How to succinctly report the correlation between two variables for 20 different subsets of the data in a table? I have 20 correlation coefficients (r) to report, for example, .34, .45, .67, .23. and so on. These are correlations between Variable A and Variable B, based on gender, age, education levels etc. 
I am after a succient method to show all the correlations in one glance. Is there any best practice in regard to reporting all these correlations in a table format?
 A: You might consider a figure along these lines (from Carr & Pickle (2011) Visualizing data patterns with micromaps).
This is basically a visual table.  It's not a set of correlations, but hopefully you get the idea.  The key features are (a) sorting the correlations, (b) putting them in groups (here, of 4), (c) white grid lines on a light gray background.

A: The micromap example that @karl suggested is very nice; I think it would, though, be similar to a dot plot, which are easy to implement in R or SAS (and probably other software). Something like 
corr <- c(.4, .4, .3, .2)
group = c("Education", "gender", "age", "more")
dotchart(corr, labels = group)

works in R
A: A table like the following would be my first thought:
.........................................
Category           r AB
.........................................
Gender
  Male             .36
  Female           .23
Age
  Under 30         .27
  30-50            .63
  ...
...
..........................................

You could add more information if you wanted, such as:


*

*A row indicating the overall correlation without subsetting

*Information on whether the correlations significantly differ across categories for a given variable (e.g., gender)

*Additional columns with things like p-values, confidence intervals, sample size for the category, etc.

