I have been looking a different way to do reporting on Aging report customer Account balances (A/R balance).

The A/R balance is presented in terms of age of debt to measure the total open debt-by-debt ages in the A/R system. The age of a debt is determined by its due date, and the number of days past the due date, for example 1-30 days, 31-60 days, and so forth.

Currently this is what I been graphing as an example described above 1-30 days, 31-60 and last bucket is 120+ and have found it boring and wanting if there are other ways doing this reporting.

Here are a few data points:

day.past.due    amt 
57              46,207.74 
321             25,275.76 
625             21,043.17 
2618            26.00 
650             2,952.40 
465             484.22 
1407            10,090.55 
979             239.10 
235             25,965.41 
218             50,715.57 
366             15,616.98 
608             15,819.15 
534             4,087.55 
695             7,138.07 
844             3,680.72 
684             24,511.03 
1797            1,786.16 
985             7,004.80 
108             38,570.32 
258             22,897.39 
148             24,200.36 
461             13,240.68 
444             9,386.73 
489             2,665.33
  • $\begingroup$ "Not boring" is a problematic and subjective criterion for designing a visualization. Please provide objective guidelines that describe what you want viewers of your visualization to learn from it. $\endgroup$
    – whuber
    Apr 1 '14 at 15:45
  • $\begingroup$ I don't really have one but rather want to see if you can process inspecting, transforming, and modeling data with the goal of discovering useful information. What approach would you take if you were given such data points to present the information. $\endgroup$
    – user42860
    Apr 1 '14 at 17:48

Below, there are two bar charts built from your data. The day.past.due observations were offset by -57 days (to start on day 1).

I don't feel visualizing 2D data in a bar plot as being boring. Maybe the differential on your report could be the discussion and information that you can pull out from the data.

enter image description here

For example:

63% of total debt is inside a 300 day.past.due interval. This window of time represents less than 20% of total. Can you provide an explanation in the report why this could be happening?

enter image description here


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