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=44.9322, lon=-123.0280)
```

![](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)
```

---
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 Expense.Ratio Return.2009
1          FN-1 Intermediate Government  7268.1   No          0.45         6.9
2          FN-2 Intermediate Government   475.1   No          0.50         9.8
3          FN-3 Intermediate Government   193.0   No          0.71         6.3
4          FN-4 Intermediate Government 18603.5   No          0.13         5.4
5          FN-5 Intermediate Government   142.6   No          0.60         5.9
6          FN-6 Intermediate Government  1401.6   No          0.54         5.7
7          FN-7 Intermediate Government   985.6   No          0.49         3.0
8          FN-8 Intermediate Government  2188.8   No          0.55         7.4
9          FN-9 Intermediate Government   390.6   No          0.67         5.3
10        FN-10 Intermediate Government   544.1   No          0.63         5.7
11        FN-11 Intermediate Government  1407.4   No          0.45         0.9
12        FN-12 Intermediate Government   155.5   No          0.91        12.4
13        FN-13 Intermediate Government   397.5   No          0.60         6.0
14        FN-14 Intermediate Government   906.1   No          0.51         8.1
15        FN-15 Intermediate Government   379.1   No          0.57         5.7
16        FN-16 Intermediate Government    62.7   No          0.74         4.5
17        FN-17 Intermediate Government  1539.1   No          0.66         6.4
18        FN-18 Intermediate Government   167.3   No          0.60         5.8
19        FN-19 Intermediate Government   448.0   No          0.77        10.1
20        FN-20 Intermediate Government   238.1   No          1.13         4.4
21        FN-21 Intermediate Government   557.8   No          0.74         9.0
22        FN-22 Intermediate Government   192.7   No          0.70         3.2
23        FN-23 Intermediate Government   261.8  Yes          1.08         3.5
24        FN-24 Intermediate Government   135.8   No          0.65        -1.1
25        FN-25 Intermediate Government  1807.3  Yes          0.80         7.8
26        FN-26 Intermediate Government  4615.4   No          0.45         1.3
27        FN-27 Intermediate Government    86.2   No          0.61        -0.2
28        FN-28 Intermediate Government   291.1  Yes          1.10         5.1
29        FN-29 Intermediate Government  6332.5  Yes          0.73         4.8
30        FN-30 Intermediate Government   110.7   No          1.08         1.9
31        FN-31 Intermediate Government   619.7  Yes          0.96         5.0
32        FN-32 Intermediate Government   628.7   No          0.55         2.1
33        FN-33 Intermediate Government   274.3   No          0.80        12.7
34        FN-34 Intermediate Government   891.8  Yes          0.88         4.2
35        FN-35 Intermediate Government    94.7  Yes          0.83         3.5
36        FN-36 Intermediate Government   676.3  Yes          0.93        22.3
37        FN-37 Intermediate Government  1182.1  Yes          0.92        28.6
38        FN-38 Intermediate Government   135.9   No          0.47        13.0
39        FN-39 Intermediate Government   588.8  Yes          0.96         5.2
40        FN-40 Intermediate Government    40.0   No          1.94        17.1
41        FN-41 Intermediate Government  1080.4   No          0.95         3.5
42        FN-42 Intermediate Government   309.1  Yes          0.98         0.0
43        FN-43 Intermediate Government  4472.2  Yes          0.64         2.2
44        FN-44 Intermediate Government   498.7  Yes          0.95         0.9
45        FN-45 Intermediate Government   278.2   No          0.20        -3.8
46        FN-46 Intermediate Government  1525.5   No          0.13        -0.7
47        FN-47 Intermediate Government   556.6  Yes          0.94         7.9
48        FN-48 Intermediate Government   588.6  Yes          0.96         6.2
49        FN-49 Intermediate Government   332.5  Yes          1.00         3.7
50        FN-50 Intermediate Government    84.9   No          0.71        11.2
51        FN-51 Intermediate Government    17.3   No          1.58         2.1
52        FN-52 Intermediate Government   104.1  Yes          0.95        -3.6
53        FN-53 Intermediate Government   530.7  Yes          0.92         4.6
54        FN-54 Intermediate Government   333.6   No          0.37        12.0
55        FN-55 Intermediate Government  1133.3   No          0.46         3.4
56        FN-56 Intermediate Government   322.5  Yes          1.13         4.7
57        FN-57 Intermediate Government   123.8   No          0.75         6.4
