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 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.)