Visualizing Two Qualitative 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

There are three primary visualizations that we might use for a combination of two qualities. We will have a look at geom_tile(), geom_point(), and geom_bar(aes(fill=)) to visualize these. We can also make use of facet. Let me work with Risk and Fees. I will first display the relevant table.

Building a Table

BTDF <- Bonds %>%
    group_by(Risk, Fees) %>%
    summarise(Count = n()) %>%
    data.frame()

`summarise()` has grouped output by 'Risk'. You can override using the
`.groups` argument.

BTDF %>%
    pivot_wider(names_from = Fees, values_from = Count)

# A tibble: 3 × 3
  Risk             No   Yes
  <chr>         <int> <int>
1 Above average    37    22
2 Average          49    20
3 Below average    44    12

The table as a data.frame.

BTDF

           Risk Fees Count
1 Above average   No    37
2 Above average  Yes    22
3       Average   No    49
4       Average  Yes    20
5 Below average   No    44
6 Below average  Yes    12

geom_tile

BTDF %>%
    ggplot(., aes(x = Risk, y = Fees, fill = Count)) + geom_tile() + scale_fill_viridis_c() +
    theme_minimal() + labs(fill = "Number of Funds")

geom_label a visual table

BTDF %>%
    ggplot(., aes(x = Risk, y = Fees, label = Count)) + geom_label() + theme_minimal() +
    labs(subtitle = "Counts of Funds Shown")

A Tile Table

BTDF %>%
    ggplot(., aes(x = Risk, y = Fees, label = Count, fill = Count)) + geom_tile(alpha = 0.2) +
    scale_fill_viridis_c() + geom_text() + theme_minimal() + labs(subtitle = "Counts of Funds Shown")

Raw Data Plots

geom_count()

Bonds %>%
    ggplot() + aes(x = Risk, y = Fees) + geom_count() + theme_minimal()

geom_bar(fill=)

Now let us look at the bar plot. First, start with Risk.

Bonds %>%
    ggplot() + aes(x = Risk) + geom_bar()

To add Fees to the plot, we can fill the bars.

Bonds %>%
    ggplot() + aes(x = Risk, fill = Fees) + geom_bar()

Conditional Probability: (position="fill")

We can also plot this normalized by the number in each category of x so that the plots represent conditional probability.

Bonds %>%
    ggplot() + aes(x = Risk, fill = Fees) + geom_bar(position = "fill")