merged <- merged %>%
remove_rownames %>%
filter(Codi != 12) %>%
column_to_rownames(var="Nom_Barri") %>%
select("n.tot","pc.esp","pc.ue27-esp","pc.20.34","2019-2014","n.esp.M1419",
"hotel2019","rest1614",
"RFD.2017",
"tot_ann","pmedio","pmedio.M1519","pm_ent.M1519","pm_priv.M1519",
"alq.num","alq.pm","alq.pm.M1519","alq.num.M1519",
"tot.comp","tot.eur","perc.nou.comp","perc.usat.comp","tot.comp.M1419",
"nou.eur.M1419","usat.eur.M1419",
"percbar.filt.A", "percbar.filt"
)
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.:11558 1st Qu.:77.85 1st Qu.: 3.030 1st Qu.:17.07
## Median :21791 Median :83.44 Median : 4.947 Median :18.01
## Mean :22905 Mean :80.67 Mean : 7.199 Mean :18.96
## 3rd Qu.:31359 3rd Qu.:85.61 3rd Qu.: 8.534 3rd Qu.:20.11
## Max. :58642 Max. :94.09 Max. :43.067 Max. :36.59
## 2019-2014 n.esp.M1419 hotel2019 rest1614
## Min. :-8766.0 Min. :-2311.00 Min. : 0.00 Min. :-75.000
## 1st Qu.: 205.0 1st Qu.: -648.50 1st Qu.: 20.25 1st Qu.: -5.000
## Median : 495.0 Median : -232.50 Median : 73.50 Median : 0.500
## Mean : 664.8 Mean : -392.72 Mean : 279.61 Mean : 1.069
## 3rd Qu.: 980.2 3rd Qu.: -8.25 3rd Qu.: 272.25 3rd Qu.: 6.000
## Max. : 3989.0 Max. : 462.00 Max. :3115.00 Max. : 79.000
## RFD.2017 tot_ann pmedio pmedio.M1519
## Min. : 38.60 Min. : 0.00 Min. : 0.00 Min. :-73.150
## 1st Qu.: 66.83 1st Qu.: 29.75 1st Qu.: 45.37 1st Qu.: -7.244
## Median : 83.10 Median : 99.50 Median : 70.81 Median : 10.828
## Mean : 94.42 Mean : 283.53 Mean : 90.80 Mean : 24.807
## 3rd Qu.:105.80 3rd Qu.: 268.25 3rd Qu.:105.40 3rd Qu.: 30.089
## Max. :248.80 Max. :2099.00 Max. :344.80 Max. :260.705
## pm_ent.M1519 pm_priv.M1519 alq.num alq.pm
## Min. :-107.1184 Min. :-59.0000 Min. : 2.00 Min. : 0.0
## 1st Qu.: -0.0525 1st Qu.: 0.7741 1st Qu.: 52.75 1st Qu.: 754.3
## Median : 23.7477 Median : 8.4181 Median :109.50 Median : 865.2
## Mean : 53.0906 Mean : 6.8620 Mean :169.62 Mean : 869.9
## 3rd Qu.: 73.2969 3rd Qu.: 14.8500 3rd Qu.:249.75 3rd Qu.:1002.3
## Max. : 635.8342 Max. : 44.2917 Max. :659.00 Max. :1950.1
## alq.pm.M1519 alq.num.M1519 tot.comp tot.eur
## Min. :-303.8 Min. :-72.00 Min. : 5.0 Min. : 0.0
## 1st Qu.: 184.0 1st Qu.: 11.00 1st Qu.: 80.0 1st Qu.: 193.2
## Median : 237.3 Median : 27.00 Median :153.0 Median : 267.8
## Mean : 239.0 Mean : 53.97 Mean :187.7 Mean : 312.7
## 3rd Qu.: 292.3 3rd Qu.: 75.00 3rd Qu.:259.8 3rd Qu.: 369.3
## Max. : 714.4 Max. :244.00 Max. :619.0 Max. :1125.3
## perc.nou.comp perc.usat.comp tot.comp.M1419 nou.eur.M1419
## Min. : 0.000 Min. : 50.00 Min. :-62.00 Min. :-495.300
## 1st Qu.: 0.000 1st Qu.: 86.65 1st Qu.: 1.50 1st Qu.: -7.125
## Median : 2.683 Median : 97.21 Median : 37.00 Median : 0.350
## Mean : 7.794 Mean : 91.99 Mean : 40.72 Mean : 111.703
## 3rd Qu.:12.123 3rd Qu.:100.00 3rd Qu.: 62.00 3rd Qu.: 205.350
## Max. :50.000 Max. :100.00 Max. :291.00 Max. :2600.700
## usat.eur.M1419 percbar.filt.A percbar.filt
## Min. :-141.30 Min. : 0.00 Min. : 0.00
## 1st Qu.: 55.73 1st Qu.: 39.86 1st Qu.: 46.46
## Median : 87.35 Median : 46.31 Median : 57.54
## Mean : 92.74 Mean : 45.78 Mean : 53.22
## 3rd Qu.: 119.85 3rd Qu.: 50.15 3rd Qu.: 62.71
## Max. : 517.50 Max. :100.00 Max. :100.00for (i in 1:25)
{
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] 1 2 3 4
