Friends don’t let friends copy-paste

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### Friends don't let friends copy-paste

![:pull right, 35%](

)

# Preventing code rot

#### Frederik Aust & Marius Barth

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<div class="my-footer">
  <div style="float: left;"><span>Frederik Aust & Marius Barth</span></div>
  <div style="text-align: right;"><span>Friends don't let friends copy-paste</span></div>
  <div style="float: center;"><span>21.04.2023</span></div>
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# Preventing code rot

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# Preventing code rot

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### Recap

*Computational reproducibility is hard!*

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- Shared reproducibility packages often fail<br />.grey[(e.g., Eubank, 2016)]

--
- Code is increasingly likely to break as time passes<br />(it "rots")
    - This is true even if code is *untouched*

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- Computing environments inevitably change

--
    - Installation or removal of software
    - Software updates
    - Removal or relocation of files
    - (Hardware updates)
--
- Complex computing environments exacerbate the problem

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<small>
- Computational reproducibility requires a stable computing environment
- But how?
    - Archive the computer used to run the analysis?
    - Trade-off between robustness and feasibility
</small>
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  <span style = "font-size: 75%">Adapted from Grüning et al. (2018)</span>
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1. [`checkpoint`](https://github.com/RevolutionAnalytics/checkpoint/) by Microsoft
  - Requires a project-based workflow
  - Package database will gradually grow
--

2. [`groundhog`](https://groundhogr.com/)
  - Package database will gradually grow
--

3. [`renv`](https://rstudio.github.io/renv/articles/renv.html) by RStudio
  - Most flexible and powerful
  - Least straight forward to use
  - No "forensic" applications

---

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Dependencies are detected automatically

.pull-left-50[

.center[<small>R script</small>]

```r
library("checkpoint")
checkpoint("2022-04-27", r_version = "4.1.2")

library("ggplot2")
```

]

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.center[<small>Console</small>]

```r
checkpoint("2022-04-27")
install.packages("ggplot2")
```

]

Uses a date-specific directory outside of usual library

```bash
~/.checkpoint/...
```

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<br /><br />
✅
<br /><br />
(✅)
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### Virtual machines

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### Containers

.footnote[<small>(Piccolo & Frampton, 2016)</small>]

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- Virtual machines (e.g., [Vagrant](https://www.vagrantup.com/))
    - Encapsulate an entire operating system
    - Require a lot of disk space
    - Take little longer to set up and boot
--
- Containers (e.g., [Docker](https://www.docker.com/))
    - Widely used in software development
    - Depend on OS kernel, but can be run on all common operating systems

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<br /><br />
✅
<br /><br />
✅
<br /><br />
✅
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- Virtual machines and containers requires some expertise
- Online services simplify setup, collaboration, sharing, and archiving
    - [PsychNotebooks](https://www.psychnotebook.org/)
    - [CodeOcean](https://codeocean.com/)
    - [RStudio.cloud](https://rstudio.cloud)

---
[An unobtrusive Docker workflow for papaja](https://github.com/crsh/papaja_docker)

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# Time for a demonstration!

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### Summary

- Computing environments inevitably change

--
- Robust computational reproducibility requires a fixed computing environment
    1. prevents code rot
    2. facilitates sharing reproducible analyses

- Mind software licenses when sharing software environments