#tab=read.table("nomduficier.csv",sep=";",)
data("iris")
head(iris)

summary(iris)
var(iris$Petal.Length)

# représentation de distributions de variables quantitatives
hist(iris$Sepal.Length)
boxplot(iris$Sepal.Length)

# représentation de variables quantitatives ~ variables qualitatives
plot(iris$Sepal.Length~iris$Species,col=c("purple","pink","red"))

install.packages("vioplot")
library(vioplot)
vioplot(iris$Sepal.Length~iris$Species,col=c("purple","pink","red"))

# représentation de deux variables quantitatives
plot(iris$Sepal.Length~iris$Sepal.Width,pch=19)
plot(iris[,c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")])

plot(iris$Sepal.Length~iris$Sepal.Width,pch=19,col=c("purple","pink","red")[as.factor(iris$Species)])
legend("topleft",levels(iris$Species),pch=19,col=c("purple","pink","red"))

plot(iris[,c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")],,pch=19,col=c("purple","pink","red")[as.factor(iris$Species)])


# variables qualitatives ~ variables qualitatives

## On crée une nouvelle variable facteur : 
iris$Sepal.Length_cat[iris$Sepal.Length<=5]<-"petit"
iris$Sepal.Length_cat[iris$Sepal.Length<=6 & iris$Sepal.Length>5]<-"moyen"
iris$Sepal.Length_cat[iris$Sepal.Length>6]<-"grand"

# création d'une table de contingence
conting=table(iris[,c("Sepal.Length_cat","Species")])

barplot(conting,beside=T,legend=T)


# variables quanti ~ plusieurs variables qualitatives
iris2=iris[,c("Sepal.Length","Species")]
colnames(iris2)[1]="length"
iris2$measure="Sepal.Length"

iris3=iris[,c("Sepal.Width","Species")]
colnames(iris3)[1]="length"
iris3$measure="Sepal.Width"

iris_bis=rbind(iris2,iris3)
interaction.plot(x.factor=iris_bis$measure,trace.factor=iris_bis$Species,response=iris_bis$length,fun=mean,type="b", col=c("black","red","green"), pch=19, fixed=TRUE, leg.bty = "o")


# changements de variables

exempleA=c(0.05,0.03,0.2,0.17,0.045,0.5,0.6,3)
plot(exempleA)
plot(log(exempleA))