library(pracma)
data(titanium)
head(titanium)

xi = seq(595,1075,1)
yi = interp1(titanium$x, titanium$y, xi = xi, method = "cubic")
plot(xi,yi,type="l")
xi[which(yi==max(yi))]
points(xi[which(yi==max(yi))],max(yi),col=2,pch=16)

N <- 10 # predpokladany pocet bodu
npeaks <- identify(xi,yi,pos=FALSE,n=N,plot=FALSE)  
##    zadej N bodu
npeaks <-sort(npeaks)
xtime <- xi[npeaks]
ytic <- yi[npeaks]
min(npeaks)
max(npeaks)

plot(xi,yi,type="l")
points(xtime,ytic,type="p",col=2,lwd=2)
bsln.loess <- loess(ytic ~ xtime)
pred <- predict(bsln.loess, xi)
points(xi,pred, type="l",lty=2,col=4,lwd=2)

intsc <- yi-pred
plot(xi,intsc,type="l")
abline(h=0,lty=2)

library(pracma)
plsplint = trapz(xi[min(npeaks):max(npeaks)], intsc[min(npeaks):max(npeaks)]) 
plsplint




####################### GRAFY ##############################################################


### data Iris 
data(iris)
summary(iris)
head(iris)
# edit(iris)
rownames(iris)=seq(1:length(iris[,1]))


#### pairwise scatterplot

pairs(iris[,-5])

library(ellipse)
ellipse::pairs(iris[,-5])

library(gclus)
cpairs(iris[,-5], order = NULL, panel.colors = NULL, border.color = "grey70",show.points = TRUE)

library(ggplot2)
library(GGally)
ggpairs(iris[,-5])

library(psych)
pairs.panels(iris[,-5],smooth=FALSE, scale=FALSE,density=TRUE,ellipses=FALSE,digits=2,
             method="spearman", pch = 20,lm=FALSE,cor=TRUE,jiggle=FALSE,factor=2,
             hist.col="cyan",show.points=TRUE,rug=FALSE)

library(pgirmess)
pairsrp(iris[,-5], meth = "spearman", pansmo = FALSE, abv = FALSE, lwt.cex = NULL)

library(PerformanceAnalytics)
chart.Correlation(iris[,-5], histogram=TRUE, pch=19)

library(TeachingDemos)
TeachingDemos::pairs2(iris[,c("Petal.Length", "Petal.Width")], iris[,c("Sepal.Length", "Sepal.Width")], panel = points, row1attop = TRUE, gap =.5)


#### Korelacni matice

library(mvoutlier)
corr.plot(iris$Petal.Length, iris$Petal.Width, quan=1/2, alpha=0.025)

library(TeachingDemos)
cor.rect.plot(iris$Petal.Length,iris$Petal.Width, corr = TRUE, col = c("#ff000055", "#0000ff55"))


cP <- cor(iris[,-5],method="pearson")
cS <- cor(iris[,-5],method="spearman")
cK <- cor(iris[,-5],method="kendall")
library(robustbase)
cR <- covMcd(iris[,-5],alpha=.75,nsamp=500,seed=NULL,use.correction=TRUE,control=rrcov.control(alpha=0.75),cor=T)$cor # robustni korelace MCD

library(ellipse)
plotcorr(cP, outline = TRUE, col = "gray", main="",numbers = FALSE, type = "full",cex.lab=0.8)

colors <- c("#A50F15","#DE2D26","#FB6A4A","#FCAE91","#FEE5D9","white",
            "#EFF3FF","#BDD7E7","#6BAED6","#3182BD","#08519C")
plotcorr(cP, col=colors)


library(DescTools)
cols =colors
PlotCorr(cP, cols = colors, breaks = seq(-1, 1, length = length(cols) + 1), border = "grey", lwd = 1, args.colorlegend = NULL, xaxt = par("xaxt"), yaxt = par("yaxt"), cex.axis = 0.8, las = 2, mar = c(3, 8, 8, 8))