## [1] 2 67 68
## integer(0)
## [1] 1 2 6 7 8 30
## [1] 1 2 4 7 11 14 16 17 30 58 66 68
## [1] 20
## [1] 1 2 4 6 7 8 10 11 30
## [1] 24
## [1] 10 24 26
## [1] 24 35
## [1] 52 55
## integer(0)
## [1] 20 41 46 53 55 57
## [1] 20 48 57
## integer(0)
## integer(0)
## [1] 20
## [1] 41
## integer(0)
## [1] 59
## [1] 5 20
## [1] 57 68VALORES MINMAX outl_min<-(Q[1])-1.5IRQ outl_max<-(Q[3])-1.5IRQ
minmax## minmax V2 V3
## 1 n.tot -47846.000000 90762.75000
## 2 pc.esp 54.538055 108.92201
## 3 pc.ue27-esp -13.481301 25.04596
## 4 pc.20.34 7.963631 29.21494
## 5 2019-2014 -2120.750000 3306.00000
## 6 n.esp.M1419 -2569.250000 1912.50000
## 7 hotel2019 -735.750000 1028.25000
## 8 rest1614 -38.000000 39.00000
## 9 RFD.2017 -50.100000 222.72500
## 10 tot_ann -685.750000 983.75000
## 11 pmedio -134.720993 285.49738
## 12 pmedio.M1519 -119.241496 142.08705
## 13 pm_ent.M1519 -220.100757 293.34521
## 14 pm_priv.M1519 -41.453688 57.07773
## 15 alq.num -538.250000 840.75000
## 16 alq.pm 10.161773 1746.46574
## 17 alq.pm.M1519 -141.089218 617.38882
## 18 alq.num.M1519 -181.000000 267.00000
## 19 tot.comp -459.250000 799.00000
## 20 tot.eur -335.175000 897.70000
## 21 perc.nou.comp -36.370510 48.49401
## 22 perc.usat.comp 46.614173 140.03937
## 23 tot.comp.M1419 -180.000000 243.50000
## 24 nou.eur.M1419 -644.550000 842.77500
## 25 usat.eur.M1419 -136.650000 312.22500
## 26 percbar.filt.A NA NA
## 27 percbar.filt NA NAOUTLIERS 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 Raval 29.23370
## el Barri Gòtic 36.59020
## la Barceloneta 30.46200
## Sant Pere, Santa Caterina i la Ribera 30.17264
## 2019-2014
## el Barri Gòtic -8766
## el Poblenou 3989
## Diagonal Mar i el Front Marítim del Poblenou 3850
## 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
## Sant Gervasi - la Bonanova 344.7963
## pmedio.M1519
## Sant Antoni 173.7692
## Sant Gervasi - la Bonanova 260.7054
## el Putxet i el Farró 163.8440
## pm_ent.M1519
## Sant Gervasi - la Bonanova 635.8342
## la Font d'en Fargues 491.0000
## pm_priv.M1519
## la Trinitat Nova -48.77778
## Vallbona -59.00000
## alq.pm
## Pedralbes 1950.055
## la Clota 0.000
## Can Peguera 0.000
## Torre Baró 0.000
## Vallbona 0.000
## Baró de Viver 0.000
## alq.pm.M1519
## Pedralbes 699.2251
## Canyelles 714.4444
## Baró de Viver -303.7962
## tot.eur
## Pedralbes 1125.3
## perc.nou.comp
## la Clota 50
## tot.comp.M1419
## Sant Andreu 291
## nou.eur.M1419
## el Fort Pienc 2040.3
## Pedralbes 2600.7
## usat.eur.M1419
## Baró de Viver 517.5
## Diagonal Mar i el Front Marítim del Poblenou -141.3SCALED
merged.sc <- scale(merged)
summary(merged.sc)## n.tot pc.esp pc.ue27-esp pc.20.34
## Min. :-1.51816 Min. :-4.0958 Min. :-0.8614 Min. :-1.4824
## 1st Qu.:-0.77532 1st Qu.:-0.3127 1st Qu.:-0.5683 1st Qu.:-0.4843
## Median :-0.07612 Median : 0.3075 Median :-0.3070 Median :-0.2420
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.57764 3rd Qu.: 0.5487 3rd Qu.: 0.1820 3rd Qu.: 0.2961
## Max. : 2.44180 Max. : 1.4883 Max. : 4.8896 Max. : 4.5333
## 2019-2014 n.esp.M1419 hotel2019 rest1614
## Min. :-6.2892 Min. :-3.3793 Min. :-0.56748 Min. :-2.89006
## 1st Qu.:-0.3066 1st Qu.:-0.4506 1st Qu.:-0.52638 1st Qu.:-0.23059
## Median :-0.1132 Median : 0.2823 Median :-0.41831 Median :-0.02163
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.2104 3rd Qu.: 0.6773 3rd Qu.:-0.01494 3rd Qu.: 0.18732
