Skip to main content

How to Cite Packages

(This article was first published on Dominique Makowski, and kindly contributed to R-bloggers)

Citing the packages, modules and softwares you used for your analysis is important, both from a reproducibility perspective (statistical routines are often implemented in different ways by different packages, which could explain slight discrepancies in the results. Saying "I did this using this function from that package version 1.2.3� is a way of protecting yourself by being clear about what you have found doing what you have done) but also for acknowledging the work and time that people spent creating tools for others (sometimes at the expense of their own research).

  • That's great, but how to actually cite them?
  • I used about 100 packages, should I cite them all?

What should I cite?

Ideally, you should indeed cite all the packages that you used. However, it's not very diegetic. Therefore, I would recommand the following:

  1. Cite the main / important packages in the manuscript

This should be done for the packages that were central to your specific analysis (i.e., that got you the results that you reported) rather than data manipulation tools (even though these are as much important).

For example:

Statistics were done using R 3.5.0 (R Core Team, 2018), the rstanarm (v2.13.1; Gabry & Goodrich, 2016) and the psycho (v0.3.4; Makowski, 2018) packages. The full reproducible code is available in Supplementary Materials.

  1. Present everything in Supplementary Materials

Then, in Supplementary Materials, you show the packages and functions you used. Moreover, in R, you can include (usually at the end) every used package and their version using the sessionInfo() function.

How should I cite it?

Finding the right citation information is sometimes complicated. In R, this process is made quite easy, you simply run citation("packagename"). For instance, citation("dplyr"):

To cite ‘dplyr' in publications use:

  Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2018). dplyr: A Grammar of Data Manipulation. R package version
  0.7.6. https://CRAN.R-project.org/package=dplyr

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {dplyr: A Grammar of Data Manipulation},
    author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
    year = {2018},
    note = {R package version 0.7.6},
    url = {https://CRAN.R-project.org/package=dplyr},
  }

For other languages, such as Python or Julia, it might be a little trickier, but a quick search on google (or github) should provide you with all the necessary information (version, authors, date). It's better to have a slightly incomplete citation than no citation at all.

Previous blogposts

To leave a comment for the author, please follow the link and comment on their blog: Dominique Makowski.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...


from R-bloggers https://ift.tt/2PPmUZY
via IFTTT

Comments

Popular posts from this blog

Controlling legend appearance in ggplot2 with override.aes

[This article was first published on Very statisticious on Very statisticious , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here ) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In ggplot2 , aesthetics and their scale_*() functions change both the plot appearance and the plot legend appearance simultaneously. The override.aes argument in guide_legend() allows the user to change only the legend appearance without affecting the rest of the plot. This is useful for making the legend more readable or for creating certain types of combined legends. In this post I’ll first introduce override.aes with a basic example and then go through three additional plotting scenarios to how other instances where override.aes comes in handy. Table of Contents R packages Introducing override.aes Adding a guides() layer Using the guide argument in scale_*() Changing multiple aesthetic par...

Using RStudio and LaTeX

(This article was first published on r – Experimental Behaviour , and kindly contributed to R-bloggers) This post will explain how to integrate RStudio and LaTeX, especially the inclusion of well-formatted tables and nice-looking graphs and figures produced in RStudio and imported to LaTeX. To follow along you will need RStudio, MS Excel and LaTeX. Using tikzdevice to insert R Graphs into LaTeX I am a very visual thinker. If I want to understand a concept I usually and subconsciously try to visualise it. Therefore, more my PhD I tried to transport a lot of empirical insights by means of  visualization . These range from histograms, or violin plots to show distributions, over bargraphs including error bars to compare means, to interaction- or conditional effects of regression models. For quite a while it was very tedious to include such graphs in LaTeX documents. I tried several ways, like saving them as pdf and then including them in LaTeX as pdf, or any other file ...