58        FN-58 Intermediate Government   425.6  Yes          0.97         9.1
59        FN-59 Intermediate Government   184.7  Yes          1.03         1.7
60        FN-60 Intermediate Government   122.7   No          0.26        -3.6
61        FN-61 Intermediate Government   107.2   No          0.90         0.1
62        FN-62 Intermediate Government   135.6  Yes          1.15         3.7
63        FN-63 Intermediate Government    82.2   No          0.57        -3.3
64        FN-64 Intermediate Government   305.7  Yes          0.99         3.0
65        FN-65 Intermediate Government   551.3   No          0.52        -1.4
66        FN-66 Intermediate Government    24.2   No          0.52         0.7
67        FN-67 Intermediate Government   661.2  Yes          0.97         0.0
68        FN-68 Intermediate Government   154.6  Yes          0.90         2.9
69        FN-69 Intermediate Government   106.9  Yes          0.79        -4.8
70        FN-70 Intermediate Government  1011.1  Yes          0.87         1.4
71        FN-71 Intermediate Government   634.8  Yes          0.90         7.3
72        FN-72 Intermediate Government   168.7  Yes          0.99        -2.9
73        FN-73 Intermediate Government   792.1  Yes          1.00         1.2
74        FN-74 Intermediate Government   583.5   No          0.50         6.0
75        FN-75 Intermediate Government    56.1   No          0.65         0.7
76        FN-76 Intermediate Government   285.5   No          0.36         7.1
77        FN-77 Intermediate Government   640.3   No          0.27         0.2
78        FN-78 Intermediate Government    84.7  Yes          1.00        -0.6
79        FN-79 Intermediate Government    81.3  Yes          1.16         7.0
80        FN-80 Intermediate Government   611.7   No          0.51         6.5
81        FN-81 Intermediate Government   595.1   No          0.55         5.5
82        FN-82 Intermediate Government   131.3   No          0.65        -3.8
83        FN-83 Intermediate Government    88.0   No          0.82         2.0
84        FN-84 Intermediate Government   686.6  Yes          0.94         7.2
85        FN-85 Intermediate Government  3531.8   No          0.12        -1.6
86        FN-86 Intermediate Government   358.0  Yes          1.06         0.5
87        FN-87 Intermediate Government    33.0   No          1.00         0.2
88        FN-88    Short Term Corporate   139.1   No          0.51         5.5
89        FN-89    Short Term Corporate   123.9   No          0.32         5.0
90        FN-90    Short Term Corporate  1922.0  Yes          1.08        12.1
91        FN-91    Short Term Corporate   203.4  Yes          1.00         8.3
92        FN-92    Short Term Corporate    66.1   No          0.71         6.8
93        FN-93    Short Term Corporate  1346.0   No          0.65         8.6
94        FN-94    Short Term Corporate  4772.9   No          0.56         5.0
95        FN-95    Short Term Corporate    77.5   No          0.51         2.2
96        FN-96    Short Term Corporate    76.2   No          0.68         2.5
97        FN-97    Short Term Corporate   146.3   No          0.55        12.2
98        FN-98    Short Term Corporate    49.2   No          0.60        10.0
99        FN-99    Short Term Corporate    96.1   No          0.73         7.3
100      FN-100    Short Term Corporate   226.8   No          0.45         2.9
101      FN-101    Short Term Corporate   122.3   No          0.53         7.3
102      FN-102    Short Term Corporate   725.9   No          0.68        15.5
103      FN-103    Short Term Corporate  1248.4   No          0.72        14.0
104      FN-104    Short Term Corporate    36.5   No          0.76         5.5
105      FN-105    Short Term Corporate   127.4   No          0.45         3.6
106      FN-106    Short Term Corporate   605.2   No          0.30         8.1
107      FN-107    Short Term Corporate  1879.2   No          0.48         9.6
108      FN-108    Short Term Corporate   964.7  Yes          0.85        12.9
109      FN-109    Short Term Corporate   325.3   No          0.53        10.7
110      FN-110    Short Term Corporate    69.8   No          0.90         6.0
111      FN-111    Short Term Corporate   177.8  Yes          0.94         8.2
112      FN-112    Short Term Corporate   156.6   No          0.63        12.0
113      FN-113    Short Term Corporate   244.9   No          0.81        16.4
114      FN-114    Short Term Corporate   475.5  Yes          0.91         6.0
115      FN-115    Short Term Corporate    46.1   No          0.92         5.4
116      FN-116    Short Term Corporate   181.3   No          0.36         4.9
117      FN-117    Short Term Corporate    63.8  Yes          0.62         8.6
