ggplot visualizations

.title[
# ggplot visualizations
]
.subtitle[
## Risk and Assets of Bond Funds
]
.author[
### Robert W. Walker
]

---

# A Grammar of Graphics

--
- data

--
- a mapping from data to aesthetics

--
- a geometry for representing the aesthetic

---

# My Primitive: A canvas

--
### Data as coordinates in 1 or 2-d with aesthetics and geometries

--
Graphics are generically flat; they are (at most) **two dimensional**.

That is caused by the screen.

*Our visuals must respect these limitations but we can add colors, shapes, and sizes,*
--
 or even emojis.  🔥

![](https://media.giphy.com/media/10pQQtsBUKz0Nq/giphy.gif)

---
class:inverse, center, middle, bounceIn, bounceOut

# The canvas: Every graphic fits here

---
class: tada
# One Important Caveat

There is one notable and widely seen exception to this charaterization.  
Size is total tenure.<sup>1<sup>

[1] The install script for the packages used is in the talk directory as `source(url("http://rww.science/talk/ggsc/PackagesUsed.R"))`.  The interested reader can issue `source(url("https://rww.science/talk/ggsc/XSCSetup.R"))`.

]

---
class: heartBeat

## The Illustration

What determines x and y?    **Nothing.**    
Coordinates are arbitrary.

![](img/WordCloudLeaders.jpeg)

---
class: center, middle

# A Simple Example to Cement our Thinking

[Where am I?](https://where-am-i.net)

---
# I am in Oregon
## Let's Start There

Get the map of Oregon and then use `ggmap` to map it.

```r
OR_map <- get_map(getbb("Oregon"))
# ggmap(OR_map) 
```

---
class: inverse, wobble

## The Result

---
class: inverse

# Me as Data

For now, just my GPS coordinates.

```r
Me <- data.frame(lat=45.5115834, lon=-122.6783126)
```

![](https://media.giphy.com/media/tFV89pJJz3MoU/giphy.gif)

---
# Mapping Me...

- Put my data on the earlier map.  
- My data is a set of coordinates that are already defined on the map.  
- Let's locate me on the map.

```r
Me <- data.frame(lat=44.924943, lon=-123.043461)
ggmap(OR_map) +
ggimage::geom_emoji(data=Me, aes(y=lat, x=lon), image="1f4aa", size=0.125) + theme_nothing()
```

---
class: inverse
# Me on the Map

I stand out.  It must have worked.

---
# 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

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

Bonds:
1. Type: **Intermediate Government** or **Short-Term Corporate**
2. Fees: **Yes** or **No**
3. Risk:  **Above Average**, **Average**, or **Below Average**
4. Assets: Assets under management (in $ Millions).
5. Return 2009: The annual returns in 2009.
6. 3-Year-Return: The average annual return over three years.
7. 5-Year-Return: The average annual return over five years.

---
## A Summary

<div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:300px; overflow-x: scroll; width:800px; "><table>
 <thead>
  <tr>
   <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> skim_type </th>
   <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> skim_variable </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n_missing </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> complete_rate </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.min </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.max </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.empty </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.n_unique </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.whitespace </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.mean </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.sd </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p0 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p25 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p50 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p75 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p100 </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fund.Number </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 184 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Type </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 20 </td>
   <td style="text-align:right;"> 23 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fees </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Risk </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 7 </td>
   <td style="text-align:right;"> 13 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Assets </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 910.6483696 </td>
   <td style="text-align:right;"> 2253.2667273 </td>
   <td style="text-align:right;"> 12.40 </td>
   <td style="text-align:right;"> 113.7250 </td>
   <td style="text-align:right;"> 268.4 </td>
   <td style="text-align:right;"> 621.9500 </td>
   <td style="text-align:right;"> 18603.50 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Expense.Ratio </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 0.7117935 </td>
   <td style="text-align:right;"> 0.2563373 </td>
   <td style="text-align:right;"> 0.12 </td>
   <td style="text-align:right;"> 0.5275 </td>
   <td style="text-align:right;"> 0.7 </td>
   <td style="text-align:right;"> 0.9025 </td>
   <td style="text-align:right;"> 1.94 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Return.2009 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 7.1641304 </td>
   <td style="text-align:right;"> 6.0908438 </td>
   <td style="text-align:right;"> -8.80 </td>
   <td style="text-align:right;"> 3.4750 </td>
   <td style="text-align:right;"> 6.4 </td>
   <td style="text-align:right;"> 10.7250 </td>
   <td style="text-align:right;"> 32.00 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X3.Year.Return </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 4.6625000 </td>
   <td style="text-align:right;"> 2.5164065 </td>
   <td style="text-align:right;"> -13.80 </td>
   <td style="text-align:right;"> 4.0500 </td>
   <td style="text-align:right;"> 5.1 </td>
   <td style="text-align:right;"> 6.1000 </td>
   <td style="text-align:right;"> 9.40 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X5.Year.Return </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 3.9858696 </td>
   <td style="text-align:right;"> 1.4852338 </td>
   <td style="text-align:right;"> -7.30 </td>
   <td style="text-align:right;"> 3.6000 </td>
   <td style="text-align:right;"> 4.3 </td>
   <td style="text-align:right;"> 4.9000 </td>
   <td style="text-align:right;"> 6.80 </td>
  </tr>
</tbody>
</table></div>

```r
Bonds %>% 
  skim_without_charts()
```

Most data types are represented.  There is no time variable so dates and the visualizations that go with time series are omitted.  `kableExtra` is necessary to render my tables.  You can ignore that.