## Max. : 2.2169 Max. : 1.5057 Max. : 5.75454 Max. : 2.96077
## RFD.2017 tot_ann pmedio pmedio.M1519
## Min. :-1.3124 Min. :-0.63599 Min. :-1.3446 Min. :-1.78829
## 1st Qu.:-0.6488 1st Qu.:-0.56926 1st Qu.:-0.6727 1st Qu.:-0.58512
## Median :-0.2661 Median :-0.41280 Median :-0.2961 Median :-0.25521
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.2677 3rd Qu.:-0.03427 3rd Qu.: 0.2162 3rd Qu.: 0.09642
## Max. : 3.6301 Max. : 4.07236 Max. : 3.7610 Max. : 4.30652
## pm_ent.M1519 pm_priv.M1519 alq.num alq.pm
## Min. :-1.4640 Min. :-4.01762 Min. :-1.0535 Min. :-2.51910
## 1st Qu.:-0.4856 1st Qu.:-0.37137 1st Qu.:-0.7346 1st Qu.:-0.33488
## Median :-0.2681 Median : 0.09492 Median :-0.3779 Median :-0.01371
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.1847 3rd Qu.: 0.48727 3rd Qu.: 0.5036 3rd Qu.: 0.38339
## Max. : 5.3253 Max. : 2.28323 Max. : 3.0758 Max. : 3.12773
## alq.pm.M1519 alq.num.M1519 tot.comp tot.eur
## Min. :-3.8223 Min. :-1.9321 Min. :-1.2347 Min. :-1.7110
## 1st Qu.:-0.3874 1st Qu.:-0.6591 1st Qu.:-0.7277 1st Qu.:-0.6537
## Median :-0.0116 Median :-0.4137 Median :-0.2342 Median :-0.2458
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.3756 3rd Qu.: 0.3225 3rd Qu.: 0.4873 3rd Qu.: 0.3101
## Max. : 3.3482 Max. : 2.9146 Max. : 2.9157 Max. : 4.4471
## perc.nou.comp perc.usat.comp tot.comp.M1419 nou.eur.M1419
## Min. :-0.7483 Min. :-3.9519 Min. :-1.67972 Min. :-1.3914
## 1st Qu.:-0.7483 1st Qu.:-0.5019 1st Qu.:-0.64137 1st Qu.:-0.2724
## Median :-0.4908 Median : 0.4922 Median :-0.06087 Median :-0.2553
## Mean : 0.0000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.4157 3rd Qu.: 0.7544 3rd Qu.: 0.34794 3rd Qu.: 0.2147
## Max. : 4.0522 Max. : 0.7544 Max. : 4.09257 Max. : 5.7055
## usat.eur.M1419 percbar.filt.A percbar.filt
## Min. :-2.90215 Min. :-3.21081 Min. :-2.9322
## 1st Qu.:-0.45904 1st Qu.:-0.41538 1st Qu.:-0.3723
## Median :-0.06689 Median : 0.03696 Median : 0.2381
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.33611 3rd Qu.: 0.30688 3rd Qu.: 0.5231
## Max. : 5.26698 Max. : 3.80305 Max. : 2.5779CLUSTER
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 17790.94 12119.641
## 3 3 19689.73 8549.702
## 4 4 43952.08 7202.346
## 5 5 15505.42 10515.134
## pc.esp mean sd
## 1 1 51.94028 7.582845
## 2 2 82.60935 5.549095
## 3 3 84.27693 5.028063
## 4 4 78.71882 5.378709
## 5 5 83.85759 5.318977
## pc.ue27-esp mean sd
## 1 1 32.556091 9.146157
## 2 2 3.998640 2.640868
## 3 3 7.666938 3.082958
## 4 4 9.649185 3.404287
## 5 5 4.196735 1.676709
## pc.20.34 mean sd
## 1 1 31.61463 3.358223
## 2 2 17.76062 1.833073
## 3 3 16.91618 2.581906
## 4 4 20.94842 1.835231
## 5 5 17.63229 1.484560
## 2019-2014 mean sd
## 1 1 -1803.5000 4748.5513
## 2 2 651.0000 907.3169
## 3 3 1440.0000 1441.6681
## 4 4 874.3846 685.4564
## 5 5 586.6667 875.3134
## n.esp.M1419 mean sd
## 1 1 -1134.5000 335.9926
## 2 2 -161.5938 300.5875
## 3 3 -140.6364 211.1569
## 4 4 -1166.3846 621.8460
## 5 5 -154.7500 379.2915
## hotel2019 mean sd
## 1 1 751.50000 416.0389
## 2 2 79.43750 107.9925
## 3 3 134.36364 100.4990
## 4 4 978.92308 749.6974
## 5 