Introduction to Graphics in R - PowerPoint Presentation

Slide1

Introduction to Graphics in R

3/12/2014Slide2

First, let’s get some data

Load the Duncan dataset

It’s in the car package. Remember how to get it?

library(car)

data(Duncan)Slide3

Getting started

Okay, now plot income levels:

plot(

Duncan$income

)

What is this graph? Can you make it a line plot instead?

plot(

Duncan$income

, type=“l”)Slide4

Histogram

The X axis is useless. Wouldn’t a histogram be more informative?

Make a histogram

If you’re stuck, use google

hist

(

Duncan$income

)Slide5

Fix the title

‘Histogram of

Duncan$income

’ is not a good title

Change it to ‘Income Distribution in Duncan Dataset’

hist

(

Duncan$income

, main="Income Distribution in Duncan Dataset")Slide6

Another option

There’s another way to set the title. Maybe some of you will have done this (my crystal ball is murky):

hist

(

Duncan$income

)

title("Income Distribution in Duncan Dataset“)

But wait. That looks awful. We need to not print the title as part of the

hist

() call. How do we do that?

hist

(

Duncan$income

, main="")Slide7

Scatterplot

Okay, let’s look at income vs. prestige

Make a scatterplot comparing income (x-axis) to prestige (y-axis)

plot(

Duncan$income

,

Duncan$prestige

)

Did you get the x- and y- axes right?

Add a title: Income vs. Prestige

title(“Income vs. Prestige”)Slide8

Scatterplot: Axis labels

The axis labels display the variable names. Can we do better than that?

Label the X axis “Income” and the Y axis “Prestige”

plot(

Duncan$income

,

Duncan$prestige

,

xlab

="Income",

ylab

="Prestige")Slide9

Scatterplot: Axis range

How come income doesn’t have ticks at 0 and 100 but prestige does?

Make both axes run from 0 to 100

plot(

Duncan$income

,

Duncan$prestige

,

xlab

="Income",

ylab

="Prestige",

xlim

=c(0,100))Slide10

Scatterplot Axis Tick Marks

Actually, your

collaborator wants

tick marks every 5 points on the X axis.

DO IT

Caveat: this is trickier:

plot(

Duncan$income

,

Duncan$prestige

,

xlab

="Income",

ylab

="Prestige",

xlim

=c(0,100),

xaxt

="n")

axis(1, at=

seq

(0,100, by=5))Slide11

Axis labels sideways

Your

collaborator still

isn’t happy. Turn the x labels sideways.

plot(

Duncan$income

,

Duncan$prestige

,

xlab

="Income",

ylab

="Prestige",

xlim

=c(0,100),

xaxt

="n")

axis(1,

las

=2, at=

seq

(0,100, by=5))Slide12

More columns

Now your

collaborator wants

to see how education affect this relationship. Create a dichotomous variable named ‘

high_education

’ categorizing education > 50 as TRUE and <= 50 as FALSE

Duncan$high_education

<-

Duncan$education

> 50Slide13

High education: sanity check

How many high and low education jobs are there?

table(

Duncan$high_education

)

Plot education (y-axis) by

high_education

(x-axis)

plot(

Duncan$high_education

,

Duncan$education

)

Does it look right?Slide14

Adding color

Okay, now color your income/prestige graph so high-education jobs are blue and low-education jobs are red

This is a little tricky

colors <-

as.numeric

(

Duncan$high_education

)+1

plot(Duncan$income

,

Duncan$prestige

, col=c("red", "blue")[colors],

xlab

="Income",

ylab

="Prestige",

xlim

=c(0,100),

xaxt

="n")

axis(1, at=seq(0,100, by=5))Slide15

Bar plot

Okay, now run this code:

plot(Duncan$type

,

Duncan$income

)

What happened? Why didn't we get a

scatterplot

? Can you get one?

plot(as.numeric(Duncan$type

),

Duncan$income

)Slide16

More than one plot at a time

Now your collaborator wants your

scatterplot

and histogram side-by-side. (Don’t worry about color if you don't want to)

opar

<-par()

par(mfrow

=c(1,2))

hist(Duncan$income

, main="Income Distribution in Duncan Dataset")

plot(Duncan$income

,

Duncan$prestige

,

xlab

="Income",

ylab

="Prestige",

xlim

=c(0,100),

xaxt

="n")

axis(1, at=seq(0,100, by=5))

par(opar

)Slide17

ggplot

ggplot

is a whole different beast from base graphics

ggplot

is like R itself – some work to get oriented, but powerful once you do

You don't have to know

ggplot

to be successful using R

But you do have to experiment with it for this classSlide18

Load the ggplot library

Hint: the package name, confusingly, is ggplot2Slide19

Plot income vs. prestige

It will be easiest to start using

qplot

.

Qplot

mimics plot(), but uses the

ggplot

layout engine.

qplot(Duncan$income

,

Duncan$prestige

)Slide20

ggplot

qplot

is the training wheels version of

ggplot

ggplot's

syntax takes some getting used to. Try this:

ggplot(Duncan

) +

aes(x

=income,

y

=prestige) +

geom_point

(

)

Huh? What are the pluses about?Slide21

ggplot syntax

ggplot

objects are weird

You execute them (like a command) to draw their plot

But you construct them by adding options to them

Options specify data source, data columns, etc, resulting in code like this:

p

<-

ggplot

(Duncan

)

p

<-

p

+

aes

(x

=income,

y

=prestige

)

p

+

geom_point

()Slide22

Where ggplot shines

In my opinion, it's harder to think about doing simple plots in

ggplot

But when I want to do something multi-faceted (e.g. with different colors, sizes, etc.),

ggplot

makes it really easy

I use it a lot for to understand 3+-way relationships in dataSlide23

ggplot example (one of many)Slide24

ggplot code for that example

ggplot(data

=

nycnames

)

+

aes

(x

=

as.factor(race

),

y

=n1_013002p, color=

as.factor(

nbhdarkwalk

)) +

geom_point

(position

="jitter")

+

scale_x_discrete

(breaks

=1:7, limits=1:7, name="Subject Race", labels=

c('Asian

', 'Black', 'First\

nPeoples

', 'Pacific\

nIslander

', 'Non-Hispanic\

nWhite

', 'Other', 'Hispanic')

) +

scale_color_discrete

(breaks

=1:4, limits=1:4, name="Neighborhood Safe After Dark", labels=

c('Strongly

Agree', 'Somewhat Agree', 'Somewhat disagree', 'Strongly Disagree')

) +

scale_y_continuous

(name

="Neighborhood percent white (1km buffer)")Slide25

Exercises