ISA 401: Business Intelligence & Data Visualization

class: center, middle, inverse, title-slide

.title[
# ISA 401: Business Intelligence & Data Visualization
]
.subtitle[
## 02: Introduction to <svg viewBox="0 0 581 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <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>
]
.author[
### <br>Fadel M. Megahed, PhD <br><br>Professor of Information Systems and Business Analytics <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <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> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <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> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z" /></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z" /></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Fall 2024
]

---

## Quick Refresher from Last Class

✅ Describe course objectives & structure

✅ Define data visualization & describe its main goals

✅ Describe the BI methodology and major concepts

---

## Quick Refresher from Last Class

.footnote[
<html>
<hr>
</html>

**Note:** Image generated using [Midjourney AI](https://www.midjourney.com/) v6.1 on Aug 27, 2024. I used the **following prompt:** Taylor Swift holding a sign that says "3D Charts are Awful" with Cincinnati skyline in the background. 
]

---

## Learning Objectives for Today's Class

- Describe why we are using <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><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> in this course?

- Understand the syntax, data structures and functions.

- Utilize the project workflow in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><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> and create your first <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><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> script.

---
class: inverse, center, middle

# <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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>

---

## Pedagogy Behind Using <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> in this Course

.font80[

``` r
crashes = 
  # reading the data directly from the source
  readr::read_csv("https://data.cincinnati-oh.gov/api/views/rvmt-pkmq/rows.csv?accessType=DOWNLOAD") |> 
  
  # changing all variable names to snake_case
  janitor::clean_names() |> 
  
  # selecting variables of interest
  dplyr::select(address_x, latitude_x, longitude_x, cpd_neighborhood, datecrashreported, instanceid, typeofperson, weather) |>
  
  # engineering some features from the data
  dplyr::mutate(
    datetime = lubridate::parse_date_time(datecrashreported, orders = "'%m/%d/%Y %I:%M:%S %p", tz =  'America/New_York'),
    hour = lubridate::hour(datetime),
    date = lubridate::as_date(datetime)   
    )
```
]

### The Beauty of Programming Languages

- Programming languages are **languages**.

- **It's just text** -- which gives you access to **two extremely powerful techniques**!!!

.footnote[
<html>
<hr>
</html>

**Source:** Content in "The Beauty of Programming Languages" is from [Hadley Wickham's You Can't Do Data Science in a GUI](https://speakerdeck.com/hadley/you-cant-do-data-science-in-a-gui?slide=14)
]

---

### The Beauty of Programming Languages (Continued)

- .large[`Ctrl` + `C` <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M384 336H192c-8.8 0-16-7.2-16-16V64c0-8.8 7.2-16 16-16l140.1 0L400 115.9V320c0 8.8-7.2 16-16 16zM192 384H384c35.3 0 64-28.7 64-64V115.9c0-12.7-5.1-24.9-14.1-33.9L366.1 14.1c-9-9-21.2-14.1-33.9-14.1H192c-35.3 0-64 28.7-64 64V320c0 35.3 28.7 64 64 64zM64 128c-35.3 0-64 28.7-64 64V448c0 35.3 28.7 64 64 64H256c35.3 0 64-28.7 64-64V416H272v32c0 8.8-7.2 16-16 16H64c-8.8 0-16-7.2-16-16V192c0-8.8 7.2-16 16-16H96V128H64z"/></svg>]

- .large[`Ctrl` + `V`] <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M80 96v16c0 17.7 14.3 32 32 32h60.8c16.6-28.7 47.6-48 83.2-48h62c-7.1-27.6-32.2-48-62-48H215.4C211.6 20.9 188.2 0 160 0s-51.6 20.9-55.4 48H64C28.7 48 0 76.7 0 112V384c0 35.3 28.7 64 64 64h96V400H64c-8.8 0-16-7.2-16-16V112c0-8.8 7.2-16 16-16H80zm64-40a16 16 0 1 1 32 0 16 16 0 1 1 -32 0zM256 464c-8.8 0-16-7.2-16-16V192c0-8.8 7.2-16 16-16H384v48c0 17.7 14.3 32 32 32h48V448c0 8.8-7.2 16-16 16H256zm192 48c35.3 0 64-28.7 64-64V227.9c0-12.7-5.1-24.9-14.1-33.9l-51.9-51.9c-9-9-21.2-14.1-33.9-14.1H256c-35.3 0-64 28.7-64 64V448c0 35.3 28.7 64 64 64H448z"/></svg>

