Reproducible Research in R

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.title[
# Reproducible Research in R
]
.subtitle[
## How to do the same thing more than once
]
.author[
### Aaron Peikert, Hannes Diemerling, Maximilian S. Ernst, Andreas M. Brandmaier
]
.date[
### 2025-06-12
]

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## In a nutshell

.left[

1. aaronpeikert.github.io/repro-workshop/self-paced
2. Do the .todo[green stuff].
3. Take your time.

]

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# Why should we work reproducibly?
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# ~~Why~~ How should we work reproducibly?

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# Reproducible Research

# =

# same data + same analysis

# =

# same results

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.tiny[No idea how to attribute self-made memes.]

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.tiny[No idea how to attribute self-made memes.]

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.left[

Let me present to you today:

]

.center[

# Reproducible Research: The Bleeding Edge

]

.right[

An action movie.

]

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## Properties we are after:

* fully automated
* independent of hardware
* independent of users
* continually verified

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## Problems we want to solve:

1. copy&paste mistakes
2. inconsistent versions of code or data
3. missing or incompatible software
4. complicated or ambiguous procedure for reproduction

---

## Software we will use:

1. R Markdown
2. Git
3. Docker
4. Make

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## Services we will use:

* Posit Cloud (= RStudio Cloud)
* GitHub (≠ Git)
* GitHub Actions (∈ GitHub)

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## TL;DR: This is almost impossible.

## Don't freak out. 
--
Ok, maybe a bit.

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# Today:

### I push you to the brink of what is possible†

† in the form of a cozy precooked meal.

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.center[

# Alone in the wild:

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## 1. do the necessary,

## 2. than the possible,

## 3. and you reach the impossible.

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.center.pull-left[

# Goal for today:

### Understanding what is possible!

]

.center.pull-left[

# Not today:

### Reaching a deep technical understanding of what is possible.

]

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.left[Level 1:]

# The necessary

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.center[

.large[code + data]

]

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## A Note on Computing Infrastructure

Usually you work on a private laptop. 
--
**Not today.**

For ease of setup we work in the Cloud. But confusingly we than ask another cloud provider to reproduce everything again somewhere else.

An incomplete list of computational infrastructures:

- laptops
- virtual machines
- single servers
- cloud computers
- high performance computers

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# Posit / RStudio Cloud

Access the project:

https://posit.cloud/content/10490110

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# A single script

.todo[Look around, become comfortable. Then, locate the files pane.]

.todo[Open the R folder.]

.todo[Click on the file `R/prepare_games.R`]

***

.optional[Take a look at `R/prepare_inflation.R` (same thing but more complicated)]

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# An incomplete list of best practices

1. list requirements early
2. use relative locations
3. document relevant information

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# A few principles of documentation

.center[

document relevant information = lot of writing

## No
--
, not necessarily.

1. What is *standard* does not have to be documented.
2. What is *easy* needs only little documentation.
3. What is *consistent* only has to be documented once.

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.left[Congratulations, you have completed:]

# Necessary

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# Expanded Goals

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.center[

.large[code + data + text + history + software + workflow]

]

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.center[

# Todo list

]

.pull-left[

## Problems

1. Copy&paste mistakes
2. Inconsistent versions of code or data
3. Missing or incompatible software
4. Complicated or ambiguous procedure for reproduction

]

.pull-right[

## Solutions

1. RMarkdown
2. Git
3. Docker
4. Make

]
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.center[

]

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.left[Level 2:]

# Reproducibility: Current practice

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.left[Tool to learn:]

# RMarkdown

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.left[Level 3:]

# Reproducibility: best practice

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.left[Tool to learn:]

# Git/GitHub

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.left[Level 4:]

# Reproducibility: longterm perspektive

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.left[Tool to learn:]

# Docker

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.left[Level 5:]

# Reproducibility: ultimate

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.left[Tools to learn:]

# Make + GitHub Actions

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.center[

.large[code + data + text + history + software + workflow]

]

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<https://github.com/aaronpeikert/repro-workshop/actions>

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![](images/qr_slides.svg)

[aaronpeikert.github.io/repro-workshop/self-paced/](https://aaronpeikert.github.io/repro-workshop/self-paced/)