
@online{yau_comparing_2016,
	title = {Comparing ggplot2 and R Base Graphics},
	url = {https://flowingdata.com/2016/03/22/comparing-ggplot2-and-r-base-graphics/},
	titleaddon = {{FlowingData}},
	author = {Yau, Nathan},
	date = {2016-03-22}
}

@online{robinson_why_2016,
	title = {Why I use ggplot2},
	url = {http://varianceexplained.org/r/why-I-use-ggplot2/},
	titleaddon = {Variance Explained},
	author = {Robinson, David},
	date = {2016-02-12}
}

@online{mock_tidytuesday_nodate,
	title = {{TidyTuesday} - A Weekly Social Data Project in R},
	url = {https://github.com/rfordatascience/tidytuesday},
	titleaddon = {{GitHub}},
	author = {Mock, Thomas}
}

@book{wilke_fundamentals_2019,
	title = {Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures},
	url = {https://serialmentor.com/dataviz/},
	publisher = {O'Reilly Media},
	author = {Wilke, Claus O.},
	date = {2019-04-14}
}

@book{healy_data_2018,
	title = {Data Visualization: A Practical Introduction},
	publisher = {Princeton University Press},
	author = {Healy, Kieran},
	date = {2018-12-18}
}

@book{grolemund_r_2016,
	title = {R for Data Science},
	url = {https://r4ds.had.co.nz/},
	abstract = {This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.},
	author = {Grolemund, Garrett and Wickham, Hadley},
	urldate = {2019-12-24},
	date = {2016-12},
	file = {Snapshot:/Users/LKP/Zotero/storage/8MWD9SWU/r4ds.had.co.nz.html:text/html}
}

@book{xie_r_2019,
	title = {R Markdown: The Definitive Guide},
	url = {https://bookdown.org/yihui/rmarkdown/},
	shorttitle = {R Markdown},
	abstract = {The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.},
	author = {Xie, Yihui and Allaire, J. J. and Grolemund, Garrett},
	urldate = {2019-12-24},
	date = {2019-12-02},
	file = {Snapshot:/Users/LKP/Zotero/storage/WBAI2VCJ/rmarkdown.html:text/html}
}

@book{imai_quantitative_2017,
	location = {Princeton},
	title = {Quantitative Social Science : an Introduction},
	isbn = {978-0-691-17546-1},
	abstract = {"Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it–or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results–it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors"– Provided by publisher.},
	publisher = {Princeton University Press},
	author = {Imai, Kosuke},
	date = {2017},
	lccn = {2016962298},
	keywords = {Social sciences – Methodology, Social sciences – Research}
}

@book{bailey_real_2016,
	title = {Real Stats: Using Econometrics for Political Science and Public Policy},
	isbn = {978-0-19-998194-6},
	url = {https://books.google.com/books?id=f5o_jgEACAAJ},
	publisher = {Oxford University Press},
	author = {Bailey, M.A.},
	date = {2016},
	lccn = {2015032221}
}