7 Cluster - S05

pacman::p_load(
     tidyverse
     ,maditr
     ,highcharter
     ,car
     ,psych
     ,Hmisc
     ,ca
     ,FactoMineR
     ,factoextra
     ,readr
     ,readxl
     ,tidyverse
     ,kableExtra
     ,igraph
     ,highcharter
     ,sparkline
     ,fontawesome
     ,car
     ,outliers
     ,nortest
     ,vcd
     ,ca
     ,corrplot
     ,gplots
     ,DT
     ,lmtest
     ,sjstats
     ,igraph
     ,expss
)

Cargamos los paquetes necesarios y los datos del archivo hatco.sav

options(scipen=9,width=80)
data <- read_spss("data/hatco.sav")
colnames(data) <- tolower(colnames(data))

No retenemos todo el archivo, sino que hacemos un subfichero con las 7 variables de trabajo y el identificador del registro.

#selecciono las variables con las que voy a trabajar
data01 <- subset(data, select = c(id, x1,x2,x3,x4,x5,x6,x7))

Recordemos que si las variables estuvieran medidas en distinta escala, deberíamos estandarizar. No es el caso.

# si fuera necesario debería estandarizar, si todas miden igual no sería necesario
# data01<-  scale(data01, center= TRUE, scale=TRUE)

mh <- round(mahalanobis(data01[,-1], colMeans(data01[,-1]), cov(data01[,-1])),5)
pmh <- round(pchisq(mh, df=7, lower.tail=FALSE),5)
data01 <- cbind(data01, mh,pmh)
dim(data01)

[1] 100  10

data01 <- subset(data01, pmh>0.001)
dim(data01)

[1] 98 10

datatable(data01)

data01 <- data01[,c(-9,-10)]

7.1 Cálculo de distancias

Calculamos las distancias, euclídeas.

#calculo las distancias (package stats)
dist01 <- dist(data01[,-1])

7.2 Clúster jerárquico

7.2.1 Cálculo

Realizamos el cluster jerárquico y listamos sus resultados. Nótese en la agenda de casos, que un número negativo significa referencia al caso original, mientras que un número positivo indica una referencia un grupo creado en el paso idncado por el número

#calculo los grupos method -> the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
clusters <- hclust(dist01, method = "complete")
clusters[["method"]]

[1] "complete"

clusters[["call"]]

hclust(d = dist01, method = "complete")

clusters[["dist.method"]]

[1] "euclidean"

clusters[["merge"]]

      [,1] [,2]
 [1,]  -15  -20
 [2,]   -5  -41
 [3,]  -23  -26
 [4,]  -46  -59
 [5,]  -19  -27
 [6,]  -65  -88
 [7,]  -35  -40
 [8,]  -50  -75
 [9,]  -18  -90
[10,]  -32  -60
[11,]  -24  -43
[12,]  -83  -85
[13,]  -42  -45
[14,]  -63  -77
[15,]  -37  -61
[16,]  -67  -79
[17,]  -49  -70
[18,]  -54  -89
[19,]  -92  -96
[20,]  -16  -71
[21,]  -73  -97
[22,]  -36  -47
[23,]   -1  -93
[24,]  -11  -98
[25,]   -4  -87
[26,]  -22  -31
[27,]  -82  -86
[28,]   -2  -81
[29,]   -3  -69
[30,]  -28  -76
[31,]  -17  -62
[32,]   -8  -66
[33,]  -12  -74
[34,]   -9  -72
[35,]  -51  -58
[36,]  -10  -33
[37,]  -25  -57
[38,]  -48  -95
[39,]   -7    6
[40,]    7   27
[41,]  -13  -21
[42,]  -39  -53
[43,]  -80  -91
[44,]  -64  -78
[45,]  -29   36
[46,]   -6  -68
[47,]  -44  -84
[48,]  -38  -94
[49,]   16   17
[50,]  -52   45
[51,]  -14   15
[52,]    1    5
[53,]  -34   41
[54,]   20   30
[55,]    3   25
[56,]    9   49
[57,]   42   47
[58,]  -56   34
[59,]  -30   22
[60,]   10   11
[61,]   32   40
[62,]   18   21
[63,]   37   60
[64,]   44   51
[65,]   35   46
[66,]   29   50
[67,]   33   58
[68,]   12   24
[69,]    8   23
[70,]   14   48
[71,]   39   52
[72,]   28   62
[73,]   13   56
[74,]   19   57
[75,]    4   54
[76,]   59   66
[77,]   64   68
[78,]   31   61
[79,]   67   71
[80,]   55   74
[81,]  -55   26
[82,]   63   73
[83,]   70   80
[84,]   53   69
[85,]   38   82
[86,]   65   72
[87,]   75   77
[88,]   76   81
[89,]   84   87
[90,]   78   86
[91,]   43   88
[92,]   85   89
[93,]   83   90
[94,]    2   79
[95,]   91   93
[96,]   92   94
[97,]   95   96

