--- On Thu, 6/24/10, Jim Lemon <j...@bitwrit.com.au> wrote:
> From: Jim Lemon <j...@bitwrit.com.au> > Subject: Re: [R] Analyzing large transition matrix > To: "Bill Harris" <bill_har...@facilitatedsystems.com> > Cc: "r-help" <r-help@r-project.org> > Received: Thursday, June 24, 2010, 7:44 AM > 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. > Subset by manufacturer and done one per page? ______________________________________________ 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.