- In addition, programming languages are generally

+ Readable (IMO way easier than trying to figure what someone did in an <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:green;overflow:visible;position:relative;"><path d="M48 448V64c0-8.8 7.2-16 16-16H224v80c0 17.7 14.3 32 32 32h80V448c0 8.8-7.2 16-16 16H64c-8.8 0-16-7.2-16-16zM64 0C28.7 0 0 28.7 0 64V448c0 35.3 28.7 64 64 64H320c35.3 0 64-28.7 64-64V154.5c0-17-6.7-33.3-18.7-45.3L274.7 18.7C262.7 6.7 246.5 0 229.5 0H64zm90.9 233.3c-8.1-10.5-23.2-12.3-33.7-4.2s-12.3 23.2-4.2 33.7L161.6 320l-44.5 57.3c-8.1 10.5-6.3 25.5 4.2 33.7s25.5 6.3 33.7-4.2L192 359.1l37.1 47.6c8.1 10.5 23.2 12.3 33.7 4.2s12.3-23.2 4.2-33.7L222.4 320l44.5-57.3c8.1-10.5 6.3-25.5-4.2-33.7s-25.5-6.3-33.7 4.2L192 280.9l-37.1-47.6z"/></svg>)  
  
  + Open (so you can <svg aria-hidden="true" role="img" viewBox="0 0 488 512" style="height:1em;width:0.95em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z"/></svg> it)  
  
  + Reusable and reproducible (so you can reuse your code for similar problems and other people can get the same results as you easily)  
  
  + Diffable (version control is extremely powerful)  
  
.footnote[
<html>
<hr>
</html>

**Source:** The bullets in "The Beauty of Programming Languages" are based on [Hadley Wickham's You Can't Do Data Science in a GUI](https://speakerdeck.com/hadley/you-cant-do-data-science-in-a-gui?slide=17)
]

---

### Why <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> Specifically?

- It is a general-purpose programming language, which originated for statistical analysis.

- As of Aug 28, 2024, there are 20,332 packages on [CRAN](https://cran.r-project.org/) (not including those on <svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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>).

- The `tidyverse` group of packages have simplified the workflow for data analytics/science.

---

## How to Learn <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> (Any Programming Language)

* 🗣 **Get hands dirty**‼️

* 📖 Documentation! Documentation! Documentation!

* 🔎 (Not surprisingly) Learn to Google/ChatGPT/[ChatISA](https://chatisa.fsb.miamioh.edu/): what that error message means(I do that a lot 😆)

.footnote[
<html>

<hr>

</html>

**Source:** Slide is based on [Kia Ora's How I Learn a Technology](https://stats220.earo.me/01-intro.html#7).
]

---

class: inverse center middle

# The RStudio Interface, Setup and a Project-Oriented Workflow for your Analysis

---

## RStudio Interface

.center[<img src="../../figures/rstudio-interface.png" width="80%">]

.footnote[
<html>
<hr>
</html>
image credit: Stuart Lee]

???

live

---

## Setting up RStudio (do this once)

.pull-left[
Go to **Tools** > **Global Options**:
.center[<img src="../../figures/rstudio-setup.PNG" width="100%">]
]
.pull-right[
<br>
<br>
<br>
<br>
Uncheck `Workspace` and `History`, which helps to keep <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> working environment fresh and clean every time you switch between projects.
]

---

## What is a project?

* Each university course is a project, and get your work organised.
* A self-contained project is a folder that contains all relevant files, for example my `ISA 401/` 📁 includes:
   + `isa401.Rproj`
   + `lectures/`  
     +  `01_introduction/`  
          *  `01-introduction.Rmd`, etc.   
     +  `02_introduction_to_r/`  
          *  `02_introduction_to_r.Rmd`, etc. 
* All working files are **relative** to the **project root** (i.e. `isa401/`).
* The project should just work on a different computer.

