B Extra Reading
Further reading, including books, links, demos and packages. You don’t need to read all of this, but you will want to dig around. If I could recommend one book to accompany the course it would be
Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.
B.1 Visualisation (theory)
Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.
Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders.
Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, CT: Graphics press.
McCandless, D. (2012). Information is beautiful. London: Collins.
Wilke, C.O. (2019). Fundamentals of Data Visualization. O’Reilly. [free online]
Rougier, N. P., Droettboom, M., & Bourne, P. E. (2014). Ten simple rules for better figures. PLoS Comput Biol, 10(9), e1003833. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833
Weissgerber, T. L., Milic, N. M., Winham, S. J., & Garovic, V. D. (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLoS biology, 13(4).
Nightingale: The Journal of The Data Visualisation Society
Podcast: Explore Explain: A Video and Podcast Series
Why you sometimes need to break the rules in data viz by Rosamund Pearce
The Encyclopedia of Human-Computer Interaction, 2nd Ed.: Data Visualization for Human Perception
The Economist newsletter: “Off the Charts” is highly recommended. Examples: better bar charts, using log scales
The Do’s and Don’ts of Chart Making
Riffe, T., Sander, N., & Kluesener, S. (2021). Editorial to the Special Issue on Demographic Data Visualization: Getting the point across–Reaching the potential of demographic data visualization. Demographic research. Rostock: Max Planck Institute for Demographic Research, 2021, Vol. 44.
Franconeri, S. L., Padilla, L. M., Shah, P., Zacks, J. M., & Hullman, J. (2021). The science of visual data communication: What works. Psychological Science in the Public Interest, 22(3), 110-161.
Lisa Charlotte Muth: What to consider when using text in data visualizations
B.2 The Reproducibility Crisis
Cancer Biology Reproducibility Project https://www.enago.com/academy/the-reproducibility-project-cancer-biology-to-replicate-only-18-studies-now/
Economics reproducibility https://www.wired.com/story/econ-statbias-study/
Video: Is Most Published Research Wrong https://www.youtube.com/watch?v=42QuXLucH3Q
Demo: p-hacking https://fivethirtyeight.com/features/science-isnt-broken/#part1
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.
B.3 Better practice
Munafo, M. R., et al. (2017). A manifesto for reproducible science . Nature Human Behaviour, 1, 0021. DOI: 10.0138/s41562-016-0021.
Markowetz, F. (2015). Five selfish reasons to work reproducibly. Genome biology, 16(1), 274. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0850-7
A Guide to Reproducible Code in Ecology and Evolution https://www.britishecologicalsociety.org/wp-content/uploads/2017/12/guide-to-reproducible-code.pdf
Gael Varoquaux: Computational practices for reproducible science https://www.slideshare.net/GaelVaroquaux/computational-practices-for-reproducible-science
Axelrod, V. (2014). Minimizing bugs in cognitive neuroscience programming. Frontiers in psychology, 5, 1435.
“our wishlist for what knowledge and skills we’d find in a well-prepared data scientist candidate coming from a masters program.” https://github.com/brohrer/academic_advisory/blob/master/curriculum_roadmap.md
Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLoS computational biology, 13(6), e1005510.
B.4 Project organisation
Mike Frank onboarding guide http://babieslearninglanguage.blogspot.co.uk/2017/01/onboarding.html
Jenny Bryan’s advice on filenames: Naming Things
Emily Riederer Column naming contracts
Broman & Woo (2017) Data Organization in Spreadsheets https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1375989
Video: Data Sharing and Management Snafu in 3 Short Acts https://www.youtube.com/watch?time_continue=2&v=N2zK3sAtr-4
Hadley Wickham: Tidy Data: http://vita.had.co.nz/papers/tidy-data.pdf
B.5 Coding
Readings in Applied Data Science https://github.com/hadley/stats337#readings
Stack overflow: asking good questions https://stackoverflow.com/help/how-to-ask
Stack overflow: provide a minimal, complete, verifable example https://stackoverflow.com/help/mcve
Our Software Dependency Problem https://research.swtch.com/deps
From Psychologist to Data Scientist https://www.neurotroph.de/2019/01/from-psychologist-to-data-scientist/
Bret Victor: Learnable Programming: Designing a programming system for understanding programs
Top 10 Coding Mistakes Made by Data Scientists
Coding error postmortem by Russ Poldrack, McKenzie Hagen, and Patrick Bissett (August 10, 2020)
B.6 R
B.6.1 Hints
Prime Hints For Running A Data Project In R
RStudio Cheat Sheets: https://www.rstudio.com/resources/cheatsheets/
Here::Here https://github.com/jennybc/here_here
We are R-ladies - Twitter account with a rotating curator featuring discussions, package highlights, and tips
B.6.2 Courses / books
I recommend you start with swirl: https://swirlstats.com/
Lisa DeBruine, & Dale Barr. (2019). Data Skills for Reproducible Science. Zenodo. doi:10.5281/zenodo.3564348 https://psyteachr.github.io/msc-data-skills/
You may also enjoy:
Chester Ismay and Patrick C. Kennedy: Getting Used to R, RStudio, and R Markdown
Matt Crump: Reproducible statistics for psychologists with R
Danielle Navarro: Learning Statistics With R
* Particularly chapter 3 https://learningstatisticswithr-bookdown.netlify.com/intror
Data Science with R: An introductory course by Danielle Navarro
Adler, J. (2010). R in a nutshell: A desktop quick reference. ” O’Reilly Media, Inc.”.
Intro to R (Liz Page-Gould): http://www.page-gould.com/r/uoft/
Grolemund, G., & Wickham, H. (2018). R for data science. * See also https://r4ds.had.co.nz/
B.7 Making graphs (practice)
Graphing in R (Eric-Jan Wagenmakers and Quentin F. Gronau): http://shinyapps.org/apps/RGraphCompendium/index.php
r-charts.com: “Over 1100 graphs with reproducible code divided in 8 big categories and over 50 chart types, in addition of tools to choose and create colors and color palettes”
r-graph-gallery.com/: Similar!
Cédric Scherer: A ggplot2 Tutorial for Beautiful Plotting in R
B.8 Presentations
Kieran Healy : Making Slides
B.9 Statistics
Discovering Statistics Using R
Hox, J. (2010) Multilevel Analysis: Techniques and Applications
Statistical Rethinking: A Bayesian Course with Examples in R and Stan
model checking package: Performance
B.10 Advanced Reading, Background & Other Recommends
Data Feminism by Catherine D’Ignazio and Lauren F. Klein. The MIT Press. 2020
Rachel Thomas’s Applied Data Ethics Syllabus
Data Visualization course by Dr. Andrew Heiss of Georgia State University
www.datascienceglossary.org - there’s lots of new terminology, don’t be afraid to ask (or google)
B.11 Pedagogy
Brown, N. C., & Wilson, G. (2018). Ten quick tips for teaching programming. PLoS computational biology, 14(4), e1006023.
Hudiburgh, L. M., & Garbinsky, D. (2020). Data Visualization: Bringing Data to Life in an Introductory Statistics Course. Journal of Statistics Education, 28(3), 262-279.