clusters[["height"]]

 [1] 0.0000000 0.1000000 0.1000000 0.1414214 0.2000000 0.2449490 0.2645751
 [8] 0.2645751 0.2645751 0.2645751 0.3162278 0.3162278 0.3162278 0.3162278
[15] 0.3605551 0.4000000 0.4358899 0.4358899 0.4358899 0.4690416 0.4690416
[22] 0.4690416 0.4924429 0.5000000 0.5291503 0.5291503 0.5291503 0.5291503
[29] 0.5291503 0.5291503 0.6164414 0.6557439 0.6557439 0.7280110 0.7615773
[36] 0.8185353 0.8366600 0.9165151 0.9539392 0.9848858 1.0198039 1.0246951
[43] 1.0246951 1.0344080 1.0488088 1.1489125 1.2884099 1.2961481 1.3601471
[50] 1.4899664 1.5652476 1.5968719 1.6278821 1.6703293 1.7804494 1.8055470
[57] 1.8627936 1.8627936 1.8947295 1.9078784 1.9949937 1.9974984 2.0976177
[64] 2.1447611 2.1725561 2.2338308 2.2383029 2.2737634 2.3643181 2.3916521
[71] 2.4269322 2.4331050 2.4617067 2.4698178 2.6532998 2.8548205 2.9000000
[78] 3.1016125 3.1096624 3.1968735 3.3630343 3.3941125 3.4292856 3.6000000
[85] 3.9076847 3.9230090 4.0435133 4.0472213 4.3335897 4.6957428 4.9061186
[92] 5.0892043 5.4899909 5.9665736 7.1554175 7.3464277 8.8960665

clusters[["order"]]

 [1] 80 91 30 36 47  3 69 52 29 10 33 55 22 31 63 77 38 94 23 26  4 87 92 96 39
[26] 53 44 84 17 62  8 66 35 40 82 86 51 58  6 68  2 81 54 89 73 97 48 95 25 57
[51] 32 60 24 43 42 45 18 90 67 79 49 70 34 13 21 50 75  1 93 46 59 16 71 28 76
[76] 64 78 14 37 61 83 85 11 98  5 41 12 74 56  9 72  7 65 88 15 20 19 27

plot(clusters)

7.2.2 Creación de variables de grupo

7.2.2.1 Solución de 5 grupos

Retenemos una variable con la pertenencia a cada grupo (5).

clusterCut.05 <- cutree(clusters, 5)
clusterCut.05

  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   2   3   2   4   2   5   2   5   3   1   5   1   1   5   1   2   1   5   5 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   3   2   1   1   2   5   1   3   3   3   1   3   1   2   3   1   2   2   2 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  4   1   1   2   1   1   3   1   1   1   2   3   2   2   3   5   1   2   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  1   2   2   1   5   2   1   2   3   1   1   5   2   5   1   1   2   1   1   3 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  2   2   1   2   1   2   2   5   2   1   3   2   1   2   1   2   2   1

7.2.2.2 Solución de 4 grupos

Retenemos una variable con la pertenencia a cada grupo (4).

