/racisminarchy

This repository contains our data and R code for our article on computational text analysis of racism in archaeology.

Primary LanguageROtherNOASSERTION

Computational text analysis of archaeological writing about race

Binder Docker build status

This repository contains the data and code for our paper, which is online here:

G. Park, L-Y. Wang, B. Marwick, (2020) How do archaeologists write about race? Computational text analysis of 41 years of Society of American Archaeology Annual Meeting Abstracts. Antiquity http://doi.org/10.15184/aqy.2021.181

How to cite

Please cite this compendium as:

G. Park, L-Y. Wang, B. Marwick, (2020), (2021). Compendium of R code and data for How do archaeologists write about race? Computational text analysis of 41 years of Society of American Archaeology Annual Meeting Abstracts. Accessed 21 Jun 2021. Online at https://doi.org/10.17605/OSF.IO/2N3RF

Contents

The most important parts of the compendium, for most users, are:

  • 🎯 _targets.R: workflow instructions and information indicating the order that code needs to be run to generate the results. Run targets::tar_make() at the R console to start the analysis workflow.
  • 📁 analysis/scripts: R script files that include code to reproduce the figures and tables generated by the analysis.
  • 📁 analysis/data: Data used in the analysis.
  • 📁 analysis/figures): Plots and other illustrations
  • 📁 analysis/paper: R Markdown document that combines our narrative text and code

How to run in your broswer or download and run locally

This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and optionally RStudio Desktop.

The simplest way to explore the text, code and data is to click on binder to open an instance of RStudio in your browser, which will have the compendium files ready to work with. Binder uses rocker-project.org Docker images to ensure a consistent and reproducible computational environment. These Docker images can also be used locally.

You can download the compendium as a zip from from this URL: master.zip. After unzipping:

  • open the .Rproj file in RStudio, this will open our project in RStudio on your computer
  • run renv::restore() to ensure you have the packages this analysis depends on (also listed in the DESCRIPTION file).
  • run targets::tar_make() to run our reproducible workflow. This will run the R code that produces the figures and numerical results presented in the paper, and generate our manuscript by rendering our R Markdown document into a Microsoft Word document.

Licenses

Text and figures : CC-BY-4.0

Code : See the DESCRIPTION file

Data : CC-0 attribution requested in reuse

Contributions

We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.