#جواب_تمرین_شماره_11

#تولید_نمونه_تصادفی_از_توزیع #وایبل

@R_Experts

#Points

 points(...)

function adds a group of points to plot. It's usage is:

points(x, y, ...)

x,y:Vector of coordinates

First let's make a scatter plot:

>x <- c(1.2,3.4,1.3,-2.1,5.6,2.3,3.2,2.4,2.1,1.8,1.7,2.2)

>y <- c(2.4,5.7,2.0,-3,13,5,6.2,4.8,4.2,3.5,3.7,5.2)

>plot(x,y,cex=.8,pch=1,xlab="x",ylab="y",col="black")

Add some points to the plot:

>x2 <- c(4.1,1.1,-2.3,-0.2,-1.2,2.3)

>y2 <- c(2.3,4.2,1.2,2.1,-2,4.3)

>points(x2,y2,cex=.8,pch=3,col="blue")

Notice that there is a point almost out of the left border. If the added points are out of the plot border, they were not be added to the plot. In the example above, the smallest value of x is -2.1, and largest is 5.6, the y value range is -3 < y < 13, so the added points should be inside that range.

The cex= controls the size of the points, pch= controls the point shape, and col= controls the point color. Here is a list of all pch symbols, and here is a complete chart of R color names. Let add some points of filled diamond shape, large size, and red color:

>x3 <- c(0,4)

>y3 <- c(10,-0.5)

>points(x3,y3,cex=4,pch=18,col="red")

@R_Experts

#Scatter_plot

Following is a csv file example, we will draw a Scatter Plot of the "Expression" and "Quality" values,
Let first read in the data from the file:

 > x <- read.csv("scatterplot.csv",header=T,sep="\t")
    > x <- t(x)
    > ex <- as.numeric(x[2,1:ncol(x)])
    > qu <- as.numeric(x[3,1:ncol(x)])

Draw a Scatter Plot:

> plot(ex,qu)

If we want to draw different subtype in different color and symbol, we need more work like follows:

 > plot(ex,qu,col="white",xlab="Expression", ylab="Quality")
    > points(ex[1:143],qu[1:143],col="red",pch=3,cex=.6) #Subtype A
    > points(ex[144:218],qu[144:218],col="blue",pch=19,cex=.6) #Subtype B
    > points(ex[219:ncol(x)],qu[219:ncol(x)],col="black",,pch=1,cex=.6) #Subtype C
    > abline(lm(ex[144:218] ~ qu[144:218]),col="blue")

#regression expression ~ quality of BFollowing code can add a legend on the right:

  > layout(matrix(c(1,2), nrow = 1), widths = c(0.7, 0.3))
    > par(mar = c(5, 4, 4, 2) + 0.1)
    > plot(ex,qu,col="white",xlab="Expression", ylab="Quality")
    > points(ex[219:ncol(x)],qu[219:ncol(x)],col="black",,pch=1,cex=.6)
    > points(ex[144:218],qu[144:218],col="blue",pch=19,cex=.6)
    > points(ex[1:143],qu[1:143],col="red",cex=.6,pch=3)
    > abline(lm(ex[144:218] ~ qu[144:218]),col="blue")
    > par(mar = c(5, 0, 4, 1) + 0.1)
    > plot(ex,qu,axes=FALSE,ann=FALSE, col="white")
    > legend(x=-2.5,y=1.2,c("A (n=146)","B (n=77)","C (n=85)"),cex=.8, pch=c(1,19,3),col=c("black","blue", "red"))

R package "scatterplot3d" can be used to draw 3D scatter plots, to install this package:

 > install.packages("scatterplot3d")

To draw a 3D scatter plot based on the "Expression", "Quality" and "Height" values:

  > library(scatterplot3d)
    > hi <- as.numeric(x[4,1:ncol(x)])
    > scatterplot3d(ex,qu,hi,pch=20,highlight.3d=T)

We can add more parameters like:

 scatterplot3d(ex,qu,hi,pch=20,highlight.3d=T,type="h")

@R_Experts

#Lattice
#Graphs

The lattice package, written by Deepayan Sarkar, attempts to improve on base R graphics by providing better defaults and the ability to easily display multivariate relationships.
In particular, the package supports the creation of trellis graphs - graphs that display a variable or the relationship between variables, conditioned on one or more other variables.

The typical format is

graph_type(formula, data=) 

where graph_type is selected from the listed below.
formula specifies the variable(s) to display and any conditioning variables .
For example ~x|A means display numeric variable x for each level of factor A. y~x | A*B means display the relationship between numeric variables y and x separately for every combination of factor A and B levels. ~x means display numeric variable x alone.


@R_Experts

#Example _1

install.packages("lattice")
library(lattice)
attach(mtcars)

#create factors with value labels
gear.f<-factor(gear,levels=c(3,4,5),
   labels=c("3gears","4gears","5gears"))
cyl.f <-factor(cyl,levels=c(4,6,8),
   labels=c("4cyl","6cyl","8cyl"))

# kernel density plot
densityplot(~mpg,
   main="Density Plot",
   xlab="Miles per Gallon")

@R_Experts

densityplot(~mpg,
main="Density Plot",
xlab="Miles per Gallon")

@R_Experts

#Example_2

# 3d scatterplot by factor level
cloud(mpg~wt*qsec|cyl.f,
   main="3D Scatterplot by Cylinders")

@R_Experts

#Example_3

# dotplot for each combination of two factors

dotplot(cyl.f~mpg|gear.f,
   main="Dotplot Plot by Number of Gears and Cylinders",
   xlab="Miles Per Gallon")

# scatterplot matrix

``splom(mtcars[c(1,3,4,5,6)],

   main="MTCARS Data")

@R_Experts