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)##CLUSTER 5 (between_SS / total_SS = 41.0 %) K-means clustering with 5 clusters of sizes 12, 12, 12, 1, 36
set.seed(123)
finalK <- kmeans(df, centers = 5, nstart = 100, algorithm = c("Lloyd"), trace=FALSE)## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterationsprint(finalK)## K-means clustering with 5 clusters of sizes 15, 32, 10, 12, 4
##
## Cluster means:
## n.tot pc.esp pc.ue27-esp pc.20.34 2019-2014 n.esp.V1419
## 1 1.4657983 -0.1241054 0.29275438 0.3359906 0.37003963 -0.32466204
## 2 -0.4048967 0.1758249 -0.43067252 -0.2607653 -0.07224749 -0.01125433
## 3 -0.4296796 0.4941950 0.02706972 -0.5534141 0.39471383 0.19374192
## 4 -0.4813801 0.3414739 -0.39857588 -0.3501026 -0.04810179 0.73261515
## 5 0.2607690 -3.2011136 3.47560459 3.2600005 -1.65214794 -1.37468295
## hotel2019 rest1614 RFD.2017 tot_ann pmedio pmedio.V1519
## 1 1.2482798 -0.06339989 0.4580240 1.0554551 0.4416244 0.5652920
## 2 -0.4203858 0.13244132 -0.5888431 -0.4612479 -0.4192780 -0.3292515
## 3 -0.3169013 0.47816995 1.5530114 -0.4074535 1.3050082 0.9495875
## 4 -0.4985165 -0.02007431 -0.2605859 -0.4677258 -0.4803683 -0.4811563
## 5 0.9698396 -1.95698293 -0.1076155 2.1538382 -0.1232834 -0.4163330
## pm_ent.V1519 pm_priv.V1519 pm_sha.V1519 alq.num alq.pm alq.pm.V1519
## 1 0.2462257 0.1992936 0.49290635 1.5268687 0.5078776 0.37043340
## 2 -0.2459251 -0.1638581 -0.08124108 -0.5296721 -0.5036375 -0.22658713
## 3 1.1445328 0.4829801 -0.20732369 -0.2002266 1.1067416 -0.07459540
## 4 -0.4340199 -0.3500613 -0.19049830 -0.5447242 -0.3098866 -0.03821903
## 5 -0.5152180 0.4062477 -0.10866604 0.6463580 0.2873644 0.72471736
## alq.num.V1519 tot.comp tot.eur perc.nou.comp perc.usat.comp
## 1 0.098278691 0.9083897 0.40903579 -0.01889347 0.02813566
## 2 -0.337693113 -0.2565086 -0.50438111 -0.57724452 0.59825258
## 3 1.137998251 -0.5558614 1.49333051 -0.31631647 0.22935041
## 4 0.005718211 -0.2146745 -0.37945872 1.77715327 -1.78280018
## 5 -0.529150450 0.6792846 -0.09378548 0.14813806 -0.11650487
## tot.comp.V1519 nou.eur.V1519 usat.eur.V1519
## 1 -0.4005995 0.3883824 0.285858024
## 2 0.3392008 -0.3536856 -0.001860212
## 3 -0.7745214 -0.5713827 -0.450045601
## 4 0.4541385 0.5559168 0.026366535
## 5 -0.6374699 1.1337574 -0.011071497
##
## Clustering vector:
## el Raval
## 5
## el Barri Gòtic
## 5
## la Barceloneta
## 5
## Sant Pere, Santa Caterina i la Ribera
## 5
## el Fort Pienc
## 1
## la Sagrada Família
## 1
## la Dreta de l'Eixample
## 1
## l'Antiga Esquerra de l'Eixample
## 1
## la Nova Esquerra de l'Eixample
## 1
## Sant Antoni
## 1
## el Poble Sec
## 1
## la Marina del Prat Vermell
## 3
## la Marina de Port
## 2
## la Font de la Guatlla
## 2
## Hostafrancs
## 2
## la Bordeta
## 2
## Sants - Badal
## 2
## Sants
## 1
## les Corts
## 1
## la Maternitat i Sant Ramon
## 3
## Pedralbes
## 3
## Vallvidrera, el Tibidabo i les Planes
## 3
## Sarrià
## 3
## les Tres Torres
## 3
## Sant Gervasi - la Bonanova
## 3
## Sant Gervasi - Galvany
## 1
## el Putxet i el Farró
## 3
## Vallcarca i els Penitents
## 4
## el Coll
## 4
## la Salut
## 2
## la Vila de Gràcia
## 1
## el Camp d'en Grassot i Gràcia Nova
## 1
## el Baix Guinardó
## 2
## Can Baró
## 2
## el Guinardó
## 2
## la Font