Meet the Toolkit

# Meet the Toolkit
### K Arnold, based on IntroDS.org

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## Q&A

**Will we use databases / SQL?** Yes, in the later part of the class.

**Will everything be on Moodle?** Moodle Calendar will have all dates, and direct links to anything outside of Moodle that you'll need.

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## Do you remember your take-away point from Monday?

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## Logistics

* "Check off" all Moodle activities under "Introduction"
  * Prep 1
  * Lec 1.1
  * Discussion 1
  * Quiz 1
  * Lab 1.2
  * Lec 1.3
* Start Prep 2 for Wednesday (no class Monday)
* Homework 1 posted soon
* Piazza: keep it up!

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## So far...

- Monday: Overall objectives: projects, topics, dispositions
- Wednesday:
  - Hands on practice with R, RStudio, Git, GitHub
  - First look at summarizing data in R
- Today:
  - Review Wednesday's activity
  - Overview of the toolkit we're using

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# Reproducible data analysis

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## Reproducibility checklist

Near-term goals:

- Can you re-make all tables and figures easily?
- 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

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

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## Tour: R and RStudio

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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 loaded with the `library` function:

```r
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

- write code and prose in reproducible computational documents
]

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# R Markdown

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## 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

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## Tour: R Markdown

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## Environments

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

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## Environments

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

```r
x * 3
```
]

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## 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;" />
]

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## 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

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# Getting help in R

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# Version Control

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## Git and GitHub

.pull-left[
<img src="img/git-logo.png" width="25%" style="display: block; margin: auto;" />
- Git is a version control system -- like “Track Changes” features from Microsoft Word, on steroids
- It's not the only version control system, but it's a very popular one
]
.pull-right[
<img src="img/github-logo.png" width="25%" style="display: block; margin: auto;" />
- GitHub is the home for your Git-based projects on the internet

- We will use GitHub as a platform for web hosting and collaboration
]

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## Versioning

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## Versioning

### with human readable messages

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## Why do we need version control?

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## How will we use Git and GitHub?

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## How will we use Git and GitHub?

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## How will we use Git and GitHub?

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## How will we use Git and GitHub?