vinos_Rconcentracion
2025-02-19
# Contexto Estos datos son el resultado de
un analisis quimico de vinos cultivados en la misma region de
Italia
Pero derivados de tres cultivares distintos
Librerias
#install.packages("Cluster") # para agrupamiento
library(cluster)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("factoextra") # visualizar cluster
library(factoextra)
#install.packages("data.table") # Conjunto de datos grande
library(data.table)
library(tidyverse)Importa la base de datos
datos <- read.csv("~/Documents/Clases/Semestre 6/R clase eeee/wine_dataset.csv")
datos## alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols
## 1 14.23 1.71 2.43 15.6 127 2.80
## 2 13.20 1.78 2.14 11.2 100 2.65
## 3 13.16 2.36 2.67 18.6 101 2.80
## 4 14.37 1.95 2.50 16.8 113 3.85
## 5 13.24 2.59 2.87 21.0 118 2.80
## 6 14.20 1.76 2.45 15.2 112 3.27
## 7 14.39 1.87 2.45 14.6 96 2.50
## 8 14.06 2.15 2.61 17.6 121 2.60
## 9 14.83 1.64 2.17 14.0 97 2.80
## 10 13.86 1.35 2.27 16.0 98 2.98
## 11 14.10 2.16 2.30 18.0 105 2.95
## 12 14.12 1.48 2.32 16.8 95 2.20
## 13 13.75 1.73 2.41 16.0 89 2.60
## 14 14.75 1.73 2.39 11.4 91 3.10
## 15 14.38 1.87 2.38 12.0 102 3.30
## 16 13.63 1.81 2.70 17.2 112 2.85
## 17 14.30 1.92 2.72 20.0 120 2.80
## 18 13.83 1.57 2.62 20.0 115 2.95
## 19 14.19 1.59 2.48 16.5 108 3.30
## 20 13.64 3.10 2.56 15.2 116 2.70
## 21 14.06 1.63 2.28 16.0 126 3.00
## 22 12.93 3.80 2.65 18.6 102 2.41
## 23 13.71 1.86 2.36 16.6 101 2.61
## 24 12.85 1.60 2.52 17.8 95 2.48
## 25 13.50 1.81 2.61 20.0 96 2.53
## 26 13.05 2.05 3.22 25.0 124 2.63
## 27 13.39 1.77 2.62 16.1 93 2.85
## 28 13.30 1.72 2.14 17.0 94 2.40
## 29 13.87 1.90 2.80 19.4 107 2.95
## 30 14.02 1.68 2.21 16.0 96 2.65
## 31 13.73 1.50 2.70 22.5 101 3.00
## 32 13.58 1.66 2.36 19.1 106 2.86
## 33 13.68 1.83 2.36 17.2 104 2.42
## 34 13.76 1.53 2.70 19.5 132 2.95
## 35 13.51 1.80 2.65 19.0 110 2.35
## 36 13.48 1.81 2.41 20.5 100 2.70
## 37 13.28 1.64 2.84 15.5 110 2.60
## 38 13.05 1.65 2.55 18.0 98 2.45
## 39 13.07 1.50 2.10 15.5 98 2.40
## 40 14.22 3.99 2.51 13.2 128 3.00
## 41 13.56 1.71 2.31 16.2 117 3.15
## 42 13.41 3.84 2.12 18.8 90 2.45
## 43 13.88 1.89 2.59 15.0 101 3.25
## 44 13.24 3.98 2.29 17.5 103 2.64
## 45 13.05 1.77 2.10 17.0 107 3.00
## 46 14.21 4.04 2.44 18.9 111 2.85
## 47 14.38 3.59 2.28 16.0 102 3.25
## 48 13.90 1.68 2.12 16.0 101 3.10
## 49 14.10 2.02 2.40 18.8 103 2.75
## 50 13.94 1.73 2.27 17.4 108 2.88
