Reproducible Science with LATEX

Bar, Haim; Wang, HaiYing

doi:10.6339/21-JDS998

Journal of Data Science

Reproducible Science with LATEX

Volume 19, Issue 1 (2021), pp. 111–125

Haim Bar HaiYing Wang

https://doi.org/10.6339/21-JDS998

Pub. online: 28 January 2021 Type: Computing In Data Science

Received
1 August 2020

Accepted
1 November 2020

Published
28 January 2021

Abstract

This paper proposes a procedure to execute external source codes from a LATEX document and include the calculation outputs in the resulting Portable Document Format (pdf) file automatically. It integrates programming tools into the LATEX writing tool to facilitate the production of reproducible research. In our proposed approach to a LATEX-based scientific notebook the user can easily invoke any programming language or a command-line program when compiling the LATEX document, while using their favorite LATEX editor in the writing process. The required LATEX setup, a new Python package, and the defined preamble are discussed in detail, and working examples using R, Julia, and MatLab to reproduce existing research are provided to illustrate the proposed procedure. We also demonstrate how to include system setting information in a paper by invoking shell scripts when compiling the document.

Supplementary material

Supplementary Material

Detailed examples and documentation are available in a GitHub repository at https://github.com/Ossifragus/runcode. The runcode LATEX package is available via CTAN, and the talk2stat Python package for the server-mode is available at https://pypi.org/project/talk2stat/.

References

Allaire J, Xie Y, McPherson J, Luraschi J, Ushey K, Atkins A, et al. (2020). rmarkdown: Dynamic Documents for R. package version 2.1.

Bates D, Maechler M (2019). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.2-16.

Chang W, Cheng J, Allaire J, Xie Y, McPherson J (2020). shiny: Web Application Framework for R. R package version 1.5.0.

Csardi G, Nepusz T, et al. (2006). The igraph software package for complex network research. InterJournal, complex systems, 1695(5): 1–9.

Gentleman R, Temple Lang D (2007). Statistical analyses and reproducible research. Journal of Computational and Graphical Statistics, 16(1): 1–23.

Ioannidis JPA (2005). Why most published research findings are false. PLOS Medicine, 2(8).

Ji P, Jin J (2016). Coauthorship and citation networks for statisticians. Annals of Applied Statistics, 10(4): 1779–1812.

Jin J (2015). Fast community detection by SCORE. Annals of Statistics, 43(1): 57–89.

Knuth DE (1984). Literate programming. The Computer Journal, 27(2): 97–111.

Leisch F (2002). Sweave: Dynamic generation of statistical reports using literate data analysis. In: Compstat, 575–580. Springer.

Schulte E, Davison D, Dye T, Dominik C (2012). A multi-language computing environment for literate programming and reproducible research. Journal of Statistical Software, 46(3): 1–24.

Xie Y (2014). knitr: A comprehensive tool for reproducible research in R. In: Implementing Reproducible Computational Research (V Stodden, F Leisch, RD Peng, eds.). Chapman and Hall/CRC. ISBN 978-1466561595.

Xie Y (2015). Dynamic Documents with R and knitr. Chapman and Hall/CRC, Boca Raton, Florida, 2nd edition edition. ISBN 978-1498716963.

Xie Y (2020). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.28.

Xie Y, Allaire J, Grolemund G (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9781138359338.

Zeileis A (2014). ineq: Measuring Inequality, Concentration, and Poverty. R package version 0.2-13.

This is a free to read article.

Keywords

literate programming reproducibility scientific notebook --shell-escape

Metrics

since February 2021

2586 Article info views	0 Full article views
1038 PDF downloads	353 XML downloads

RSS

Authors

Abstract

Supplementary material

References

Export citation

Copy and paste formatted citation

Download citation in file