library(corrplot) 
corrplot(cR, method = "pie", type = "upper", order = "original")
corrplot(cR, method="ellipse")
corrplot(cR, type="full", order="FPC", method = "ellipse",
         p.mat = NULL, sig.level = 0.05, insig = "blank",tl.col=1,tl.cex = 1,cl.cex =0.8)
# method = c("circle", "square", "ellipse", "number", "shade","color", "pie")
# type = c("full", "lower", "upper"),
# order = c("original","AOE", "FPC", "hclust", "alphabet")
# hclust.method = c("complete", "ward","ward.D", "ward.D2", "single", "average", "mcquitty", "median", "centroid"),


library(openair)
corPlot(iris[,-5], pollutants = NULL, type = "default",cluster = TRUE, cols = "hue", r.thresh = 0.8,text.col = c("black", "black"), auto.text = FALSE)
cols =c("default","increment","heat","spectral","hue", "brewer1","greyscale") 

corPlot(m, pollutants = NULL, type = "default",cluster = TRUE, cols = "hue", r.thresh = 0.8,text.col = c("black", "black"), auto.text = FALSE)
cols =c("default","increment","heat","spectral","hue", "brewer1","greyscale") 


### Heat map
col<- colorRampPalette(c("blue", "white", "red"))(20)
heatmap(x = cK, col = col, symm = TRUE)

heatmap(x = cor(m), col = col, symm = TRUE)


#############



## matematicke funkce

curve(5*x**3 + 6*x**2 - 5, -2, 2 , col = "blue", main = "CUBIC CURVE")

curve(3 * exp(-x/2), from = 0, to = 10, ylim = c(0, 2), ylab = "",col = "red", lwd = 2)
curve(4 * exp(-x), add = T, lty = 4, col = "blue", lwd = 2)
curve(2.5 * exp(-x/3), add = T, lty = 3, col = "darkgreen", lwd = 2)
text(3, 1.5, expression(paste(3 * e^(-x/2))), col = "red")
text(0.5, 1, expression(paste(4 * e^(-x))), col = "blue" )
text(7, 0.6, expression(paste(2.5 * e^(-x/3))), col = "darkgreen" )



x <- seq(-4, 4, 0.2)
y <- 2*x^2 + 4*x - 7

plot(x, y)

plot(x, y, pch = 16, col = "red")

plot(x, y, pch = 16, col = 1, xlim = c(-8, 8), ylim = c(-20, 50), main = "MY PLOT", xlab = "X VARIABLE" , ylab = "Y VARIABLE")

plot(x, y,pch=16,xlab="First",ylab="Second", main="RATINGS", cex = 1.5, cex.lab = 1.5, cex.main = 1.6, xlim=c(0,100), ylim=c(0,100))

points(x, y,col=4, pch=19)
rug(x) # jen osa x
rug(y, side=2) # jen osa y
lines(x, y,col=2)
abline(h = 0)
abline(v = 0)
abline(-10, 2) # primka: usek -10 a smernice 2.
abline(lm(y ~ x))
text(4, 20, "TEXT")
text(location, "text", pos=2)
mtext("text to place", side, line=n, ...)
legend(-8,36,"This is my Legend")
legend(bottomleft,"This is my Legend")
title(main="PLOT", col.main="red", font.main=2)


x <- seq(-pi, pi, length = 50)
y <- sin(x)
plot(x, y, pch = 17, cex = 0.7, col = "darkgreen")
lines(x, y, col = "darkgreen")

x <- seq(-pi, pi, length = 1000)
y <- sin(x)
plot(x, y, type = "l")
points(x, y, col = "darkgreen")