## 51 13.05 1.73 2.04 12.4 92 2.72
## 52 13.83 1.65 2.60 17.2 94 2.45
## 53 13.82 1.75 2.42 14.0 111 3.88
## 54 13.77 1.90 2.68 17.1 115 3.00
## 55 13.74 1.67 2.25 16.4 118 2.60
## 56 13.56 1.73 2.46 20.5 116 2.96
## 57 14.22 1.70 2.30 16.3 118 3.20
## 58 13.29 1.97 2.68 16.8 102 3.00
## 59 13.72 1.43 2.50 16.7 108 3.40
## 60 12.37 0.94 1.36 10.6 88 1.98
## 61 12.33 1.10 2.28 16.0 101 2.05
## 62 12.64 1.36 2.02 16.8 100 2.02
## 63 13.67 1.25 1.92 18.0 94 2.10
## 64 12.37 1.13 2.16 19.0 87 3.50
## 65 12.17 1.45 2.53 19.0 104 1.89
## 66 12.37 1.21 2.56 18.1 98 2.42
## 67 13.11 1.01 1.70 15.0 78 2.98
## 68 12.37 1.17 1.92 19.6 78 2.11
## 69 13.34 0.94 2.36 17.0 110 2.53
## 70 12.21 1.19 1.75 16.8 151 1.85
## 71 12.29 1.61 2.21 20.4 103 1.10
## 72 13.86 1.51 2.67 25.0 86 2.95
## 73 13.49 1.66 2.24 24.0 87 1.88
## 74 12.99 1.67 2.60 30.0 139 3.30
## 75 11.96 1.09 2.30 21.0 101 3.38
## 76 11.66 1.88 1.92 16.0 97 1.61
## 77 13.03 0.90 1.71 16.0 86 1.95
## 78 11.84 2.89 2.23 18.0 112 1.72
## 79 12.33 0.99 1.95 14.8 136 1.90
## 80 12.70 3.87 2.40 23.0 101 2.83
## 81 12.00 0.92 2.00 19.0 86 2.42
## 82 12.72 1.81 2.20 18.8 86 2.20
## 83 12.08 1.13 2.51 24.0 78 2.00
## 84 13.05 3.86 2.32 22.5 85 1.65
## 85 11.84 0.89 2.58 18.0 94 2.20
## 86 12.67 0.98 2.24 18.0 99 2.20
## 87 12.16 1.61 2.31 22.8 90 1.78
## 88 11.65 1.67 2.62 26.0 88 1.92
## 89 11.64 2.06 2.46 21.6 84 1.95
## 90 12.08 1.33 2.30 23.6 70 2.20
## 91 12.08 1.83 2.32 18.5 81 1.60
## 92 12.00 1.51 2.42 22.0 86 1.45
## 93 12.69 1.53 2.26 20.7 80 1.38
## 94 12.29 2.83 2.22 18.0 88 2.45
## 95 11.62 1.99 2.28 18.0 98 3.02
## 96 12.47 1.52 2.20 19.0 162 2.50
## 97 11.81 2.12 2.74 21.5 134 1.60
## 98 12.29 1.41 1.98 16.0 85 2.55
## 99 12.37 1.07 2.10 18.5 88 3.52
## 100 12.29 3.17 2.21 18.0 88 2.85
## 101 12.08 2.08 1.70 17.5 97 2.23
## 102 12.60 1.34 1.90 18.5 88 1.45
## 103 12.34 2.45 2.46 21.0 98 2.56
## 104 11.82 1.72 1.88 19.5 86 2.50
## 105 12.51 1.73 1.98 20.5 85 2.20
## 106 12.42 2.55 2.27 22.0 90 1.68
## 107 12.25 1.73 2.12 19.0 80 1.65
## 108 12.72 1.75 2.28 22.5 84 1.38
## 109 12.22 1.29 1.94 19.0 92 2.36
## 110 11.61 1.35 2.70 20.0 94 2.74
## 111 11.46 3.74 1.82 19.5 107 3.18
## 112 12.52 2.43 2.17 21.0 88 2.55
## 113 11.76 2.68 2.92 20.0 103 1.75
## 114 11.41 0.74 2.50 21.0 88 2.48
## 115 12.08 1.39 2.50 22.5 84 2.56
## 116 11.03 1.51 2.20 21.5 85 2.46
## 117 11.82 1.47 1.99 20.8 86 1.98
## 118 12.42 1.61 2.19 22.5 108 2.00
