=====================================================================================

# RETTE DI REGRESSIONE

x <- c(1,2,3,4,5)
y <- c(0.5, 0.6, 0.7, 0.75, 0.8)

plot(x,y)

model1 <- lm(y~x)
coefficienti <- coef(model1)

abline(coefficienti[1], coefficienti[2])
=====================================================================================
# PIE CHARTS


(1)


# Esempio Pie Chart
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")

(2)


# Pie Chart CON PERCENTUALI
slices <- c(10, 12, 4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, pct) # add percents to labels 
lbls <- paste(lbls,"%",sep="") # ad % to labels 
pie(slices,labels = lbls, col=rainbow(length(lbls)), main="Pie Chart of Countries")


(3) pie Chart INSENSATI

slices <- c(30,45,10,15)
etichette <- c("100%", "200%", "300%", "500%")
pie(slices, labels=etichette)

======================================================================================

# ISTOGRAMMI (esplorazione per scegliere visivamente il miglior binsize (via breaks)  

BMI<-rnorm(n=1000, m=24.2, sd=2.2)
hist(BMI)
histinfo<-hist(BMI)

hist(BMI, breaks=5, main=”Breaks=5″)
hist(BMI, breaks=c(17,20,23,26,29,32), main="Breaks is vector of breakpoints")