### Przykładowe dane
x=c(3,6,4,8)
barplot(x)


barplot(x,col=3)

barplot(x,col=1:4)

### Wykresy dla danych Orange
barplot(Orange$circumference)

barplot(Orange$age,col=3)

barplot(Orange$age,col=rainbow(7))

barplot(Orange$age,col=rainbow(35))

barplot(sort(Orange$circumference))


### Wykrews słupkowy z biblioteki gplots

library(gplots)
# Dodane 95% słupki błędów
hh <- t(VADeaths)[, 5:1]
mybarcol <- "gray20"
ci.l <- hh * 0.85
ci.u <- hh * 1.15
mp <- barplot2(hh, beside = TRUE,
        col = c("lightblue", "mistyrose",
                "lightcyan", "lavender"),
        legend = colnames(VADeaths), ylim = c(0, 100),
        main = "Death Rates in Virginia", font.main = 4,
        sub = "Faked 95 percent error bars", col.sub = mybarcol,
        cex.names = 1.5, plot.ci = TRUE, ci.l = ci.l, ci.u = ci.u,
        plot.grid = TRUE)
mtext(side = 1, at = colMeans(mp), line = 2,
      text = paste("Mean", formatC(colMeans(hh))), col = "red")
box()


Wykresy słupkowe z biblioteki UsingR

library(UsingR)
record.high=c(95,95,93,96,98,96,97,96,95,97)
record.low= c(49,47,48,51,49,48,52,51,49,52)
normal.high=c(78,78,78,79,79,79,79,80,80,80)
normal.low= c(62,62,62,63,63,63,64,64,64,64)
actual.high=c(80,78,80,68,83,83,73,75,77,81)
actual.low =c(62,65,66,58,69,63,59,58,59,60)
x=rbind(record.low,record.high,normal.low,normal.high,actual.low,actual.high)
the.names=c("S","M","T","W","T","F","S")[c(3:7,1:5)]
superbarplot(x,names=the.names)



### Histogram
attach(chickwts)
hist(weight)

hist(weight,col=2)

hist(weight,col=1:7)

hist(weight,col=2,breaks=3)

hist(weight,col=2,breaks=18)

hist(weight,col=2,breaks=18,border="blue")

hist(rnorm(1000),col=3)

hist(rnorm(1000),col=3,density=15)

hist(rivers)

hist(rivers,n=20, col=rainbow(20))

hist(rivers,breaks=c(20,100,200,300,400,500,1000,4000),col=rainbow(6))

### Histogram - funkcje z biblioteki lattice


library("lattice")
histogram(~ iris$Sepal.Width | factor(Species), data = iris)

histogram(~ weight | factor(feed), data = chickwts)



### Histogram - z zaznaczonymi pojedynczymi obserwacjami
# mieszanka rozkładów normalnych
# - N( 0, 1) waga 0.3
# - N( 3, 1) waga 0.7
x <- rnorm( 1000,mean = sample( c(0, 3), size = 1000, prob = c(.3, .7),  replace = TRUE ) )

par( las = 1, mar = c(3,3,4,1)+.1 )
h <- hist( x, plot = FALSE, breaks = 15 )
d <- density( x )
plot( h , border = NA, freq = FALSE, xlab = "", ylab = "",main= "Use of clipping and translucency" )
usr <- par( "usr" )
ncolors <- 100
dy <- ( usr[4] - usr[3] ) / ncolors
colors <- colorRampPalette( c("yellow","orange","red") )(ncolors)

# poziome linie na wykresie
abline( h = axTicks(2) , col = "gray", lwd = .5 )

for( i in 1:ncolors){
  clip( usr[1], usr[2], usr[3] + (i-1) * dy, usr[3] + i*dy )
  plot( h, add = TRUE, axes = FALSE, ylab = "", xlab = "",
        col = colors[i], border = NA, freq = FALSE)
}
do.call( clip, as.list( usr) )

plot( h, add = TRUE, lwd = .5 , freq = FALSE, xlab = "", ylab = "", axes = FALSE )
lines( d, lwd = 4, col = "#77222288" )

#Dodanie znaczników pojedynczych obserwacji
rug( x, col = "#000000FF" )
box()