118      FN-118    Short Term Corporate   135.4   No          0.70         6.8
119      FN-119    Short Term Corporate   388.0   No          0.56         6.7
120      FN-120    Short Term Corporate    18.6   No          0.89         4.1
121      FN-121    Short Term Corporate   298.1   No          0.51        11.2
122      FN-122    Short Term Corporate   461.3   No          0.46         8.4
123      FN-123    Short Term Corporate   159.9  Yes          0.80         5.9
124      FN-124    Short Term Corporate  3662.7   No          0.52         9.0
125      FN-125    Short Term Corporate    42.6   No          0.74         5.4
126      FN-126    Short Term Corporate   111.4   No          0.97         9.1
127      FN-127    Short Term Corporate   114.5   No          0.65         9.5
128      FN-128    Short Term Corporate  5282.9   No          0.22         4.3
129      FN-129    Short Term Corporate    17.4   No          0.75         2.8
130      FN-130    Short Term Corporate   518.0   No          0.72        10.8
131      FN-131    Short Term Corporate   262.5   No          0.85         9.5
132      FN-132    Short Term Corporate    93.9   No          0.74         9.4
133      FN-133    Short Term Corporate  1130.0  Yes          0.86        13.2
134      FN-134    Short Term Corporate  1101.9   No          0.64        13.0
135      FN-135    Short Term Corporate    49.9   No          0.60         1.6
136      FN-136    Short Term Corporate   640.3   No          0.54         7.0
137      FN-137    Short Term Corporate   477.4   No          0.60        12.9
138      FN-138    Short Term Corporate   569.4   No          0.52        12.0
139      FN-139    Short Term Corporate    36.8   No          0.38         3.6
140      FN-140    Short Term Corporate   145.4  Yes          0.90        10.1
141      FN-141    Short Term Corporate   148.9   No          1.10         7.7
142      FN-142    Short Term Corporate   195.8   No          0.52         7.2
143      FN-143    Short Term Corporate  2361.1  Yes          0.80        17.4
144      FN-144    Short Term Corporate    12.4   No          0.70         3.4
145      FN-145    Short Term Corporate    26.3   No          0.49         4.0
146      FN-146    Short Term Corporate   681.0  Yes          0.75        11.3
147      FN-147    Short Term Corporate   486.1   No          0.50        10.3
148      FN-148    Short Term Corporate 10744.6   No          0.46        13.4
149      FN-149    Short Term Corporate   108.9   No          0.63        11.4
150      FN-150    Short Term Corporate    69.0  Yes          0.98         5.2
151      FN-151    Short Term Corporate   128.3   No          0.55         8.8
152      FN-152    Short Term Corporate   374.4   No          0.73        13.2
153      FN-153    Short Term Corporate   280.6  Yes          0.99        16.6
154      FN-154    Short Term Corporate   456.2   No          0.37        13.6
155      FN-155    Short Term Corporate 16297.1   No          0.14        14.2
156      FN-156    Short Term Corporate   517.0   No          0.65        10.6
157      FN-157    Short Term Corporate    80.5   No          0.70        13.4
158      FN-158    Short Term Corporate   119.3   No          0.82         6.0
159      FN-159    Short Term Corporate    88.3   No          0.92         4.5
160      FN-160    Short Term Corporate  5701.6  Yes          0.69         6.7
161      FN-161    Short Term Corporate   478.4   No          0.64         6.8
162      FN-162    Short Term Corporate   369.3   No          0.86        13.5
163      FN-163    Short Term Corporate   297.4  Yes          1.05        12.8
164      FN-164    Short Term Corporate   230.6   No          0.95        13.9
165      FN-165    Short Term Corporate   115.7  Yes          0.70        11.5
166      FN-166    Short Term Corporate  6981.5   No          0.45         7.3
167      FN-167    Short Term Corporate   190.1   No          0.50        11.6
168      FN-168    Short Term Corporate   278.4  Yes          0.85        15.5
169      FN-169    Short Term Corporate   246.1   No          0.75        11.9
170      FN-170    Short Term Corporate   143.8   No          0.80        24.8
171      FN-171    Short Term Corporate  1469.6  Yes          1.08        29.7
172      FN-172    Short Term Corporate    80.0   No          0.70         9.9
173      FN-173    Short Term Corporate    95.5   No          0.66        19.2
174      FN-174    Short Term Corporate   170.5   No          0.46        15.6
175      FN-175    Short Term Corporate    95.4  Yes          1.02         1.5
176      FN-176    Short Term Corporate   237.1   No          0.50        14.5
177      FN-177    Short Term Corporate   983.0   No          0.60        15.2
178      FN-178    Short Term Corporate    51.9   No          0.70        13.4