---

## A Quick Pivot Table

```r
library(kableExtra)
Bonds %>% 
  group_by(Risk) %>% 
  skim()
```

---

<div style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:550px; overflow-x: scroll; width:800px; "><table class="table" style="margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> skim_type </th>
   <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> skim_variable </th>
   <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> Risk </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> n_missing </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> complete_rate </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.min </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.max </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.empty </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.n_unique </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> character.whitespace </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.mean </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.sd </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p0 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p25 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p50 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p75 </th>
   <th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;"> numeric.p100 </th>
   <th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;"> numeric.hist </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fund.Number </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 59 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fund.Number </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 69 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fund.Number </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:right;"> 6 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 56 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Type </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 20 </td>
   <td style="text-align:right;"> 23 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Type </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 20 </td>
   <td style="text-align:right;"> 23 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Type </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 20 </td>
   <td style="text-align:right;"> 23 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fees </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fees </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> character </td>
   <td style="text-align:left;"> Fees </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:left;"> NA </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Assets </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 969.4067797 </td>
   <td style="text-align:right;"> 2558.1215000 </td>
   <td style="text-align:right;"> 12.40 </td>
   <td style="text-align:right;"> 99.8000 </td>
   <td style="text-align:right;"> 278.400 </td>
   <td style="text-align:right;"> 637.5500 </td>
   <td style="text-align:right;"> 16297.10 </td>
   <td style="text-align:left;"> ▇▁▁▁▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Assets </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 965.7753623 </td>
   <td style="text-align:right;"> 2432.7903066 </td>
   <td style="text-align:right;"> 17.30 </td>
   <td style="text-align:right;"> 123.8000 </td>
   <td style="text-align:right;"> 298.100 </td>
   <td style="text-align:right;"> 681.0000 </td>
   <td style="text-align:right;"> 18603.50 </td>
   <td style="text-align:left;"> ▇▁▁▁▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Assets </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 780.8178571 </td>
   <td style="text-align:right;"> 1627.9671828 </td>
   <td style="text-align:right;"> 17.40 </td>
   <td style="text-align:right;"> 107.5750 </td>
   <td style="text-align:right;"> 232.450 </td>
   <td style="text-align:right;"> 547.5250 </td>
   <td style="text-align:right;"> 7268.10 </td>
   <td style="text-align:left;"> ▇▁▁▁▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Expense.Ratio </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 0.7093220 </td>
   <td style="text-align:right;"> 0.3113478 </td>
   <td style="text-align:right;"> 0.12 </td>
   <td style="text-align:right;"> 0.5050 </td>
   <td style="text-align:right;"> 0.700 </td>
   <td style="text-align:right;"> 0.9350 </td>
   <td style="text-align:right;"> 1.94 </td>
   <td style="text-align:left;"> ▃▇▆▁▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Expense.Ratio </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 0.7218841 </td>
   <td style="text-align:right;"> 0.2424706 </td>
   <td style="text-align:right;"> 0.13 </td>
   <td style="text-align:right;"> 0.5400 </td>
   <td style="text-align:right;"> 0.700 </td>
   <td style="text-align:right;"> 0.9000 </td>
   <td style="text-align:right;"> 1.58 </td>
   <td style="text-align:left;"> ▁▇▇▁▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Expense.Ratio </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 0.7019643 </td>
   <td style="text-align:right;"> 0.2085310 </td>
   <td style="text-align:right;"> 0.32 </td>
   <td style="text-align:right;"> 0.5475 </td>
   <td style="text-align:right;"> 0.675 </td>
   <td style="text-align:right;"> 0.8925 </td>
   <td style="text-align:right;"> 1.13 </td>
   <td style="text-align:left;"> ▅▇▇▃▅ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Return.2009 </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 8.3135593 </td>
   <td style="text-align:right;"> 9.2392054 </td>
   <td style="text-align:right;"> -8.80 </td>
   <td style="text-align:right;"> 0.8000 </td>
   <td style="text-align:right;"> 7.900 </td>
   <td style="text-align:right;"> 13.8500 </td>
   <td style="text-align:right;"> 32.00 </td>
   <td style="text-align:left;"> ▅▇▇▂▂ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Return.2009 </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 6.8710145 </td>
   <td style="text-align:right;"> 4.3899317 </td>
   <td style="text-align:right;"> -1.10 </td>
   <td style="text-align:right;"> 3.5000 </td>
   <td style="text-align:right;"> 6.000 </td>
   <td style="text-align:right;"> 10.8000 </td>
   <td style="text-align:right;"> 16.40 </td>
   <td style="text-align:left;"> ▆▇▇▇▂ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> Return.2009 </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 6.3142857 </td>
   <td style="text-align:right;"> 2.7074596 </td>
   <td style="text-align:right;"> 0.20 </td>
   <td style="text-align:right;"> 4.9500 </td>
   <td style="text-align:right;"> 6.100 </td>
   <td style="text-align:right;"> 8.1250 </td>
   <td style="text-align:right;"> 13.00 </td>
   <td style="text-align:left;"> ▂▅▇▅▁ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X3.Year.Return </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 4.1661017 </td>
   <td style="text-align:right;"> 3.8050606 </td>
   <td style="text-align:right;"> -13.80 </td>
   <td style="text-align:right;"> 2.8000 </td>
   <td style="text-align:right;"> 5.500 </td>
   <td style="text-align:right;"> 6.2500 </td>
   <td style="text-align:right;"> 9.40 </td>
   <td style="text-align:left;"> ▁▁▁▃▇ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X3.Year.Return </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 5.0130435 </td>
   <td style="text-align:right;"> 1.5361251 </td>
   <td style="text-align:right;"> 0.40 </td>
   <td style="text-align:right;"> 4.5000 </td>
   <td style="text-align:right;"> 5.400 </td>
   <td style="text-align:right;"> 6.0000 </td>
   <td style="text-align:right;"> 7.30 </td>
   <td style="text-align:left;"> ▁▁▃▇▆ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X3.Year.Return </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 4.7535714 </td>
   <td style="text-align:right;"> 1.5673909 </td>
   <td style="text-align:right;"> -0.20 </td>
   <td style="text-align:right;"> 3.9000 </td>
   <td style="text-align:right;"> 4.950 </td>
   <td style="text-align:right;"> 6.1000 </td>
   <td style="text-align:right;"> 7.50 </td>
   <td style="text-align:left;"> ▁▂▅▇▆ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X5.Year.Return </td>
   <td style="text-align:left;"> Above average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 3.6186441 </td>
   <td style="text-align:right;"> 2.2002723 </td>
   <td style="text-align:right;"> -7.30 </td>
   <td style="text-align:right;"> 2.8500 </td>
   <td style="text-align:right;"> 4.300 </td>
   <td style="text-align:right;"> 4.8000 </td>
   <td style="text-align:right;"> 6.80 </td>
   <td style="text-align:left;"> ▁▁▁▅▇ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X5.Year.Return </td>
   <td style="text-align:left;"> Average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 4.2000000 </td>
   <td style="text-align:right;"> 0.9617692 </td>
   <td style="text-align:right;"> 1.50 </td>
   <td style="text-align:right;"> 3.7000 </td>
   <td style="text-align:right;"> 4.400 </td>
   <td style="text-align:right;"> 4.9000 </td>
   <td style="text-align:right;"> 6.20 </td>
   <td style="text-align:left;"> ▂▂▇▇▃ </td>
  </tr>
  <tr>
   <td style="text-align:left;"> numeric </td>
   <td style="text-align:left;"> X5.Year.Return </td>
   <td style="text-align:left;"> Below average </td>
   <td style="text-align:right;"> 0 </td>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> NA </td>
   <td style="text-align:right;"> 4.1089286 </td>
   <td style="text-align:right;"> 0.9344472 </td>
   <td style="text-align:right;"> 1.80 </td>
   <td style="text-align:right;"> 3.5750 </td>
   <td style="text-align:right;"> 4.100 </td>
   <td style="text-align:right;"> 4.9000 </td>
   <td style="text-align:right;"> 6.10 </td>
   <td style="text-align:left;"> ▂▃▇▇▁ </td>
  </tr>
</tbody>
</table></div>

---

# Playing Along

A basic graphing template is handy to work from.  [You can access it here.](https://github.com/robertwwalker/DADMStuff/raw/master/GraphingTemplate.Rmd)

---

# One Qualitative Variable

---

## Bar plots and column plots

There are two ways to construct a barplot; we can let ggplot handle it on the raw data or calculate it ourselves.  Let me focus on *Risk*.

---
### Raw Data Bar Plot

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

---
### Raw Data Bar Plot

```r
*Bonds
```
]
 
