Meet the toolkit

# Meet the toolkit
## Intro to Data Science
### Shawn Santo

---

## Today's agenda

- R and RStudio

- R Markdown

- Git and GitHub

- Reproducible data analysis

---

# R and RStudio

---

## What are R and RStudio?

- R is a statistical programming language.
<br/><br/>
- RStudio is a convenient interface for R (an integrated development environment, IDE).
<br/><br/>
- At its simplest:
    - R is like a car’s engine
    - RStudio is like a car’s dashboard

The RStudio interface makes working with R much easier.

*Source*: [Modern Dive](https://moderndive.com/)

---

## RStudio

Access RStudio at https://vm-manage.oit.duke.edu/containers/rstudio

---

## Package `tidyverse`

- The tidyverse is an opinionated collection of R packages designed 
  for data science.

- All packages share an underlying philosophy and a common grammar.

---

## R essentials (a short overview)

**Packages** are installed with the `install.packages()` function and loaded 
with function `library()` once per session.

```r
install.packages("package_name")
library(package_name)
```

**Data frames** are rectangular objects that contain data. Each column in a
data frame is a **vector**.

```
#>                    mpg cyl    wt
#> Mazda RX4         21.0   6 2.620
#> Mazda RX4 Wag     21.0   6 2.875
#> Datsun 710        22.8   4 2.320
#> Hornet 4 Drive    21.4   6 3.215
#> Hornet Sportabout 18.7   8 3.440
#> Valiant           18.1   6 3.460
```

**Functions** are (most often) verbs, followed by what they will be applied 
to in parentheses:

```r
do_this(to_this)
do_that(to_this, to_that, with_those)
```

---

# R Markdown

---

## R Markdown

- Generate fully reproducible reports - the analysis is run from the beginning 
  each time you knit

- Simple Markdown syntax for text

- Code goes in chunks, defined by three backticks, narrative goes outside of 
  chunks

---

## Sample R Markdown syntax

.pull-left[
| Header syntax       | Example                                                  |
|:--------------------|:---------------------------------------------------------|
| \#      Level one   | <h1 style="font-size:16pt; color:black"> Level one</h1>  |
| \##     Level two   | <h2 style="font-size:15pt; color:black"> Level two</h2>  |
| \###    Level three | <h3 style="font-size:14pt; color:black"> Level three</h3>|
| \####   Level four  | <h4 style="font-size:13pt; color:black"> Level four</h4> |
| \#####  Level five  | <h5 style="font-size:12pt; color:black"> Level five</h5> |
| \###### Level six   | <h6 style="font-size:11pt; color:black"> Level six</h6>  |
]

.pull-[
| Syntax                      | Example                                        |
|:----------------------------|:-----------------------------------------------|
| \*\*bold text\*\*           | **bold text**                                  |
| \*italicized text\*         | *italicized text*                              |
| - one<br/>- two<br/>- three | <ul><li>one</li><li>two</li><li>three</li></ul>|
| ` `in-line code` `          | `in-line code`                                 |
]

---

## RStudio and R Markdown tour

First, recall "The murky tale of Flint’s deceptive water data"
  - How many samples were taken from each sampled home?
  - What is the EPA action level?

Live demo

- Access RStudio: https://vm-manage.oit.duke.edu/containers/rstudio
  - Create your private repository of today's notes: https://classroom.github.com/a/Tpy7C0oC
 
---

## R Markdown tips

**Resources**
- [R Markdown cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf)

- In RStudio, Markdown Quick Reference:

- `Help -> Markdown Quick Reference`
    
<br><br>

**Remember**: The workspace of the R Markdown document is separate 
              from the console.

---

## Workspace vs. console

Run the following in your console.

```r
x <- 2
x * 3
```

Then, add the following chunk in your R Markdown document and knit.

```r
x * 3
```

What happens? Why the error?

---

## How will we use R Markdown?

- Every homework, lab, and project will involve an R Markdown document.

- You'll always have a template R Markdown document to start with; however, the
  amount of scaffolding in the template will decrease over the semester.

---

# Git and GitHub

---

## Version control

- We introduced GitHub as a platform for collaboration

- But it's much more than that...

- It's actually designed for version control

---

## Versioning

---

## Versioning

We can add human readable messages.

---

## Git and GitHub tips

- **Git** is a version control system, similar to “Track Changes” features 
  from Microsoft Word.

- **GitHub** is the home for your Git-based projects on the internet 
  (like DropBox but much better).

- There are a lot of Git commands and very few people know them all; 
  most of the time you will use
  ```bash
  git add
  git commit
  git push
  git pull
  ```
  All of these commands can be executed through RStudio's Git tab.
  
---

## Git and GitHub tips

- We will be using git and interfacing with GitHub through RStudio
<br/><br/>
    - If you Google for help you might come across methods for doing these 
      things in the command line -- skip that and move on to the next resource 
      unless you feel comfortable trying it out.
<br/><br/><br/><br/>
--

- There is a great resource for working with git and R: 
  [happygitwithr.com](http://happygitwithr.com/).
  <br/><br/>
    - Some of the content in there is beyond the scope of this course, but it's 
      a good place to look for help.

---

## Git and GitHub live demo

- Concepts introduced:
  - Connect an R project to GitHub repository
  - Working with a local and remote repository
  - Making a change locally, committing, and pushing

<br/><br/>
  
- In Lab 01 you will go through the full version control cycle. As the semester
  progresses we will guide you in using Git/GitHub in a team-based
  environment.

---

# Reproducible data analysis

---

## Reproducibility checklist

What does it mean for a data analysis to be "reproducible"?

**Near-term goals:**

- Are tables and figures reproducible from the code and data?
- Does 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 updates to the current data?
- Can the code be used for other data?
- Can you extend the code to do other things?

---

## Toolkit

- Scriptability `\(\rightarrow\)` R
<br/><br/>
- Literate programming (code, narrative, output in one place) `\(\rightarrow\)` R Markdown
<br/><br/>
- Version control `\(\rightarrow\)` Git / GitHub

---

## Recap

Can you answer these questions?

- What is a reproducible data analysis, and why is it important?
  
- What is version control, and why is it important?
  
- What is R vs. RStudio? 
  
- What is git vs. GitHub?

Concepts introduced:

- Cloning a project from GitHub to RStudio
- Knitting documents
- R Markdown and (some) R syntax
- Console
- Using R as a calculator
- Environment
- Loading and viewing a data frame
- Accessing a variable in a data frame
- R functions

---

## References

1. McConville, C. (2019). Statistical Inference via Data Science. 
   Moderndive.com. Retrieved from https://moderndive.com/