---
class: inverse, center, middle

---

## Assignment

``` r
x1 <- 5

x2 = 5

5 -> x3

print(paste0("The values of x1, x2, and x3 are ", x1, ", ", x2, ", and ", x3, " respectively"))
```

```
## [1] "The values of x1, x2, and x3 are 5, 5, and 5 respectively"
```

The assignment consists of three parts:

- The left-hand side: **variable names** (`x1` or `x2`), 
  - The assignment operator: `<-` (or alternatively `=`), and 
  - The right-hand side: **values** (`5`)

---

## Retrieval

We can retrieve/call the object using its name as follows:

``` r
x1
```

```
## [1] 5
```

``` r
x3
```

```
## [1] 5
```

---

## Retrieval: Three Common Errors

**Case issue:** object names in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> are **case sensitive**.

``` r
X1 # should be x1 instead of X1 (see last slide)
```

```
## Error in eval(expr, envir, enclos): object 'X1' not found
```

**Typo:** A spelling error of some sort

``` r
y3 # should be x3 instead of y3 (see last slide)
```

```
## Error in eval(expr, envir, enclos): object 'y3' not found
```

**Object not saved:** e.g., you clicked **Enter** instead of **Ctrl + Enter** when running your code

``` r
rm(x2) # removing x2 from the global environment to mimic error
x2 # x2 is not in the global environment (see environment)
```

```
## Error in eval(expr, envir, enclos): object 'x2' not found
```

---

## Arthimetic Operators

While we will not specifically talk about doing math in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><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>, the operators below are good to know.

|Operator |Description                 |
|:--------|:---------------------------|
|+        |addition                    |
|-        |subtraction                 |
|*        |multiplication              |
|/        |division                    |
|^ or **  |exponentiation              |
|x %% y   |modulus (x mod y) 5%%2 is 1 |
|x %/% y  |integer division 5%/%2 is 2 |

.footnote[
<html>
<hr>
</html>
**Source:** The above table is scraped from [Quick-R: Operators](https://www.statmethods.net/management/operators.html).
]

---

## Logical Operators

Logical operators are operators that return `TRUE` and `FALSE` values.

|Operator  |Description              |
|:---------|:------------------------|
|<         |less than                |
|<=        |less than or equal to    |
|>         |greater than             |
|>=        |greater than or equal to |
|==        |exactly equal to         |
|!=        |not equal to             |
|!x        |Not x                    |
|**x       |y**                      |
|x & y     |x AND y                  |
|isTRUE(x) |test if X is TRUE        |

.footnote[
<html>
<hr>
</html>
**Source:** The above table is also scraped from [Quick-R: Operators](https://www.statmethods.net/management/operators.html).
]

---
class: inverse, center, middle

---

## Coding Style

> .font150[Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.
 <br> -- [The tidyverse style guide](https://style.tidyverse.org)]

### <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> style guide

✅ `snake_case`

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#34)
]

---

## <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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> is a Vector Language: Sample Output

Learning from a **sample of ten obs. from the standard normal distribution** (i.e., `$x \sim \mathcal{N}(0, \, 1)$`)

``` r
x_vec = rnorm(n=10, mean = 0, sd = 1) # generating std normal dist data

x_vec > 0 # finding which elements in x are larger than 0
```

```
##  [1]  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE
```

``` r
sum(x_vec > 0) # summing the number of elements (i.e., how many are > 0)
```

```
## [1] 5
```

If we focus on the obtained outputs, we can see that **both** are vectors:  
  - `x_vec > 0` returns a vector of size 10 (TRUE/FALSE for each element)  
  - `sum(x_vec > 0)` returns a vector of size 1

**There are no scalars in R 😨!!!**

---

.pull-left[
- **Vectors** come in **two flavors**, which differ by their **elements' types:** 
    * **atomic vectors --** all elements **must have the same type** 
    * **lists --** elements **can** be different
- Vector have two important **attributes:**  
  * **Dimension** turns vectors into matrices and arrays, checked using `dim(object_name)`. 
  * The **class** attribute powers the S3 object system, checked using `class(object_name)`.
]

.pull-right[
.center[<img src="https://d33wubrfki0l68.cloudfront.net/2ff3a6cebf1bb80abb2a814ae1cfc67b12817713/ae848/diagrams/vectors/summary-tree.png" width="100%">]

``` r
class(x_vec)
```

```
## [1] "numeric"
```
]

.footnote[
<html>
<hr>
</html>
**Source:** The content and image are from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

