#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")

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#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")

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#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")

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Diagnostics
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