Machine Learning with R

class: title-slide   
<a href="https://github.com/koalaverse/AnalyticsSummit19"><img style="position: absolute; top: 0; right: 0; border: 0;" src="https://s3.amazonaws.com/github/ribbons/forkme_right_darkblue_121621.png" alt="Fork me on GitHub"></a>

<br><br><br><br>
# .font200[Machine Learning with <span><i class="fab  fa-r-project faa-pulse animated faa-slow " style=" color:steelblue;"></i></span>]

### Brad Boehmke & Brandon Greenwell
### April 1-2, 2019

---

---

# About us

* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 496 512"><path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"/></svg>](http://bradleyboehmke.github.io/) bradleyboehmke.github.io
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>](https://github.com/bradleyboehmke/) @bradleyboehmke
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 512 512"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg>](https://twitter.com/bradleyboehmke) @bradleyboehmke
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 448 512"><path d="M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z"/></svg>](https://www.linkedin.com/in/brad-boehmke-ph-d-9b0a257/) @bradleyboehmke
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 512 512"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>](mailto:bradleyboehmke@gmail.com) bradleyboehmke@gmail.com

]

#### Family <img src="images/family.png" align="right" alt="family" width="130" />

* Dayton, OH
* Kate, Alivia (9), Jules (6)

#### Professional

* 84.51° - Data Science Enabler <img src="images/logo8451.jpg" align="right" alt="family" width="150" />

#### Academic

* University of Cincinnati <img src="images/uc.png" align="right" alt="family" width="100" />
* Air Force Institute of Technology

#### R Community

]

---

# About us

* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 496 512"><path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"/></svg>](https://bgreenwell.netlify.com/) https://bgreenwell.netlify.com/
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>](https://github.com/bgreenwell/) @bgreenwell
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 512 512"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg>](https://twitter.com/bgreenwell8) @bgreenwell8
* [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 512 512"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>](mailto:greenwell.brandon@gmail.com) greenwell.brandon@gmail.com

]

#### Professional

* 84.51° - Data Scientist <img src="images/logo8451.jpg" align="right" alt="family" width="150" />

#### Academic

* University of Cincinnati <img src="images/uc.png" align="right" alt="family" width="60" />

* Wright State University <img src="images/wsu.jpg" align="right" alt="family" width="60" />

#### R Community

]

---
# Data science courses

| Course | Dates |
|:------|:------:|
| Intro to R | Dec 13-14 |
| Intermediate R | Jan 31 - Feb 1 |
| Advanced Analytics with R | Feb 28 - Mar 1 |
| Machine Learning with R <span><i class="fas  fa-map-pin faa-flash animated " style=" color:red;"></i></span> | Apr 1-2 |

]
]

]

---
# Course objectives

<br><br><br>
.font130[
This workshop will step through the process of building, visualizing, testing, and comparing supervised models. The goal is to expose you to building machine learning models in R using a variety of packages and model types.
]

.center.bold[_You will gain deeper knowledge around the analytic modeling process and apply various supervised machine learning algorithms_]

---
# Course overview

.font110[Moving from a machine learning apprentice to journeyman with <span><i class="fab  fa-r-project faa-FALSE animated " style=" color:steelblue;"></i></span>:]

| Topic                               |     Time      |
| :---------------------------------- | :-----------: |
| .opacity[<s>Social time</s>]        |  <s>8:00 - 8:30</s>  |
| Getting started                     |  8:30 - 9:15  |
| Supervised modeling process         |  9:30 - 10:30 |
| .opacity[Break]                     | 10:30 - 10:45 |
| Feature & target engineering        | 10:45 - 11:45 |
| .opacity[Lunch]                     | 12:00 - 1:00  |
| Regression & cousins                |  1:00 - 2:30  |
| .opacity[Break]                     |  2:30 - 2:45  |
| Interpretable machine learning      |  2:45 - 4:15  |
| Q&A                                 |  4:15 - 5:00  |

]
]

.center[.bold[Day 2]]
 
| Topic                                    |     Time      |
| :----------------------------------------| :-----------: |
| .opacity[Recap / morning discussion]     |  8:30 - 9:00  |
| Tree-based methods                       | 9:00 - 10:30 |
| .opacity[Break]                          | 10:30 - 10:45 |
| Tree-based methods                       | 10:45 - 12:00 |
| .opacity[Lunch]                          | 12:00 - 1:00  |
| Support vector machines                  |  1:00 - 2:30  |
| .opacity[Break]                          |  2:30 - 2:45  |
| Stacked models & auto ML                 |  2:45 - 3:15  |
| Kaggle competition                       |  3:30 - 4:30  |
| Q&A                                      |  4:30 - 5:00  |

]
]

---
# A hands-on learning environment

### You may be overwhelmed

]

### So work together

]