.pull-left[
.center[<img src="https://d33wubrfki0l68.cloudfront.net/eb6730b841e32292d9ff36b33a590e24b6221f43/57192/diagrams/vectors/summary-tree-atomic.png" width="100%">]
]

.pull-right[

``` r
dim(x_vec) 
```

```
## NULL
```

**Atomic vectors have a dim of NULL, which distinguishes it from 1D arrays 🤯!!!**
]

.footnote[
<html>
<hr>
</html>
**Source:** The image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

## Data Types: A Visual Introduction [1]

.center[<img src="https://d33wubrfki0l68.cloudfront.net/8a3d360c80da1186b1373a0ff0ddf7803b96e20d/254c6/diagrams/vectors/atomic.png" width="60%">]

- To check the **type of** an object in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><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>, you can use the function `typeof`:

``` r
typeof(x_vec)
```

```
## [1] "double"
```

.footnote[
<html>
<hr>
</html>
**Source:** The image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

## Data Types: A Visual Introduction [2]

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/four_popular_data_types.png" alt="The four data types that we will utilize the most in our course." width="100%" />
<p class="caption">The four data types that we will utilize the most in our course.</p>
</div>

---

## Data Types: A Visual Introduction [3]

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">A visual representation of different types of atomic vectors</p>
</div>

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

## Data Types: Formal Definitions

Each of the four primary types has a special syntax to create an individual value:

- Logicals can be written in full (`TRUE` or `FALSE`), or abbreviated (`T` or `F`).

- Doubles can be specified in decimal (`0.1234`), scientific (`1.23e4`), or hexadecimal (`0xcafe`) form.   
    * There are three special values unique to doubles: `Inf`, `-Inf`, and `NaN` (not a number).   
    * These are special values defined by the floating point standard.

- Integers are written similarly to doubles but must be followed by `L`(`1234L`, `1e4L`, or `0xcafeL`), and can not contain fractional values.

- Strings are surrounded by `"` (e.g., `"hi"`) or `'` (e.g., `'bye'`). Special characters are escaped with `\` see `?Quotes` for full details.

.footnote[
<html>
<hr>
</html>
**Source:** The content of this slide is verbatim from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#scalars)
]

---

## Data Structures: Atomic Vector (1D)

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">Keeping the  visual representation of different types of atomic vectors in your head!!</p>
</div>

``` r
dept = c('ACC', 'ECO', 'FIN', 'ISA', 'MGMT')
nfaculty = c(18L, 19L, 14L, 25L, 22L)
```

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

## Data Structures: 1D ➡️ 2D [Visually]

.center[<img src="../../figures/legos-jbryan-structures.png" width="92%">]

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)  
]

---

## Data Structures: 1D ➡️ 2D [In Code]

``` r
library(tibble)

fsb_tbl <- tibble(
  department = dept, 
  count = nfaculty, 
  percentage = count / sum(count))
fsb_tbl
```

```
## # A tibble: 5 × 3
##   department count percentage
##   <chr>      <int>      <dbl>
## 1 ACC           18      0.184
## 2 ECO           19      0.194
## 3 FIN           14      0.143
## 4 ISA           25      0.255
## 5 MGMT          22      0.224
```

---

## Data Structures: Lists [1]

An object contains elements of **different data types**.

.center[<img src="../../figures/legos-jbryan-list.png" width="25%">]

.footnote[
<html>
<hr>
</html>
**Source:** The image is adapted from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats/blob/master/lego-rstats_014.jpg)
]

---

## Data Structures: Lists [2]

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png" width="100%">]

``` r
lst <- list( # list constructor/creator
* 1:3, # atomic double/numeric vector  of length = 3
* "a", # atomic character vector of length = 1 (aka scalar)
* c(TRUE, FALSE, TRUE), # atomic logical vector of length = 3
* c(2.3, 5.9) # atomic double/numeric vector of length =3
)
lst # printing the list
```

```
## [1] "1:3"                  "a"                    "c(TRUE, FALSE, TRUE)"
## [4] "c(2.3, 5.9)"
```

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#lists)
]