5 31.66667 30.8260
## rest1614 mean sd
## 1 1 -50.0000000 21.694853
## 2 2 2.6250000 13.197629
## 3 3 17.0000000 27.509998
## 4 4 -0.1538462 42.258818
## 5 5 0.6666667 2.269695
## RFD.2017 mean sd
## 1 1 89.07500 16.38849
## 2 2 69.46875 22.30390
## 3 3 155.20909 51.78844
## 4 4 116.99231 33.09734
## 5 5 82.54167 17.11472
## tot_ann mean sd
## 1 1 1235.75000 555.65659
## 2 2 68.78125 68.18321
## 3 3 158.09091 145.52488
## 4 4 820.30769 518.72607
## 5 5 72.25000 62.09395
## pmedio mean sd
## 1 1 86.93437 8.952207
## 2 2 52.86485 23.667586
## 3 3 183.95403 78.394334
## 4 4 141.19450 61.702368
## 5 5 53.28492 26.275791
## pmedio.M1519 mean sd
## 1 1 -6.2389287 45.05626
## 2 2 1.4206520 21.69803
## 3 3 89.1021506 74.67913
## 4 4 61.2275704 56.23935
## 5 5 -0.8706408 24.57228
## pm_ent.M1519 mean sd
## 1 1 -12.154780 64.65317
## 2 2 17.025960 39.68130
## 3 3 204.440804 191.63061
## 4 4 79.588133 59.39082
## 5 5 3.568255 52.34323
## pm_priv.M1519 mean sd
## 1 1 18.355969 4.897977
## 2 2 4.280258 20.535113
## 3 3 5.022687 12.248069
## 4 4 14.928615 9.722295
## 5 5 2.862696 11.618596
## alq.num mean sd
## 1 1 270.25000 153.13910
## 2 2 87.09375 66.54921
## 3 3 156.90909 100.21722
## 4 4 434.69231 126.33183
## 5 5 80.66667 51.50169
## alq.pm mean sd
## 1 1 960.8175 107.5058
## 2 2 691.7836 302.8195
## 3 3 1265.7178 336.6723
## 4 4 1058.9407 171.1282
## 5 5 747.1656 250.0849
## alq.pm.M1519 mean sd
## 1 1 301.6976 27.76881
## 2 2 181.9992 152.47071
## 3 3 358.1977 159.38244
## 4 4 298.4240 37.02829
## 5 5 196.3789 92.74132
## alq.num.M1519 mean sd
## 1 1 62.75000 93.50356
## 2 2 19.25000 21.70328
## 3 3 62.00000 45.15307
## 4 4 158.46154 60.20052
## 5 5 23.08333 19.37411
## tot.comp mean sd
## 1 1 286.0000 113.54588
## 2 2 161.6562 141.55510
## 3 3 125.0000 80.82698
## 4 4 306.0769 133.84161
## 5 5 153.3333 168.89444
## tot.eur mean sd
## 1 1 292.4000 96.19948
## 2 2 221.5281 106.51172
## 3 3 565.4455 255.55034
## 4 4 396.3615 116.39838
## 5 5 240.0250 68.10376
## perc.nou.comp mean sd
## 1 1 9.225648 8.981480
## 2 2 1.493168 2.313484
## 3 3 7.131685 7.013079
## 4 4 6.490874 5.957947
## 5 5 26.139675 9.686588
## perc.usat.comp mean sd
## 1 1 90.69345 8.942009
## 2 2 98.44440 2.377027
## 3 3 92.86832 7.013079
## 4 4 93.17759 6.126398
## 5 5 73.09209 9.555946
## tot.comp.M1419 mean sd
## 1 1 9.000000 37.97368
## 2 2 59.250000 56.26321
## 3 3 -1.909091 34.55273
## 4 4 38.615385 64.22297
## 5 5 43.250000 77.75267
## nou.eur.M1419 mean sd
## 1 1 200.75000 165.0946
## 2 2 11.69063 192.0564
## 3 3 251.08182 867.1289
## 4 4 229.59231 574.3115
## 5 5 93.24167 73.3617
## usat.eur.M1419 mean sd
## 1 1 89.50000 23.33652
## 2 2 75.45625 91.60477
## 3 3 125.48182 108.41505
## 4 4 128.83077 44.73590
## 5 5 70.82500 36.11296
## percbar.filt.A mean sd
## 1 1 46.09992 3.999831
## 2 2 44.57572 17.777032
## 3 3 50.02645 10.403905
## 4 4 49.22589 4.330275
## 5 5 41.24751 15.475492ANOVA
merged$cluster <- as.factor(merged$cluster)
for (i in 1:25)
{
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 7.417e+09 1.854e+09 15.95 3.22e-09 ***