clusterCut.04 <- cutree(clusters, 4)
clusterCut.04

  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   2   3   2   4   2   4   2   4   3   1   4   1   1   4   1   2   1   4   4 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   3   2   1   1   2   4   1   3   3   3   1   3   1   2   3   1   2   2   2 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  4   1   1   2   1   1   3   1   1   1   2   3   2   2   3   4   1   2   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  1   2   2   1   4   2   1   2   3   1   1   4   2   4   1   1   2   1   1   3 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  2   2   1   2   1   2   2   4   2   1   3   2   1   2   1   2   2   1

7.2.2.3 Solución de 3 grupos

Retenemos una variable con la pertenencia a cada grupo (3).

clusterCut.03 <- cutree(clusters, 3)
clusterCut.03

  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   2   2   2   3   2   3   2   3   2   1   3   1   1   3   1   2   1   3   3 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   2   2   1   1   2   3   1   2   2   2   1   2   1   2   2   1   2   2   2 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  3   1   1   2   1   1   2   1   1   1   2   2   2   2   2   3   1   2   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  1   2   2   1   3   2   1   2   2   1   1   3   2   3   1   1   2   1   1   2 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  2   2   1   2   1   2   2   3   2   1   2   2   1   2   1   2   2   1

7.2.2.4 Solución de 2 grupos

Retenemos una variable con la pertenencia a cada grupo (2).

clusterCut.02 <- cutree(clusters, 2)
clusterCut.02

  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   2   2   2   1   2   1   2   1   2   1   1   1   1   1   1   2   1   1   1 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   2   2   1   1   2   1   1   2   2   2   1   2   1   2   2   1   2   2   2 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  1   1   1   2   1   1   2   1   1   1   2   2   2   2   2   1   1   2   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  1   2   2   1   1   2   1   2   2   1   1   1   2   1   1   1   2   1   1   2 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  2   2   1   2   1   2   2   1   2   1   2   2   1   2   1   2   2   1

Procedemos a unir las variables con el fichero original.

data02 <- cbind(id=data01[,1],clusterCut.02,clusterCut.03,clusterCut.04,clusterCut.05)

7.3 Clúster K-medias

7.3.1 Cálculo

Hacemos el análisis K-means para 5, 4 3 y 2 grupos. Listamos secuencialmente sus resúmenes.

#cluster kmeans
set.seed(31121965)
kclusterCut.05 <- kmeans(data01[,-1], 5, nstart = 10)
kclusterCut.05

K-means clustering with 5 clusters of sizes 31, 14, 16, 18, 19

Cluster means:
        x1       x2       x3       x4       x5       x6       x7
1 4.125806 1.687097 8.638710 4.525806 2.922581 2.091935 5.264516
2 3.614286 4.128571 5.950000 6.064286 3.842857 3.164286 7.950000
3 2.300000 3.625000 7.843750 5.562500 2.956250 2.675000 7.881250
4 2.011111 2.133333 6.544444 5.266667 2.038889 2.672222 8.483333
5 4.884211 1.510526 9.368421 5.810526 3.178947 3.300000 7.000000

Clustering vector:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   4   2   4   5   3   5   3   5   2   1   5   1   5   5   1   2   1   5   5 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   3   4   1   1   4   5   1   2   2   3   1   2   1   3   2   5   4   4   3 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  5   1   1   4   1   1   2   5   1   1   3   2   4   3   2   5   1   3   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  5   2   4   1   5   3   1   3   2   1   1   5   4   5   1   1   4   1   1   2 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  4   3   3   4   3   3   4   5   3   1   2   4   1   4   5   4   4   1 

Within cluster sum of squares by cluster:
[1] 130.54274  54.23429  51.28062  60.38611  73.35368
 (between_SS / total_SS =  61.6 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"

set.seed(31121965)
kclusterCut.04 <- kmeans(data01[,-1], 4, nstart = 10)
kclusterCut.04