---

## Data Structures: Lists [3]

.pull-left[
### data type

``` r
typeof(lst) # primitive type
```

```
## [1] "list"
```
### data class

``` r
class(lst) # type + attributes
```

```
## [1] "list"
```
]
.pull-right[
### data structure

``` r
str(lst)
# sublists can be of diff lengths and types
```

```
## List of 4
##  $ : int [1:3] 1 2 3
##  $ : chr "a"
##  $ : logi [1:3] TRUE FALSE TRUE
##  $ : num [1:2] 2.3 5.9
```
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/02-import-export.html#6).
]

---

## Data Structures: Lists [3]

A list can contain other lists, i.e. **recursive**

``` r
# a named list
str(
* list(first_el = lst, second_el = iris)
  )
```

```
## List of 2
##  $ first_el :List of 4
##   ..$ : int [1:3] 1 2 3
##   ..$ : chr "a"
##   ..$ : logi [1:3] TRUE FALSE TRUE
##   ..$ : num [1:2] 2.3 5.9
##  $ second_el:'data.frame':	150 obs. of  5 variables:
##   ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##   ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##   ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##   ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##   ..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

---

## Data Structures: Lists [4]

.pull-left[
Subset by `[]`

``` r
lst[1]
```

```
## [[1]]
## [1] 1 2 3
```
]
.pull-right[
Subset by `[[]]`

``` r
lst[[1]]
```

```
## [1] 1 2 3
```
]

.center[<img src="../../figures/pepper.png" width="50%">]

.footnote[
<html>
<hr>
</html>
**Sources:** The slide is based on [Earo Wang's STAT 220 slides](https://stats220.earo.me/02-import-export.html#10) and image is from [Hadley Wickham's Tweet on Indexing lists in R](https://twitter.com/hadleywickham/status/643381054758363136?lang=en).
]

---

## Data Structures: Matrices

A matrix is a **2D data structure** made of **one/homogeneous data type.**

.pull-left[

``` r
x_mat = matrix( sample(1:10, size = 4), nrow = 2, ncol = 2 ) 
str(x_mat) # its structure?
```

```
##  int [1:2, 1:2] 7 9 5 10
```

``` r
x_mat # printing it nicely
print('-----------------')
*x_mat[1, 2] # subsetting
```

```
##      [,1] [,2]
## [1,]    7    5
## [2,]    9   10
## [1] "-----------------"
## [1] 5
```

]

.pull-right[

``` r
x_char = matrix(
  sample(letters, size = 12), nrow = 3, ncol =4)
x_char
```

```
##      [,1] [,2] [,3] [,4]
## [1,] "w"  "s"  "o"  "d" 
## [2,] "l"  "h"  "v"  "n" 
## [3,] "p"  "g"  "z"  "u"
```

``` r
*x_char[1:2, 2:3] # subsetting
```

```
##      [,1] [,2]
## [1,] "s"  "o" 
## [2,] "h"  "v"
```
]

---

## Data Structures: Data Frames [1]

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9ec5e1f8982238a413847eb5c9bbc5dcf44c9893/bc590/diagrams/vectors/summary-tree-s3-2.png" width="22%">]

> .font150[If you do data analysis in R, you’re going to be using data frames. A data frame is a named list of vectors with attributes for `(column)` `names`, `row.names`, and its class, “data.frame”.  -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)]

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#list-array)
]

---

## Data Structures: Data Frames [2]

``` r
df1 <- data.frame(x = 1:3, y = letters[1:3])
typeof(df1) # showing that its a special case of a list
```

```
## [1] "list"
```

``` r
attributes(df1) # but also is of class data.frame
```

```
## $names
## [1] "x" "y"
## 
## $class
## [1] "data.frame"
## 
## $row.names
## [1] 1 2 3
```

In contrast to a regular list, a data frame has **an additional constraint: the length of each of its vectors must be the same.** This gives data frames their **rectangular structure.**

.footnote[
<html>
<hr>
</html>
**Source:** Content is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#list-array)
]

---

## Data Structures: Data Frames [3]

As noted in the creation of `df1`, columns in a data frame can be of different types. Hence, it is more widely used in data analysis than matrices.

.center[<img src="../../figures/legos-jbryan-dataframe-w-text.png" width="40%">]

---

## Data Structures: So What is a Tibble Anyway?

> Tibble is a **modern reimagining of the data frame**. Tibbles are designed to be (as much as possible) **drop-in replacements for data frames** that fix those frustrations. A concise, and fun, way to summarise the main differences is that tibbles are **lazy and surly: they do less and complain more**. -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)

.pull-left[[<img src="https://d33wubrfki0l68.cloudfront.net/565916198b0be51bf88b36f94b80c7ea67cafe7c/7f70b/cover.png" height="320px">](https://adv-r.hadley.nz)]

To learn more about the basics of tibble, please consult the reference below: 
* [Data frames and tibbles (Click and read from 3.6 up to and including 3.6.5)](https://adv-r.hadley.nz/vectors-chap.html#list-array)

---

## Functions

A function call consists of the **function name** followed by one or more **argument** within parentheses.

