1. aoliveira@univ-ab.pt, toliveir@univ-ab.pt

6. plot( ), boxplot( ), hist( )

7. nome(argumento1,argumento2,...)
>plot(x,y,xlab="Peso",ylab="Altura",
main=’’Diagrama de dispers˜o’’, col=2)
a

9. graphics
lattice

10. > demo(graphics) # programa de demonstrac˜o
¸a

11. plot( ) - caso unidimensional
(base de dados "iris" dispon´vel no R)
ı
>iris.joclad<-iris
>names(iris.joclad)<-c(Comp.S´pala,"Larg.S´pala",
e e
"Comp.P´tala","Larg.P´tala", "Esp´cies")
e e e
>plot(Comp.S´pala)
e

12. plot( )
>iris.joclad<-iris
>names(iris.joclad)<-c(Comp.S´pala,"Larg.S´pala",
e e
"Comp.P´tala","Larg.P´tala", "Esp´cies")
e e e
>plot(Comp.S´pala, ylab="Comprimento S´pala (cm)",
e e
col="red", pch=20, cex=1.4, main="Anderson Iris data")

13. plot( ) - caso bidimensional
>iris.joclad<-iris
>names(iris.joclad)<-c(Comp.S´pala,"Larg.S´pala","Comp.P´tala",
e e e
"Larg.P´tala", "Esp´cies")
e e
>plot(Comp.S´pala,Comp.P´tala)
e e

14. plot( )
>iris.joclad<-iris
>names(iris.joclad)<-c(Comp.S´pala,"Larg.S´pala","Comp.P´tala",
e e e
"Larg.P´tala", "Esp´cies")
e e
>plot(Comp.S´pala,Comp.P´tala, xlab="Comprimento S´pala
e e e
(cm)", ylab="Comprimento P´tala (cm)", col=3, pch=6)
e
>abline(lm(Comp.P´tala Comp.S´pala),col=2)
e e

15. pairs( ), para matrizes de diagramas de
dispers˜o
a

16. barplot( ) - unidimensional
>require(grDevices) # for colours
>tN <- table(Ni <- stats::rpois(1000, lambda=4))
>barplot(tN, col=rainbow(20))

17. barplot( ) - multidimensional
>barplot(height = cbind(Jovem = c(465, 91) / 465 * 100,
Adulto = c(840, 200) / 840 * 100, Idoso = c(37, 17) / 37
* 100), beside = FALSE, width = c(465, 840, 37), col =
c(1, 2), legend.text = c("Antes tratamento", "Ap´s
o
tratamento"), args.legend = list(x = "topleft"))

18. boxplot( )

19. hist( )
>hist(Comp.P´tala, breaks = "Sturges", + main =
e
paste("Histograma"), + xlab = "Comprimentos das P´talas
e
(cm)", ylab="Frequˆncia", + axes = TRUE, plot = TRUE,
e
ylim=c(0,40), col=c(1,2,3,4,5,6,7,8,9,10,11,12))

20. persp( )

21. ¸˜
> demo(lattice) # programa de demonstracao

22. barchart( )
>barchart(peso,variedade | local, data = cevada, groups
= ano, layout = c(1,6), stack = TRUE, auto.key =
list(points = FALSE, rectangles = TRUE, space =
"right"), ylab = "Peso de cevada (Ton./ha)", scales =
list(x = list(rot = 45)))

23. densityplot( )
> densityplot( altura | voz, data = cantor, layout =
c(2, 4), xlab = "Altura (polegadas)", bw = 5)

24. dotplot( )
> dotplot(variedade,peso | ano * local, data=cevada)

25. histogram( )
>histogram( altura | voz, data = cantor, nint = 17,
endpoints = c(59.5, 76.5), layout = c(2,4), aspect = 1,
xlab = "Altura (polegadas)")

26. xyplot( )
>xyplot(Comp.P´tala
e Larg.P´tala, data=iris,
e
groups=Esp´cies, auto.key=T)
e

27. cloud( )
>par.set <- list(axis.line = list(col = "transparent"), clip = list(panel = "off"))
print(cloud(Larg.S´pala
e Comp.P´tala * Larg.P´tala, data = iris, cex = .8, groups =
e e
Esp´cies, screen = list(z = 20, x = -70, y = 3), par.settings = par.set, scales =
e
list(col = "black")), split = c(1,1,2,1), more = TRUE) print(cloud(Comp.S´pala
e
Comp.P´tala * Larg.P´tala, data = iris, cex = .8, groups = Esp´cies, screen = list(z
e e e
= 20, x = -70, y = 0), par.settings = par.set, scales = list(col = "black")), split =
c(2,1,2,1))

28. biplot e triplot)
> library(agricolae)
> library(klaR)
> data(Oat2)
> startgraph
> biplot
> model<- AMMI(Oat2[,1], Oat2[,2], Oat2[,3], Oat2[,4],xlim=c(-35,20),ylim=c(-20,20),
graph="biplot")
> model<- AMMI(Oat2[,1], Oat2[,2], Oat2[,3], Oat2[,4],xlim=c(-35,20),ylim=c(-20,20),
graph="biplot",number=FALSE)
> triplot
> model<- AMMI(Oat2[,1], Oat2[,2], Oat2[,3], Oat2[,4],graph="triplot")