d'en Fargues
## 2
## el Carmel
## 4
## la Teixonera
## 4
## Sant Genís dels Agudells
## 4
## Montbau
## 4
## la Vall d'Hebron
## 4
## la Clota
## 4
## Horta
## 4
## Vilapicina i la Torre Llobeta
## 2
## Porta
## 2
## el Turó de la Peira
## 2
## Can Peguera
## 2
## la Guineueta
## 2
## Canyelles
## 2
## les Roquetes
## 2
## Verdun
## 2
## la Prosperitat
## 2
## la Trinitat Nova
## 2
## Torre Baró
## 2
## Ciutat Meridiana
## 2
## Vallbona
## 2
## la Trinitat Vella
## 2
## Baró de Viver
## 2
## el Bon Pastor
## 2
## Sant Andreu
## 1
## la Sagrera
## 2
## el Congrés i els Indians
## 2
## Navas
## 2
## el Camp de l'Arpa del Clot
## 1
## el Clot
## 2
## el Parc i la Llacuna del Poblenou
## 2
## la Vila Olímpica del Poblenou
## 3
## el Poblenou
## 1
## Diagonal Mar i el Front Marítim del Poblenou
## 3
## el Besòs i el Maresme
## 4
## Provençals del Poblenou
## 4
## Sant Martí de Provençals
## 4
## la Verneda i la Pau
## 2
##
## Within cluster sum of squares by cluster:
## [1] 301.21668 333.61509 239.32777 186.67077 72.59433
## (between_SS / total_SS = 39.5 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"kkk <- merged %>%
mutate(Cluster = finalK$cluster) %>%
group_by(Nom_Barri) #%>%
#summarise_all("mean")##GRUPO 1
K1 <- subset(kkk, Cluster == "1")
K1$Nom_Barri## [1] "el Fort Pienc" "la Sagrada Família"
## [3] "la Dreta de l'Eixample" "l'Antiga Esquerra de l'Eixample"
## [5] "la Nova Esquerra de l'Eixample" "Sant Antoni"
## [7] "el Poble Sec" "Sants"
## [9] "les Corts" "Sant Gervasi - Galvany"
## [11] "la Vila de Gràcia" "el Camp d'en Grassot i Gràcia Nova"
## [13] "Sant Andreu" "el Camp de l'Arpa del Clot"
## [15] "el Poblenou"##GRUPO 2
K2 <- subset(kkk, Cluster == "2")
K2$Nom_Barri## [1] "la Marina de Port" "la Font de la Guatlla"
## [3] "Hostafrancs" "la Bordeta"
## [5] "Sants - Badal" "la Salut"
## [7] "el Baix Guinardó" "Can Baró"
## [9] "el Guinardó" "la Font d'en Fargues"
## [11] "Vilapicina i la Torre Llobeta" "Porta"
## [13] "el Turó de la Peira" "Can Peguera"
## [15] "la Guineueta" "Canyelles"
## [17] "les Roquetes" "Verdun"
## [19] "la Prosperitat" "la Trinitat Nova"
## [21] "Torre Baró" "Ciutat Meridiana"
## [23] "Vallbona" "la Trinitat Vella"
## [25] "Baró de Viver" "el Bon Pastor"
## [27] "la Sagrera" "el Congrés i els Indians"
## [29] "Navas" "el Clot"
## [31] "el Parc i la Llacuna del Poblenou" "la Verneda i la Pau"##GRUPO 3
K3 <- subset(kkk, Cluster == "3")
K3$Nom_Barri## [1] "la Marina del Prat Vermell"
## [2] "la Maternitat i Sant Ramon"
## [3] "Pedralbes"
## [4] "Vallvidrera, el Tibidabo i les Planes"
## [5] "Sarrià"
## [6] "les Tres Torres"
## [7] "Sant Gervasi - la Bonanova"
## [8] "el Putxet i el Farró"
## [9] "la Vila Olímpica del Poblenou"
## [10] "Diagonal Mar i el Front Marítim del Poblenou"##GRUPO 4
K4 <- subset(kkk, Cluster == "4")
K4$Nom_Barri## [1] "Vallcarca i els Penitents" "el Coll"
## [3] "el Carmel" "la Teixonera"
## [5] "Sant Genís dels Agudells" "Montbau"
## [7] "la Vall d'Hebron" "la Clota"
## [9] "Horta" "el Besòs i el Maresme"
## [11] "Provençals del Poblenou" "Sant Martí de Provençals"##GRUPO 5
K5 <- subset(kkk, Cluster == "5")
K5$Nom_Barri## [1] "el Raval"
## [2] "el Barri Gòtic"
## [3] "la Barceloneta"
## [4] "Sant Pere, Santa Caterina i la Ribera"