## 119 12.77 3.43 1.98 16.0 80 1.63
## 120 12.00 3.43 2.00 19.0 87 2.00
## 121 11.45 2.40 2.42 20.0 96 2.90
## 122 11.56 2.05 3.23 28.5 119 3.18
## 123 12.42 4.43 2.73 26.5 102 2.20
## 124 13.05 5.80 2.13 21.5 86 2.62
## 125 11.87 4.31 2.39 21.0 82 2.86
## 126 12.07 2.16 2.17 21.0 85 2.60
## 127 12.43 1.53 2.29 21.5 86 2.74
## 128 11.79 2.13 2.78 28.5 92 2.13
## 129 12.37 1.63 2.30 24.5 88 2.22
## 130 12.04 4.30 2.38 22.0 80 2.10
## 131 12.86 1.35 2.32 18.0 122 1.51
## 132 12.88 2.99 2.40 20.0 104 1.30
## 133 12.81 2.31 2.40 24.0 98 1.15
## 134 12.70 3.55 2.36 21.5 106 1.70
## 135 12.51 1.24 2.25 17.5 85 2.00
## 136 12.60 2.46 2.20 18.5 94 1.62
## 137 12.25 4.72 2.54 21.0 89 1.38
## 138 12.53 5.51 2.64 25.0 96 1.79
## 139 13.49 3.59 2.19 19.5 88 1.62
## 140 12.84 2.96 2.61 24.0 101 2.32
## 141 12.93 2.81 2.70 21.0 96 1.54
## 142 13.36 2.56 2.35 20.0 89 1.40
## 143 13.52 3.17 2.72 23.5 97 1.55
## 144 13.62 4.95 2.35 20.0 92 2.00
## 145 12.25 3.88 2.20 18.5 112 1.38
## 146 13.16 3.57 2.15 21.0 102 1.50
## 147 13.88 5.04 2.23 20.0 80 0.98
## 148 12.87 4.61 2.48 21.5 86 1.70
## 149 13.32 3.24 2.38 21.5 92 1.93
## 150 13.08 3.90 2.36 21.5 113 1.41
## 151 13.50 3.12 2.62 24.0 123 1.40
## 152 12.79 2.67 2.48 22.0 112 1.48
## 153 13.11 1.90 2.75 25.5 116 2.20
## 154 13.23 3.30 2.28 18.5 98 1.80
## 155 12.58 1.29 2.10 20.0 103 1.48
## 156 13.17 5.19 2.32 22.0 93 1.74
## 157 13.84 4.12 2.38 19.5 89 1.80
## 158 12.45 3.03 2.64 27.0 97 1.90
## 159 14.34 1.68 2.70 25.0 98 2.80
## 160 13.48 1.67 2.64 22.5 89 2.60
## 161 12.36 3.83 2.38 21.0 88 2.30
## 162 13.69 3.26 2.54 20.0 107 1.83
## 163 12.85 3.27 2.58 22.0 106 1.65
## 164 12.96 3.45 2.35 18.5 106 1.39
## 165 13.78 2.76 2.30 22.0 90 1.35
## 166 13.73 4.36 2.26 22.5 88 1.28
## 167 13.45 3.70 2.60 23.0 111 1.70
## 168 12.82 3.37 2.30 19.5 88 1.48
## 169 13.58 2.58 2.69 24.5 105 1.55
## 170 13.40 4.60 2.86 25.0 112 1.98
## 171 12.20 3.03 2.32 19.0 96 1.25
## 172 12.77 2.39 2.28 19.5 86 1.39
## 173 14.16 2.51 2.48 20.0 91 1.68
## 174 13.71 5.65 2.45 20.5 95 1.68
## 175 13.40 3.91 2.48 23.0 102 1.80
## 176 13.27 4.28 2.26 20.0 120 1.59
## 177 13.17 2.59 2.37 20.0 120 1.65
## 178 14.13 4.10 2.74 24.5 96 2.05
## flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue
## 1 3.06 0.28 2.29 5.640000 1.040
## 2 2.76 0.26 1.28 4.380000 1.050
## 3 3.24 0.30 2.81 5.680000 1.030
## 4 3.49 0.24 2.18 7.800000 0.860
## 5 2.69 0.39 1.82 4.320000 1.040