K-means clustering with 4 clusters of sizes 19, 29, 19, 31

Cluster means:
        x1       x2       x3       x4       x5       x6       x7
1 3.415789 3.989474 6.578947 6.210526 3.684211 3.200000 8.026316
2 2.024138 2.703448 6.951724 5.196552 2.337931 2.565517 8.193103
3 4.884211 1.510526 9.368421 5.810526 3.178947 3.300000 7.000000
4 4.125806 1.687097 8.638710 4.525806 2.922581 2.091935 5.264516

Clustering vector:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  4   2   1   2   3   2   3   2   3   1   4   3   4   3   3   4   1   4   3   3 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  4   1   2   4   4   2   3   4   1   1   1   4   1   4   2   1   3   2   2   2 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  3   4   4   2   4   4   1   3   4   4   1   1   2   1   1   3   4   2   4   4 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  3   1   2   4   3   2   4   2   1   4   4   3   2   3   4   4   2   4   4   1 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  2   2   2   2   2   2   2   3   1   4   1   2   4   2   3   2   2   4 

Within cluster sum of squares by cluster:
[1]  92.03474 116.63724  73.35368 130.54274
 (between_SS / total_SS =  57.2 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"

set.seed(31121965)
kclusterCut.03 <- kmeans(data01[,-1], 3, nstart = 10)
kclusterCut.03

K-means clustering with 3 clusters of sizes 31, 19, 48

Cluster means:
        x1       x2       x3       x4       x5       x6       x7
1 4.125806 1.687097 8.638710 4.525806 2.922581 2.091935 5.264516
2 4.884211 1.510526 9.368421 5.810526 3.178947 3.300000 7.000000
3 2.575000 3.212500 6.804167 5.597917 2.870833 2.816667 8.127083

Clustering vector:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   3   3   3   2   3   2   3   2   3   1   2   1   2   2   1   3   1   2   2 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   3   3   1   1   3   2   1   3   3   3   1   3   1   3   3   2   3   3   3 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  2   1   1   3   1   1   3   2   1   1   3   3   3   3   3   2   1   3   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  2   3   3   1   2   3   1   3   3   1   1   2   3   2   1   1   3   1   1   3 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  3   3   3   3   3   3   3   2   3   1   3   3   1   3   2   3   3   1 

Within cluster sum of squares by cluster:
[1] 130.54274  73.35368 289.03208
 (between_SS / total_SS =  48.8 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"

set.seed(31121965)
kclusterCut.02 <- kmeans(data01[,-1], 2, nstart = 10)
kclusterCut.02

K-means clustering with 2 clusters of sizes 50, 48

Cluster means:
     x1     x2       x3       x4       x5       x6       x7
1 4.414 1.6200 8.916000 5.014000 3.020000 2.551000 5.924000
2 2.575 3.2125 6.804167 5.597917 2.870833 2.816667 8.127083

Clustering vector:
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
  1   2   2   2   1   2   1   2   1   2   1   1   1   1   1   1   2   1   1   1 
 21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41 
  1   2   2   1   1   2   1   1   2   2   2   1   2   1   2   2   1   2   2   2 
 42  43  44  45  46  47  48  49  50  51  52  53  54  56  57  58  59  60  61  62 
  1   1   1   2   1   1   2   1   1   1   2   2   2   2   2   1   1   2   1   1 
 63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82 
  1   2   2   1   1   2   1   2   2   1   1   1   2   1   1   1   2   1   1   2 
 83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  2   2   2   2   2   2   2   1   2   1   2   2   1   2   1   2   2   1 

Within cluster sum of squares by cluster:
[1] 290.2013 289.0321
 (between_SS / total_SS =  39.8 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"

7.3.2 Creación de las variables de grupo

g2 <- kclusterCut.02[["cluster"]]
g3 <- kclusterCut.03[["cluster"]]
g4 <- kclusterCut.04[["cluster"]]
g5 <- kclusterCut.05[["cluster"]]

Procedemos a unir las variables de pertenencia del K-means con el fichero original.

data03 <- cbind(id=data01[,1], g2, g3, g4, g5)
data <- merge(data, data02, by = "id")
data <- merge(data, data03, by = "id")

El fichero resultante tras la adición de las variables es:

datatable(data)

