by Joseph Rickert
Basically, there are two kinds of graphics or plots you can make from a data set: (1) those that allow you to see what is going on with the data, and (2) those you make to communicate what you have found to someone else. When making the first kind, you want to select plots that will enable you to see as much as possible while taking great care not to fool yourself. With the second kind, you ought to select plots that make the features of the data you want to communicate seem obvious. They should be focused on the story you are trying to tell, be free from clutter and have an impact on your target audience.
Anthony Unwin’s Graphical Data Analysis with R (CRC Press 2015) is a very good read that thoroughly discusses the process and principles behind plots of the first kind while offering considerable guidance about producing those of the second kind. In 14 chapters that extend to nearly 300 pages, Unwin makes superb use of the R language to develop the principles of Graphical Data Analysis (GDA) while demonstrating the interplay of plot making and basic statistical inference that together make for a comprehensive, exploratory analysis of a data set.
The Preface and Chapter 1 set the scene by defining the scope of GDA, illustrating some of its basic principles and offering a new metaphor that ought to replace the tired and misleading idea that good graphics let you “drill down” into the data. This mechanical notion assumes you know where to drill and possess a clear idea of what you are looking for. In stark contrast, Unwin offers the metaphor of a photographer who takes many photographs of an object from multiple angles and in different lighting conditions in order to “grasp a …read more
Source:: r-bloggers.com