Interactive Elements Slides Template

class: center, middle, inverse, title-slide

.title[
# Interactive Elements Slides Template
]
.author[
### S. Mason Garrison
]

---

class: middle

# Your Turn

.your-turn[
- On Github, download the assignment called `AE01a - UN Votes`.
- In the Files pane in the bottom right corner, spot the file called `unvotes.Rmd`. Open it, and then click on the "Knit" button.
- Go back to the file and change your name on top (in the `yaml` -- we'll talk about what this means later) and knit again.
- Change the country names to those you're interested in. Your spelling and capitalization should match how the countries appear in the data, so take a peek at the Appendix to confirm spelling. Knit again. Voila, your first data visualization!
]

---

# Question Slide

.question[
What does it mean for a data analysis to be "reproducible"?
]

Near-term goals:

- Are the tables and figures reproducible from the code and data?
- Does the code actually do what you think it does?
- In addition to what was done, is it clear **why** it was done? 
(e.g., how were parameter settings chosen?)

Long-term goals:

- Can the code be used for other data?
- Can you extend the code to do other things?

---

# Tip Slide

.tip[
When working in an R Markdown document, your analysis is re-run each time you knit. If web scraping in an R Markdown document, you'd be re-scraping the data each time you knit, which is undesirable (and not *nice*)!

An alternative workflow: 
- Use an R script to save your code 
- Save interim data scraped using the code in the script as CSV or RDS files
- Use the saved data in your analysis in your R Markdown document
]

---

# FAQ Slide

.question[
Q: What programming language will we use in this course?
]

.answer[
A: We will be using R in this course. R is a powerful language for statistical computing and graphics, widely used in data science and research.
]

.question[
Q: Do I need prior programming experience?
]

.answer[
A: No prior programming experience is required. We'll start from the basics and build up your skills throughout the course.
]

---

# Interactive Poll

.large[
What's your primary goal for taking this course?
]

- [ ] Learn data analysis skills
- [ ] Improve programming abilities
- [ ] Understand statistical concepts better
- [ ] Apply data science to my field
- [ ] Other (please specify in the chat)

*Please raise your hand or use the reaction feature to indicate your choice.*

---

# Think-Pair-Share

1. **Think** (1 minute): Reflect on the following question:
   .question[
   What's the most interesting data analysis you've seen recently?
   ]

2. **Pair** (2 minutes): Turn to your neighbor and discuss your thoughts.

3. **Share** (5 minutes): We'll hear from several pairs about their discussions.

---

# Q&A Session

.large[
Now it's your turn to ask questions!
]

- Raise your hand or type in the chat to ask a question
- We'll take questions for the next 10 minutes
- If we don't get to your question, please email me or ask during office hours

---

# Discussion

.large[
Let's discuss the ethical implications of data science.
]

Consider these questions:
1. What responsibilities do data scientists have?
2. How can we ensure privacy in the age of big data?
3. What are potential negative consequences of data-driven decision making?

We'll break into small groups for 10 minutes, then come back for a full-class discussion.

```