merged <- 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.V1419",
"nou.eur.V1419","usat.eur.V1419",
)
list <- colnames(merged)
minmax <- list
minmax <- as.data.frame(minmax)
list2 <- list()SUMMARY
summary(merged)## n.tot pc.esp pc.ue27-esp pc.20.34
## Min. : 686 Min. :43.73 Min. : 0.880 Min. :13.19
## 1st Qu.:10864 1st Qu.:77.91 1st Qu.: 2.984 1st Qu.:17.17
## Median :21303 Median :83.48 Median : 4.630 Median :18.02
## Mean :22608 Mean :80.78 Mean : 7.114 Mean :18.99
## 3rd Qu.:31087 3rd Qu.:85.63 3rd Qu.: 8.498 3rd Qu.:20.43
## Max. :58642 Max. :94.09 Max. :43.067 Max. :36.59
## 2019-2014 n.esp.V1419 hotel2019 rest1614
## Min. :-8766.0 Min. :-11.479 Min. : 0.0 Min. :-75.000
## 1st Qu.: 199.0 1st Qu.: -3.201 1st Qu.: 20.0 1st Qu.: -5.000
## Median : 494.0 Median : -1.528 Median : 72.0 Median : 1.000
## Mean : 658.3 Mean : -1.234 Mean : 276.1 Mean : 1.192
## 3rd Qu.: 931.0 3rd Qu.: 0.000 3rd Qu.: 272.0 3rd Qu.: 6.000
## Max. : 3989.0 Max. : 35.550 Max. :3115.0 Max. : 79.000
## RFD.2017 tot_ann pmedio pmedio.V1519
## Min. : 38.60 Min. : 0.0 Min. : 0.00 Min. :-100.00
## 1st Qu.: 65.10 1st Qu.: 29.0 1st Qu.: 45.41 1st Qu.: -10.14
## Median : 82.90 Median : 97.0 Median : 71.14 Median : 19.96
## Mean : 93.67 Mean : 279.8 Mean : 98.55 Mean : 35.61
## 3rd Qu.:105.70 3rd Qu.: 261.0 3rd Qu.:108.19 3rd Qu.: 53.82
## Max. :248.80 Max. :2099.0 Max. :656.43 Max. : 310.03
## pm_ent.V1519 pm_priv.V1519 pm_sha.V1519 alq.num
## Min. :-100.0000 Min. :-100.0000 Min. :-100.0 Min. : 2.0
## 1st Qu.: -0.0462 1st Qu.: 0.9837 1st Qu.: 0.0 1st Qu.: 52.0
## Median : 27.1100 Median : 25.8011 Median : 0.0 Median :109.0
## Mean : 59.5332 Mean : 24.5238 Mean : 113.2 Mean :167.4
## 3rd Qu.: 85.8829 3rd Qu.: 40.7103 3rd Qu.: 0.0 3rd Qu.:247.0
## Max. : 590.4439 Max. : 247.2222 Max. :4625.8 Max. :659.0
## alq.pm alq.pm.V1519 alq.num.V1519 tot.comp
## Min. : 0.0 Min. : 0.00 Min. :-50.00 Min. : 5.0
## 1st Qu.: 749.2 1st Qu.:29.76 1st Qu.: 19.23 1st Qu.: 74.0
## Median : 864.6 Median :37.61 Median : 43.11 Median :148.0
## Mean : 858.0 Mean :34.61 Mean : 50.83 Mean :185.2
## 3rd Qu.: 996.4 3rd Qu.:44.27 3rd Qu.: 71.43 3rd Qu.:257.0
## Max. :1950.1 Max. :62.78 Max. :200.00 Max. :619.0
## tot.eur perc.nou.comp perc.usat.comp tot.comp.V1419
## Min. : 0.0 Min. : 0.000 Min. : 50.00 Min. :-45.88
## 1st Qu.: 192.6 1st Qu.: 0.000 1st Qu.: 86.67 1st Qu.: 0.00
## Median : 264.9 Median : 2.672 Median : 97.12 Median : 35.62
## Mean : 309.6 Mean : 7.688 Mean : 91.92 Mean : 55.06
## 3rd Qu.: 367.7 3rd Qu.:11.720 3rd Qu.:100.00 3rd Qu.: 70.13
## Max. :1125.3 Max. :50.000 Max. :100.00 Max. :466.67
## nou.eur.V1419 usat.eur.V1419
## Min. :-100.00 Min. :-38.12
## 1st Qu.: 0.00 1st Qu.: 31.67
## Median : 0.00 Median : 43.00
## Mean : 21.81 Mean : 49.56
## 3rd Qu.: 72.57 3rd Qu.: 67.51
## Max. : 472.73 Max. :201.26for (i in 1:26)
{
IRQ <- IQR(merged[,i])
Q <- quantile(merged[,i], c(0.25,0.5,0.75), type=7)
minmax[i,2] <- outl_min<-as.numeric(Q[1])-3*IRQ
minmax[i,3] <- outl_max<-as.numeric(Q[3])+3*IRQ
#bp <- boxplot(merged[,1][merged[,1]>outl_min & merged[,1]<outl_max], decreasing = FALSE)
out <- print(which(merged[,i] < outl_min | merged[,i] > outl_max))
