mapbr.r

AC <- lm(PVOTOS~PGASTOS+I(PGASTOSc^2),

subset(dados, p==5 & t==2006 & j==”AC”),

na.action=na.omit)

ac <- as.data.frame(summary(AC)$coef)

ac <- ac[-c(1,3),]

# repeat for all states

SP <- lm(PVOTOS~PGASTOS+I(PGASTOSc^2),

subset(dados, p==5 & t==2006 & j==”SP”),

na.action=na.omit)

sp <- as.data.frame(summary(SP)$coef)

sp <- sp[-c(1,3),]

TO <- lm(PVOTOS~PGASTOS+I(PGASTOSc^2),

subset(dados, p==5 & t==2006 & j==”TO”),

na.action=na.omit)

to <- as.data.frame(summary(TO)$coef)

to <- to[-c(1,3),]


# Then I merge the results into a matrix:

sen6<- rbind(ac, al, am, ap, ba, ce, df, es, go, ma, mg, ms, mt, pa, pb, pe, pi, pr, rj, rn, ro, rr, rs, sc, se, sp, to)

sen6$Estimate <- format(round(sen6$Estimate, 2))

colnames(sen6) <-c(“sen06″, “Std.Error”, “tvalue”, “Pr(>|t|)”)

###

coefmap <- as.data.frame (cbind(UF, sen6$sen06, sen6$Std.Error))

colnames (coefmap) <- c(“UF”,”sen02″, “Erro02″,”sen06″, “Erro06″)

Calling the shapefile

## ======> Maps <=======

# loads sp library for maps and for nice color schemes

require(maptools)

gpclibPermit()

require(classInt)

require(RColorBrewer)

# ===> Shapefile Ivocation <==

brasil <- readShapeSpatial(file.choose(),

proj4string=CRS(“+proj=longlat”))

summary(brasil)

coefmap <- read.csv(“/Users/Daniel/D1/coefmap.csv”, sep = “,”, header=TRUE)

data <- subset(coefmap, T, select = c(

“UF”, “sen02″, “sen06″))

data.br <- attr(brasil, “data”)#getting names from shape

data.br $indice <- 1:dim(data.br)[1]

data$UF <- as.character(data$UF)

data.br$UF <- as.character(data.br$UF)

data$cam02 <- as.numeric(as.character(data$cam02))

data.br <- merge(data.br, data)

data.br <- data.br[order(data.br$indice),]

attr(brasil, “data”) <- data.br #attributing data to shape

# Finally, I have the map of money effects accross states for the senatorial elections in Brazil

## Variables

var2plot <- as.numeric(brasil@data$sen06)

nclr <- 12

clr2plot <- palette(c(“#FFFF99″, “#FFFF66″,”#FFEB5C”,

“#FFD652″, “#FFC247″, “#FFAD3D”,”#FF9933″, “#FF8529″,

“#FF701F”, “#FF5C14″, “#FF470A”, “#FF3300″))

class <- classIntervals(var2plot, nclr, style=”fisher”,

dataPrecision = 2)

colors <- findColours(class, clr2plot)

#Plot Map <===========

plot(brasil, xlim=c(-73.83943,-34.85810),

ylim=c(-33.77086,5.38289))

plot(brasil, border = gray (.9), col=colors, add=T)

title(paste(“Senado Federal, 2006″))

legend(“bottomleft”, border=”white”,

xjust = 0, yjust = 0, x.intersp = 1, y.intersp = .8,

legend=names(attr(colors, “table”)),

fill=attr(colors, “palette”), cex=0.8, bty=”n”,

title=”%Gastos sobre %Votos”)

##======> Labels <========

br.polgns <- attr(brasil, “polygons”)

br.cntrd <- lapply(br.polgns, slot, “labpt”)

text(as.character(data$UF), cex = 0.6)

br.x <- sapply(br.cntrd, function(x) x[1])

br.y <- sapply(br.cntrd, function(x) x[2])

text(br.x, br.y, as.character(

brasil$sen06), cex = 0.6)#=====> Senado 2006 <===========

#Variables

var2plot <- as.numeric(brasil@data$sen06)

nclr <- 12

clr2plot <- palette(c(“#FFFF99″, “#FFFF66″,”#FFEB5C”,

“#FFD652″, “#FFC247″, “#FFAD3D”,”#FF9933″, “#FF8529″,

“#FF701F”, “#FF5C14″, “#FF470A”, “#FF3300″))

class <- classIntervals(var2plot, nclr, style=”fisher”,

dataPrecision = 2)

colors <- findColours(class, clr2plot)

#Plot Map <===========

plot(brasil, xlim=c(-73.83943,-34.85810),

ylim=c(-33.77086,5.38289))

plot(brasil, border = gray (.9), col=colors, add=T)

title(paste(“Senado Federal, 2006″))

legend(“bottomleft”, border=”white”,

xjust = 0, yjust = 0, x.intersp = 1, y.intersp = .8,

legend=names(attr(colors, “table”)),

fill=attr(colors, “palette”), cex=0.8, bty=”n”,

title=”%Gastos sobre %Votos”)

##======> Labels <========

br.polgns <- attr(brasil, “polygons”)

br.cntrd <- lapply(br.polgns, slot, “labpt”)

text(as.character(data$UF), cex = 0.6)

br.x <- sapply(br.cntrd, function(x) x[1])

br.y <- sapply(br.cntrd, function(x) x[2])

text(br.x, br.y, as.character(

brasil$sen06), cex = 0.6)