Slides and Course Materials
This section has links to each week’s materials. Follow the links under the Topics menu for weekly course readings, notes, slides, and so on. The Data section below has links to a variety of data sources that you can practice on. The bibliography is a general reading list. Specific items to look at from week to week will be listed in each week’s topic page.
Practice Data Sources
Some places you can go to get data (as CSVs, Excel files, JSON blobs, or other formats) to play with as you’re learning how to work with the tools we’re learning about.
| Resource | URL |
|---|---|
| Data is Plural | https://www.data-is-plural.com |
| Tidy Tuesday | https://github.com/rfordatascience/tidytuesday |
| NCES | https://nces.ed.gov |
| OECD | https://www.oecd.org/en/data.html |
| US Census | https://data.census.gov |
| US Census (API) | https://www.census.gov/data/developers/data-sets.html |
| BLS | https://www.bls.gov |
| FRED | https://fred.stlouisfed.org |
| FRED (API) | https://fred.stlouisfed.org/docs/api/fred/ |
| NCHS | https://www.cdc.gov/nchs/index.htm |
| General Social Survey (GSS) | https://gss.norc.org |
| gssr | https://kjhealy.github.io/gssr/ |
Course Bibliography
Armstrong, Zan, and Martin Wattenberg. 2014. “Visualizing Statistical Mix Effects and Simpson’s Paradox.” In Proceedings of IEEE InfoVis 2014, https://research.google.com/pubs/pub42901.html.
Barrett, Daniel J. 2022. Efficient Linux at the Command Line. Sebastopol, CA: O’Reilly Media.
Bertin, Jacques. 2010. Semiology of Graphics. Redlands, CA: ESRI Press.
Bouk, Dan. 2017. “The History and Political Economy of Personal Data over the Last Two Centuries in Three Acts.” OSIRIS 32: 856–106.
Broman, Karl. 2023. “Minimal Make.” https://kbroman.org/minimal_make/.
Cairo, Alberto. 2013. The Functional Art: An Introduction to Information Graphics and Visualization. Berkeley, California: New Riders.
Chacon, Scott, and Ben Straub. 2014. Pro Git. Second. Apress. https://git-scm.com/book/en/v2.
Cleveland, William S. 1993. The Elements of Graphing Data. Hobart Press.
———. 1994. Visualizing Data. Hobart Press.
Few, Stephen. 2008. “Dual-Scaled Axes in Graphs: Are They Ever the Best Solution?” Visual Business Intelligence Newsletter. http://www.perceptualedge.com/articles/visual_business_intelligence/dual-scaled_axes.pdf (September 20, 2017).
———. 2009. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press.
Field, Kenneth. 2018. Cartography. Redlands, CA: ESRI Press.
Friedl, Jeffrey E F. 2006. Mastering Regular Expressions. 3rd ed. Sebastopol, CA: O’Reilly Media.
Friendly, Michael, and David Meyer. 2017. Discrete Data Analysis with r. Boca Raton, FL: CRC/Chapman; Hall.
Gabasova, Evelina. 2016. “Star Wars Social Network.” https://doi.org/10.5281/zenodo.1411479.
Healy, Kieran. 2019. Data Visualization: A Practical Introduction. Princeton: Princeton University Press. http://socviz.co/.
Heer, Jeffrey, and Michael Bostock. 2010. “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.” In ACM Human Factors in Computing Systems, 203–12. http://vis.stanford.edu/papers/crowdsourcing-graphical-perception.
Hyndman, Rob J, and George Athanasopoulos. 2021. Forecasting: Principles and Practice. Third edition. Melbourne, Australia: OTexts. http://otexts.com/fpp3.
Isenberg, Petra, Anastasia Bezerianos, Pierre Dragicevic, and and Jean-Daniel Fekete. 2011. “A Study on Dual-Scale Data Charts.” IEEE Transactions on Visualization and Computer Graphics 17(12): 2469–78.
Ismay, Chester, and Albert Y. Kim. 2018. ModernDive: An Introduction to Statistical and Data Sciences via R. https://moderndive.com/.
———. 2019. Statistical Inference via Data Science. CRC Press. https://moderndive.com.
Janssens, Jeroen. 2021b. Data Science at the Command Line. Second. O’Reilly Media. https://jeroenjanssens.com/dsatcl/.
———. 2021a. Data Science at the Command Line. 2nd ed. Sebastopol, CA: O’Reilly Media.
Landau, Will. 2022. “The {Targets} R Package User Manual.” https://books.ropensci.org/targets/.
Munzner, Tamara. 2014. Visualization Analysis and Design. Boca Raton, FL: CRC Press.
Neil, Drew. 2015. Practical Vim. Second. Raleigh, NC: Pragmatic Programmers.
Ognyanova, Katya. 2019. “Network Visualization with R.” www.kateto.net/network-visualization.
Petersen, Mickey. 2022. “Mastering Emacs.” https://www.masteringemacs.org/book.
Petzold, Charles. 2022. Code: The Hidden Language of Computer Hardware and Software. Second. Microsoft Press.
Powers, Shelley, Jerry Peek, Tim O’Reilly, and Mike Loukides. 2002. Unix Power Tools. 3rd ed. Sebastopol, CA: O’Reilly Media.
Robbins, Arnold. 2005. Unix in a Nutshell. 4th ed. Sebastopol, CA: O’Reilly Media.
Robbins, Arnold, and Nelson H. F. Beebe. 2005. Classic Shell Scripting. Sebastopol: O’Reilly.
Rodrigues, Bruno. 2023. Building Reproducible Analytical Pipelines with r. Leanpub. https://raps-with-r.dev.
Silge, Julia, and David Robinson. 2017. Text Mining with R. Sebastopol, California: O’Reilly Media. https://www.tidytextmining.com/.
Sylor-Miller, Katie, and Julia Evans. 2021. Oh Shit, Git! Recipes for Getting Out of a Git Mess. Wizard Zines. https://wizardzines.com/zines/oh-shit-git/.
Tufte, Edward R. 1983. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.
Tukey, John. 1977. Exploratory Data Analysis. Addison Wesley.
Ushey, Kevin. 2024. “Introduction to Renv.” https://rstudio.github.io/renv/articles/renv.html.
Walker, Francis. 1870. Statistical Atlas of the United States Based on the Results of the Ninth Census 1870 with Contributions from Many Eminent Men of Science and Several Departments of the Government. Washington, DC: United States Census Bureau. https://www.loc.gov/item/05019329/.
Walker, Kyle. 2023. Analyzing US Census Data: Methods, Maps, and Models in r. CRC Press. https://walker-data.com/census-r/.
Ware, Colin. 2008. Visual Thinking for Design. Waltham, MA: Morgan Kaufman.
Wickham, Hadley, and Jennifer Bryan. 2023a. R Packages. Sebastopol, CA: O’Reilly. https://r-pkgs.org.
———. 2023b. R Packages: Organize, Test, Document, and Share Your Code. Second. O’Reilly Media. https://r-pkgs.org.
Wickham, Hadley, Garrett Grolemund, and Mine Çetinkaya-Rundel. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Second. Sebastopol, CA: O’Reilly Media. https://r4ds.hadley.nz.
Wilke, Claus E. 2019. Fundamentals of Data Visualization. Sebastopol, California: O’Reilly Media. https://serialmentor.com/dataviz/.