if (length(out) != 0)
{
bout <-merged[which(merged[,i] < outl_min | merged[,i] > outl_max), ]
# print(bout)
assign(list[i], bout)
list2 <- append(list2, list[i])
}
}## integer(0)
## [1] 1 2
## [1] 2 3 4
## [1] 2 3
## [1] 2 68 69
## [1] 42
## [1] 1 2 6 7 8 31
## [1] 1 2 4 7 11 15 17 18 31 59 67 69
## [1] 21
## [1] 1 2 4 6 7 8 10 11 31
## [1] 12 25
## [1] 25
## [1] 12 25
## [1] 12
## [1] 1 2 4 5 6 7 8 18 22 30 31 33 35 64 65 68
## integer(0)
## [1] 12 21 42 47 54 56 58
## integer(0)
## integer(0)
## integer(0)
## [1] 21
## [1] 42
## integer(0)
## [1] 39 47 55
## [1] 5
## [1] 11VALORES MINMAX outl_min<-(Q[1])-1.5IRQ outl_max<-(Q[3])-1.5IRQ
minmax## minmax V2 V3
## 1 n.tot -49805.000000 91756.000000
## 2 pc.esp 54.755821 108.778372
## 3 pc.ue27-esp -13.557280 25.038782
## 4 pc.20.34 7.395624 30.203934
## 5 2019-2014 -1997.000000 3127.000000
## 6 n.esp.V1419 -12.804598 9.603448
## 7 hotel2019 -736.000000 1028.000000
## 8 rest1614 -38.000000 39.000000
## 9 RFD.2017 -56.700000 227.500000
## 10 tot_ann -667.000000 957.000000
## 11 pmedio -142.928507 296.536086
## 12 pmedio.V1519 -202.025733 245.711297
## 13 pm_ent.V1519 -257.833650 343.670317
## 14 pm_priv.V1519 -118.195993 159.889944
## 15 pm_sha.V1519 0.000000 0.000000
## 16 alq.num -533.000000 832.000000
## 17 alq.pm 7.556394 1737.983899
## 18 alq.pm.V1519 -13.766003 87.792632
## 19 alq.num.V1519 -137.362637 228.021978
## 20 tot.comp -475.000000 806.000000
## 21 tot.eur -332.700000 893.000000
## 22 perc.nou.comp -35.160681 46.880907
## 23 perc.usat.comp 46.666667 140.000000
## 24 tot.comp.V1419 -210.389610 280.519481
## 25 nou.eur.V1419 -217.699115 290.265487
## 26 usat.eur.V1419 -75.861203 175.039897OUTLIERS DE CADA VARIABLE solo n.tot no tiene
for (i in list2)
{x <- get(i)
x <- x[i]
print(x)
}## pc.esp
## el Raval 48.09823
## el Barri Gòtic 43.72784
## pc.ue27-esp
## el Barri Gòtic 43.06665
## la Barceloneta 28.23924
## Sant Pere, Santa Caterina i la Ribera 36.62403
## pc.20.34
## el Barri Gòtic 36.5902
## la Barceloneta 30.4620
## 2019-2014
## el Barri Gòtic -8766
## el Poblenou 3989
## Diagonal Mar i el Front Marítim del Poblenou 3850
## n.esp.V1419
## la Clota 35.55046
## hotel2019
## el Raval 1085
## el Barri Gòtic 1112
## la Sagrada Família 1106
## la Dreta de l'Eixample 3115
## l'Antiga Esquerra de l'Eixample 1559
## la Vila de Gràcia 1327
## rest1614
## el Raval -75
## el Barri Gòtic -59
## Sant Pere, Santa Caterina i la Ribera -41
## la Dreta de l'Eixample 79
## el Poble Sec -48
## Hostafrancs 62
## Sants - Badal 43
## Sants 68
## la Vila de Gràcia -65
## el Bon Pastor 44
## la Vila Olímpica del Poblenou 68
## Diagonal Mar i el Front Marítim del Poblenou 45
## RFD.2017
## Pedralbes 248.8
## tot_ann
## el Raval 1781
## el Barri Gòtic 1423
## Sant Pere, Santa Caterina i la Ribera 1273
## la Sagrada Família 1101
## la Dreta de l'Eixample 2099
## l'Antiga Esquerra de l'Eixample 1079
## Sant Antoni 999
## el Poble Sec 1112
## la Vila de Gràcia 1122
## pmedio
## la Marina del Prat Vermell 656.4286
## Sant Gervasi - la Bonanova 344.7963
## pmedio.V1519
## Sant Gervasi - la Bonanova 310.028
## pm_ent.V1519
## la Marina del Prat Vermell 473.0769
