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
?dotchart perhaps? --- On Wed, 6/23/10, Bill Harris <bill_harris at facilitatedsystems.com> wrote:> From: Bill Harris <bill_harris at facilitatedsystems.com> > Subject: [R] Analyzing large transition matrix > To: "r-help" <r-help at r-project.org> > Received: Wednesday, June 23, 2010, 9:30 AM > 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 at 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. >
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