Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/aaronpeikert/reproducible-research
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker
https://github.com/aaronpeikert/reproducible-research
containerization dependency-management literate-programming open-science r reproducibility version-management
Last synced: 3 months ago
JSON representation
A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker
- Host: GitHub
- URL: https://github.com/aaronpeikert/reproducible-research
- Owner: aaronpeikert
- License: cc-by-4.0
- Created: 2019-09-03T12:48:03.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-03-31T12:32:12.000Z (almost 3 years ago)
- Last Synced: 2024-10-31T17:38:39.015Z (3 months ago)
- Topics: containerization, dependency-management, literate-programming, open-science, r, reproducibility, version-management
- Language: TeX
- Homepage: https://psyarxiv.com/8xzqy/
- Size: 2.43 MB
- Stars: 122
- Watchers: 5
- Forks: 17
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - aaronpeikert/reproducible-research - A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker (TeX)
README
---
output:
md_document:
variant: gfm
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(here)
library(readr)
library(pander)
```
This is the accompanying GitHub repository to a work in progress paper by Aaron Peikert[](https://orcid.org/0000-0001-7813-818X) and Andreas M. Brandmaier [](http://orcid.org/0000-0001-8765-6982).
[](https://creativecommons.org/licenses/by/4.0) [](https://github.com/aaronpeikert/reproducible-research/issues/new) 
# Abstract
```{r abstract, child = 'abstract.Rmd'}
```
```{r, echo=FALSE}
knitr::include_graphics("Images/nutshell.svg")
```
# Resources
```{r, include=FALSE}
resources <- read_csv(here("data", "resources.csv"), trim_ws = FALSE)
```
```{r, echo=FALSE, asis=TRUE}
pander(
resources,
split.tables = Inf,
split.cells = Inf,
missing = "",
keep.line.breaks = TRUE,
style = "multiline"
)
```
# Compile
The following paragraphs describe how you can obtain a copy of the source files of our manuscript describing reproducible workflows, and create the PDF. Either, you can go the 'standard' way of downloading a local copy of the repository and knit the manuscript file in R, or you can use the reproducible workflow as suggested and use Make to create a container and build the final PDF file in exactly the same virtual computational environment that we used to render the PDF.
## Standard Way
Requires: `Git`, `RStudio`, `pandoc`, `pandoc-citeproc` & `rmarkdown`.
Open RStudio -> File -> New Project -> Version Control -> Git
Insert:
```
https://github.com/aaronpeikert/reproducible-research.git
```
Open `manuscript.Rmd` click on `Knit`.
## Using a Reproducible Workflow
Does not require R or RStudio, but `make` & `docker`.
Execute in Terminal:
```{bash, eval=FALSE}
git clone https://github.com/aaronpeikert/reproducible-research.git
cd reproducible-research
make build
make all DOCKER=TRUE
```
**Note: Windows user need to manually edit the `Makefile` and set current_path to the current directory and use `make all DOCKER=TRUE WINDOWS=TRUE`. We hope that future releases of Docker for Windows will not require that workaround.**
## Rebuild Everything
In case you experience some unexpected behavior with this workflow, you should check that you have the most recent version (`git pull`), rebuild the docker image (`make build`) and force the rebuild of all targets (`make -B DOCKER`).
```{bash, eval=FALSE}
git pull && make rebuild && make -B DOCKER=TRUE
```
# Session Info
```{r}
sessioninfo::session_info()
```