lluisvaradd
CORRELACION
corrm <- cor(cat[,3:17])
catnum <- cat[,3:17]
#write.xlsx(corrm, file = "corr.xlsx")HIER
hh <- dist(catnum, method = "euclidean")
hc <- hclust(hh, method = "ward.D2")
hc5 <- cutree(hc, k = 5)
table(hc5)## hc5
## 1 2 3 4 5
## 138 93 106 166 102cat$hc5 <- hc5
#write_xlsx(cat, "catK.xlsx")
K1 <- subset(cat, hc5 == "1")
K1[,1:2]## # A tibble: 138 x 2
## CIUDAD BARRIO
## <chr> <chr>
## 1 Austin Barton Hills
## 2 Austin Clarksville
## 3 Austin North Loop
## 4 Austin Old West Austin
## 5 Austin Travis Heights
## 6 Austin Zilker
## 7 Bangkok Din Daeng/Huai Khwang
## 8 Bangkok Phra Khanong
## 9 Bangkok Thonburi
## 10 Barcelona El Poblenou
## # ... with 128 more rowsK2 <- subset(cat, hc5 == "2")
K2[,1:2]## # A tibble: 93 x 2
## CIUDAD BARRIO
## <chr> <chr>
## 1 Austin Bouldin Creek
## 2 Austin South Lamar
## 3 Austin Universidad de Texas
## 4 Bangkok Aree
## 5 Bangkok Bang Na
## 6 Bangkok Chatuchak
## 7 Bangkok Klong Toey
## 8 Bangkok Pathum Wan
## 9 Bangkok Ratchathewi/Phaya Thai
## 10 Bangkok Siam
## # ... with 83 more rowsK3 <- subset(cat, hc5 == "3")
K3[,1:2]## # A tibble: 106 x 2
## CIUDAD BARRIO
## <chr> <chr>
## 1 Austin Centro
## 2 Austin Hancock
## 3 Austin St. Edwards
## 4 Bangkok Sathorn
## 5 Bangkok Upper Sukhumvit
## 6 Barcelona El Raval
## 7 Barcelona Grà cia
## 8 Barcelona L'Antiga Esquerra de l'Eixample
## 9 Barcelona Les Corts
## 10 Barcelona Sant Andreu
## # ... with 96 more rowsK4 <- subset(cat, hc5 == "4")
K4[,1:2]## # A tibble: 166 x 2
## CIUDAD BARRIO
## <chr> <chr>
## 1 Austin Dawson
## 2 Austin East Downtown
## 3 Austin East Riverside
## 4 Austin Galindo
## 5 Austin South Congress
## 6 Bangkok Banglampoo
## 7 Bangkok Chinatown
## 8 Bangkok Monumento a la Victoria
## 9 Bangkok Thong Lo
## 10 Barcelona Dreta de l'Eixample
## # ... with 156 more rowsK5 <- subset(cat, hc5 == "5")
K5[,1:2]## # A tibble: 102 x 2
## CIUDAD BARRIO
## <chr> <chr>
## 1 Austin Hyde Park
## 2 Austin Parker Lane
## 3 Austin Upper Boggy Creek
## 4 Bangkok Dusit
## 5 Barcelona La Nova Esquerra de l'Eixample
## 6 Barcelona La Vila OlÃmpica
## 7 Barcelona Sant MartÃ
## 8 BerlÃn Wedding
## 9 Boston Back Bay
## 10 Boston Beacon Hill
## # ... with 92 more rowsfviz_cluster(list(data = catnum, cluster = hc5))
plot(hc, cex = 0.6)
rect.hclust(hc, k = 5, border = 2:5)
df <- scale(catnum)
set.seed(123)
fviz_nbclust(df, kmeans, method = "wss")
finalK <- kmeans(df, centers = 5, nstart = 100)
fviz_cluster(finalK, data = df)
print(finalK)## K-means clustering with 5 clusters of sizes 79, 75, 255, 158, 38