## Sant Gervasi - la Bonanova 590.4439
## pm_priv.V1519
## la Marina del Prat Vermell 247.2222
## pm_sha.V1519
## el Raval 46.00000
## el Barri Gòtic 11.61290
## Sant Pere, Santa Caterina i la Ribera 136.93182
## el Fort Pienc 52.17391
## la Sagrada Família -21.50376
## la Dreta de l'Eixample 1438.54167
## l'Antiga Esquerra de l'Eixample -20.63492
## Sants 161.11111
## Vallvidrera, el Tibidabo i les Planes -100.00000
## la Salut 154.54545
## la Vila de Gràcia 4625.78947
## el Baix Guinardó 1623.40426
## el Guinardó -100.00000
## el Camp de l'Arpa del Clot -42.85714
## el Clot 400.00000
## el Poblenou -100.00000
## alq.pm
## la Marina del Prat Vermell 0.000
## Pedralbes 1950.055
## la Clota 0.000
## Can Peguera 0.000
## Torre Baró 0.000
## Vallbona 0.000
## Baró de Viver 0.000
## tot.eur
## Pedralbes 1125.3
## perc.nou.comp
## la Clota 50
## tot.comp.V1419
## Sant Genís dels Agudells 466.6667
## Can Peguera 300.0000
## Ciutat Meridiana 293.7500
## nou.eur.V1419
## el Fort Pienc 472.7294
## usat.eur.V1419
## el Poble Sec 201.2632SCALED
merged.sc <- scale(merged)
summary(merged.sc)## n.tot pc.esp pc.ue27-esp pc.20.34
## Min. :-1.48582 Min. :-4.1126 Min. :-0.8517 Min. :-1.4973
## 1st Qu.:-0.79597 1st Qu.:-0.3188 1st Qu.:-0.5643 1st Qu.:-0.4693
## Median :-0.08843 Median : 0.2991 Median :-0.3394 Median :-0.2505
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.57472 3rd Qu.: 0.5377 3rd Qu.: 0.1890 3rd Qu.: 0.3720
## Max. : 2.44236 Max. : 1.4769 Max. : 4.9115 Max. : 4.5446
## 2019-2014 n.esp.V1419 hotel2019 rest1614
## Min. :-6.3247 Min. :-1.97135 Min. :-0.563112 Min. :-2.912694
## 1st Qu.:-0.3083 1st Qu.:-0.37849 1st Qu.:-0.522315 1st Qu.:-0.236702
## Median :-0.1103 Median :-0.05649 Median :-0.416242 Median :-0.007332
## Mean : 0.0000 Mean : 0.00000 Mean : 0.000000 Mean : 0.000000
## 3rd Qu.: 0.1830 3rd Qu.: 0.23748 3rd Qu.:-0.008271 3rd Qu.: 0.183811
## Max. : 2.2352 Max. : 7.07815 Max. : 5.791038 Max. : 2.974488
## RFD.2017 tot_ann pmedio pmedio.V1519
## Min. :-1.2894 Min. :-0.63052 Min. :-1.0458 Min. :-1.8315
## 1st Qu.:-0.6690 1st Qu.:-0.56517 1st Qu.:-0.5639 1st Qu.:-0.6178
## Median :-0.2522 Median :-0.41196 Median :-0.2909 Median :-0.2113
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2816 3rd Qu.:-0.04244 3rd Qu.: 0.1023 3rd Qu.: 0.2460
## Max. : 3.6322 Max. : 4.09889 Max. : 5.9201 Max. : 3.7063
## pm_ent.V1519 pm_priv.V1519 pm_sha.V1519 alq.num
## Min. :-1.4363 Min. :-2.61530 Min. :-0.3588 Min. :-1.0390
## 1st Qu.:-0.5364 1st Qu.:-0.49440 1st Qu.:-0.1905 1st Qu.:-0.7248
## Median :-0.2919 Median : 0.02683 Median :-0.1905 Median :-0.3667
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2372 3rd Qu.: 0.33995 3rd Qu.:-0.1905 3rd Qu.: 0.5003
## Max. : 4.7797 Max. : 4.67720 Max. : 7.5926 Max. : 3.0887
## alq.pm alq.pm.V1519 alq.num.V1519 tot.comp
## Min. :-2.39854 Min. :-2.2085 Min. :-2.0803 Min. :-1.2142
## 1st Qu.:-0.30429 1st Qu.:-0.3093 1st Qu.:-0.6519 1st Qu.:-0.7493
## Median : 0.01837 Median : 0.1919 Median :-0.1593 Median :-0.2506
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.38676 3rd Qu.: 0.6166 3rd Qu.: 0.4251 3rd Qu.: 0.4839