X <- c(1, 2, 3, 4, 5, 6, 7)
Y1 <- c(2, 4, 5, 7, 12, 14, 16)
Y2 <- c(3, 6, 7, 8, 9, 11, 12)

plot(X, Y1, type="b", pch = 17, cex=1.2, col="darkgreen", ylim=c(0,20))
lines(Y2, type="b", pch=16, lty=2, col="blue")

plot(X, Y1, type="b", pch = 17, cex=1.2, col="darkgreen", ylim=c(0,20))
points(X, Y2, type="b", pch=16, lty=2, col="blue")

plot(X, Y1, type="h", pch = 17, cex=1.2, col="darkgreen", ylim=c(0,20))
points(X, Y1, type="b", pch=16, lty=2, col="blue")

plot(X, Y1, type="s", pch = 17, cex=1.2, col="darkgreen", ylim=c(0,20))
points(X, Y1, type="p", pch=16, lty=2, col="blue")



# growth of orange trees
data(Orange)
head(Orange)

plot(Orange$age, Orange$circumference)
plot(circumference~age, data=Orange)

library(lattice)
xyplot(circumference~age, data=Orange)

library(ggplot2)
qplot(age, circumference, data=Orange)
qplot(Orange$age, Orange$circumference)

ggplot(Orange,aes(x=age,y=circumference))+
  geom_point()
ggplot(Orange)+
  geom_point(aes(x=age,y=circumference))
ggplot()+
  geom_point(aes(x=age,y=circumference),data=Orange)


plot(Petal.Length~Petal.Width, data=iris)
rug(iris$Petal.Width,col=3,ticksize=0.02)
rug(iris$Petal.Length,side=2,col=2,ticksize=0.02)
rug(iris$Petal.Width,side=3,col=3,ticksize=0.02)
rug(iris$Petal.Length,side=4,col=2,ticksize=0.02)





plot(circumference~age, data=Orange)
abline(lm(circumference~age, data=Orange), col="blue",lty=1,lwd=1)
lines(loess.smooth(Orange$age,Orange$circumference), col="red",lty=1,lwd=1)

library(lattice)
myPanel <- function(x,y){
  panel.xyplot(x,y,col="black") # Add the observations
  panel.lmline(x,y,col="blue") # Add the regression
  panel.loess(x,y,col="red") # Add the smooth line
}
xyplot(circumference~age, data=Orange, panel=myPanel)

qplot(age,circumference, data=Orange, geom = c("point","smooth"),
      xlab = "age", ylab = "circumference", method = "lm") + geom_smooth(color="red")


####

coplot(circumference ~ age | Tree, data = Orange, show.given = FALSE)

library(ggplot2)
qplot(age, circumference, data=Orange, facets=~Tree)
qplot(circumference,age, data=Orange, geom=c("line","point"),facets=~Tree)

library(lattice)
xyplot(circumference ~ age|Tree, data=Orange)
xyplot(circumference ~ age|Tree, data=Orange, type="b")

library(lattice)
myPanel <- function(x,y){
  panel.xyplot(x,y, type="b") #the observations
}
xyplot(circumference ~ age|Tree, data=Orange, panel=myPanel)


par(mfrow=c(2,3))
plot(circumference~age, data=Orange[Orange$Tree==1,], type="p")
plot(circumference~age, data=Orange[Orange$Tree==2,], type="p")
plot(circumference~age, data=Orange[Orange$Tree==3,], type="p")
plot(circumference~age, data=Orange[Orange$Tree==4,], type="p")
plot(circumference~age, data=Orange[Orange$Tree==5,], type="p")
par(mfrow=c(1,1))


library(ggplot2)
g1 = qplot(age,circumference, data=Orange[Orange$Tree==1,], geom="point")
g2 = qplot(age,circumference, data=Orange[Orange$Tree==2,], geom="point")
g3 = qplot(age,circumference, data=Orange[Orange$Tree==3,], geom="point")
g4 = qplot(age,circumference, data=Orange[Orange$Tree==4,], geom="point")
g5 = qplot(age,circumference, data=Orange[Orange$Tree==5,], geom="point")
library(gridExtra)
grid.arrange(g1,g2,g3,g4,g5,nrow=2,ncol=3)