.panel2-Bas1-auto[

```
    Fund.Number                    Type  Assets Fees
1          FN-1 Intermediate Government  7268.1   No
2          FN-2 Intermediate Government   475.1   No
3          FN-3 Intermediate Government   193.0   No
4          FN-4 Intermediate Government 18603.5   No
5          FN-5 Intermediate Government   142.6   No
6          FN-6 Intermediate Government  1401.6   No
7          FN-7 Intermediate Government   985.6   No
8          FN-8 Intermediate Government  2188.8   No
9          FN-9 Intermediate Government   390.6   No
10        FN-10 Intermediate Government   544.1   No
11        FN-11 Intermediate Government  1407.4   No
12        FN-12 Intermediate Government   155.5   No
13        FN-13 Intermediate Government   397.5   No
14        FN-14 Intermediate Government   906.1   No
15        FN-15 Intermediate Government   379.1   No
16        FN-16 Intermediate Government    62.7   No
17        FN-17 Intermediate Government  1539.1   No
18        FN-18 Intermediate Government   167.3   No
19        FN-19 Intermediate Government   448.0   No
20        FN-20 Intermediate Government   238.1   No
21        FN-21 Intermediate Government   557.8   No
22        FN-22 Intermediate Government   192.7   No
23        FN-23 Intermediate Government   261.8  Yes
24        FN-24 Intermediate Government   135.8   No
25        FN-25 Intermediate Government  1807.3  Yes
26        FN-26 Intermediate Government  4615.4   No
27        FN-27 Intermediate Government    86.2   No
28        FN-28 Intermediate Government   291.1  Yes
29        FN-29 Intermediate Government  6332.5  Yes
30        FN-30 Intermediate Government   110.7   No
31        FN-31 Intermediate Government   619.7  Yes
32        FN-32 Intermediate Government   628.7   No
33        FN-33 Intermediate Government   274.3   No
34        FN-34 Intermediate Government   891.8  Yes
35        FN-35 Intermediate Government    94.7  Yes
36        FN-36 Intermediate Government   676.3  Yes
37        FN-37 Intermediate Government  1182.1  Yes
38        FN-38 Intermediate Government   135.9   No
39        FN-39 Intermediate Government   588.8  Yes
40        FN-40 Intermediate Government    40.0   No
41        FN-41 Intermediate Government  1080.4   No
42        FN-42 Intermediate Government   309.1  Yes
43        FN-43 Intermediate Government  4472.2  Yes
44        FN-44 Intermediate Government   498.7  Yes
45        FN-45 Intermediate Government   278.2   No
46        FN-46 Intermediate Government  1525.5   No
47        FN-47 Intermediate Government   556.6  Yes
48        FN-48 Intermediate Government   588.6  Yes
49        FN-49 Intermediate Government   332.5  Yes
50        FN-50 Intermediate Government    84.9   No
51        FN-51 Intermediate Government    17.3   No
52        FN-52 Intermediate Government   104.1  Yes
53        FN-53 Intermediate Government   530.7  Yes
54        FN-54 Intermediate Government   333.6   No
55        FN-55 Intermediate Government  1133.3   No
56        FN-56 Intermediate Government   322.5  Yes
57        FN-57 Intermediate Government   123.8   No
58        FN-58 Intermediate Government   425.6  Yes
59        FN-59 Intermediate Government   184.7  Yes
60        FN-60 Intermediate Government   122.7   No
61        FN-61 Intermediate Government   107.2   No
62        FN-62 Intermediate Government   135.6  Yes
63        FN-63 Intermediate Government    82.2   No
64        FN-64 Intermediate Government   305.7  Yes
65        FN-65 Intermediate Government   551.3   No
66        FN-66 Intermediate Government    24.2   No
67        FN-67 Intermediate Government   661.2  Yes
68        FN-68 Intermediate Government   154.6  Yes
69        FN-69 Intermediate Government   106.9  Yes
70        FN-70 Intermediate Government  1011.1  Yes
71        FN-71 Intermediate Government   634.8  Yes
72        FN-72 Intermediate Government   168.7  Yes
73        FN-73 Intermediate Government   792.1  Yes
74        FN-74 Intermediate Government   583.5   No
75        FN-75 Intermediate Government    56.1   No
76        FN-76 Intermediate Government   285.5   No
77        FN-77 Intermediate Government   640.3   No
78        FN-78 Intermediate Government    84.7  Yes
79        FN-79 Intermediate Government    81.3  Yes
80        FN-80 Intermediate Government   611.7   No
81        FN-81 Intermediate Government   595.1   No
82        FN-82 Intermediate Government   131.3   No
83        FN-83 Intermediate Government    88.0   No
84        FN-84 Intermediate Government   686.6  Yes
85        FN-85 Intermediate Government  3531.8   No
86        FN-86 Intermediate Government   358.0  Yes
87        FN-87 Intermediate Government    33.0   No
88        FN-88    Short Term Corporate   139.1   No
89        FN-89    Short Term Corporate   123.9   No
90        FN-90    Short Term Corporate  1922.0  Yes
91        FN-91    Short Term Corporate   203.4  Yes
92        FN-92    Short Term Corporate    66.1   No
93        FN-93    Short Term Corporate  1346.0   No
94        FN-94    Short Term Corporate  4772.9   No
95        FN-95    Short Term Corporate    77.5   No
96        FN-96    Short Term Corporate    76.2   No
97        FN-97    Short Term Corporate   146.3   No
98        FN-98    Short Term Corporate    49.2   No
99        FN-99    Short Term Corporate    96.1   No
100      FN-100    Short Term Corporate   226.8   No
101      FN-101    Short Term Corporate   122.3   No
102      FN-102    Short Term Corporate   725.9   No
103      FN-103    Short Term Corporate  1248.4   No
104      FN-104    Short Term Corporate    36.5   No
105      FN-105    Short Term Corporate   127.4   No
106      FN-106    Short Term Corporate   605.2   No
107      FN-107    Short Term Corporate  1879.2   No
108      FN-108    Short Term Corporate   964.7  Yes
109      FN-109    Short Term Corporate   325.3   No
110      FN-110    Short Term Corporate    69.8   No
111      FN-111    Short Term Corporate   177.8  Yes
    Expense.Ratio Return.2009 X3.Year.Return
1            0.45         6.9            6.9
2            0.50         9.8            7.5
3            0.71         6.3            7.0
4            0.13         5.4            6.6
5            0.60         5.9            6.7
6            0.54         5.7            6.4
7            0.49         3.0            6.8
8            0.55         7.4            6.4
9            0.67         5.3            6.1
10           0.63         5.7            6.2
11           0.45         0.9            6.2
12           0.91        12.4            6.4
13           0.60         6.0            6.3
14           0.51         8.1            6.1
15           0.57         5.7            6.6
16           0.74         4.5            7.8
17           0.66         6.4            6.2
18           0.60         5.8            6.2
19           0.77        10.1            6.5
20           1.13         4.4            5.5
21           0.74         9.0            6.0
22           0.70         3.2            6.0
23           1.08         3.5            5.1
24           0.65        -1.1            6.0
25           0.80         7.8            6.1
26           0.45         1.3            6.7
27           0.61        -0.2            5.6
28           1.10         5.1            5.8
29           0.73         4.8            6.0
30           1.08         1.9            5.4
31           0.96         5.0            5.9
32           0.55         2.1            6.3
33           0.80        12.7            6.1
34           0.88         4.2            6.6
35           0.83         3.5            6.4
36           0.93        22.3            9.4
37           0.92        28.6            9.4
38           0.47        13.0            5.1
39           0.96         5.2            6.7
40           1.94        17.1            6.6
41           0.95         3.5            6.0
42           0.98         0.0            5.8
43           0.64         2.2            5.5
44           0.95         0.9            4.9
45           0.20        -3.8            6.0
46           0.13        -0.7            7.1
47           0.94         7.9            5.5
48           0.96         6.2            5.1
49           1.00         3.7            4.7
50           0.71        11.2            3.9
51           1.58         2.1            4.9
52           0.95        -3.6            4.7
53           0.92         4.6            5.5
54           0.37        12.0            4.9
55           0.46         3.4            2.3
56           1.13         4.7            5.6
57           0.75         6.4            3.3
58           0.97         9.1            4.7
59           1.03         1.7            5.6
60           0.26        -3.6            6.1
61           0.90         0.1            5.3
62           1.15         3.7            5.9
63           0.57        -3.3            5.1
64           0.99         3.0            6.2
65           0.52        -1.4            7.3
66           0.52         0.7           -0.1
67           0.97         0.0            3.2
68           0.90         2.9            2.4
69           0.79        -4.8            5.6
70           0.87         1.4            1.6
71           0.90         7.3            3.7
72           0.99        -2.9            6.0
73           1.00         1.2            2.3
74           0.50         6.0            7.3
75           0.65         0.7            5.2
76           0.36         7.1            5.5
77           0.27         0.2            6.5
78           1.00        -0.6            2.5
79           1.16         7.0            1.0
80           0.51         6.5            6.8
81           0.55         5.5            6.3
82           0.65        -3.8            4.4
83           0.82         2.0            5.8
84           0.94         7.2            3.7
85           0.12        -1.6            7.2
86           1.06         0.5            4.9
87           1.00         0.2            6.1
88           0.51         5.5            5.1
89           0.32         5.0            4.4
90           1.08        12.1            5.5
91           1.00         8.3            5.2
92           0.71         6.8            4.9
93           0.65         8.6            6.1
94           0.56         5.0            4.9
95           0.51         2.2            3.7
96           0.68         2.5            5.2
97           0.55        12.2            5.8
98           0.60        10.0            5.0
99           0.73         7.3            4.8
100          0.45         2.9            3.6
101          0.53         7.3            5.9
102          0.68        15.5            5.9
103          0.72        14.0            5.5
104          0.76         5.5            5.0
105          0.45         3.6            3.3
106          0.30         8.1            4.1
107          0.48         9.6            4.9
108          0.85        12.9            7.1
109          0.53        10.7            4.5
110          0.90         6.0            4.6
111          0.94         8.2            5.4
    X5.Year.Return          Risk
1              5.5 Below average
2              6.1 Below average
3              5.6       Average
4              5.5       Average
5              5.4       Average
6              6.2       Average
7              5.3       Average
8              5.2 Below average
9              5.0 Below average
10             5.1 Below average
11             4.7       Average
12             5.1 Below average
13             5.1 Below average
14             5.1 Below average
15             5.2 Below average
16             5.6 Above average
17             5.0 Below average
18             5.1 Below average
19             5.3 Below average
20             4.3 Below average
21             4.9 Below average
22             4.9       Average
23             4.0 Below average
24             4.6       Average
25             4.9       Average
26             5.2 Above average
27             4.4       Average
28             4.6 Below average
29             4.9 Below average
30             4.2       Average
31             4.9       Average
32             5.1 Above average
33             5.0       Average
34             5.1       Average
35             5.1       Average
36             6.4 Above average
37             6.8 Above average
38             4.5 Below average
39             5.3       Average
40             5.1 Above average
41             4.7       Average
42             4.4 Above average
43             4.4       Average
44             4.1 Above average
45             4.7 Above average
46             5.3 Above average
47             4.3 Above average
48             4.4 Below average
49             4.0       Average
50             3.7 Below average
51             3.7       Average
52             3.7 Above average
53             4.5       Average
54             4.5 Above average
55             2.5 Below average
56             4.4 Above average
57             3.2       Average
58             4.1 Below average
59             4.4 Above average
60             4.7 Above average
61             4.0       Average
62             4.4       Average
63             4.2 Above average
64             4.9 Above average
65             5.2 Above average
66             1.2 Above average
67             3.0 Above average
68             2.6       Average
69             4.6 Above average
70             2.3       Average
71             3.4 Above average
72             4.1 Above average
73             2.6 Above average
74             5.7       Average
75             4.4       Average
76             4.8 Below average
77             5.2 Above average
78             2.5       Average
79             1.7 Above average
80             5.5       Average
81             5.2 Below average
82             3.8 Above average
83             4.5       Average
84             3.5 Below average
85             5.4 Above average
86             4.0       Average
87             4.8 Below average
88             4.3 Below average
89             4.1 Below average
90             5.0       Average
91             4.4 Below average
92             4.0 Below average
93             4.8 Below average
94             4.2 Below average
95             3.5 Below average
96             4.2 Below average
97             4.8       Average
98             4.6       Average
99             4.1 Below average
100            3.6 Below average
101            4.6       Average
102            4.7       Average
103            4.7       Average
104            4.0 Below average
105            3.3 Below average
106            3.7       Average
107            4.2       Average
108            5.3       Average
109            4.1       Average
110            3.8       Average
111            4.4 Below average
 [ reached 'max' / getOption("max.print") -- omitted 73 rows ]
```
]

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-Bas1-auto[
<img src="index_files/figure-html/Bas1_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk)
```
]
 
.panel2-Bas1-auto[
<img src="index_files/figure-html/Bas1_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk) +
* geom_bar()
```
]
 
.panel2-Bas1-auto[
<img src="index_files/figure-html/Bas1_auto_04_output-1.svg" width="432" />
]

---

```r
*Bonds
```
]
 
.panel2-QQ31-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-QQ31-auto[
<img src="index_files/figure-html/QQ31_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk, color=Risk)
```
]
 
.panel2-QQ31-auto[
<img src="index_files/figure-html/QQ31_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, color=Risk) +
* geom_bar()
```
]
 
.panel2-QQ31-auto[
<img src="index_files/figure-html/QQ31_auto_04_output-1.svg" width="432" />
]

---

### Raw Data Bar Plot [Color]

We could color it.

