Hier vAR k1b

Variables

merged2 <- merged %>% 
           remove_rownames %>% 
           column_to_rownames(var="Nom_Barri") %>% 
           select("n.tot","pc.esp","pc.ue27-esp","pc.20.34","2019-2014","n.esp.V1419",
                  "hotel2019","rest1614",
                  "RFD.2017",
                  "tot_ann","pmedio","pmedio.V1519","pm_ent.V1519","pm_priv.V1519","pm_sha.V1519",
                  "alq.num","alq.pm","alq.pm.V1519","alq.num.V1519",
                  "tot.comp","tot.eur","perc.nou.comp","perc.usat.comp","tot.comp.V1519",
                  "nou.eur.V1519","usat.eur.V1519",
                   )

Standardizar los datos

df <- scale(merged2)

Hier

d <- dist(df, method = "euclidean")

#ward hier
res.hc <- hclust(d, method = "ward" )

## The "ward" method has been renamed to "ward.D"; note new "ward.D2"

#plot
plot(res.hc, cex = 0.6, hang = -1)

Cut 3

grp3 <- cutree(res.hc, k = 3)
table(grp3)

## grp3
##  1  2  3 
##  5 17 51

plot(res.hc, cex = 0.6)
rect.hclust(res.hc, k = 3, border = 2:5)

Cut 4

grp4 <- cutree(res.hc, k = 4)
table(grp4)

## grp4
##  1  2  3  4 
##  5 17 38 13

plot(res.hc, cex = 0.6)
rect.hclust(res.hc, k = 4, border = 2:5)

Cut 5

grp5 <- cutree(res.hc, k = 5)
table(grp5)

## grp5
##  1  2  3  4  5 
##  5 17 11 27 13

plot(res.hc, cex = 0.6)
rect.hclust(res.hc, k = 5, border = 2:5)