4. March 2012
> setwd("E:/Data/counties/pajek2")
> load("../pajek/rankDat.RData")
> objects()
[1] "da" "rankPart" "s"
> dim(da)
[1] 3110 92
> del <- c(1,2,4,6,9,10,11,14,15,20,25,30,33,35,36,37,38,39,40,41,42,45,46,48,
+ 52,53,56,57,58,60,61,62,63,65,67,69,71,72,73,74,75,76,77,78,79,81,82,89,90,91)
> V <- da[,-del]
> for(i in 1:nrow(V)) if(length(which(is.na(V[i,])))>0) cat(i,"\n")
1620
2888
> V[1620,] <- V[1597,] # Yellowstone National Park, MT = Park, MT
> V[2888,] <- V[2789,] # Clifton Forge, VA = Alleghany, VA
> for(i in 1:nrow(V)) if(length(which(is.na(V[i,])))>0) cat(i,"\n")
> names(V)
[1] "AGE050200D" "CLF040200D"
[3] "EDU685200D" "HSG045200D"
[5] "IPE010200D" "IPE120200D"
[7] "PIN020200D" "POP050200D"
[9] "POP060200D" "POP165200D"
[11] "POP255200D" "POP285200D"
[13] "POP325200D" "POP405200D"
[15] "VST020200D" "VST220200D"
[17] "VST420200D" "WAT130200D"
[19] "P.pop.under18" "P.pop.over85"
[21] "P.land.farms" "P.emply.ind.CONSTRUCTION"
[23] "P.emply.ind.MANUFACTORING" "P.emply.ind.TRANSPORT.WAREHOUSING"
[25] "P.emply.ind.FINANC.INSUR" "P.emply.ind.PROFscientTECH"
[27] "P.emply.ind.EDUC.HEALTH" "P.25overLESS9thGRADE"
[29] "P.employ.FARMING" "P.employ.GOV.stateLoc"
[31] "P.OWNERoccupiedHousingUnits" "P.occupiedHousingUnitsLackingPlumb"
[33] "P.RURALpopul" "P.CHANGEurban90to00"
[35] "CHANGEperCapitaIncome89to99" "GroundWaterUsePerCapita"
[37] "P.NET.DOMESTIC.MIGRATIONS" "P.NativePopulationBornInStateOfRes"
[39] "R.LABOR.FORCEmaleFemale" "R.VOTING.DEMOCRATESoverREPUBLICANS"
[41] "P.PUBLIC.SCHOOL.ENROLNEMT" "P.CHANGEpverty95to00"
> names(da)
[1] "STCOU"
[2] "Areaname"
[3] "AGE050200D"
...
[91] "LIFESTOCKvaluePerFARM"
[92] "P.CHANGEpverty95to00"
> unitNams <- da[,"Areaname"]
> unitIDs <- da[,"STCOU"]
> save(V,unitNams,unitIDs,file="vars42org.RData")
> setwd("E:/Data/counties/pajek2")
> load("vars42org.RData")
> objects()
[1] "unitIDs" "unitNams" "V"
> z <- function(x) (x-mean(x,na.rm=TRUE))/sd(x,na.rm=TRUE)
> U <- apply(V,2,z)
> rownames(U) <- unitNams
> save(U,file="vars42std.Rdata")
> r <- hclust(d<-dist(U),method="ward")
> pdf("DendroWard.pdf",width=58.5,height=41.5)
> plot(r,hang=-1,cex=0.08,main="Ward / Free",lwd=0.01)
> dev.off()
> ant <- read.csv(file="../ANT/ANTcorr.csv",stringsAsFactors=FALSE,sep=",",header=TRUE)$cluster
> library(RColorBrewer)
> library(graphics)
> display.brewer.pal(8,"Dark2")
> dark <- c(brewer.pal(8,"Dark2"),"red","blue","purple","black")
> pie(rep(1,12), col=dark)