## Residuals 67 7.791e+09 1.163e+08
## ---
## 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 3736 934.1 30.71 1.6e-14 ***
## Residuals 67 2038 30.4
## ---
## 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 3088.3 772.1 70.65 <2e-16 ***
## Residuals 67 732.2 10.9
## ---
## 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 4 805.1 201.27 50.07 <2e-16 ***
## Residuals 67 269.3 4.02
## ---
## 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 31630691 7907673 4.139 0.00468 **
## Residuals 67 128016347 1910692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "n.esp.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 13070174 3267543 22.32 9.58e-12 ***
## Residuals 67 9808275 146392
## ---
## 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 9500206 2375052 20.57 4.31e-11 ***
## Residuals 67 7736807 115475
## ---
## 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 13323 3331 6.222 0.000256 ***
## Residuals 67 35866 535
## ---
## 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 69002 17250 19.45 1.16e-10 ***
## Residuals 67 59415 887
## ---
## 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 9557086 2389271 35.16 8.31e-16 ***
## Residuals 67 4553488 67963
## ---
## 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 191468 47867 24.23 2e-12 ***
## Residuals 67 132343 1975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pmedio.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 91985 22996 12.73 9.24e-08 ***
## Residuals 67 121051 1807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_ent.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 349182 87295 11.67 2.99e-07 ***
## Residuals 67 501041 7478
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "pm_priv.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1817 454.2 1.763 0.147
## Residuals 67 17264 257.7
## [1] "alq.num"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 1268597 317149 40.19 <2e-16 ***
## Residuals 67 528776 7892
## ---
## 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 3417005 854251 11.33 4.4e-07 ***
## Residuals 67 5050229 75377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.pm.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 343688 85922 5.291 0.000918 ***
## Residuals 67 1088071 16240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "alq.num.M1519"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 192981 48245 29.7 3.24e-14 ***
## Residuals 67 108837 1624
## ---
## 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 299944 74986 4.007 0.00566 **
## Residuals 67 1253923 18715
## ---
## 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 1124695 281174 15.12 7.41e-09 ***
## Residuals 67 1246112 18599
## ---
## 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 5344 1336.1 37.97 <2e-16 ***
## Residuals 67 2358 35.2
## ---
## 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 5652 1413.1 40.09 <2e-16 ***
## Residuals 67 2362 35.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "tot.comp.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 35136 8784 2.554 0.0468 *
## Residuals 67 230392 3439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "nou.eur.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 750250 187563 0.985 0.422
## Residuals 67 12761554 190471
## [1] "usat.eur.M1419"
## Df Sum Sq Mean Sq F value Pr(>F)
## cluster 4 44090 11022 1.768 0.146
## Residuals 67 417668 6234