Meet the toolkit: programming

.title[
# Meet the toolkit: programming
]
.subtitle[
## Data Science in a Box
]
.author[
### <a href="https://datasciencebox.org/">datasciencebox.org</a>
]

---

layout: true
 
<div class="my-footer">

<a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a>

</div>

---

## Course toolkit

.pull-left[
### .gray[Course operation]
.gray[
- introds.org
- Learn
- Zoom
- Teams
- Piazza
]
]
.pull-right[
### .pink[Doing data science]
- .pink[Programming:]
  - .pink[R]
  - .pink[RStudio]
  - .pink[tidyverse]
  - .pink[R Markdown]
- .gray[Version control and collaboration:]
  - .gray[Git]
  - .gray[GitHub]
]

---

## Learning goals

By the end of the course, you will be able to...

- gain insight from data

--
- gain insight from data, **reproducibly**

--
- gain insight from data, reproducibly, **using modern programming tools and techniques**

--
- gain insight from data, reproducibly **and collaboratively**, using modern programming tools and techniques

--
- gain insight from data, reproducibly **(with literate programming and version control)** and collaboratively, using modern programming tools and techniques

---

# Reproducible data analysis

---

## Reproducibility checklist

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?

Long-term goals:

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

---

## Toolkit for reproducibility

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

---

# R and RStudio

---

## R and RStudio

.pull-left[
<img src="img/r-logo.png" width="25%" style="display: block; margin: auto;" />
- R is an open-source statistical **programming language**
- R is also an environment for statistical computing and graphics
- It's easily extensible with *packages*
]
.pull-right[
<img src="img/rstudio-logo.png" width="50%" style="display: block; margin: auto;" />
- RStudio is a convenient interface for R called an **IDE** (integrated development environment), e.g. *"I write R code in the RStudio IDE"*
- RStudio is not a requirement for programming with R, but it's very commonly used by R programmers and data scientists
]

---

## R packages

- **Packages** are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data1

- As of September 2020, there are over 16,000 R packages available on **CRAN** (the Comprehensive R Archive Network)2

- We're going to work with a small (but important) subset of these!

2 [CRAN contributed packages](https://cran.r-project.org/web/packages/).
]

---

## Tour: R and RStudio

---

## A short list (for now) of R essentials

- 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)
```

- Packages are installed with the `install.packages` function and loaded with the `library` function, once per session:

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

---

## R essentials (continued)

- Columns (variables) in data frames are accessed with `$`:

```r
dataframe$var_name
```
]

- Object documentation can be accessed with `?`

```r
?mean
```

---

## tidyverse

- The **tidyverse** is an opinionated collection of R packages designed for data science
- All packages share an underlying philosophy and a common grammar
]

---

## rmarkdown

- **rmarkdown** and the various packages that support it enable R users to write their code and prose in reproducible computational documents
- We will generally refer to R Markdown documents (with `.Rmd` extension), e.g. *"Do this in your R Markdown document"* and rarely discuss loading the rmarkdown package
]

---

# R Markdown

---

## R Markdown

- Fully reproducible reports -- each time you knit the analysis is ran from the beginning
- Simple markdown syntax for text
- Code goes in chunks, defined by three backticks, narrative goes outside of chunks

---

## Tour: R Markdown

---

## Environments

Remember this, and expect it to bite you a few times as you're learning to work 
with R Markdown!

---

## Environments

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

```r
x * 3
```
]

---

## R Markdown help

.pull-left[
.center[
.midi[R Markdown Cheat Sheet 
`Help -> Cheatsheets`]
]
<img src="img/rmd-cheatsheet.png" width="80%" style="display: block; margin: auto;" />
]
.pull-right[
.center[
.midi[Markdown Quick Reference 
`Help -> Markdown Quick Reference`]
]
<img src="img/md-cheatsheet.png" width="80%" style="display: block; margin: auto;" />
]

---

## How will we use R Markdown?

- Every assignment / report / project / etc. is an R Markdown document
- You'll always have a template R Markdown document to start with
- The amount of scaffolding in the template will decrease over the semester

---

## What's with all the hexes?

.footnote[
Mitchell O'Hara-Wild, [useR! 2018 feature wall](https://www.mitchelloharawild.com/blog/user-2018-feature-wall/)
]

---

.your-turn[
.light-blue[.hand[Your turn:]] `AE 02 - Bechdel + R Markdown`
- [The Bechdel test](https://en.wikipedia.org/wiki/Bechdel_test) asks whether a work of fiction features at least two women who talk to each other about something other than a man, and there must be two women named characters.
- Go to [RStudio Cloud](https://rstudio.cloud/) and start the assignment `AE 02 - Bechdel + R Markdown`. 
- Open and knit the R Markdown document `bechdel.Rmd`, review the document, and fill in the blanks.
]