## Max. : 3.05272 Max. : 1.7979 Max. : 3.0778 Max. : 2.9232
## tot.eur perc.nou.comp perc.usat.comp tot.comp.V1419
## Min. :-1.6886 Min. :-0.7404 Min. :-3.9689 Min. :-1.2071
## 1st Qu.:-0.6381 1st Qu.:-0.7404 1st Qu.:-0.4977 1st Qu.:-0.6584
## Median :-0.2438 Median :-0.4831 Median : 0.4921 Median :-0.2325
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.3169 3rd Qu.: 0.3884 3rd Qu.: 0.7645 3rd Qu.: 0.1802
## Max. : 4.4492 Max. : 4.0752 Max. : 0.7645 Max. : 4.9220
## nou.eur.V1419 usat.eur.V1419
## Min. :-1.2656 Min. :-2.4404
## 1st Qu.:-0.2266 1st Qu.:-0.4979
## Median :-0.2266 Median :-0.1826
## Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.5273 3rd Qu.: 0.4997
## Max. : 4.6848 Max. : 4.2224CLUSTER
set.seed(123)
finalK <- kmeans(merged.sc, centers = 5, nstart = 100)
ann <- finalK$centers
ann <- as.data.frame(ann)
merged$cluster <- finalK$clusterANOVA MEAN+SD CLUSTER
for (i in 1:26)
{
min <- aggregate(merged[,i] ~ cluster, data = merged, FUN = mean)
sdd <- aggregate(merged[,i] ~ cluster, data = merged, FUN = sd)
sdmin <- merge(min,sdd,by="cluster",all.y=TRUE)
colnames(sdmin)<- c(paste0(list[i]),"mean","sd")
print(sdmin)
}## n.tot mean sd
## 1 1 26455.00 14922.839
## 2 2 15505.42 10515.134
## 3 3 16633.84 9499.795
## 4 4 43336.44 8588.847
## 5 5 14756.33 9000.682
## pc.esp mean sd
## 1 1 51.94028 7.582845
## 2 2 83.85759 5.318977
## 3 3 82.36516 5.765888
## 4 4 80.02038 5.934031
## 5 5 85.21701 3.437829
## pc.ue27-esp mean sd
## 1 1 32.556091 9.146157
## 2 2 4.196735 1.676709
## 3 3 3.961785 2.451312
## 4 4 9.143068 3.657832
## 5 5 7.299551 3.769939
## pc.20.34 mean sd
## 1 1 31.61463 3.358223
## 2 2 17.63229 1.484560
## 3 3 17.97830 2.030386
## 4 4 20.19599 2.385660
## 5 5 16.62853 2.317656
## 2019-2014 mean sd
## 1 1 -1803.5000 4748.5513
## 2 2 586.6667 875.3134
## 3 3 550.6875 828.5086
## 4 4 1189.4375 1077.6702
## 5 5 1286.6667 1242.4975
## n.esp.V1419 mean sd
## 1 1 -8.378314 2.243839
## 2 2 2.573161 10.696455
## 3 3 -1.292675 2.284297
## 4 4 -2.714491 2.076334
## 5 5 -0.295874 1.706409
## hotel2019 mean sd
## 1 1 751.50000 416.03886
## 2 2 31.66667 30.82600
## 3 3 69.96875 76.93880
## 4 4 849.56250 731.49190
## 5 5 103.77778 82.44662
## rest1614 mean sd
## 1 1 -50.0000000 21.694853
## 2 2 0.6666667 2.269695
## 3 3 4.6562500 16.757491
## 4 4 -0.0625000 37.914762
## 5 5 14.5555556 25.070456
## RFD.2017 mean sd
## 1 1 89.07500 16.38849
## 2 2 82.54167 17.11472
## 3 3 68.52187 18.97167
## 4 4 115.19375 32.34364
## 5 5 161.71111 60.79982
## tot_ann mean sd
## 1 1 1235.75000 555.65659
## 2 2 72.25000 62.09395
## 3 3 75.12500 78.90286
## 4 4 716.00000 520.98228
## 5 5 84.22222 77.30423
## pmedio mean sd
## 1 1 86.93437 8.952207
## 2 2 53.28492 26.275791
## 3 3 59.04169 42.742610
## 4 4 146.26637 63.268923
## 5 5 219.72562 184.115582
## pmedio.V1519 mean sd
## 1 1 4.78081483 35.44938
## 2 2 -0.01884583 62.98126
## 3 3 11.22853192 44.83083
## 4 4 86.47770461 70.97761
## 5 5 93.05125963 111.96817
## pm_ent.V1519 mean sd
## 1 1 2.304752 38.18935
## 2 2 11.323927 78.53286
## 3 3 32.216796 72.72542
## 4 4 98.877303 73.86415
## 5 5 176.427234 211.45441
## pm_priv.V1519 mean sd
## 1 1 43.86672 10.54872
## 2 2 7.85612 41.52803
## 3 3 