179      FN-179    Short Term Corporate   249.7   No          0.55         2.4
180      FN-180    Short Term Corporate    33.8   No          0.53        16.4
181      FN-181    Short Term Corporate   249.8  Yes          0.43         6.7
182      FN-182    Short Term Corporate    52.9   No          0.87         5.2
183      FN-183    Short Term Corporate    39.7   No          0.51        -8.8
184      FN-184    Short Term Corporate   182.3   No          0.53        32.0
    X3.Year.Return X5.Year.Return          Risk
1              6.9            5.5 Below average
2              7.5            6.1 Below average
3              7.0            5.6       Average
4              6.6            5.5       Average
5              6.7            5.4       Average
6              6.4            6.2       Average
7              6.8            5.3       Average
8              6.4            5.2 Below average
9              6.1            5.0 Below average
10             6.2            5.1 Below average
11             6.2            4.7       Average
12             6.4            5.1 Below average
13             6.3            5.1 Below average
14             6.1            5.1 Below average
15             6.6            5.2 Below average
16             7.8            5.6 Above average
17             6.2            5.0 Below average
18             6.2            5.1 Below average
19             6.5            5.3 Below average
20             5.5            4.3 Below average
21             6.0            4.9 Below average
22             6.0            4.9       Average
23             5.1            4.0 Below average
24             6.0            4.6       Average
25             6.1            4.9       Average
26             6.7            5.2 Above average
27             5.6            4.4       Average
28             5.8            4.6 Below average
29             6.0            4.9 Below average
30             5.4            4.2       Average
31             5.9            4.9       Average
32             6.3            5.1 Above average
33             6.1            5.0       Average
34             6.6            5.1       Average
35             6.4            5.1       Average
36             9.4            6.4 Above average
37             9.4            6.8 Above average
38             5.1            4.5 Below average
39             6.7            5.3       Average
40             6.6            5.1 Above average
41             6.0            4.7       Average
42             5.8            4.4 Above average
43             5.5            4.4       Average
44             4.9            4.1 Above average
45             6.0            4.7 Above average
46             7.1            5.3 Above average
47             5.5            4.3 Above average
48             5.1            4.4 Below average
49             4.7            4.0       Average
50             3.9            3.7 Below average
51             4.9            3.7       Average
52             4.7            3.7 Above average
53             5.5            4.5       Average
54             4.9            4.5 Above average
55             2.3            2.5 Below average
56             5.6            4.4 Above average
57             3.3            3.2       Average
58             4.7            4.1 Below average
59             5.6            4.4 Above average
60             6.1            4.7 Above average
61             5.3            4.0       Average
62             5.9            4.4       Average
63             5.1            4.2 Above average
64             6.2            4.9 Above average
65             7.3            5.2 Above average
66            -0.1            1.2 Above average
67             3.2            3.0 Above average
68             2.4            2.6       Average
69             5.6            4.6 Above average
70             1.6            2.3       Average
71             3.7            3.4 Above average
72             6.0            4.1 Above average
73             2.3            2.6 Above average
74             7.3            5.7       Average
75             5.2            4.4       Average
76             5.5            4.8 Below average
77             6.5            5.2 Above average
78             2.5            2.5       Average
79             1.0            1.7 Above average
80             6.8            5.5       Average
81             6.3            5.2 Below average
82             4.4            3.8 Above average
83             5.8            4.5       Average
84             3.7            3.5 Below average
85             7.2            5.4 Above average
86             4.9            4.0       Average
87             6.1            4.8 Below average
88             5.1            4.3 Below average
