)

dev.off()
name=colnames(numdata)
Nvars=ncol(numdata)
# boxplot
par(mfrow =c (4,3))
for (i in 1:Nvars)
{
  #boxplot(numdata[,i]~numdata[,Nvars],data=data,main=name[i])
  boxplot(numdata[,i],data=numdata,main=name[i])
}

# Histogram with normal curve plot
dev.off()
Nvars=ncol(numdata)
name=colnames(numdata)
par(mfrow =c (3,5))
for (i in 1:Nvars)
{
  x<- numdata[,i]
  h<-hist(x, breaks=10, freq=TRUE, col="blue", xlab=name[i],main=" ",
            font.lab=1)
  axis(1, tck=1, col.ticks="light gray")
  axis(1, tck=-0.015, col.ticks="black")
  axis(2, tck=1, col.ticks="light gray", lwd.ticks="1")
  axis(2, tck=-0.015)
  xfit<-seq(min(x),max(x),length=40)
  yfit<-dnorm(xfit,mean=mean(x),sd=sd(x))
  yfit <- yfit*diff(h$mids[1:2])*length(x)
  lines(xfit, yfit, col="blue", lwd=2)
}


library(reshape2)
library(ggplot2)
d <- melt(diamonds[,-c(2:4)])
ggplot(d,aes(x = value)) +
  facet_wrap(~variable,scales = "free_x") +
  geom_histogram()