---

```r
*Bonds
```
]
 
.panel2-QQ33-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-QQ33-auto[
<img src="index_files/figure-html/QQ33_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk, color=Risk)
```
]
 
.panel2-QQ33-auto[
<img src="index_files/figure-html/QQ33_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, color=Risk) +
* geom_bar(fill="white")
```
]
 
.panel2-QQ33-auto[
<img src="index_files/figure-html/QQ33_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, color=Risk) +
  geom_bar(fill="white") +
* guides(color=FALSE)
```
]
 
.panel2-QQ33-auto[
<img src="index_files/figure-html/QQ33_auto_05_output-1.svg" width="432" />
]

---

### Raw Data Bar Plot [Fill]

We can fill the shapes.

---

```r
*Bonds
```
]
 
.panel2-QQ45-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, fill=Risk))
```
]
 
.panel2-QQ45-auto[
<img src="index_files/figure-html/QQ45_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, fill=Risk)) +
* geom_bar()
```
]
 
.panel2-QQ45-auto[
<img src="index_files/figure-html/QQ45_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, fill=Risk)) +
  geom_bar() +
* guides(fill=FALSE)
```
]
 
.panel2-QQ45-auto[
<img src="index_files/figure-html/QQ45_auto_04_output-1.svg" width="432" />
]

---

## `geom_bar()` meets `fill`

We can also deploy `fill` but x is no longer the axis; the axis is some constant value with frequencies filled by the fill.  *This will require some prettying.*

### A Cumulative Bar Plot

The prettying will require that I eliminate the x axis [set it to empty], include a theme, and give it proper labels.

---

```r
*Basic.Bar <- Bonds
```
]
 
.panel2-QQ55-auto[

]

---
count: false

```r
Basic.Bar <- Bonds %>%
* ggplot(., aes(x="", fill=Risk))
```
]
 
.panel2-QQ55-auto[

]

---
count: false

```r
Basic.Bar <- Bonds %>%
  ggplot(., aes(x="", fill=Risk)) +
* geom_bar()
```
]
 
.panel2-QQ55-auto[

]

---
count: false

```r
Basic.Bar <- Bonds %>%
  ggplot(., aes(x="", fill=Risk)) +
  geom_bar()
*Basic.Bar
```
]
 
.panel2-QQ55-auto[
<img src="index_files/figure-html/QQ55_auto_04_output-1.svg" width="432" />
]

---

### Enhanced Cumulative Bar Plot

---

```r
*Bonds %>% ggplot(., aes(x="", fill=Risk))
```
]
 
.panel2-QQ65-auto[
<img src="index_files/figure-html/QQ65_auto_01_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
* geom_bar()
```
]
 
.panel2-QQ65-auto[
<img src="index_files/figure-html/QQ65_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
  geom_bar() +
* labs(x="", y="Number of Funds")
```
]
 
.panel2-QQ65-auto[
<img src="index_files/figure-html/QQ65_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
  geom_bar() +
  labs(x="", y="Number of Funds") +
* theme_minimal()
```
]
 
.panel2-QQ65-auto[
<img src="index_files/figure-html/QQ65_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
  geom_bar() +
  labs(x="", y="Number of Funds") +
  theme_minimal() +
* theme(axis.text.x= element_blank())
```
]
 
.panel2-QQ65-auto[
<img src="index_files/figure-html/QQ65_auto_05_output-1.svg" width="432" />
]

---

### Proportion Bar Plot

The prettying will require that I eliminate the x axis [set it to empty], include a theme, and give it proper labels.

---

```r
*Bonds
```
]
 
.panel2-QQ75-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x="", fill=Risk))
```
]
 
.panel2-QQ75-auto[
<img src="index_files/figure-html/QQ75_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x="", fill=Risk)) +
* geom_bar(position="fill")
```
]
 
.panel2-QQ75-auto[
<img src="index_files/figure-html/QQ75_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x="", fill=Risk)) +
  geom_bar(position="fill") +
* labs(x="", y="Proportion of Funds")
```
]
 
.panel2-QQ75-auto[
<img src="index_files/figure-html/QQ75_auto_04_output-1.svg" width="432" />
]

---

### Enhanced Proportion Bar Plot

```r
*Bonds %>% ggplot(., aes(x="", fill=Risk))
```
]
 
.panel2-QQ77-auto[
<img src="index_files/figure-html/QQ77_auto_01_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
* geom_bar(position="fill")
```
]
 
.panel2-QQ77-auto[
<img src="index_files/figure-html/QQ77_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
  geom_bar(position="fill") +
* labs(x="", y="Propotion of Funds")
```
]
 
.panel2-QQ77-auto[
<img src="index_files/figure-html/QQ77_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x="", fill=Risk)) +
  geom_bar(position="fill") +
  labs(x="", y="Propotion of Funds") +
* theme_minimal()
```
]
 
.panel2-QQ77-auto[
<img src="index_files/figure-html/QQ77_auto_04_output-1.svg" width="432" />
]

---

## geom_col()

```r
*Risk.Table <- table(Bonds$Risk) %>% data.frame()
```
]
 
.panel2-QQ78-auto[

]

---
count: false

```r
Risk.Table <- table(Bonds$Risk) %>% data.frame()
*Risk.Table
```
]
 
.panel2-QQ78-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table <- table(Bonds$Risk) %>% data.frame()
Risk.Table %>%
* ggplot(., aes(x=Var1, y=Freq))
```
]
 
.panel2-QQ78-auto[
<img src="index_files/figure-html/QQ78_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table <- table(Bonds$Risk) %>% data.frame()
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq)) +
* geom_col()
```
]
 
.panel2-QQ78-auto[
<img src="index_files/figure-html/QQ78_auto_04_output-1.svg" width="432" />
]

---

### Beautifying `geom_col()`

Now it really needs some beautification.

---

```r
*Risk.Table
```
]
 
.panel2-QQ88-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table %>%
* ggplot(., aes(x=Var1, y=Freq, fill=Var1))
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, fill=Var1)) +
* geom_col()
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, fill=Var1)) +
  geom_col() +
* labs(x="Risk Levels", y="Number of Funds")
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, fill=Var1)) +
  geom_col() +
  labs(x="Risk Levels", y="Number of Funds") +
* theme_minimal()
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, fill=Var1)) +
  geom_col() +
  labs(x="Risk Levels", y="Number of Funds") +
  theme_minimal() +
* theme(axis.text.x= element_blank())
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_06_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, fill=Var1)) +
  geom_col() +
  labs(x="Risk Levels", y="Number of Funds") +
  theme_minimal() +
  theme(axis.text.x= element_blank()) +
* scale_fill_viridis_d()
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_07_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, fill=Var1)) +
  geom_col() +
  labs(x="Risk Levels", y="Number of Funds") +
  theme_minimal() +
  theme(axis.text.x= element_blank()) +
  scale_fill_viridis_d() +
* guides(fill=FALSE)
```
]
 
.panel2-QQ88-auto[
<img src="index_files/figure-html/QQ88_auto_08_output-1.svg" width="432" />
]

---

## `position = "fill"`

The two commands are symmetric in the sense that x as axis always splits it into multiple parts.  `fill` will prove very useful with a two dimensional table.

```r
*Risk.Table
```
]
 
.panel2-QQ89-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table %>%
* ggplot(., aes(x=1, y=Freq, fill=Var1))
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=1, y=Freq, fill=Var1)) +
* geom_col(position = "fill")
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=1, y=Freq, fill=Var1)) +
  geom_col(position = "fill") +
* labs(x="Risk Levels", y="Number of Funds")
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=1, y=Freq, fill=Var1)) +
  geom_col(position = "fill") +
  labs(x="Risk Levels", y="Number of Funds") +
* theme_minimal()
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=1, y=Freq, fill=Var1)) +
  geom_col(position = "fill") +
  labs(x="Risk Levels", y="Number of Funds") +
  theme_minimal() +
* theme(axis.text.x= element_blank())
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_06_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=1, y=Freq, fill=Var1)) +
  geom_col(position = "fill") +
  labs(x="Risk Levels", y="Number of Funds") +
  theme_minimal() +
  theme(axis.text.x= element_blank()) +
* scale_fill_viridis_d()
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_07_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=1, y=Freq, fill=Var1)) +
  geom_col(position = "fill") +
  labs(x="Risk Levels", y="Number of Funds") +
  theme_minimal() +
  theme(axis.text.x= element_blank()) +
  scale_fill_viridis_d() +
* guides(fill=FALSE)
```
]
 
.panel2-QQ89-auto[
<img src="index_files/figure-html/QQ89_auto_08_output-1.svg" width="432" />
]

---

## A lollipop chart

A lollipop chart is a combination of two geometries.  It is a basic scatterplot combining one qualitative variable and the quantitative count of the number of observations.  The head of the lollipop is a point while there is an accompanying line segment from (x,0) to (x,Freq) where Freq is the default name for a count from a table.

