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

https://blog.datawrapper.de/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

Data Science in a Box

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”

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

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron (2019)

Efficient R Programming

SQL Database access dplyr

Data Visualization course by Dr. Andrew Heiss of Georgia State University

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.