zotero.bib

@article{freedman_economics_2015,
	title = {The {Economics} of {Reproducibility} in {Preclinical} {Research}},
	volume = {13},
	issn = {1545-7885},
	url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002165},
	doi = {10.1371/journal.pbio.1002165},
	abstract = {Low reproducibility rates within life science research undermine cumulative knowledge production and contribute to both delays and costs of therapeutic drug development. An analysis of past studies indicates that the cumulative (total) prevalence of irreproducible preclinical research exceeds 50\%, resulting in approximately US\$28,000,000,000 (US\$28B)/year spent on preclinical research that is not reproducible—in the United States alone. We outline a framework for solutions and a plan for long-term improvements in reproducibility rates that will help to accelerate the discovery of life-saving therapies and cures.},
	language = {en},
	number = {6},
	urldate = {2020-11-06},
	journal = {PLOS Biology},
	author = {Freedman, Leonard P. and Cockburn, Iain M. and Simcoe, Timothy S.},
	month = jun,
	year = {2015},
	note = {Publisher: Public Library of Science},
	keywords = {Drug discovery, Drug research and development, Drug therapy, Economics, Finance, Internet, Peer review, Reproducibility},
	pages = {e1002165},
	file = {Full Text PDF:C\:\\Users\\ries9\\Zotero\\storage\\U728UCL6\\Freedman et al. - 2015 - The Economics of Reproducibility in Preclinical Re.pdf:application/pdf;Snapshot:C\:\\Users\\ries9\\Zotero\\storage\\RGMFZXTU\\article.html:text/html},
}
@book{R_for_data_science,
author = {Wickham, Hadley and Grolemund, Garrett},
title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
year = {2017},
isbn = {1491910399},
publisher = {O'Reilly Media, Inc.},
edition = {1st},
abstract = {Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way. Youll learn how to: Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "signals" in your datasetCommunicatelearn R Markdown for integrating prose, code, and results}
}