---

### Basic Lollipop Chart

```r
*Risk.Table
```
]
 
.panel2-QQ94-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table %>%
* ggplot(., aes(x=Var1, y=Freq, color=Var1))
```
]
 
.panel2-QQ94-auto[
<img src="index_files/figure-html/QQ94_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
* geom_point(size=6)
```
]
 
.panel2-QQ94-auto[
<img src="index_files/figure-html/QQ94_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
* labs(x="Risk Level", y="Number of Funds", color="Risk Level")
```
]
 
.panel2-QQ94-auto[
<img src="index_files/figure-html/QQ94_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Level", y="Number of Funds", color="Risk Level")+
* geom_segment(aes(xend=Var1, y=0, yend=Freq))
```
]
 
.panel2-QQ94-auto[
<img src="index_files/figure-html/QQ94_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Level", y="Number of Funds", color="Risk Level")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq)) +
* theme_minimal()
```
]
 
.panel2-QQ94-auto[
<img src="index_files/figure-html/QQ94_auto_06_output-1.svg" width="432" />
]

---

### Slicked Lollipop Chart by Adjusting Segment Size

```r
*Risk.Table
```
]
 
.panel2-QQ95-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table %>%
* ggplot(., aes(x=Var1, y=Freq, color=Var1))
```
]
 
.panel2-QQ95-auto[
<img src="index_files/figure-html/QQ95_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
* geom_point(size=6)
```
]
 
.panel2-QQ95-auto[
<img src="index_files/figure-html/QQ95_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
* labs(x="Risk Levels", y="Number of Funds")
```
]
 
.panel2-QQ95-auto[
<img src="index_files/figure-html/QQ95_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
* geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5)
```
]
 
.panel2-QQ95-auto[
<img src="index_files/figure-html/QQ95_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
* theme_minimal()
```
]
 
.panel2-QQ95-auto[
<img src="index_files/figure-html/QQ95_auto_06_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  theme_minimal()  +
* guides(color=FALSE)
```
]
 
.panel2-QQ95-auto[
<img src="index_files/figure-html/QQ95_auto_07_output-1.svg" width="432" />
]

---

```r
*Risk.Table
```
]
 
.panel2-QQ96-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table %>%
* ggplot(., aes(x=Var1, y=Freq, color=Var1))
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
* geom_point(size=6)
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
* labs(x="Risk Levels", y="Number of Funds")
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
* geom_segment(aes(xend=Var1, y=0, yend=Freq))
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq)) +
* theme_minimal()
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_06_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq)) +
  theme_minimal()  +
* scale_color_viridis_d()
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_07_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq)) +
  theme_minimal()  +
  scale_color_viridis_d() +
* guides(color=FALSE)
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_08_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1)) +
  geom_point(size=6) +
  labs(x="Risk Levels", y="Number of Funds")+
  geom_segment(aes(xend=Var1, y=0, yend=Freq)) +
  theme_minimal()  +
  scale_color_viridis_d() +
  guides(color=FALSE) +
* coord_flip()
```
]
 
.panel2-QQ96-auto[
<img src="index_files/figure-html/QQ96_auto_09_output-1.svg" width="432" />
]

---

### A Lollipop Table [geom_label()]

Now I will switch up the points to be the actual values as text.  For this, I use the `geom_text` aesthetic that requires a `label` to be assigned.  I also want to put down the lines before the text to avoid overlap.

```r
Risk.Table %>% 
  ggplot(., aes(x=Var1, y=Freq, color=Var1, label=Freq)) +
  labs(x="Risk Levels", y="Number of Funds")+ 
  geom_segment(aes(xend=Var1, y=0, yend=Freq)) + 
  geom_label(size=6) + 
  theme_minimal()  + 
  scale_color_viridis_d() + 
  guides(color=FALSE) + 
  coord_flip()
```

---

### A Lollipop Table [geom_text() inverse]

The ggplot is built in layers so the segment before the label makes sure that the white shows up.  The fill and a discrete color are combined to create this graphical table.

---

```r
*Risk.Table
```
]
 
.panel2-QQ98-auto[

```
           Var1 Freq
1 Above average   59
2       Average   69
3 Below average   56
```
]

---
count: false

```r
Risk.Table %>%
* ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq))
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
* geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5)
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
* geom_label(size=6, color="white")
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  geom_label(size=6, color="white") +
* labs(x="Risk Levels", y="Number of Funds")
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  geom_label(size=6, color="white") +
  labs(x="Risk Levels", y="Number of Funds")+
* theme_minimal()
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_06_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  geom_label(size=6, color="white") +
  labs(x="Risk Levels", y="Number of Funds")+
  theme_minimal()  +
* scale_color_viridis_d()
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_07_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  geom_label(size=6, color="white") +
  labs(x="Risk Levels", y="Number of Funds")+
  theme_minimal()  +
  scale_color_viridis_d() +
* scale_fill_viridis_d()
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_08_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  geom_label(size=6, color="white") +
  labs(x="Risk Levels", y="Number of Funds")+
  theme_minimal()  +
  scale_color_viridis_d() +
  scale_fill_viridis_d() +
* guides(fill=FALSE, color=FALSE)
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_09_output-1.svg" width="432" />
]

---
count: false

```r
Risk.Table %>%
  ggplot(., aes(x=Var1, y=Freq, color=Var1, fill=Var1, label=Freq)) +
  geom_segment(aes(xend=Var1, y=0, yend=Freq), size=1.5) +
  geom_label(size=6, color="white") +
  labs(x="Risk Levels", y="Number of Funds")+
  theme_minimal()  +
  scale_color_viridis_d() +
  scale_fill_viridis_d() +
  guides(fill=FALSE, color=FALSE) +
* coord_flip()
```
]
 
.panel2-QQ98-auto[
<img src="index_files/figure-html/QQ98_auto_10_output-1.svg" width="432" />
]

---

## I HATE PIE CHARTS

A pie chart is fairly easy to do.  Let's go back and show something that I find pretty amazing.  A pie chart is a bar chart [the fill variety] with coordinates that fill a circle rather than a square.  We take the most basic bar plot -- Basic.Bar -- and add three things: new coordinates that are polar, labels, and a blank theme to eliminate axis labels.

---

```r
*Basic.Bar
```
]
 
.panel2-QQ99-auto[
<img src="index_files/figure-html/QQ99_auto_01_output-1.svg" width="432" />
]

---
count: false

```r
Basic.Bar +
* coord_polar("y", start=0)
```
]
 
.panel2-QQ99-auto[
<img src="index_files/figure-html/QQ99_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Basic.Bar +
  coord_polar("y", start=0) +
* labs(x="", y="")
```
]
 
.panel2-QQ99-auto[
<img src="index_files/figure-html/QQ99_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Basic.Bar +
  coord_polar("y", start=0) +
  labs(x="", y="") +
* theme_void()
```
]
 
.panel2-QQ99-auto[
<img src="index_files/figure-html/QQ99_auto_04_output-1.svg" width="432" />
]

---

# One Quantitative Variable

---
## `geom_histogram()`

```r
*Bonds
```
]
 
.panel2-FB1-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-FB1-auto[
<img src="index_files/figure-html/FB1_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Assets)
```
]
 
.panel2-FB1-auto[
<img src="index_files/figure-html/FB1_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Assets) +
* geom_histogram()
```
]
 
.panel2-FB1-auto[
<img src="index_files/figure-html/FB1_auto_04_output-1.svg" width="432" />
]

---
### Histograms [bins]

We can choose more bins. 50?  That is far more than the default of 30.

```r
*Bonds
```
]
 
.panel2-BF2-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-BF2-auto[
<img src="index_files/figure-html/BF2_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Assets)
```
]
 
.panel2-BF2-auto[
<img src="index_files/figure-html/BF2_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Assets) +
* geom_histogram(bins=50)
```
]
 
.panel2-BF2-auto[
<img src="index_files/figure-html/BF2_auto_04_output-1.svg" width="432" />
]

---

We can also choose fewer bins.  I will choose 10.

```r
*Bonds
```
]
 
.panel2-BF3-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-BF3-auto[
<img src="index_files/figure-html/BF3_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Assets)
```
]
 
.panel2-BF3-auto[
<img src="index_files/figure-html/BF3_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Assets) +
* geom_histogram(bins=10)
```
]
 
.panel2-BF3-auto[
<img src="index_files/figure-html/BF3_auto_04_output-1.svg" width="432" />
]

---

## Histograms [binwidth]

We can also set the width of bins in the metric of `x`; I will choose 500 (bigger).

```r
*Bonds
```
]
 
.panel2-BF4-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-BF4-auto[
<img src="index_files/figure-html/BF4_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Assets)
```
]
 
.panel2-BF4-auto[
<img src="index_files/figure-html/BF4_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Assets) +
* geom_histogram(binwidth=500)
```
]
 
.panel2-BF4-auto[
<img src="index_files/figure-html/BF4_auto_04_output-1.svg" width="432" />
]

---

We can also set the width of bins in the metric of `x`; I will choose 50 (smaller width makes more bins).

```r
*Bonds
```
]
 
.panel2-BF5-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-BF5-auto[
<img src="index_files/figure-html/BF5_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Assets)
```
]
 
.panel2-BF5-auto[
<img src="index_files/figure-html/BF5_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Assets) +
* geom_histogram(binwidth=50)
```
]
 
.panel2-BF5-auto[
<img src="index_files/figure-html/BF5_auto_04_output-1.svg" width="432" />
]

---

## `geom_dotplot()`

`geom_dotplot()` places a dot for every observation in the relevant bin.  We can control the size of the bins [in the original metric] with `binwidth=`.

### Small binwidth

```r
*Bonds
```
]
 
.panel2-BF6-auto[

---
count: false

```r
Bonds %>%
*   ggplot()
```
]
 
.panel2-BF6-auto[
<img src="index_files/figure-html/BF6_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot() +
*   aes(x=Assets)
```
]
 
.panel2-BF6-auto[
<img src="index_files/figure-html/BF6_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot() +
    aes(x=Assets) +
*   geom_dotplot(binwidth=10)
```
]
 
.panel2-BF6-auto[
<img src="index_files/figure-html/BF6_auto_04_output-1.svg" width="432" />
]

---

### Large binwidth

```r
*Bonds
```
]
 
.panel2-BF7-auto[

---
count: false

```r
Bonds %>%
*   ggplot()
```
]
 
.panel2-BF7-auto[
<img src="index_files/figure-html/BF7_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot() +
*   aes(x=Assets)
```
]
 
.panel2-BF7-auto[
<img src="index_files/figure-html/BF7_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot() +
    aes(x=Assets) +
*   geom_dotplot(binwidth=1000)
```
]
 
.panel2-BF7-auto[
<img src="index_files/figure-html/BF7_auto_04_output-1.svg" width="432" />
]

---

### An ?optimal? binwidth

Each dot represents a datum with bins of size 100.

