Meet the toolkit ⚒

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<span>
<a href="https://DataScience4Psych.github.io/DataScience4Psych/" target="_blank">Data Science for Psychologists</a>
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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

![toolkit](img/toolkit.png)

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

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# Toolkit overview

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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:
    - R is like a car’s engine
    - RStudio is like a car’s dashboard

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## Let's take a tour - R / RStudio

- Console
- Using R as a calculator
- Environment
- Loading and viewing a data frame
- Accessing a variable in a data frame
- R functions

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## Working with R at the command line

- Launch RStudio/R.
- Notice the default panes:
    - Console (entire left)
    - Environment/History (tabbed in upper right)
    - Files/Plots/Packages/Help (tabbed in lower right)

- FYI: You can change the default location of the panes, among many other things
  - [Customizing RStudio](https://support.rstudio.com/hc/en-us/articles/200549016-Customizing-RStudio)

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## Working with R at the command line (pt 2)

- Go into the Console, where we interact with the live R process.
- Make an assignment and then inspect the object you just created:

``` r
x <- 3 * 4
x
```

```
## [1] 12
```

- All R statements where you create objects -- "assignments" -- have this form:

``` r
objectName <- value
```

- Read this as 'x gets 12'

<!---
and in my head I hear, e.g., "x gets 12".

You will make lots of assignments and the operator `<-` is a pain to type. Don't be lazy and use `=`, although it would work, because it will just sow confusion later. Instead, utilize RStudio's keyboard shortcut: Alt + - (the minus sign).
-->

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## R essentials

A short list (for now):

- 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 -- 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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## Let's take a tour - R Markdown

Concepts introduced:

- Copying a project of mine
- Knitting documents
- R Markdown and (some) R syntax

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- The 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 github page and fork the assignment `Bechdel + R Markdown`. 
- Open and knit the R Markdown document `bechdel.Rmd` and follow along with the instructions.
]

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# Wrapping Up...

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## R Markdown help

.pull-left[
.center[
[R Markdown cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/rmarkdown-2.0.pdf)
]
![](img/rmd-cheatsheet.png)
]
.pull-right[
.center[
Markdown Quick Reference  
`Help -> Markdown Quick Reference`
]
![](img/md-cheatsheet.png)
]

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## Workspaces

Remember this, and expect it to bite you a few times as you're learning to work 
with R Markdown: The workspace of your R Markdown document is separate from the Console!

- Run the following in the console

``` r
x <- 2
x * 3
```
.question[
All looks good, eh?
]

- Then, add the following chunk in your R Markdown document

``` r
x * 3
```
.question[
What happens? Why the error?
]

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## How will we use R Markdown?

- Every assignment / report / project / etc. is an R Markdown document
- You'll 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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# Wrapping Up... R and R Markdown

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

--
- There are millions of git commands -- ok, that's an exaggeration, but there are a lot of them -- and very few people know them all. 99% of the time you will use git to add, commit, push, and pull.

--
- We will be doing Git things and interfacing with GitHub through RStudio, but 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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## Let's take a tour - Git and GitHub

- Connect an R project to GitHub repository
- Working with a local and remote repository
- Making a change locally, committing, and pushing
- Making a change on GitHub and pulling
- There is a bit more to GitHub that we'll use in this class, but for today this is enough

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# Wrapping Up... Git and GitHub

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# Getting help in R

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## Reading help files

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## Asking good questions

.pull-left[
- **Good:** Describe your intention and include your code and the error
- **Better:** Describe your intention and create a minimum working example
- **Best:** Write a **rep**roducible **ex**ample (reprex) -- we'll introduce this concept more formally and teach you the tools for it a little later in the semester
]
--
.pull-right[
![](https://media.giphy.com/media/uRb2p09vY8lEs/giphy.gif)
- Use code formatting
- For issues with R code: copy / paste your code and resulting error, don't use screenshots!
]

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# Sources

- Mine Çetinkaya-Rundel's Data Science in a Box ([link](https://datasciencebox.org/))
- Kieran Healy's [Data Visualization: A practical introduction](http://socviz.co/appendix.html#a-little-more-about-r)
- [Jenny Bryan's Stat545](https://stat545.com)
- [Modern Dive](https://moderndive.com/)

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# Wrapping Up... Getting help in R