####

plot(circumference~age, data=Orange[Orange$Tree==1,])
plot(circumference~age, data = Orange, subset = Tree == 2)

plot(circumference~age, data=Orange, col=0)
points(circumference~age, data=Orange[Orange$Tree==1,])

library(ggplot2)
qplot(age,circumference, data=Orange[Orange$Tree==1,], geom="point")
qplot(age,circumference, data=Orange[Orange$Tree==1,],geom=c("line","point"))



plot(iris$Petal.Length,iris$Petal.Width)
points(iris$Petal.Length[ff=="setosa"],iris$Petal.Width[ff=="setosa"],col=2,pch=19)
points(iris$Petal.Length[ff=="versicolor"],iris$Petal.Width[ff=="versicolor"],col=3,pch=19)
points(iris$Petal.Length[ff=="virginica"],iris$Petal.Width[ff=="virginica"],col=4,pch=19)

library(lattice)
xyplot(Petal.Width ~ Petal.Length, data=iris, groups=Species, type="p")

library(ggplot2)
ggplot(iris, aes(Petal.Length, Petal.Width, colour=Species)) +
  geom_point()



#### Ellipsoid Hull or Spanning Ellipsoid of a Point Set

library(car)
dataEllipse(iris$Petal.Length, iris$Petal.Width)
dataEllipse(iris$Petal.Length, iris$Petal.Width,groups = as.factor(iris$Species),col = c(2,3,4))


library(ggplot2)
ggplot(iris, aes(Petal.Length, Petal.Width, colour=Species)) +
  geom_point() +
  stat_ellipse()

ggplot(iris, aes(Petal.Length, Petal.Width, colour=Species)) +
  geom_point() +
  stat_ellipse(geom = "polygon",aes(fill = Species),alpha = 0.25)


#### Convex Hull

library(siar)
x <- iris$Petal.Length
y <- iris$Petal.Width
ff = iris$Species
plot(x,y, col=1,xlab="Sepal.Length",ylab="Sepal.Width")
points(x[ff=="setosa"], y[ff=="setosa"], pch=16, col=2, cex=1.2)
chnsu <-convexhull(x[ff=="setosa"], y[ff=="setosa"])
points(chnsu$xcoords, chnsu$ycoords,type="b", col=2, cex=1.2)
points(x[ff=="versicolor"], y[ff=="versicolor"], pch=16, col=3, cex=1.2)
chnxt <-convexhull(x[ff=="versicolor"], y[ff=="versicolor"])
points(chnxt$xcoords, chnxt$ycoords,type="b", col=3, cex=1.2)
points(x[ff=="virginica"], y[ff=="virginica"], pch=16, col=4, cex=1.2)
chnps <-convexhull(x[ff=="virginica"], y[ff=="virginica"])
points(chnps$xcoords, chnps$ycoords,type="b", col=4, cex=1.2)




plot(circumference~age, data=Orange[Orange$Tree==1,],col=0)
text(circumference~age,data=Orange[Orange$Tree==1,],names=rownames(Orange),cex=0.7,col=2)

plot(circumference~age, data=Orange[Orange$Tree==1,],col=1)
text(circumference~age,data=Orange[Orange$Tree==1,],labels=rownames(Orange[Orange$Tree==1,]),pos=3,cex=0.7,col=2)

plot(circumference~age, data=Orange[Orange$Tree==1,],col=1)
ind = identify(Orange[Orange$Tree==1,]$age,Orange[Orange$Tree==1,]$circumference,pos=FALSE,n=1,plot=FALSE)
points(Orange[Orange$Tree==1,]$age[ind],Orange[Orange$Tree==1,]$circumference[ind],pch=19,col=3)
text(Orange[Orange$Tree==1,]$age[ind],Orange[Orange$Tree==1,]$circumference[ind],labels=rownames(Orange)[ind],pos=3,cex=0.7,col=4)

plot(circumference~age, data=Orange[Orange$Tree==1,],col=1)
ind = locator(1)
points(ind$x,ind$y,pch=19,col=3)
text(ind$x,ind$y,labels=":-)",pos=3,cex=1,col=4)



