Data Science: Data Visualization Boot CampRelationshipBubble Plot

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

24 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 202024 January 2020

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Table of contents (1 of 1)

1 TypeUsesGeneral considerations

2 Sample data

3 Hands on

4 Q & A

5 Conclusion6 References7 Files

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A definition

“A bubble graph is a vari-ation of a point or line graphwhere the data points (dots)have been replaced by circles(bubbles). The major advan-tage of a bubble graph versus apoint or line graph is the abil-ity to encode one or more addi-tional variables by means of thebubble symbol. Bubble graphsmight be two or three dimen-sional, . . . ”

R. L. Harris [1]

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R supplied data set (1 of 2)

Included in the R package ggplot2.

“This dataset contains a subset of the fuel econ-omy data that the EPA makes available on . It contains only models which hada new release every year between 1999 and 2008 - this wasused as a proxy for the popularity of the car.”

H. Wickham [2]

library(ggplot2)

?mpg

head(mpg)

Resulting in:

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R supplied data set (2 of 2)

# A tibble: 6 x 11

manufacturer model displ year cyl trans drv cty hwy fl class

1 audi a4 1.8 1999 4 auto(l5) f 18 29 p comp

2 audi a4 1.8 1999 4 manual(m5) f 21 29 p comp

3 audi a4 2 2008 4 manual(m6) f 20 31 p comp

4 audi a4 2 2008 4 auto(av) f 21 30 p comp

5 audi a4 2.8 1999 6 auto(l5) f 16 26 p comp

6 audi a4 2.8 1999 6 manual(m5) f 18 26 p comp

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More recent mileage data

Downloaded from:https://www.fueleconomy.gov/feg/download.shtml

Described at: https://www.fueleconomy.gov/feg/ws/index.shtml#vehicle

We will:

1 Extract csv data from a zip file (39,865 rows)

2 Select certain makes (attempt to replicate the sample data)

3 Display different data for selected makes/models

https://www.fueleconomy.gov/feg/download.shtmlhttps://www.fueleconomy.gov/feg/ws/index.shtml#vehiclehttps://www.fueleconomy.gov/feg/ws/index.shtml#vehicle

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The first codes. (1 of 3)

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The first codes. (2 of 3)

rm(list=ls())

library(ggplot2)

data(mpg, package="ggplot2")

mpg_select

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The first codes. (3 of 3)

g + geom_point(aes(col=manufacturer))

g + geom_jitter(aes(col=manufacturer))

g + geom_jitter(aes(col=manufacturer, size=hwy)) +

geom_smooth(aes(col=manufacturer), method="lm", se=F)

g + geom_jitter(aes(col=manufacturer, size=hwy)) +

geom_smooth(aes(col=manufacturer), method="lm", se=F) +

labs(size = "Highway\n mpg",

colour = "Brand")

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The second codes. (1 of 4)

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The second codes. (2 of 4)

rm(list=ls())

library(ggplot2)

saveFileName

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The second codes. (3 of 4)

labs(subtitle="mpg: Displacement vs City Mileage",

title="Bubble chart",

x="Engine displacement (liters)",

y="City mpg",

color="Manufacturer")

g + geom_point()

g + geom_point(aes(col=make))

g + geom_jitter(aes(col=make))

g + geom_jitter(aes(col=make, size=highway08)) +

geom_smooth(aes(col=make), method="lm", se=F) +

labs(size = "Highway\n mpg",

colour = "Brand")

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The second codes. (4 of 4)

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The third codes. (1 of 4)

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The third codes. (2 of 4)

rm(list=ls())

library(ggplot2)

saveFileName

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The third codes. (3 of 4)

" and average CO2 over time"),

title="Bubble chart",

x="Year",

y="City mpg",

caption = paste0("Idea taken from \"Practical",

" Statistics for Data",

" Scientists\", Bruce and Bruce."

)

)

g + geom_point()

g + geom_jitter()

g + geom_jitter(aes(size=co2,

shape=as.factor(cylinders)

), alpha=0.5) +

geom_smooth(colour="green", method="lm", se=F) +

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The third codes. (4 of 4)

labs(size = "CO2\nmeasurements",

shape = "Number\nof cylinders"

)

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Hands-on

1 The supervisor would like to see the effect of different“default” themes on the first plot. Show how to use the gray,linedraw, and classical themes.

2 The CO2 plot displays data for Hondas only. Change the dataselection command to include Fords, and discuss how theresulting plot could be improved.

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Q & A time.

Q: How many Harvard MBA’sdoes it take to screw in a lightbulb?A: Just one. He grasps it firmlyand the universe revolves aroundhim.

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What have we covered?

Bubble plots are:

Require slightly more thoughtand consideration than scatterplotsUsed to show 3, or more relateddata sets

Good for showing gross differencesin the third dimension.

Next: Columnar histograms (how grouping data can show patterns)

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References (1 of 1)

[1] Robert L. Harris,Information Graphics: A Comprehensive Illustrated Reference,Oxford University Press, 2000.

[2] Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis,Springer-Verlag New York, 2009.

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Files of interest

1 Code snippet to createimages in this presentation

2 Extract Federal fuel data

## First codesrm(list=ls())

library(ggplot2)data(mpg, package="ggplot2")

mpg_select