---
# Class material

<a href="https://github.com/koalaverse/AnalyticsSummit19" class="github-corner" aria-label="View source on Github"><svg width="80" height="80" viewBox="0 0 250 250" style="fill:#fff; color:#151513; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="M128.3,109.0 C113.8,99.7 119.0,89.6 119.0,89.6 C122.0,82.7 120.5,78.6 120.5,78.6 C119.2,72.0 123.4,76.3 123.4,76.3 C127.3,80.9 125.5,87.3 125.5,87.3 C122.9,97.6 130.6,101.9 134.4,103.2" fill="currentColor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>

<br>

### Source code

- <span><i class="fab  fa-github faa-pulse animated-hover "></i> GitHub</span>: [https://github.com/koalaverse/AnalyticsSummit19](https://github.com/koalaverse/AnalyticsSummit19)

- <span><i class="fab  fa-slideshare faa-pulse animated-hover "></i> Slides</span>

- <span><i class="fas  fa-code faa-pulse animated-hover "></i> Student Scripts</span>

- <span><i class="fas  fa-database faa-pulse animated-hover "></i> Data</span>

---
class: yourturn
# Your Turn!

<br>
## .font140[Meet your neighbors:]

2. What programming experience other than R do they have?

3. How are they using, or how do they plan to use, R and machine learning in their job?
]

---
class: yourturn
# Your Turn!

<br>
## .font140[Meet your neighbors:]

---
class: clear, center, middle

---

# Software

### R (programming language) <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

1. Go to https://cran.r-project.org/
2. Click "Download R for Mac/Windows"
3. Download the appropriate file:
   - Windows users click Base, and download the installer for the latest R version
   - Mac users select the file R-3.X.X that aligns with your OS version
4. Follow the instructions of the installer

]

### RStudio (IDE) <img src="https://dfsuknfbz46oq.cloudfront.net/p/icons/rstudio.png" width="35" align="center"/>

1. Go to RStudio for desktop https://www.rstudio.com/products/rstudio/download/#download
2. Select the install file for your OS
3. Follow the instructions of the installer

]

---
# Environment

This course uses several R 📦 .  You should've ran the `00-run-this-script-first.R` to ensure you have all required packages.

```r
###############################
# Setting Up Your Environment #
###############################
# the following packages will be used
list_of_pkgs <- c(
  "alr3",          # for Swiss banknote data
  "AmesHousing",   # provides data we'll use
  "tidyverse",     # data munging & visualization
  "reshape2",      # data transformation for one example
  "extracat",      # visualizing missing data (one example)
  "rsample",       # sampling procedures
  "recipes",       # feature engineering procedures
  "caret",         # meta modeling package,
  "h2o",           # meta modeling, model stacking, & auto ML
  "glmnet",        # regularized regression
  "earth",         # multivariate adaptive regression splines
  'investr',       # for plotFit() function
  'randomForest',  # for random forest 
  "ranger",        # fast random forest
  'rpart',         # for binary recursive partitioning (i.e., decision trees)
  'rpart.plot',    # for plotting decision tree diagrams
  "gbm",           # gradient boosting machines
  "xgboost",       # extreme gradient boosting
  'svmpath',       # for fitting the entire SVM regularization path
  "broom",         # provides model result clean up
  "vip",           # model interpretation
  "pdp",           # model interpretation
  "iml",           # model interpretation
  "DALEX",         # model interpretation
  "lime"           # model interpretation
)

# run the following line of code to install the packages you currently do not have
new_pkgs <- list_of_pkgs[!(list_of_pkgs %in% installed.packages()[,"Package"])]
if(length(new_pkgs)) install.packages(new_pkgs)
```

]

---
# Knowledge

This course makes some assumptions about your prior knowledge. To ensure your success, you should have reviewed the material covered in the Intro to R [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>](https://github.com/uc-r/Intro-R) and Intermediate R [<svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg>](https://github.com/uc-r/Intermediate-R) courses.

| Topics                  | &nbsp;&nbsp; Slides &nbsp;&nbsp; |
| :---------------------- | :-----: |
| R & RStudio fundamentals &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-1a-intro.html) |
| Importing data          | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-1b-import.html) |
| Data transformation     | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-1c-transform.html) |
| Data visualization      | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-1e-visualization.html) |
| Data types              | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-2b-data-types.html) |
| Tidy data               | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-2c-tidy.html) |
| Joining data            | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-2d-joins.html) |
| Data structures         | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intro-R/day-2e-data-structures.html) |

]

| Topics                  | &nbsp;&nbsp; Slides &nbsp;&nbsp; |
| :---------------------- | :-----: |
| Scoped variable transformation | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intermediate-R/day-1b-scoped-dplyr.html) |
| Control statements          | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intermediate-R/day-1c-control-flow.html) |
| Workflow     | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intermediate-R/day-1d-workflow.html) |
| Iteration with loops      | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intermediate-R/day-2b-loops.html) |
| Iteration with functional programming              | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intermediate-R/day-2c-fp.html) |
| Writing functions               | [<span><i class="fab  fa-slideshare faa-pulse animated-hover " style=" color:steelblue;"></i></span>](https://uc-r.github.io/Intermediate-R/day-2d-functions.html) |

