What makes a good graph? When faced with a slew of numeric data, graphical visualization can be a more efficient way of getting a feel for the data than going through the rows of a spreadsheet. But do we know if we are getting an accurate or useful picture? How do we pick an effective visualization that neither obscures important details, or drowns us in confusing clutter? In 1968, William Cleveland published a text called The Elements of Graphing Data, inspired by Strunk and White’s classic writing handbook The Elements of Style . The Elements of Graphing Data puts forward Cleveland’s philosophy about how to produce good, clear graphs — not only for presenting one’s experimental results to peers, but also for the purposes of data analysis and exploration. Cleveland’s approach is based on a theory of graphical perception: how well the human perceptual system accomplishes certain tasks involved in reading a graph. For a given data analysis task, the goal is to align the information being presented with the perceptual tasks the viewer accomplishes the best. Continue reading Good Graphs: Graphical Perception and Data Visualization
Our first “exciting technique” article is about a statistical language called “R.”
R is a language for statistical analysis available from http://cran.r-project.org/ . The things you can immediately do with it are incredible. You can import a spreadsheet and immediately spot relationships, trend and anomalies. R gives you instant access to top notch visualization methods and sophisticated statistical methods.