The Visual Display of Quantitative Information by Edward Tufte
Graphical displays should
- show the data
- induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
- avoid distorting what the data have to say
- present many numbers in a small space
- make large data sets coherent (tell a story)
- encourage the eye to compare different pieces of data
- reveal the data at several levels of detail, from a broad overview to the fine structure
- server a reasonably clear purpose: description, exploration, tabulation, or decoration
- be closely integrated with the statistical and verbal descriptions of a data set
Data maps are a combination of cartographic and statistical skills. Maps can show large amounts of data on a single page. Good graphics reward careful study. Data can be examined at many different levels - from the contemplation of general overall patterns to the detection of fine detail.
The time-series plot sets on dimension to a regular rhythm of seconds, minutes, hours, days, weeks, etc. Multiple time-series can be combined (same chart or above and below) to encourage comparison.
Time-series displays are at their best for big data sets with real variability. Why waste the power of data graphics on simple linear changes, which can usually be better summarized in one or two numbers? Instead, graphics should be reserved for the richer, more complex, more difficult statistical material.
Small, noncomparative, highly labeled data sets usually belong in tables.
The problem with time-series is that the simple passage of time is not a good explanatory variable: descriptive chronology is not causal explanation. There are occasional exceptions, especially when there is a clear mechanism that drives the Y-variable. Time-series plots can be moved toward causal explanation by smuggling additional variables into the graphic design.