16.72192 46.16958
## 4 4 32.83508 23.63401
## 5 5 51.11492 83.07949
## pm_sha.V1519 mean sd
## 1 1 48.63618 62.01954
## 2 2 0.00000 0.00000
## 3 3 64.93593 294.81498
## 4 4 380.78877 1188.52101
## 5 5 -11.11111 33.33333
## alq.num mean sd
## 1 1 270.25000 153.13910
## 2 2 80.66667 51.50169
## 3 3 83.06250 59.68138
## 4 4 405.18750 130.54614
## 5 5 114.22222 92.32250
## alq.pm mean sd
## 1 1 960.8175 107.5058
## 2 2 747.1656 250.0849
## 3 3 677.8560 280.4989
## 4 4 1041.6091 165.8154
## 5 5 1274.3400 537.5784
## alq.pm.V1519 mean sd
## 1 1 45.96124 2.972726
## 2 2 34.00642 17.339406
## 3 3 31.05479 16.404754
## 4 4 40.45353 5.318256
## 5 5 32.58383 22.556199
## alq.num.V1519 mean sd
## 1 1 25.18087 48.46123
## 2 2 51.10443 31.45348
## 3 3 34.46026 50.30180
## 4 4 54.38179 20.52141
## 5 5 113.73090 49.28406
## tot.comp mean sd
## 1 1 286.00000 113.54588
## 2 2 153.33333 168.89444
## 3 3 147.12500 113.57980
## 4 4 317.81250 146.50516
## 5 5 82.44444 66.84892
## tot.eur mean sd
## 1 1 292.4000 96.19948
## 2 2 240.0250 68.10376
## 3 3 217.1219 98.75582
## 4 4 390.1938 112.56555
## 5 5 595.5000 290.78490
## perc.nou.comp mean sd
## 1 1 9.225648 8.981480
## 2 2 26.139675 9.686588
## 3 3 1.694017 2.691958
## 4 4 7.856486 6.206252
## 5 5 3.410993 5.870455
## perc.usat.comp mean sd
## 1 1 90.69345 8.942009
## 2 2 73.09209 9.555946
## 3 3 98.24355 2.742043
## 4 4 91.87414 6.275282
## 5 5 95.20012 6.415918
## tot.comp.V1419 mean sd
## 1 1 1.751993 12.83624
## 2 2 93.038282 134.26008
## 3 3 83.426558 74.60757
## 4 4 19.861046 31.31801
## 5 5 -10.162327 24.83364
## nou.eur.V1419 mean sd
## 1 1 130.93821 126.18673
## 2 2 75.32115 51.74995
## 3 3 -12.22792 64.86229
## 4 4 60.03164 130.37796
## 5 5 -44.93095 52.26143
## usat.eur.V1419 mean sd
## 1 1 49.15994 14.05369
## 2 2 50.50504 33.15581
## 3 3 49.49089 36.06916
## 4 4 58.61978 42.92035
## 5 5 32.59875 32.36611ANOVA
merged$cluster <- as.factor(merged$cluster)
for (i in 1:26)
{
maov <- aov(merged[,i] ~ cluster, data = merged)
vvv <- summary(maov)
print(paste0(list[i]))
print(vvv)
}## [1] "n.tot"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 9.236e+09 2.309e+09 24.39 1.52e-12 ***
## Residuals 68 6.437e+09 9.466e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pc.esp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 3707 926.9 29.49 3.15e-14 ***
## Residuals 68 2137 31.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pc.ue27-esp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 3075.5 768.9 66.81 <2e-16 ***
## Residuals 68 782.6 11.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pc.20.34"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 765.9 191.46 41.44 <2e-16 ***
## Residuals 68 314.2 4.62
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "2019-2014"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 32741302 8185325 4.378 0.00329 **