## 6 3.39 0.34 1.97 6.750000 1.050
## 7 2.52 0.30 1.98 5.250000 1.020
## 8 2.51 0.31 1.25 5.050000 1.060
## 9 2.98 0.29 1.98 5.200000 1.080
## 10 3.15 0.22 1.85 7.220000 1.010
## 11 3.32 0.22 2.38 5.750000 1.250
## 12 2.43 0.26 1.57 5.000000 1.170
## 13 2.76 0.29 1.81 5.600000 1.150
## 14 3.69 0.43 2.81 5.400000 1.250
## 15 3.64 0.29 2.96 7.500000 1.200
## 16 2.91 0.30 1.46 7.300000 1.280
## 17 3.14 0.33 1.97 6.200000 1.070
## 18 3.40 0.40 1.72 6.600000 1.130
## 19 3.93 0.32 1.86 8.700000 1.230
## 20 3.03 0.17 1.66 5.100000 0.960
## 21 3.17 0.24 2.10 5.650000 1.090
## 22 2.41 0.25 1.98 4.500000 1.030
## 23 2.88 0.27 1.69 3.800000 1.110
## 24 2.37 0.26 1.46 3.930000 1.090
## 25 2.61 0.28 1.66 3.520000 1.120
## 26 2.68 0.47 1.92 3.580000 1.130
## 27 2.94 0.34 1.45 4.800000 0.920
## 28 2.19 0.27 1.35 3.950000 1.020
## 29 2.97 0.37 1.76 4.500000 1.250
## 30 2.33 0.26 1.98 4.700000 1.040
## 31 3.25 0.29 2.38 5.700000 1.190
## 32 3.19 0.22 1.95 6.900000 1.090
## 33 2.69 0.42 1.97 3.840000 1.230
## 34 2.74 0.50 1.35 5.400000 1.250
## 35 2.53 0.29 1.54 4.200000 1.100
## 36 2.98 0.26 1.86 5.100000 1.040
## 37 2.68 0.34 1.36 4.600000 1.090
## 38 2.43 0.29 1.44 4.250000 1.120
## 39 2.64 0.28 1.37 3.700000 1.180
## 40 3.04 0.20 2.08 5.100000 0.890
## 41 3.29 0.34 2.34 6.130000 0.950
## 42 2.68 0.27 1.48 4.280000 0.910
## 43 3.56 0.17 1.70 5.430000 0.880
## 44 2.63 0.32 1.66 4.360000 0.820
## 45 3.00 0.28 2.03 5.040000 0.880
## 46 2.65 0.30 1.25 5.240000 0.870
## 47 3.17 0.27 2.19 4.900000 1.040
## 48 3.39 0.21 2.14 6.100000 0.910
## 49 2.92 0.32 2.38 6.200000 1.070
## 50 3.54 0.32 2.08 8.900000 1.120
## 51 3.27 0.17 2.91 7.200000 1.120
## 52 2.99 0.22 2.29 5.600000 1.240
## 53 3.74 0.32 1.87 7.050000 1.010
## 54 2.79 0.39 1.68 6.300000 1.130
## 55 2.90 0.21 1.62 5.850000 0.920
## 56 2.78 0.20 2.45 6.250000 0.980
## 57 3.00 0.26 2.03 6.380000 0.940
## 58 3.23 0.31 1.66 6.000000 1.070
## 59 3.67 0.19 2.04 6.800000 0.890
## 60 0.57 0.28 0.42 1.950000 1.050
## 61 1.09 0.63 0.41 3.270000 1.250
## 62 1.41 0.53 0.62 5.750000 0.980
## 63 1.79 0.32 0.73 3.800000 1.230
## 64 3.10 0.19 1.87 4.450000 1.220
## 65 1.75 0.45 1.03 2.950000 1.450
## 66 2.65 0.37 2.08 4.600000 1.190
## 67 3.18 0.26 2.28 5.300000 1.120
## 68 2.00 0.27 1.04 4.680000 1.120
## 69 1.30 0.55 0.42 3.170000 1.020
## 70 1.28 0.14 2.50 2.850000 1.280
## 71 1.02 0.37 1.46 3.050000 0.906
## 72 2.86 0.21 1.87 3.380000 1.360
## 73 1.84 