```r
*Bonds
```
]
 
.panel2-BF8-auto[

---
count: false

```r
Bonds %>%
*   ggplot()
```
]
 
.panel2-BF8-auto[
<img src="index_files/figure-html/BF8_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot() +
*   aes(x=Assets)
```
]
 
.panel2-BF8-auto[
<img src="index_files/figure-html/BF8_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot() +
    aes(x=Assets) +
*   geom_dotplot(binwidth=100) + labs(y="")
```
]
 
.panel2-BF8-auto[
<img src="index_files/figure-html/BF8_auto_04_output-1.svg" width="432" />
]

---
## `geom_freqpoly()`

`geom_freqpoly()` is the line equivalent of a histogram.  The arguments are similar, the output doesn't include the bars as it does in the histogram.

```r
*Bonds
```
]
 
.panel2-BF9-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets))
```
]
 
.panel2-BF9-auto[
<img src="index_files/figure-html/BF9_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets)) +
* geom_freqpoly()
```
]
 
.panel2-BF9-auto[
<img src="index_files/figure-html/BF9_auto_03_output-1.svg" width="432" />
]

---

### More bins

```r
*Bonds
```
]
 
.panel2-BF10-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets))
```
]
 
.panel2-BF10-auto[
<img src="index_files/figure-html/BF10_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets)) +
* geom_freqpoly(bins=50)
```
]
 
.panel2-BF10-auto[
<img src="index_files/figure-html/BF10_auto_03_output-1.svg" width="432" />
]

---

### Fewer bins

```r
*Bonds
```
]
 
.panel2-BF11-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets))
```
]
 
.panel2-BF11-auto[
<img src="index_files/figure-html/BF11_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets)) +
* geom_freqpoly(bins=10)
```
]
 
.panel2-BF11-auto[
<img src="index_files/figure-html/BF11_auto_03_output-1.svg" width="432" />
]

---
## `geom_area()`

Is a relative of the histogram with lines connecting the midpoints of the bins and an associated fill from zero.  It defaults to 30 bins.

```r
*Bonds
```
]
 
.panel2-BF12-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets))
```
]
 
.panel2-BF12-auto[
<img src="index_files/figure-html/BF12_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets)) +
* geom_area(stat="bin")
```
]
 
.panel2-BF12-auto[
<img src="index_files/figure-html/BF12_auto_03_output-1.svg" width="432" />
]

---
### Large binwidth with a small number of bins

```r
*Bonds
```
]
 
.panel2-BF13-auto[

---
count: false

```r
Bonds %>%
*  ggplot(., aes(x=Assets))
```
]
 
.panel2-BF13-auto[
<img src="index_files/figure-html/BF13_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
   ggplot(., aes(x=Assets)) +
*  geom_area(stat="bin", bins=10) + theme_minimal()
```
]
 
.panel2-BF13-auto[
<img src="index_files/figure-html/BF13_auto_03_output-1.svg" width="432" />
]

---
### Small binwidth with a large number of bins

I will color in the area with magenta and clean up the theme.

```r
*Bonds
```
]
 
.panel2-BF14-auto[

---
count: false

```r
Bonds %>%
*  ggplot(., aes(x=Assets))
```
]
 
.panel2-BF14-auto[
<img src="index_files/figure-html/BF14_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
   ggplot(., aes(x=Assets)) +
*  geom_area(stat="bin", bins=100, fill="magenta") + theme_minimal()
```
]
 
.panel2-BF14-auto[
<img src="index_files/figure-html/BF14_auto_03_output-1.svg" width="432" />
]

---

## `geom_density()`

A relative of the histogram and the area plots above, the density plot smooths out the blocks of a histogram with a moving window [known as the bandwidth].

---

### `geom_density()` outlines

```r
*Bonds
```
]
 
.panel2-BF15-auto[

---
count: false

```r
Bonds %>%
*  ggplot(., aes(x=Assets))
```
]
 
.panel2-BF15-auto[
<img src="index_files/figure-html/BF15_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
   ggplot(., aes(x=Assets)) +
*  geom_density(outline.type = "upper")
```
]
 
.panel2-BF15-auto[
<img src="index_files/figure-html/BF15_auto_03_output-1.svg" width="432" />
]

---

```r
Bonds %>% 
   ggplot(., aes(x=Assets)) + 
   geom_density(outline.type = "lower")
```

```r
*Bonds
```
]
 
.panel2-BF16-auto[

---
count: false

```r
Bonds %>%
*  ggplot(., aes(x=Assets))
```
]
 
.panel2-BF16-auto[
<img src="index_files/figure-html/BF16_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
   ggplot(., aes(x=Assets)) +
*  geom_density(outline.type = "lower")
```
]
 
.panel2-BF16-auto[
<img src="index_files/figure-html/BF16_auto_03_output-1.svg" width="432" />
]

---

```r
Bonds %>% 
   ggplot(., aes(x=Assets)) + 
   geom_density(outline.type = "full")
```

```r
*Bonds
```
]
 
.panel2-BF17-auto[

---
count: false

```r
Bonds %>%
*  ggplot(., aes(x=Assets))
```
]
 
.panel2-BF17-auto[
<img src="index_files/figure-html/BF17_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
   ggplot(., aes(x=Assets)) +
*  geom_density(outline.type = "full")
```
]
 
.panel2-BF17-auto[
<img src="index_files/figure-html/BF17_auto_03_output-1.svg" width="432" />
]

---

### `geom_density()` adjust

Adjust applies a numeric correction to the bandwidth.  Numbers greater than 1 make the bandwidth bigger [and the graphic smoother] and numbers less than 1 [but greater than zero] make the bandwidth smaller and the graphic more jagged.

```r
*Bonds
```
]
 
.panel2-BF18-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets))
```
]
 
.panel2-BF18-auto[
<img src="index_files/figure-html/BF18_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets)) +
* geom_density(adjust=2)
```
]
 
.panel2-BF18-auto[
<img src="index_files/figure-html/BF18_auto_03_output-1.svg" width="432" />
]

---

```r
*Bonds
```
]
 
.panel2-BF19-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets))
```
]
 
.panel2-BF19-auto[
<img src="index_files/figure-html/BF19_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets)) +
* geom_density(adjust=1/2)
```
]
 
.panel2-BF19-auto[
<img src="index_files/figure-html/BF19_auto_03_output-1.svg" width="432" />
]

---

## `geom_boxplot`

A boxplot shows a box of the first and third quartiles and a notch at the median.  The dots above or below denote points outside the hinges.  The hinges [default to 1.5*IQR] show a range of expected data while the individual dots show possible outliers outside the hinges.  To adjust the hinges, the argument `coef=1.5` can be adjusted.

```r
*Bonds %>% ggplot(., aes(x=Assets))
```
]
 
.panel2-BF20-auto[
<img src="index_files/figure-html/BF20_auto_01_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(., aes(x=Assets)) +
* geom_boxplot()
```
]
 
.panel2-BF20-auto[
<img src="index_files/figure-html/BF20_auto_02_output-1.svg" width="432" />
]

---

## `geom_qq()`

To compare empirical and theoretical quantiles.  Comparing a distribution to the normal or others is common and this provides the tool for doing so.  The default is a normal.

The empirical cumulative distribution function arises when we sort a quantitative variable and show the percentiles below said value.

---

## `stat_ecdf(geom = )`

We could do this with most geometries.  I will show a few.

---

### `stat_ecdf(geom = "step")`

```r
*Bonds
```
]
 
.panel2-BF22-auto[

---
count: false

```r
Bonds %>%
* ggplot(aes(x=Assets))
```
]
 
.panel2-BF22-auto[
<img src="index_files/figure-html/BF22_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
* stat_ecdf(geom = "step")
```
]
 
.panel2-BF22-auto[
<img src="index_files/figure-html/BF22_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "step") +
* labs(y="ECDF: Proportion less than Assets")
```
]
 
.panel2-BF22-auto[
<img src="index_files/figure-html/BF22_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "step") +
  labs(y="ECDF: Proportion less than Assets") +
* theme_minimal()
```
]
 
.panel2-BF22-auto[
<img src="index_files/figure-html/BF22_auto_05_output-1.svg" width="432" />
]

---

### `stat_ecdf(geom = "point")`

```r
*Bonds
```
]
 
.panel2-BF23-auto[

---
count: false

```r
Bonds %>%
* ggplot(aes(x=Assets))
```
]
 
.panel2-BF23-auto[
<img src="index_files/figure-html/BF23_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
* stat_ecdf(geom = "point")
```
]
 
.panel2-BF23-auto[
<img src="index_files/figure-html/BF23_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "point") +
* labs(y="ECDF: Proportion less than Assets")
```
]
 
.panel2-BF23-auto[
<img src="index_files/figure-html/BF23_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "point") +
  labs(y="ECDF: Proportion less than Assets") +
* theme_minimal()
```
]
 
.panel2-BF23-auto[
<img src="index_files/figure-html/BF23_auto_05_output-1.svg" width="432" />
]

---
### Combining two

