As everyday computing power increases, data becomes more available, and more researchers become familiar with powerful programming languages like Python and R, data visualization will continue to become a larger and larger part of social science. I hope that not too far in the future, pages like this one will seem quaint (maybe they already do) and visualization will be a standard part of methodological training in many social science graduate programs. We are not quite there yet, though. Here, I have compiled a list of resources for making visualization work easier.
Books and the like
Although not on visualization per se, Matt Salganik’s book, Bit by Bit, on social science in the digital age, is also a great resource for those interested in “big data” and the like.
Anyone who programs in R knows ggplot2, which is part of the
colorbrewer – Ideal for generating low-n color schemes.
iwanthue – Ideal for generating high-n color schemes (e.g. 10+ colors). Hosted and developed by the MediaLab at SciencesPo, which is also home to a number of other useful tools like Table 2 Net, for drawing graphs (networks) from tables of data.
htmlcolorcodes – A simple site for getting html hex color codes
8-digit hex codes – Rather than designating an “alpha” (a transparency factor) for a plot, it can sometimes be useful to specify transparency directly in specific color codes, e.g. when you want to highlight a specific line or trend in a plot with many lines and trends. You can do this with 8-digit hex codes. (See also here.)