maps were created using google fusion tables
correlation matrix of district location data
district<-read.csv(“placedistrictopendata.csv”)
library(corrplot)
COR<-cor(district)
corrplot(COR, order="AOE", method="circle", tl.pos="lt", type="upper",
tl.col="black", tl.cex=0.6, tl.srt=45,
addCoef.col="black", addCoefasPercent = TRUE,
sig.level=0.50, insig = "black")
adoptaf<-read.csv(“afcarsadopt.csv”)
COR<-cor(adopaf)
corrplot(COR, order="AOE", method="circle", tl.pos="lt", type="upper",
tl.col="black", tl.cex=0.6, tl.srt=45,
addCoef.col="black", addCoefasPercent = TRUE,
sig.level=0.50, insig = "blank")
fosteraf<-read.csv(“afcarsfoster.csv”)
COR<-cor(fosteraf)
corrplot(COR, order="AOE", method="circle", tl.pos="lt", type="upper",
tl.col="black", tl.cex=0.6, tl.srt=45,
addCoef.col="black", addCoefasPercent = TRUE,
sig.level=0.50, insig = "blank")
nytdserv<-read.csv(“service.csv”)
COR<-cor(nytdserv)
corrplot(COR, order="AOE", method="circle", tl.pos="lt", type="upper",
tl.col="black", tl.cex=0.6, tl.srt=45,
addCoef.col="black", addCoefasPercent = TRUE,
sig.level=0.50, insig = "blank")
nytdoutcome<-read.csv(“outcome.csv”)
COR<-cor(nytdoutcome)
corrplot(COR, order="AOE", method="circle", tl.pos="lt", type="upper",
tl.col="black", tl.cex=0.6, tl.srt=45,
addCoef.col="black", addCoefasPercent = TRUE,
sig.level=0.50, insig = "blank")
fit <- glm(foster$goaladoption ~ ., data=foster )
summary(fit)