Visualizing Two Quantitative Variables

Bonds

A dataset for illustrating the various available visualizations needs a certain degree of richness with manageable size. The dataset on Bonds contains three categorical and a few quantitative indicators sufficient to show what we might wish.

Loading the Data

Bonds <- read.csv(url("https://raw.githubusercontent.com/robertwwalker/DADMStuff/master/BondFunds.csv"))

A Summary

library(skimr)
Bonds %>%
    skim()

Data summary

Name	Piped data
Number of rows	184
Number of columns	9
_______________________
Column type frequency:
character	4
numeric	5
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Fund.Number	0	1	4	6	0	184	0
Type	0	1	20	23	0	2	0
Fees	0	1	2	3	0	2	0
Risk	0	1	7	13	0	3	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Assets	0	1	910.65	2253.27	12.40	113.72	268.4	621.95	18603.50	▇▁▁▁▁
Expense.Ratio	0	1	0.71	0.26	0.12	0.53	0.7	0.90	1.94	▂▇▅▁▁
Return.2009	0	1	7.16	6.09	-8.80	3.48	6.4	10.72	32.00	▁▇▅▁▁
X3.Year.Return	0	1	4.66	2.52	-13.80	4.05	5.1	6.10	9.40	▁▁▁▅▇
X5.Year.Return	0	1	3.99	1.49	-7.30	3.60	4.3	4.90	6.80	▁▁▁▅▇

Most data types are represented. There is no time variable so dates and the visualizations that go with time series are omitted.

Data Visualization

The primary visualization that we might use for a combination of two quantities is the scatterplot: geom_point(). I will also make use of facet. Let me work with Assets and Returns, then with Risk.

geom_point()

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009) + geom_point()

A little better: theming

I do not like the default theme.

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009) + geom_point() + theme_minimal()

Fixing Overplotting

ggplot has an alternative geometry known as geom_jitter() that jitters – adds arbitrarily small amounts to the x and y coordinates.

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009) + geom_jitter() + theme_minimal()

The two controls for the jitter are width:

width

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009) + geom_jitter(width = 0.25) + theme_minimal()

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009) + geom_jitter(height = 0.25) + theme_minimal()

and the height

## Some other elements

Since I am going to work with Risk, let’s try that in color.

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009, color = Risk) + geom_point() + theme_minimal()

A bubble plot?

Now I will put Risk on x and Returns on y with the size of the bubble determined by Assets. First, without jitter.

Bonds %>%
    ggplot() + aes(x = Risk, y = Return.2009, size = Assets, color = Risk) + geom_point() +
    theme_minimal() + guides(color = "none")

Bonds %>%
    ggplot() + aes(x = Risk, y = Return.2009, color = Risk, size = Assets) + geom_jitter(width = 0.45) +
    theme_minimal() + guides(color = "none")

Deploying Facets

Bonds %>%
    ggplot() + aes(x = Assets, y = Return.2009, color = Risk, facet = Risk) + geom_point() +
    theme_minimal() + guides(color = "none") + facet_wrap(~Risk)