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. Suggestions? Thanks, Bill -- Bill Harris http://makingsense.facilitatedsystems.com/ Facilitated Systems Everett, WA 98208 USA http://www.facilitatedsystems.com/ phone: +1 425 374-1845 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