```r
*Bonds
```
]
 
.panel2-BF24-auto[

---
count: false

```r
Bonds %>%
* ggplot(aes(x=Assets))
```
]
 
.panel2-BF24-auto[
<img src="index_files/figure-html/BF24_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
* stat_ecdf(geom = "point")
```
]
 
.panel2-BF24-auto[
<img src="index_files/figure-html/BF24_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "point") +
* stat_ecdf(geom = "step", alpha=0.1)
```
]
 
.panel2-BF24-auto[
<img src="index_files/figure-html/BF24_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "point") +
  stat_ecdf(geom = "step", alpha=0.1) +
* labs(y="ECDF: Proportion less than Assets")
```
]
 
.panel2-BF24-auto[
<img src="index_files/figure-html/BF24_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "point") +
  stat_ecdf(geom = "step", alpha=0.1) +
  labs(y="ECDF: Proportion less than Assets") +
* theme_minimal()
```
]
 
.panel2-BF24-auto[
<img src="index_files/figure-html/BF24_auto_06_output-1.svg" width="432" />
]

---
### `stat_ecdf(geom = "line")`

```r
*Bonds
```
]
 
.panel2-BF25-auto[

---
count: false

```r
Bonds %>%
* ggplot(aes(x=Assets))
```
]
 
.panel2-BF25-auto[
<img src="index_files/figure-html/BF25_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
* stat_ecdf(geom = "line")
```
]
 
.panel2-BF25-auto[
<img src="index_files/figure-html/BF25_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "line") +
* labs(y="ECDF: Proportion less than Assets")
```
]
 
.panel2-BF25-auto[
<img src="index_files/figure-html/BF25_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "line") +
  labs(y="ECDF: Proportion less than Assets") +
* theme_minimal()
```
]
 
.panel2-BF25-auto[
<img src="index_files/figure-html/BF25_auto_05_output-1.svg" width="432" />
]

---
### `stat_ecdf(geom = "area")`

```r
*Bonds
```
]
 
.panel2-BF26-auto[

---
count: false

```r
Bonds %>%
* ggplot(aes(x=Assets))
```
]
 
.panel2-BF26-auto[
<img src="index_files/figure-html/BF26_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
* stat_ecdf(geom = "area", alpha=0.2)
```
]
 
.panel2-BF26-auto[
<img src="index_files/figure-html/BF26_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "area", alpha=0.2) +
* labs(y="ECDF: Proportion less than Assets")
```
]
 
.panel2-BF26-auto[
<img src="index_files/figure-html/BF26_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets)) +
  stat_ecdf(geom = "area", alpha=0.2) +
  labs(y="ECDF: Proportion less than Assets") +
* theme_minimal()
```
]
 
.panel2-BF26-auto[
<img src="index_files/figure-html/BF26_auto_05_output-1.svg" width="432" />
]

---

# Mixed Variables

---

## `geom_boxplot()`

This will construct a boxplot of quantitative y for each value of the qualitative variable placed on x.  This is very hard to read because of the extrema.

```r
*Bonds
```
]
 
.panel2-MF73-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets, color=Risk))
```
]
 
.panel2-MF73-auto[
<img src="index_files/figure-html/MF73_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, y=Assets, color=Risk)) +
* geom_dotplot(binaxis="y", bins=50, dotsize=0.3)
```
]
 
.panel2-MF73-auto[
<img src="index_files/figure-html/MF73_auto_03_output-1.svg" width="432" />
]

---

## `geom_dotplot()`

The number of bins, the axis they are placed on, and the size of the dots are core to dotplots.  It is often simply a matter of trial and error.

```r
*Bonds
```
]
 
.panel2-MF73-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets, color=Risk))
```
]
 
.panel2-MF73-auto[
<img src="index_files/figure-html/MF73_auto_02_output-1.svg" width="432" />
]

---
count: false

---

### Improved

```r
*Bonds
```
]
 
.panel2-MF74-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets, color=Risk))
```
]
 
.panel2-MF74-auto[
<img src="index_files/figure-html/MF74_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, y=Assets, color=Risk)) +
* geom_dotplot(binaxis="y", bins=50, dotsize=0.3) + guides(color=FALSE) + theme_minimal()
```
]
 
.panel2-MF74-auto[
<img src="index_files/figure-html/MF74_auto_03_output-1.svg" width="432" />
]

---

## `geom_violin()`

The most basic violin plot shows a two-sided density plot for each value of x.  By default, all violins have the same area.

```r
*Bonds
```
]
 
.panel2-MF75-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets))
```
]
 
.panel2-MF75-auto[
<img src="index_files/figure-html/MF75_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, y=Assets)) +
* geom_violin()
```
]
 
.panel2-MF75-auto[
<img src="index_files/figure-html/MF75_auto_03_output-1.svg" width="432" />
]

---
### Adjusting the area: `scale=count`

We can adjust the violins to have area proportional to the count of observations by deploying the scale argument set equal to count.

```r
*BSC <- Bonds
```
]
 
.panel2-MF76-auto[

]

---
count: false

```r
BSC <- Bonds %>%
* ggplot(., aes(x=Risk, y=Assets))
```
]
 
.panel2-MF76-auto[

]

---
count: false

```r
BSC <- Bonds %>%
  ggplot(., aes(x=Risk, y=Assets)) +
* geom_violin(scale="count")
```
]
 
.panel2-MF76-auto[

]

---
count: false

```r
BSC <- Bonds %>%
  ggplot(., aes(x=Risk, y=Assets)) +
  geom_violin(scale="count")
*BSC
```
]
 
.panel2-MF76-auto[
<img src="index_files/figure-html/MF76_auto_04_output-1.svg" width="432" />
]

---

### Adjusting the area: `scale=width`

We can also adjust the violins to have equal width.

```r
*Bonds
```
]
 
.panel2-MF77-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets))
```
]
 
.panel2-MF77-auto[
<img src="index_files/figure-html/MF77_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, y=Assets)) +
* geom_violin(scale="width")
```
]
 
.panel2-MF77-auto[
<img src="index_files/figure-html/MF77_auto_03_output-1.svg" width="432" />
]

---

### Adjusting the bandwidth

We can also make the violins smoother or more rigid with the `adjust=` argument.  Numbers greater than one make it smoother.

Numbers less than one make it less smooth.

```r
*Bonds
```
]
 
.panel2-MF78-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets))
```
]
 
.panel2-MF78-auto[
<img src="index_files/figure-html/MF78_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, y=Assets)) +
* geom_violin(scale="count", adjust=2)
```
]
 
.panel2-MF78-auto[
<img src="index_files/figure-html/MF78_auto_03_output-1.svg" width="432" />
]

---

```r
*Bonds
```
]
 
.panel2-MF79-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Risk, y=Assets))
```
]
 
.panel2-MF79-auto[
<img src="index_files/figure-html/MF79_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Risk, y=Assets)) +
* geom_violin(scale="count", adjust=1/2)
```
]
 
.panel2-MF79-auto[
<img src="index_files/figure-html/MF79_auto_03_output-1.svg" width="432" />
]

---

## `geom_density()` with color/fill

We can color the lines of a density plot to try and showcase the various distributions.

We fill the shape of density plot to try and showcase the various distributions.

```r
*Bonds
```
]
 
.panel2-MF87-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets, color=Risk))
```
]
 
.panel2-MF87-auto[
<img src="index_files/figure-html/MF87_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets, color=Risk)) +
* geom_density()
```
]
 
.panel2-MF87-auto[
<img src="index_files/figure-html/MF87_auto_03_output-1.svg" width="432" />
]

---

This almost always needs lightening.

```r
*Bonds
```
]
 
.panel2-MF88-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets, fill=Risk))
```
]
 
.panel2-MF88-auto[
<img src="index_files/figure-html/MF88_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets, fill=Risk)) +
* geom_density()
```
]
 
.panel2-MF88-auto[
<img src="index_files/figure-html/MF88_auto_03_output-1.svg" width="432" />
]

---

In this case, it helps to remove the outline.

```r
*Bonds
```
]
 
.panel2-MF89-auto[

---
count: false

```r
Bonds %>%
* ggplot(., aes(x=Assets, fill=Risk))
```
]
 
.panel2-MF89-auto[
<img src="index_files/figure-html/MF89_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(., aes(x=Assets, fill=Risk)) +
* geom_density(alpha=0.2)
```
]
 
.panel2-MF89-auto[
<img src="index_files/figure-html/MF89_auto_03_output-1.svg" width="432" />
]

---

```r
*Bonds
```
]
 
.panel2-MF90-auto[

---
count: false

```r
Bonds %>%
*   ggplot(., aes(x=Assets, fill=Risk))
```
]
 
.panel2-MF90-auto[
<img src="index_files/figure-html/MF90_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
    ggplot(., aes(x=Assets, fill=Risk)) +
*   geom_density(alpha=0.2, color=NA)
```
]
 
.panel2-MF90-auto[
<img src="index_files/figure-html/MF90_auto_03_output-1.svg" width="432" />
]

---

## `geom_beeswarm()`

Related to the dotplot is the beeswarm.  It requires installing a package with the geometry known as ggbeeswarm.

```
install.packages("ggbeeswarm")
```
---

```r
*library(ggbeeswarm)
```
]
 
.panel2-MF94-auto[

]

