Introduction

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## Data science

.pull-left-wide[
- Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.

- We're going to learn to do this in a `tidy` way -- more on that later!

- This is a course on introduction to data science, with an emphasis on statistical thinking.
]

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## Software

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## Data science life cycle

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.pull-left[
<img src="img/google-trend-index.png" width="75%" style="display: block; margin: auto auto auto 0;" />
]
.pull-right[
<img src="img/data-science-cycle/data-science-cycle.006.png" width="90%" style="display: block; margin: auto 0 auto auto;" />
]

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```
## # A tibble: 5 × 2
## date season
## <chr> <chr> 
## 1 23 January 2017 winter
## 2 4 March 2017 spring
## 3 14 June 2017 summer
## 4 1 September 2017 fall 
## 5 ... ...
```
]

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class: middle

## Course toolkit

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.pull-left[
### .pink[Doing data science]
- .pink[Programming:]
  - .pink[R]
  - .pink[RStudio]
  - .pink[tidyverse]
  - .pink[R Markdown]
]

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## 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**

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

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

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## Reproducible data analysis

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## 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?

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## Toolkit for reproducibility

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

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## R and RStudio

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## 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
]

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## 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/).
]

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## 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)
```

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## R essentials (continued)

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

```r
dataframe$var_name
```
]

- Object documentation can be accessed with `?`

```r
?mean
```

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## tidyverse

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

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## 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
]

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## Quarto

.center[.large[
[https://quarto.org/](https://quarto.org//)
]]
.center[
<img src="img/quarto-logo.png" width="30%" style="display: block; margin: auto;" />
]

- An open-source scientific and technical publishing system
- Create dynamic content with Python, R, Julia, and Observable.
- Publish reproducible, production quality articles, presentations, dashboards, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more.

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## R Markdown and Quarto

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## R Markdown and Quarto

- 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

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## Tour: Quarto

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## Environments

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

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## How will we use R Markdown and Quarto?

- Every assignment / report / project / etc. is an R Markdown document
- You'll always have a template R Markdown document to start with

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## 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[Practical:]]  `Bechdel + Quarto`
- [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.

<img src="img/bechdel.png" width="35%" style="display: block; margin: auto;" />
]