``` r
temp_high_forecast = c(86, 84, 85, 89, 89, 84, 81)
mean(x = temp_high_forecast)
```

```
## [1] 85.42857
```

* function name: `mean()`, a built-in R function to compute mean of a vector
* argument: the first argument (LHS `x`) to specify the data (RHS `temp_high_forecast`)

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#41)
]

---

## Function Help Page

Check the function's help page with `?mean`

### Class Activity

> _Please take 2 minutes to investigate the help page for `mean` in R Studio._

```r
mean(x = temp_high_forecast, trim = 0, na.rm = FALSE, ...)
```

* Read **Usage** section
  + What arguments have default values?
* Read **Arguments** section
  + What does `trim` do?
* Run **Example** code

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#42)
]

---

## Function Arguments

.pull-left[
### Match by **positions**

``` r
mean(temp_high_forecast, 0.1, TRUE)
```

```
## [1] 85.42857
```
]

.pull-right[
### Match by **names**

``` r
mean(x = temp_high_forecast, trim = 0.1, na.rm = TRUE)
```

```
## [1] 85.42857
```
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#43)
]

---

## Use Functions from Packages

.pull-left[

``` r
dplyr::cummean(temp_high_forecast)
```

```
## [1] 86.00000 85.00000 85.00000 86.00000 86.60000 86.16667 85.42857
```

``` r
dplyr::first(temp_high_forecast)
```

```
## [1] 86
```

``` r
dplyr::last(temp_high_forecast)
```

```
## [1] 81
```
]
.pull-right[
<br>
<br>
<br>
<br>
.center[
<img src="https://stats220.earo.me/img/install-library.JPG" height="240px">
]
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#44)
]

---

# Write Your Own Functions

``` r
# function_name <- function(arguments) {
#   function_body
# }
my_mean <- function(x, na.rm = FALSE) {
  summation <- sum(x, na.rm = na.rm)
  summation / length(x)
}

my_mean(temp_high_forecast)
```

```
## [1] 85.42857
```

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#45)
]

---
class: inverse, center, middle

# Demo

---

# Your First <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><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> Script File

Refer to our in class code.

---
class: inverse, center, middle

# Recap

---

# Summary of Main Points

By now, you should be able to do the following:

- Understand the syntax, data structures and functions.

---

# Things to Do to Prepare for Our Next Class

- Go over your notes, read the **references below**, and **complete** the [self-paced R tutorial](http://rstudio.fsb.miamioh.edu:3838/megahefm/isa401/datatypes/).

- Complete [Assignment 02](https://miamioh.instructure.com/courses/223961/quizzes/663249) on Canvas.

.pull-left[
.center[[<img src="https://d33wubrfki0l68.cloudfront.net/b88ef926a004b0fce72b2526b0b5c4413666a4cb/24a30/cover.png" height="250px">](https://r4ds.had.co.nz)]
.small[
* [Workflow: basics](https://r4ds.had.co.nz/workflow-basics.html)
* [Workflow: scripts](https://r4ds.had.co.nz/workflow-scripts.html)
* [Workflow: project](https://r4ds.had.co.nz/workflow-projects.html)
]
]
.pull-right[
.center[[<img src="https://d33wubrfki0l68.cloudfront.net/565916198b0be51bf88b36f94b80c7ea67cafe7c/7f70b/cover.png" height="250px">](https://adv-r.hadley.nz)]
.small[
* [Names and values](https://adv-r.hadley.nz/names-values.html)
* [Vectors](https://adv-r.hadley.nz/vectors-chap.html)
* [Subsetting](https://adv-r.hadley.nz/subsetting.html)
]
]