]

---
# Data

Ames, IA property sales information (De Cock, 2011) [<span><i class="ai  ai-google-scholar faa-tada animated-hover "></i></span>](https://www.tandfonline.com/doi/pdf/10.1080/10691898.2011.11889627).
- .bold[problem type]: supervised regression
- .bold[response variable]: sale price (i.e. $195,000, $215,000)
- .bold[features]: 80
- .bold[observations]: 2,930
- .bold[objective]: use property attributes to predict the sale price of a home
- .bold[access]: provided by the `AmesHousing` package
- .bold[more details]: See `?AmesHousing::ames_raw`

```r
# access data
ames <- AmesHousing::make_ames()

# initial dimension
dim(ames)
## [1] 2930   81

# response variable
head(ames$Sale_Price)
## [1] 215000 105000 172000 244000 189900 195500

# first few observations
head(ames)
## # A tibble: 6 x 81
##   MS_SubClass MS_Zoning Lot_Frontage Lot_Area Street Alley Lot_Shape
##   <fct>       <fct>            <dbl>    <int> <fct>  <fct> <fct>    
## 1 One_Story_… Resident…          141    31770 Pave   No_A… Slightly…
## 2 One_Story_… Resident…           80    11622 Pave   No_A… Regular  
## 3 One_Story_… Resident…           81    14267 Pave   No_A… Slightly…
## 4 One_Story_… Resident…           93    11160 Pave   No_A… Regular  
## 5 Two_Story_… Resident…           74    13830 Pave   No_A… Slightly…
## 6 Two_Story_… Resident…           78     9978 Pave   No_A… Slightly…
## # … with 74 more variables: Land_Contour <fct>, Utilities <fct>,
## #   Lot_Config <fct>, Land_Slope <fct>, Neighborhood <fct>,
## #   Condition_1 <fct>, Condition_2 <fct>, Bldg_Type <fct>,
## #   House_Style <fct>, Overall_Qual <fct>, Overall_Cond <fct>,
## #   Year_Built <int>, Year_Remod_Add <int>, Roof_Style <fct>,
## #   Roof_Matl <fct>, Exterior_1st <fct>, Exterior_2nd <fct>,
## #   Mas_Vnr_Type <fct>, Mas_Vnr_Area <dbl>, Exter_Qual <fct>,
## #   Exter_Cond <fct>, Foundation <fct>, Bsmt_Qual <fct>, Bsmt_Cond <fct>,
## #   Bsmt_Exposure <fct>, BsmtFin_Type_1 <fct>, BsmtFin_SF_1 <dbl>,
## #   BsmtFin_Type_2 <fct>, BsmtFin_SF_2 <dbl>, Bsmt_Unf_SF <dbl>,
## #   Total_Bsmt_SF <dbl>, Heating <fct>, Heating_QC <fct>,
## #   Central_Air <fct>, Electrical <fct>, First_Flr_SF <int>,
## #   Second_Flr_SF <int>, Low_Qual_Fin_SF <int>, Gr_Liv_Area <int>,
## #   Bsmt_Full_Bath <dbl>, Bsmt_Half_Bath <dbl>, Full_Bath <int>,
## #   Half_Bath <int>, Bedroom_AbvGr <int>, Kitchen_AbvGr <int>,
## #   Kitchen_Qual <fct>, TotRms_AbvGrd <int>, Functional <fct>,
## #   Fireplaces <int>, Fireplace_Qu <fct>, Garage_Type <fct>,
## #   Garage_Finish <fct>, Garage_Cars <dbl>, Garage_Area <dbl>,
## #   Garage_Qual <fct>, Garage_Cond <fct>, Paved_Drive <fct>,
## #   Wood_Deck_SF <int>, Open_Porch_SF <int>, Enclosed_Porch <int>,
## #   Three_season_porch <int>, Screen_Porch <int>, Pool_Area <int>,
## #   Pool_QC <fct>, Fence <fct>, Misc_Feature <fct>, Misc_Val <int>,
## #   Mo_Sold <int>, Year_Sold <int>, Sale_Type <fct>, Sale_Condition <fct>,
## #   Sale_Price <int>, Longitude <dbl>, Latitude <dbl>
```

]

---
class: yourturn
# Your Turn!

.font120[
To get warmed up, let's do some basic exploratory data analysis such as exploratory visualizations or summary statistics with these data sets. The idea is to get a feel for the data.

Let's take 5-10 minutes and work with your neighbors.
]

---
# Ready to get started?

---
# Back home

<br><br><br><br>
[.center[<span><i class="fas  fa-home fa-10x faa-FALSE animated "></i></span>]](https://github.com/koalaverse/AnalyticsSummit19)