On 06/23/2010 11:30 PM, Bill Harris wrote:
Let's say you have a dataframe of car trade-ins. For example, each row
contains
oldcar newcar qty
and a typical entry could be
lexus bmw 1
I put the qty column to allow for fleet purchases, where one purchase
may convert multiple cars at once.
I'd like to show what's going on. I could do a histogram of newcar to
show the frequency each type of car is bought. If there are 5-10 car
types, that works. If there are 50-100 or more, the legend gets
illegible.
I could also do a histogram of oldcar to see what people gave up, but
that's less interesting.
I'm considering a correlogram using the corrgram package, but a heat map
might work, too. Any tips on making the legends useful in any of this?
Any better approaches to try?
I tried table() and prop.table() to see if I could get transition
probabilities as if this were a Markov chain, but dim() comes out 108
78, which is still too big to print or visualize.
Hi Bill,
You could use sizetree (plotrix) if you have one car per line, but with
50-100 initial categories, you're going to need a long piece of paper.
Jim
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