## Residuals 68 127124344 1869476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "n.esp.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 421.2 105.3 4.7 0.00208 **
## Residuals 68 1523.4 22.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "hotel2019"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 8509678 2127419 16.45 1.79e-09 ***
## Residuals 68 8793810 129321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "rest1614"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 12502 3125.6 5.781 0.000458 ***
## Residuals 68 36765 540.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "RFD.2017"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 70887 17722 19.93 6.75e-11 ***
## Residuals 68 60450 889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot_ann"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 8901408 2225352 28.66 5.78e-14 ***
## Residuals 68 5280816 77659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pmedio"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 243657 60914 10.47 1.15e-06 ***
## Residuals 68 395703 5819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pmedio.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 109153 27288 6.498 0.000173 ***
## Residuals 68 285569 4200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_ent.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 212613 53153 5.349 0.000834 ***
## Residuals 68 675718 9937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_priv.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 14247 3562 1.626 0.178
## Residuals 68 148981 2191
## [1] "pm_sha.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1529726 382431 1.088 0.37
## Residuals 68 23903553 351523
## [1] "alq.num"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1290332 322583 41.1 <2e-16 ***
## Residuals 68 533771 7850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.pm"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 3327603 831901 9.611 3.21e-06 ***
## Residuals 68 5886054 86560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.pm.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1508 376.9 1.585 0.188
## Residuals 68 16171 237.8
## [1] "alq.num.V1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 47018 11754 6.546 0.000162 ***
## Residuals 68 122115 1796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 475624 118906 7.284 6.08e-05 ***
## Residuals 68 1110075 16325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.eur"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1172517 293129 15.98 2.88e-09 ***
## Residuals 68 1247628 18347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "perc.nou.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 5410 1352.5 39.1 <2e-16 ***
## Residuals 68 2352 34.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "perc.usat.comp"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 5636 1409.1 39.97 <2e-16 ***
## Residuals 68 2397 35.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.comp.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 112533 28133 4.893 0.00158 **
## Residuals 68 390979 5750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "nou.eur.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 182535 45634 6.405 0.000196 ***
## Residuals 68 484475 7125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "usat.eur.V1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 3914 978.5 0.747 0.563
## Residuals 68 89028 1309.2