7.3.3 Perfil de los grupos, contrastes inferenciales

Perfilamos los grupos con otros datos del banco de datos. Hacemos la prueba Chisq para los datos nominales y la prueba T para los métricos.

data = apply_labels(data, g5 = "Grupo" )
data %>% 
    tab_cells(x8,x11,x12,x13,x14) %>% 
    tab_cols(g5) %>% 
    tab_stat_cases() %>% 
    tab_last_sig_cases() %>% 
    tab_pivot()

	Grupo
	1	2	3	4	5
tamaño de la empresa
pequeña	29.0	4.0	6.0		19.0
grande	2.0	10.0	10.0	18.0
#Chi-squared p-value	<0.05
#Total cases	31.0	14.0	16.0	18.0	19.0
especificación de las compras
especificacion	2.0	10.0	10.0	18.0
valor total	29.0	4.0	6.0		19.0
#Chi-squared p-value	<0.05
#Total cases	31.0	14.0	16.0	18.0	19.0
estructura
descentralizada	29.0				19.0
centralizada	2.0	14.0	16.0	18.0
#Chi-squared p-value	<0.05
#Total cases	31.0	14.0	16.0	18.0	19.0
tipo de industria
tipo 1	21.0	6.0	8.0	8.0	5.0
tipo 2	10.0	8.0	8.0	10.0	14.0
#Chi-squared p-value
#Total cases	31.0	14.0	16.0	18.0	19.0
situacion de compra
nueva	6.0	2.0	4.0	18.0	2.0
recompra modificada	8.0	12.0	10.0		2.0
recompra	17.0		2.0		15.0
#Chi-squared p-value	<0.05 (warn.)
#Total cases	31.0	14.0	16.0	18.0	19.0

data %>% 
    tab_cells(x9,x10) %>% 
    tab_cols(g5) %>% 
    tab_stat_mean_sd_n() %>% 
    tab_last_sig_means() %>% 
    tab_pivot()

	Grupo
	1	2	3	4	5
	A	B	C	D	E
nivel de uso
Mean	46.9 D	46.5 D	46.7 D	36.3	54.2 A B C D
Std. dev.	8.8	6.5	6.1	5.3	7.0
Unw. valid N	31.0	14.0	16.0	18.0	19.0
nivel de satisfacción
Mean	4.9 D	4.9 D	4.6 D	3.7	5.6 A B C D
Std. dev.	0.7	0.8	0.6	0.3	0.6
Unw. valid N	31.0	14.0	16.0	18.0	19.0

data = apply_labels(data, g4 = "Grupo" )
data %>% 
    tab_cells(x8,x11,x12,x13,x14) %>% 
    tab_cols(g4) %>% 
    tab_stat_cases() %>% 
    tab_last_sig_cases() %>% 
    tab_pivot()

	Grupo
	1	2	3	4
tamaño de la empresa
pequeña	8.0	2.0	19.0	29.0
grande	11.0	27.0		2.0
#Chi-squared p-value	<0.05
#Total cases	19.0	29.0	19.0	31.0
especificación de las compras
especificacion	11.0	27.0		2.0
valor total	8.0	2.0	19.0	29.0
#Chi-squared p-value	<0.05
#Total cases	19.0	29.0	19.0	31.0
estructura
descentralizada			19.0	29.0
centralizada	19.0	29.0		2.0
#Chi-squared p-value	<0.05
#Total cases	19.0	29.0	19.0	31.0
tipo de industria
tipo 1	8.0	14.0	5.0	21.0
tipo 2	11.0	15.0	14.0	10.0
#Chi-squared p-value	<0.05
#Total cases	19.0	29.0	19.0	31.0
situacion de compra
nueva	2.0	22.0	2.0	6.0
recompra modificada	15.0	7.0	2.0	8.0
recompra	2.0		15.0	17.0
#Chi-squared p-value	<0.05
#Total cases	19.0	29.0	19.0	31.0

data %>% 
    tab_cells(x9,x10) %>% 
    tab_cols(g4) %>% 
    tab_stat_mean_sd_n() %>% 
    tab_last_sig_means() %>% 
    tab_pivot()

