EDA Plotting Systems

Plotting Systems

• Video Lecture URL: https://www.youtube.com/watch?v=a4mvbyNGdBA&feature=youtu.be

There are three different plotting systems in R and they each have different characteristics and modes of operation. They three systems are the base plotting system, the lattice system, and the ggplot2 system. This chapter (and this book) will focus primarily on the base plotting system.

The BASE Plotting System

• The base plotting system is the original plotting system for R. The basic model is sometimes referred to as the “artist’s palette” model. The idea is you start with blank canvas and build up from there.

• In more R-specific terms, you typically start with plot function (or similar plot creating function) to initiate a plot and then annotate the plot with various annotation functions (text, lines, points, axis)

• The base plotting system is often the most convenient plotting system to use because it mirrors how we sometimes think of building plots and analyzing data. If we don’t have a completely well-formed idea of how we want to look at some data, often we’ll start by “throwing some data on the page” and then slowly add more information to it as our thought process evolves.

• For example, we might look at a simple scatterplot and then decide to add a linear regression line or a smoother to it to highlight the trends.

data(airquality)
 with(airquality, {
 plot(Temp, Ozone)
 lines(loess.smooth(Temp, Ozone))
 })

# Load the cars data
data(cars)

## Create the plot / draw canvas
with(cars, plot(speed, dist))

## Add annotation
title("Speed vs. Stopping distance")

The Lattice System

• The lattice plotting system is implemented in the lattice package which comes with every installation of R (although it is not loaded by default). To use the lattice plotting functions you must first load the lattice package with the library function.

• With the lattice system, plots are created with a single function call, such as xyplot or bwplot. There is no real distinction between functions that create or initiate plots and functions that annotate plots because it all happens at once.

• Lattice plots tend to be most useful for conditioning types of plots, i.e. looking at how y changes with x across levels of z. These types of plots are useful for looking at multidimensional data and often allow you to squeeze a lot of information into a single window or page.

• Useful for looking at teh relationship between X & Y over levels of Z. - conditioning over Z

• Here is an example of a lattice plot that looks at the relationship between life expectancy and income and how that relationship varies by region in the United States.

library(lattice)

state <- data.frame(state.x77, region = state.region)
xyplot(Life.Exp ~ Income | region, data = state, layout = c(4, 1))

• You can see that the entire plot was generated by the call to xyplot and all of the data for the plot were stored in the state data frame. The plot itself contains four panels-one for each region-and within each panel is a scatterplot of life expectancy and income. The notion of panels comes up a lot with lattice plots because you typically have many panels in a lattice plot (each panel typically represents a condition, like “region”).

The ggplot2 System

• The ggplot2 plottings system attempts to split the difference between base and lattice in a number of ways. Taking cues from lattice, the ggplot2 system automatically deals with spacings, text, titles but also allows you to annotate by “adding” to a plot.

• Superficially, the ggplot2 functions are similar to lattice, but the system is generally easier and more intuitive to use. The defaults used in ggplot2 make many choices for you, but you can still customize plots to your heart’s desire. A typical plot with the ggplot package looks as follows.

library(ggplot2)

data(mpg)

qplot(displ, hwy, data = mpg)