89             4.4            4.1 Below average
90             5.5            5.0       Average
91             5.2            4.4 Below average
92             4.9            4.0 Below average
93             6.1            4.8 Below average
94             4.9            4.2 Below average
95             3.7            3.5 Below average
96             5.2            4.2 Below average
97             5.8            4.8       Average
98             5.0            4.6       Average
99             4.8            4.1 Below average
100            3.6            3.6 Below average
101            5.9            4.6       Average
102            5.9            4.7       Average
103            5.5            4.7       Average
104            5.0            4.0 Below average
105            3.3            3.3 Below average
106            4.1            3.7       Average
107            4.9            4.2       Average
108            7.1            5.3       Average
109            4.5            4.1       Average
110            4.6            3.8       Average
111            5.4            4.4 Below average
112            6.3            4.9       Average
113            5.5            4.6       Average
114            6.4            4.7       Average
115            4.2            3.6       Average
116            4.5            3.8       Average
117            5.5            4.3       Average
118            4.2            3.7 Below average
119            4.6            3.8 Below average
120            3.4            3.1 Below average
121            5.0            4.3       Average
122            4.5            4.0       Average
123            4.8            4.0 Below average
124            5.2            4.3       Average
125            4.6            4.0 Below average
126            4.5            3.7 Below average
127            4.9            4.1 Below average
128            5.6            4.4       Average
129            3.9            3.4 Below average
130            6.4            4.9       Average
131            4.4            3.9 Below average
132            5.5            4.3 Above average
133            6.5            4.9 Above average
134            3.5            3.8 Above average
135            1.3            2.3 Below average
136            2.3            2.7 Below average
137            4.6            3.9       Average
138            4.9            4.0       Average
139            1.7            2.5 Below average
140            3.1            3.0 Below average
141            4.3            3.4       Average
142            4.2            3.6       Average
143            7.1            5.1 Above average
144            5.5            4.2 Above average
145            6.0            4.6 Above average
146            3.5            3.3       Average
147            5.7            4.4 Above average
148            6.5            4.9 Above average
149            4.1            3.6       Average
150            4.7            3.4 Below average
151            5.9            4.5 Above average
152            4.6            3.8 Above average
153            8.0            4.6 Above average
154            3.7            3.5       Average
155            4.9            4.4 Above average
156            4.7            3.8       Average
157            5.1            4.2 Above average
158            5.0            4.0       Average
159            1.0            1.7       Average
160            3.4            3.1       Average
161            2.2            2.4 Below average
162            2.7            2.6 Above average
163            2.6            2.7 Above average
164            3.5            3.2       Average
165            1.8            2.4       Average
166            1.7            2.4 Below average
167            5.5            4.4 Above average
168            4.4            3.8 Above average
169            0.8            2.0       Average
170            8.9            6.4 Above average
171            4.8            4.3 Above average
172            2.9            3.0 Above average
173            0.0            1.3 Above average
174            0.7            2.0 Above average
175           -0.2            1.8 Below average
176            0.7            1.7 Above average
177            0.2            1.9 Above average
178            0.1            1.2 Above average
179            0.4            1.5       Average
180            0.7            1.8 Above average
181           -4.5           -1.5 Above average
182           -3.0           -0.7 Above average
183          -13.8           -7.3 Above average
184           -2.7            0.2 Above average
```
]

---
count: false

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

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