book.bib

@article{BeaulieuJones2017,
  doi = {10.1038/nbt.3780},
  url = {https://doi.org/10.1038/nbt.3780},
  year = {2017},
  month = {Mar},
  publisher = {Springer Science and Business Media {LLC}},
  volume = {35},
  number = {4},
  pages = {342--346},
  author = {Brett K Beaulieu-Jones and Casey S Greene},
  title = {Reproducibility of computational workflows is automated using continuous analysis},
  journal = {Nature Biotechnology}
}

@Manual{rmarkdown2021,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2021},
  note = {R package version 2.10},
  url = {https://github.com/rstudio/rmarkdown},
}

@Book{Xie2018,
  title = {R Markdown: The Definitive Guide},
  author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2018},
  note = {ISBN 9781138359338},
  url = {https://bookdown.org/yihui/rmarkdown},
}

@misc{r_2023,
	title = {R (programming language)},
	copyright = {Creative Commons Attribution-ShareAlike License},
	url = {https://en.wikipedia.org/w/index.php?title=R_(programming_language)&oldid=1144363470},
	abstract = {R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. Users have created packages to augment the functions of the R language.
According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages in data mining. As of December 2022, R ranks 11th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020.The official R software environment is an open-source free software environment within the GNU package, available under the GNU General Public License. It is written primarily in C, Fortran, and R itself (partially self-hosting). Precompiled executables are provided for various operating systems. R has a command line interface. Multiple third-party graphical user interfaces are also available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface.},
	language = {en},
	urldate = {2023-03-14},
	journal = {Wikipedia},
	month = mar,
	year = {2023},
	note = {Page Version ID: 1144363470}
}

@misc{r_project,
	title = {R: {The} {R} {Project} for {Statistical} {Computing}},
	url = {https://www.r-project.org/},
	urldate = {2023-03-14},
	year = {}
}

@Book{Xie2020,
  title = {R Markdown Cookbook},
  author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2020},
  note = {ISBN 9780367563837},
  url = {https://bookdown.org/yihui/rmarkdown-cookbook},
}

@book{timbers_data_nodate,
	title = {Data {Science}},
	url = {https://datasciencebook.ca},
	abstract = {This is a textbook for teaching a first introduction to data science.},
	urldate = {2023-01-26},
	date = {2022-09-24},
	author = {Timbers, Tiffany and Campbell, Trevor and Lee, Melissa},
	file = {Snapshot:/Users/carriewright/Zotero/storage/DJ4ZSZ7B/datasciencebook.ca.html:text/html},
}

@online{riederer_column_2020,
	title = {Column {Names} as {Contracts}},
	url = {https://emilyriederer.netlify.app/post/column-name-contracts/},
	abstract = {Using controlled dictionaries for low-touch documentation, validation, and usability of tabular data},
	language = {en-us},
	urldate = {2023-01-26},
	journal = {Emily Riederer},
	author = {Riederer, Emily},
	month = sep,
	year = {2020},
}

@book{gillespie_efficient_2021,
	title = {Efficient {R} programming},
	url = {https://csgillespie.github.io/efficientR/},
	abstract = {Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency.},
	urldate = {2023-01-26},
	publisher = {O'Reilly Media Inc.},
	author = {Gillespie, Colin and Lovelace, Robin},
	month = mar,
	year = {2021},
}

@misc{bodner_10_2018,
	title = {10 {Reasons} {Why} {Code} {Reviews} {Make} {Better} {Code} and {Better} {Teams}},
	url = {https://simpleprogrammer.com/why-code-reviews-make-better-code-teams/},
	abstract = {Many programmers don't like code reviews, because they feel reviews are a waste of time. In truth, code reviews have tremendous value, offering benefits that will improve your code continuously and strengthen programming teams as a whole. Here's how.},
	language = {en-us},
	urldate = {2023-01-26},
	journal = {Simple Programmer},
	author = {Bodner, Herbert},
	month = may,
	year = {2018},
}

@techreport{parker_opinionated_2017,
	type = {preprint},
	title = {Opinionated analysis development},
	url = {https://peerj.com/preprints/3210v1},
	abstract = {Traditionally, statistical training has focused primarily on mathematical derivations and proofs of statistical tests. The process of developing the technical artifact—that is, the paper, dashboard, or other deliverable—is much less frequently taught, presumably because of an aversion to cookbookery or prescribing specific software choices. In this paper I argue that it’s critical to teach analysts how to go about developing an analysis in order to maximize the probability that their analysis is reproducible, accurate, and collaborative. A critical component of this is adopting a blameless postmortem culture. By encouraging the use of and fluency in tooling that implements these opinions, as well as a blameless way of correcting course as analysts encounter errors, we as a community can foster the growth of processes that fail the practitioners as infrequently as possible.},
	language = {en},
	urldate = {2023-01-26},
	institution = {PeerJ Preprints},
	author = {Parker, Hilary},
	month = aug,
	year = {2017},
	doi = {10.7287/peerj.preprints.3210v1},
}

@online{radigan_what_nodate,
	title = {What is a {Code} {Review} \& {How} {It} {Can} {Save} {Time}},
	url = {https://www.atlassian.com/agile/software-development/code-reviews},
	abstract = {Code review helps developers learn the code base, as well as help them learn new technologies and techniques that grow their skill sets. Learn more here.},
	language = {en},
	urldate = {2023-01-26},
	journal = {Atlassian},
	author = {Radigan, Dan},
	file = {Snapshot:/Users/carriewright/Zotero/storage/6JJGZPP6/code-reviews.html:text/html},
}

@online{hutchdatascience_code_review,
	title = {About Scientific Code Review},
	url = {https://hutchdatascience.org/code_review/},
	language = {en},
	urldate = {2023-01-26},
}