How to Display Multivariate Relationship Graphs With Lattice

INTRODUCTION

The lattice package is a special visualization package, as it takes base R graphics one step further by providing improved default graphs and the ability to display multivariate relationships.

PACKAGE INSTALLATION & DATA FRAME

The first thing you have to do is install and load all the packages that we are going to need for our examples. Moreover, we need a dataset to work with. The dataset we chose in our case is “mtcars”, which was extracted from the 1974 Motor Trend US magazine. It comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).


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


You can use head(mtcars) in order to see the variables of your dataset.

GENERAL FORM

The general form is: graph_type(formula, data=)

The graph_type is selected from the listed below. The formula sets the variable(s) to be displayed and every combination between them. For example, ~var1|A means display numeric variable var1 for each level of factor A. v1~v2 | A*B means display the relationship between numeric variables v1 and v2 separately for every combination of factor A and B levels. ~v1 means display numeric variable x alone.

GRAPH DESCRIPTION EXAMPLE
Barchart – bar chart – x~A or A~x
Bwplot – boxplot – x~A or A~x
Cloud – 3D scatterplot – z~x*y|A
Contourplot- 3D contour plot – z~x*y
Densityplot- kernal density plot – ~x|A*B
Dotplot – dotplot – ~x|A
Histogram – histogram – ~x
Levelplot – 3D level plot – z~y*x
Parallel – parallel coordinates plot – data frame
Splom – scatterplot matrix – data frame
Stripplot – strip plots – A~x or x~A
Xyplot – scatterplot – y~x|A
Wireframe – 3D wireframe graph – z~y*x

Learn more about the lattice package in the online course Comprehensive Graphic Visualizations with R. In this course you will learn how to:

• Work extensively with the lattice package and its functionality,
• Learn about the specific differences between base graphics, lattice and ggplot,
• And much more

1.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"))

2.Kernel Density Plot
densityplot(~hp,
main="Density Plot",
xlab="HP")

3.Kernel Density Plots by Factor Level
densityplot(~hp|cyl.f,
main="HP by Number of Cylinders",
xlab="HP")

4.Kernel Density Plots by Factor Level (alternate layout)
densityplot(~hp|cyl.f,
main="HP by Numer of Cylinders",
xlab="HP",
layout=c(1,3))

5.Boxplots For Each Combination of Two Factors
bwplot(cyl.f~hp|gear.f,
ylab="Cylinders", xlab="HP",
main="HP by Cylinders and Gears",
layout=(c(1,3)))

6.Scatterplots For Each Combination of Two Factors
xyplot(hp~drat|cyl.f*gear.f,
main="Scatterplots by Cylinders and Gears",
ylab="DRAT", xlab="HP")

7.3D Scatterplot by Factor Level
cloud(hp~drat*qsec|cyl.f,
main="3D Scatterplot by Cylinders")

8.Dotplot For Each Combination of Two Factors
dotplot(cyl.f~hp|gear.f,
main="Dotplot Plot by Number of Gears and Cylinders",
xlab="HP")

9.Scatterplot Matrix
splom(mtcars[c(1,3,4,5,6)],
main="MTCARS Data")

Related exercise sets:

Density-Based Clustering Exercises Data wrangling : Transforming (2/3) Building Shiny App exercises part 6 How to prepare and apply machine learning to your dataset Data Manipulation with data.table (part -2)