library(plotrix)
plot(circumference~age, data=Orange[Orange$Tree==1,],col=1)
plotCI(Orange[Orange$Tree==1,]$age,Orange[Orange$Tree==1,]$circumference,uiw=Orange[Orange$Tree==1,]$age*0.1,err="x",add=TRUE,col=1,scol=1,sfrac=.005)
plotCI(Orange[Orange$Tree==1,]$age,Orange[Orange$Tree==1,]$circumference,uiw=Orange[Orange$Tree==1,]$circumference*0.1,err="y",add=TRUE,col=1,scol=1,sfrac=.005)

library(ggplot2)
ggplot(Orange[Orange$Tree==1,], aes(x=age, y=circumference)) + 
  geom_point()+
  geom_errorbar(aes(xmin=age*0.9, xmax=age*1.1), width=.2,
                position=position_dodge(0.05))+
  geom_errorbar(aes(ymin=circumference*0.9, ymax=circumference*1.1), width=.2,
                position=position_dodge(0.05))



plot(circumference~age, data=Orange, col=0)
points(circumference~age, data=Orange[Orange$Tree==1,], type="b", col=2)
points(circumference~age, data=Orange[Orange$Tree==2,], type="b", col=3)
points(circumference~age, data=Orange[Orange$Tree==3,], type="b", col=4)
points(circumference~age, data=Orange[Orange$Tree==4,], type="b", col="brown")
points(circumference~age, data=Orange[Orange$Tree==5,], type="b", col=6)
legend("topleft",col=c(2:4,"brown",6), legend = c(1:5),pch = 1,lty = 1)

library(ggplot2)
qplot(age,circumference,data=Orange,color=Tree, geom=c("line","point"))

ggplot(data=Orange, aes(y=circumference,x=age, color=Tree))+
  geom_point()+
  geom_line()

library(lattice)
xyplot(circumference ~ age, data=Orange, groups=Tree, type="b")



qplot(age,circumference,data=Orange, color=Tree, geom=c("point","smooth"),method="lm",se=FALSE)

ggplot(data=Orange, aes(x=circumference,y=age, color=Tree))+
  geom_point()+
  stat_smooth(method="lm",se=FALSE)


### Bublinovy graf

ggplot(data=iris, aes(x=Petal.Length, y=Petal.Width)) +
  geom_point(aes(size=Sepal.Length))

ggplot(data=iris, aes(x=Petal.Length, y=Petal.Width)) +
  geom_point(aes(size=Sepal.Length, color=Species))

ggplot(data=iris, aes(x=Petal.Length, y=Petal.Width)) +
  geom_point(aes(size=Sepal.Length, color=Sepal.Width))


#### Symbols

library(ggplot2)
library(ggChernoff)
ggplot(iris, aes(Sepal.Length, Sepal.Width, smile = Petal.Length, fill = Species)) +
  geom_chernoff()

ggplot(iris, aes(Petal.Length, Petal.Width, smile = Sepal.Width, fill = Species)) +
  geom_chernoff()

library(symbols)
# type of symbol plot:bar,profile,star,sun,polygon,face,stick,icon.
symbol(iris,type="face",coorx=iris$Sepal.Length,coory=iris$Sepal.Width,colout=3)
symbol(iris,type="face",coorx=iris$Petal.Length,coory=iris$Petal.Width,colout=3)
symbol(iris,type="icon",coorx=iris$Petal.Length,coory=iris$Petal.Width,colout=3)



library(MASS)
x <- iris$Petal.Width
y <- iris$Petal.Length
z <- kde2d(x, y)
plot(x, y, pch = 19, col=4)
contour(z, lwd = 1, add = TRUE, col = 1)


smoothScatter(iris$Petal.Width,iris$Petal.Length)


library(hexbin)
x <- iris$Petal.Width
y <- iris$Petal.Length
hex <- hexbin(x, y)
plot(hex,legend = FALSE)

plot(hex,legend = FALSE,
     colramp = colorRampPalette(hcl.colors(12, "GnBu")))