[R] How to read a shapefile with areas other than the one used by default in the spplot?

[Yana Borges]

Hello!
I send the same question I asked in the stackoverflow, if you don't mind.
It is possible that it is something simple, but I really did not succeed.

I need to make a map as it is in the script I send below. The shapefile I
am using, as well as the data, are attached.
My problem: I want the divisions by Regional Health (RS), and not by
municipality, which are 399. There are 22 Health Regionals, but I can't
identify them on the map. In the shp file there is this subdivision, but I
was unable to insert it that way.
How do I read by RS and not by municipality (default)?

I used the packages ggplot2, factoextra and rgdal

Thank you!

prec <- readxl::read_xlsx('Cluster\\municipios.xlsx', sheet =
"Obito0a6dias2",col_names = T)
tard <- readxl::read_xlsx('Cluster\\municipios.xlsx', sheet =
"Obito7a27dias2",col_names = T)
pop <- readxl::read_xlsx('Cluster\\municipios.xlsx', sheet =
"pop2",col_names = T)
# TAXAS
taxa.prec <- data.frame(prec[1:22,2:18]/pop[1:22,2:18]*1000,row.names
= prec$mun)
taxa.tard <- data.frame(tard[1:22,2:18]/pop[1:22,2:18]*1000,row.names
= prec$mun)
`# head(taxa.prec)
################### Dados ######################
txp     <- txt  <- res.hc  <- grupo  <- list()
dados <- taxa.grupos <- plot <- list()
for (i in 1:ncol(taxa.prec)) {
txp[[i]]    <- scale(taxa.prec[,i])
txt[[i]]    <- scale(taxa.tard[,i])
res.hc[[i]] <- hclust(dist(data.frame(txp[[i]],
                                    txt[[i]],
                                    row.names = prec$mun)),
                    method = "ward.D2")
grupo[[i]]  <- cutree(res.hc[[i]], k = 4)
dados[[i]]  <- data.frame(RS = 1:22,taxap = taxa.prec[,i],
                        taxat = taxa.tard[,i],
                        Grupo=factor(grupo[[i]]),
                        row.names = prec$mun)
taxa.grupos[[i]] <- aggregate(cbind(taxap, taxat) ~ grupo[[i]],
dados[[i]], mean)
taxa.grupos[[i]]$media.pt <- apply(taxa.grupos[[i]][2:3], 1, mean)}
names(dados) <- 2000:2016

mapa <- maptools::readShapePoly('Cluster\\PR\\rs_pr\\RS.shp')

gp <- list(gp1=list(),gp2=list(),gp3=list(),gp4=list())for (i in 1:17) {
 for (j in 1:4) {
 gp$gp1[[i]] <- which(dados[[i]]$Grupo == 1)
 gp$gp2[[i]] <- which(dados[[i]]$Grupo == 2)
 gp$gp3[[i]] <- which(dados[[i]]$Grupo == 3)
 gp$gp4[[i]] <- which(dados[[i]]$Grupo == 4) }}

aux <- matrix(0, ncol=17, nrow=399)
for (i in 1:17) {for (j in 1:4) {
    aux[,i][mapa$RS %in% gp[[j]][[i]]] <- taxa.grupos[[i]]$media.pt[j]}}
mapa using data[6:22] <- aux

 ############# Mapas em cores####
corDegrade <- colorRampPalette(c("#FFFF99", "orange","red", "darkred"))
x11()spplot(mapa, zcol=c(6:22), col.regions =corDegrade(16),
   layout(matrix(c(1:20),nrow = 5,ncol = 4, byrow = TRUE)),
   names.attr = c(2000:2016))