	Grupo
	1	2	3	4
	A	B	C	D
nivel de uso
Mean	47.8 B	39.4	54.2 A B D	46.9 B
Std. dev.	6.2	6.8	7.0	8.8
Unw. valid N	19.0	29.0	19.0	31.0
nivel de satisfacción
Mean	4.9 B	4.0	5.6 A B D	4.9 B
Std. dev.	0.7	0.6	0.6	0.7
Unw. valid N	19.0	29.0	19.0	31.0

data = apply_labels(data, g3 = "Grupo" )
data %>% 
    tab_cells(x8,x11,x12,x13,x14) %>% 
    tab_cols(g3) %>% 
    tab_stat_cases() %>% 
    tab_last_sig_cases() %>% 
    tab_pivot()

	Grupo
	1	2	3
tamaño de la empresa
pequeña	29.0	19.0	10.0
grande	2.0		38.0
#Chi-squared p-value	<0.05
#Total cases	31.0	19.0	48.0
especificación de las compras
especificacion	2.0		38.0
valor total	29.0	19.0	10.0
#Chi-squared p-value	<0.05
#Total cases	31.0	19.0	48.0
estructura
descentralizada	29.0	19.0
centralizada	2.0		48.0
#Chi-squared p-value	<0.05
#Total cases	31.0	19.0	48.0
tipo de industria
tipo 1	21.0	5.0	22.0
tipo 2	10.0	14.0	26.0
#Chi-squared p-value	<0.05
#Total cases	31.0	19.0	48.0
situacion de compra
nueva	6.0	2.0	24.0
recompra modificada	8.0	2.0	22.0
recompra	17.0	15.0	2.0
#Chi-squared p-value	<0.05
#Total cases	31.0	19.0	48.0

data %>% 
    tab_cells(x9,x10) %>% 
    tab_cols(g3) %>% 
    tab_stat_mean_sd_n() %>% 
    tab_last_sig_means() %>% 
    tab_pivot()

	Grupo
	1	2	3
	A	B	C
nivel de uso
Mean	46.9 C	54.2 A C	42.7
Std. dev.	8.8	7.0	7.7
Unw. valid N	31.0	19.0	48.0
nivel de satisfacción
Mean	4.9 C	5.6 A C	4.4
Std. dev.	0.7	0.6	0.8
Unw. valid N	31.0	19.0	48.0

data = apply_labels(data, g2 = "Grupo" )
data %>% 
    tab_cells(x8,x11,x12,x13,x14) %>% 
    tab_cols(g2) %>% 
    tab_stat_cases() %>% 
    tab_last_sig_cases() %>% 
    tab_pivot()

	Grupo
	1	2
tamaño de la empresa
pequeña	48.0	10.0
grande	2.0	38.0
#Chi-squared p-value	<0.05
#Total cases	50.0	48.0
especificación de las compras
especificacion	2.0	38.0
valor total	48.0	10.0
#Chi-squared p-value	<0.05
#Total cases	50.0	48.0
estructura
descentralizada	48.0
centralizada	2.0	48.0
#Chi-squared p-value	<0.05
#Total cases	50.0	48.0
tipo de industria
tipo 1	26.0	22.0
tipo 2	24.0	26.0
#Chi-squared p-value
#Total cases	50.0	48.0
situacion de compra
nueva	8.0	24.0
recompra modificada	10.0	22.0
recompra	32.0	2.0
#Chi-squared p-value	<0.05
#Total cases	50.0	48.0

data %>% 
    tab_cells(x9,x10) %>% 
    tab_cols(g2) %>% 
    tab_stat_mean_sd_n() %>% 
    tab_last_sig_means() %>% 
    tab_pivot()

	Grupo
	1	2
	A	B
nivel de uso
Mean	49.7 B	42.7
Std. dev.	8.9	7.7
Unw. valid N	50.0	48.0
nivel de satisfacción
Mean	5.2 B	4.4
Std. dev.	0.7	0.8
Unw. valid N	50.0	48.0