Meet the Toolkit

# Meet the Toolkit
### Becky Tang
### 05.13.21

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## Agenda

- Reproducible data analysis 
- R and RStudio 
- R Markdown 
- Git and GitHub

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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? 
(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?
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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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# R and RStudio

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## What is R/RStudio?

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

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## R essentials (a short list)

- **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)
```

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

```r
dataframe$var_name
```

- **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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## 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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# R Markdown

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## R Markdown

- 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

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## How will we use R Markdown?

- Every assignment/lab/project/etc. is an R Markdown document

- You will always have a template R Markdown document to start with

- The amount of scaffolding in the template will decrease over the course of the class

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## R Markdown tips

**Resources**
- [R Markdown cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf)
- Markdown Quick Reference: 
    - `Help -> Markdown Quick Reference`
<br><br>
--

**Remember**: The workspace of the R Markdown document is <u>separate</u> from the console

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# Git and GitHub

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## Version control

- We introduced GitHub as a platform for collaboration

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

- It's actually designed for version control

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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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## Git and GitHub tips

- **Git** is a version control system -- like “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. 99% of the time you will use git to add, commit, push, and pull.

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## Git and GitHub tips

- We will be doing git things and interfacing with GitHub through RStudio
    - 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.

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

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## 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?

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## Before next class

- Accept the invite to join `sta199-summer2021` organization on GitHub TODAY!!

- If you have not already done so "Getting to know you" survey on Sakai - due Sun, 5/16

- Start Reading 01 posted on the course schedule - due Mon, 5/17