##
## Cluster means:
## Amenities (commercial fabric) Amenities (leisure & tourism attractions)
## 1 -0.3732624 -0.19764491
## 2 0.2065275 -0.02433894
## 3 -0.3365329 -0.48323000
## 4 0.7478567 0.98020115
## 5 -0.4828235 -0.37391480
## Amenities (natural resources) AMEN COMM Destinations LAND
## 1 -0.1252825 -0.4267594 0.0425462 0.01096325 0.259183160
## 2 -0.2356818 0.0703529 -0.2009592 0.12334850 0.607050040
## 3 -0.2107066 -0.6177004 -0.1487582 -0.01776377 -0.136916432
## 4 -0.2848352 1.1255266 0.4210822 -0.07708230 -0.197516673
## 5 3.3238840 0.2136297 -0.4443911 0.17346135 0.003081627
## NEG PHYS POS
## 1 2.2258904 -0.02706587 0.03655598
## 2 -0.3506826 -0.13454760 -0.10210217
## 3 -0.4084927 0.26562519 -0.08745779
## 4 -0.2438437 -0.30143171 0.22268091
## 5 -0.1802950 -0.20734050 -0.21347650
## Territory (tangible & intangible attributes) Time / moment of the day
## 1 0.31113738 -0.11271150
## 2 -0.11534944 0.59341467
## 3 0.06668949 -0.14681694
## 4 -0.28961805 -0.04405187
## 5 0.33750493 0.23149007
## URB VERB Weather / climate conditions
## 1 -0.14946540 -0.008191888 0.01304072
## 2 1.71529883 0.264491118 0.97888942
## 3 -0.22812380 -0.050481722 -0.19538969
## 4 -0.34871600 -0.076754425 -0.12734426
## 5 -0.09397236 0.152904300 -0.11847784
##
## Clustering vector:
## [1] 5 4 4 1 3 1 3 2 3 3 3 5 3 5 4 3 5 4 1 3 4 2 3 4 1 1 3 2 3 4 2 5 4 2 1 4 4
## [38] 3 4 2 4 3 4 2 3 4 2 3 3 3 1 3 3 2 3 4 4 3 4 4 3 4 3 3 4 4 4 2 1 3 3 3 3 4
## [75] 4 3 3 3 1 2 1 3 3 4 3 3 3 4 3 1 3 3 3 4 3 4 3 1 2 4 3 1 3 3 3 4 3 4 1 1 1
## [112] 4 3 3 4 4 4 3 3 2 2 2 3 1 3 3 3 3 3 2 2 3 1 3 5 5 3 5 5 5 5 2 3 3 4 4 1 4
## [149] 4 3 2 3 1 4 4 3 3 2 3 3 4 3 3 4 3 3 1 4 3 1 1 4 4 3 4 4 4 4 4 4 4 4 4 1 1
## [186] 3 4 1 3 2 3 3 3 4 3 3 3 4 4 1 4 4 1 2 3 2 1 4 5 3 3 3 3 3 1 2 3 4 3 3 3 2
## [223] 1 1 4 3 1 3 2 4 5 1 3 3 3 3 1 4 3 3 4 1 1 3 4 4 3 3 1 4 2 3 3 5 4 1 3 2 4
## [260] 3 2 4 3 1 4 3 2 3 1 3 4 4 4 3 4 4 5 2 5 3 2 4 5 4 5 3 4 1 3 4 4 2 3 2 3 3
## [297] 3 4 3 4 4 3 3 1 4 3 3 3 4 2 3 1 1 4 5 3 3 3 3 3 3 3 3 3 3 3 3 4 3 2 1 4 1
## [334] 4 2 4 3 2 1 4 3 4 3 3 3 1 3 3 3 4 4 3 3 3 2 4 3 4 3 4 4 1 4 3 3 5 4 1 3 4
## [371] 1 3 4 4 4 4 3 4 4 5 3 3 2 3 5 5 4 3 5 2 3 3 3 1 3 3 3 2 3 1 1 2 1 4 3 1 2
## [408] 2 3 1 3 4 2 4 3 3 3 4 3 3 4 4 3 2 4 4 4 2 3 1 3 1 1 3 3 4 1 3 3 3 3 3 3 3
## [445] 4 4 1 4 3 5 2 3 2 4 3 3 3 3 3 3 3 4 4 1 4 3 3 1 3 2 4 3 3 4 3 3 2 3 2 4 4
## [482] 2 1 4 4 3 3 3 2 2 3 3 3 4 4 3 3 2 3 3 1 4 2 2 2 4 2 5 5 4 4 3 3 4 2 3 3 3
## [519] 3 5 4 2 2 4 5 4 3 3 4 5 5 2 3 1 5 4 2 1 3 4 3 2 3 3 2 3 3 3 3 4 3 3 3 1 5
## [556] 1 3 3 3 4 4 3 4 4 4 1 3 3 4 3 5 1 5 5 5 5 2 4 1 3 1 2 4 3 2 2 3 4 2 1 3 1
## [593] 3 1 1 3 3 3 3 2 2 3 3 3 3
##
## Within cluster sum of squares by cluster:
## [1] 924.8241 1295.3555 2424.2227 1864.8769 423.8366
## (between_SS / total_SS = 23.5 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"