---
count: false

```r
library(ggbeeswarm)
*Bonds %>% ggplot()
```
]
 
.panel2-MF94-auto[
<img src="index_files/figure-html/MF94_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
library(ggbeeswarm)
Bonds %>% ggplot() +
* aes(x=Risk, y=Assets)
```
]
 
.panel2-MF94-auto[
<img src="index_files/figure-html/MF94_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
library(ggbeeswarm)
Bonds %>% ggplot() +
  aes(x=Risk, y=Assets) +
* geom_beeswarm()
```
]
 
.panel2-MF94-auto[
<img src="index_files/figure-html/MF94_auto_04_output-1.svg" width="432" />
]

---

```r
*Bonds
```
]
 
.panel2-MF95-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-MF95-auto[
<img src="index_files/figure-html/MF95_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk, y=Assets, color=Risk)
```
]
 
.panel2-MF95-auto[
<img src="index_files/figure-html/MF95_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, y=Assets, color=Risk) +
* geom_beeswarm()
```
]
 
.panel2-MF95-auto[
<img src="index_files/figure-html/MF95_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, y=Assets, color=Risk) +
  geom_beeswarm() +
* guides(color=FALSE)
```
]
 
.panel2-MF95-auto[
<img src="index_files/figure-html/MF95_auto_05_output-1.svg" width="432" />
]

---

## `ggridges`

```
install.packages("ggridges")
```

A ridgeline plot represents a categorical variable (y) with stacked density plots of (x).  We will make use of the most basic one first.

I do not like the overlap in the lines.  This can be controlled with a scaling factors `scale=1` that forces no overlap.

---

```r
*library(ggridges)
```
]
 
.panel2-MF97-auto[

]

---
count: false

```r
library(ggridges)
*Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk))
```
]
 
.panel2-MF97-auto[
<img src="index_files/figure-html/MF97_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
library(ggridges)
Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk)) +
* geom_density_ridges()
```
]
 
.panel2-MF97-auto[
<img src="index_files/figure-html/MF97_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
library(ggridges)
Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk)) +
  geom_density_ridges() +
* theme_ridges()
```
]
 
.panel2-MF97-auto[
<img src="index_files/figure-html/MF97_auto_04_output-1.svg" width="432" />
]

---

### `scale=1`

---

```r
*Bonds
```
]
 
.panel2-MF98-auto[

---
count: false

```r
Bonds %>%
* ggplot(aes(x=Assets, y=Risk, group=Risk))
```
]
 
.panel2-MF98-auto[
<img src="index_files/figure-html/MF98_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets, y=Risk, group=Risk)) +
* geom_density_ridges(scale=1)
```
]
 
.panel2-MF98-auto[
<img src="index_files/figure-html/MF98_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot(aes(x=Assets, y=Risk, group=Risk)) +
  geom_density_ridges(scale=1) +
* theme_ridges()
```
]
 
.panel2-MF98-auto[
<img src="index_files/figure-html/MF98_auto_04_output-1.svg" width="432" />
]

---

### Something prettier

---

```r
*Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk, color=Risk, fill=Risk))
```
]
 
.panel2-DNT11-auto[
<img src="index_files/figure-html/DNT11_auto_01_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk, color=Risk, fill=Risk)) +
* geom_density_ridges(scale = 1, alpha=0.2)
```
]
 
.panel2-DNT11-auto[
<img src="index_files/figure-html/DNT11_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk, color=Risk, fill=Risk)) +
  geom_density_ridges(scale = 1, alpha=0.2)  +
* guides(color=FALSE, fill=FALSE)
```
]
 
.panel2-DNT11-auto[
<img src="index_files/figure-html/DNT11_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot(aes(x=Assets, y=Risk, group=Risk, color=Risk, fill=Risk)) +
  geom_density_ridges(scale = 1, alpha=0.2)  +
  guides(color=FALSE, fill=FALSE) +
* theme_ridges()
```
]
 
.panel2-DNT11-auto[
<img src="index_files/figure-html/DNT11_auto_04_output-1.svg" width="432" />
]

---
class: inverse

# Two Qualitative Variables

---
## Building a Table

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

---
# A Tidy Table

The table as a data.frame.

```r
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`

```r
*BTDF
```
]
 
.panel2-Q287-auto[

```
           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
```
]

---
count: false

```r
BTDF %>%
* ggplot(., aes(x=Risk, y=Fees, fill=Count))
```
]
 
.panel2-Q287-auto[
<img src="index_files/figure-html/Q287_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, fill=Count)) +
* geom_tile()
```
]
 
.panel2-Q287-auto[
<img src="index_files/figure-html/Q287_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, fill=Count)) +
  geom_tile() +
* scale_fill_viridis_c()
```
]
 
.panel2-Q287-auto[
<img src="index_files/figure-html/Q287_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, fill=Count)) +
  geom_tile() +
  scale_fill_viridis_c() +
* theme_minimal() + labs(fill="Number of Funds")
```
]
 
.panel2-Q287-auto[
<img src="index_files/figure-html/Q287_auto_05_output-1.svg" width="432" />
]

---

## `geom_label` a visual table

```r
*BTDF
```
]
 
.panel2-Q288-auto[

```
           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
```
]

---
count: false

```r
BTDF %>%
* ggplot(., aes(x=Risk, y=Fees, label=Count)) + geom_label() + theme_minimal() + labs(subtitle="Counts of Funds Shown")
```
]
 
.panel2-Q288-auto[
<img src="index_files/figure-html/Q288_auto_02_output-1.svg" width="432" />
]

---

### A Tile Table

```r
*BTDF
```
]
 
.panel2-Q289-auto[

```
           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
```
]

---
count: false

```r
BTDF %>%
* ggplot(., aes(x=Risk, y=Fees, label=Count, fill=Count))
```
]
 
.panel2-Q289-auto[
<img src="index_files/figure-html/Q289_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, label=Count, fill=Count)) +
* geom_tile(alpha=0.2)
```
]
 
.panel2-Q289-auto[
<img src="index_files/figure-html/Q289_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, label=Count, fill=Count)) +
  geom_tile(alpha=0.2) +
* scale_fill_viridis_c()
```
]
 
.panel2-Q289-auto[
<img src="index_files/figure-html/Q289_auto_04_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, label=Count, fill=Count)) +
  geom_tile(alpha=0.2) +
  scale_fill_viridis_c() +
* geom_text()
```
]
 
.panel2-Q289-auto[
<img src="index_files/figure-html/Q289_auto_05_output-1.svg" width="432" />
]

---
count: false

```r
BTDF %>%
  ggplot(., aes(x=Risk, y=Fees, label=Count, fill=Count)) +
  geom_tile(alpha=0.2) +
  scale_fill_viridis_c() +
  geom_text() +
* theme_minimal()
```
]
 
.panel2-Q289-auto[
<img src="index_files/figure-html/Q289_auto_06_output-1.svg" width="432" />
]

---
count: false

```r
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")
```
]
 
.panel2-Q289-auto[
<img src="index_files/figure-html/Q289_auto_07_output-1.svg" width="432" />
]

---

## Raw Data Plots

---

## `geom_count()`

```r
*Bonds %>% ggplot()
```
]
 
.panel2-Q296-auto[
<img src="index_files/figure-html/Q296_auto_01_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot() +
* aes(x=Risk, y=Fees)
```
]
 
.panel2-Q296-auto[
<img src="index_files/figure-html/Q296_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot() +
  aes(x=Risk, y=Fees) +
* geom_count()
```
]
 
.panel2-Q296-auto[
<img src="index_files/figure-html/Q296_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>% ggplot() +
  aes(x=Risk, y=Fees) +
  geom_count() +
* theme_minimal()
```
]
 
.panel2-Q296-auto[
<img src="index_files/figure-html/Q296_auto_04_output-1.svg" width="432" />
]

---

# `geom_bar(fill=)`

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

---

```r
*Bonds
```
]
 
.panel2-Q297-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-Q297-auto[
<img src="index_files/figure-html/Q297_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk)
```
]
 
.panel2-Q297-auto[
<img src="index_files/figure-html/Q297_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk) +
* geom_bar()
```
]
 
.panel2-Q297-auto[
<img src="index_files/figure-html/Q297_auto_04_output-1.svg" width="432" />
]

---

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

```r
*Bonds
```
]
 
.panel2-Q298-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-Q298-auto[
<img src="index_files/figure-html/Q298_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk, fill=Fees)
```
]
 
.panel2-Q298-auto[
<img src="index_files/figure-html/Q298_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, fill=Fees) +
* geom_bar()
```
]
 
.panel2-Q298-auto[
<img src="index_files/figure-html/Q298_auto_04_output-1.svg" width="432" />
]

---

## 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.

```r
*Bonds
```
]
 
.panel2-Q299-auto[

---
count: false

```r
Bonds %>%
* ggplot()
```
]
 
.panel2-Q299-auto[
<img src="index_files/figure-html/Q299_auto_02_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
* aes(x=Risk, fill=Fees)
```
]
 
.panel2-Q299-auto[
<img src="index_files/figure-html/Q299_auto_03_output-1.svg" width="432" />
]

---
count: false

```r
Bonds %>%
  ggplot() +
  aes(x=Risk, fill=Fees) +
* geom_bar(position="fill")
```
]
 
.panel2-Q299-auto[
<img src="index_files/figure-html/Q299_auto_04_output-1.svg" width="432" />
]

@media print {
  .has-continuation {
    display: block;
  }
}
</style>