0.27 1.03 3.740000 0.980
## 74 2.89 0.21 1.96 3.350000 1.310
## 75 2.14 0.13 1.65 3.210000 0.990
## 76 1.57 0.34 1.15 3.800000 1.230
## 77 2.03 0.24 1.46 4.600000 1.190
## 78 1.32 0.43 0.95 2.650000 0.960
## 79 1.85 0.35 2.76 3.400000 1.060
## 80 2.55 0.43 1.95 2.570000 1.190
## 81 2.26 0.30 1.43 2.500000 1.380
## 82 2.53 0.26 1.77 3.900000 1.160
## 83 1.58 0.40 1.40 2.200000 1.310
## 84 1.59 0.61 1.62 4.800000 0.840
## 85 2.21 0.22 2.35 3.050000 0.790
## 86 1.94 0.30 1.46 2.620000 1.230
## 87 1.69 0.43 1.56 2.450000 1.330
## 88 1.61 0.40 1.34 2.600000 1.360
## 89 1.69 0.48 1.35 2.800000 1.000
## 90 1.59 0.42 1.38 1.740000 1.070
## 91 1.50 0.52 1.64 2.400000 1.080
## 92 1.25 0.50 1.63 3.600000 1.050
## 93 1.46 0.58 1.62 3.050000 0.960
## 94 2.25 0.25 1.99 2.150000 1.150
## 95 2.26 0.17 1.35 3.250000 1.160
## 96 2.27 0.32 3.28 2.600000 1.160
## 97 0.99 0.14 1.56 2.500000 0.950
## 98 2.50 0.29 1.77 2.900000 1.230
## 99 3.75 0.24 1.95 4.500000 1.040
## 100 2.99 0.45 2.81 2.300000 1.420
## 101 2.17 0.26 1.40 3.300000 1.270
## 102 1.36 0.29 1.35 2.450000 1.040
## 103 2.11 0.34 1.31 2.800000 0.800
## 104 1.64 0.37 1.42 2.060000 0.940
## 105 1.92 0.32 1.48 2.940000 1.040
## 106 1.84 0.66 1.42 2.700000 0.860
## 107 2.03 0.37 1.63 3.400000 1.000
## 108 1.76 0.48 1.63 3.300000 0.880
## 109 2.04 0.39 2.08 2.700000 0.860
## 110 2.92 0.29 2.49 2.650000 0.960
## 111 2.58 0.24 3.58 2.900000 0.750
## 112 2.27 0.26 1.22 2.000000 0.900
## 113 2.03 0.60 1.05 3.800000 1.230
## 114 2.01 0.42 1.44 3.080000 1.100
## 115 2.29 0.43 1.04 2.900000 0.930
## 116 2.17 0.52 2.01 1.900000 1.710
## 117 1.60 0.30 1.53 1.950000 0.950
## 118 2.09 0.34 1.61 2.060000 1.060
## 119 1.25 0.43 0.83 3.400000 0.700
## 120 1.64 0.37 1.87 1.280000 0.930
## 121 2.79 0.32 1.83 3.250000 0.800
## 122 5.08 0.47 1.87 6.000000 0.930
## 123 2.13 0.43 1.71 2.080000 0.920
## 124 2.65 0.30 2.01 2.600000 0.730
## 125 3.03 0.21 2.91 2.800000 0.750
## 126 2.65 0.37 1.35 2.760000 0.860
## 127 3.15 0.39 1.77 3.940000 0.690
## 128 2.24 0.58 1.76 3.000000 0.970
## 129 2.45 0.40 1.90 2.120000 0.890
## 130 1.75 0.42 1.35 2.600000 0.790
## 131 1.25 0.21 0.94 4.100000 0.760
## 132 1.22 0.24 0.83 5.400000 0.740
## 133 1.09 0.27 0.83 5.700000 0.660
## 134 1.20 0.17 0.84 5.000000 0.780
## 135 0.58 0.60 1.25 5.450000 0.750
## 136 0.66 0.63 0.94 7.100000 0.730
## 137 0.47 0.53 0.80 3.850000 0.750
## 138 0.60 0.63 1.10 5.000000 0.820
## 139 0.48 0.58 0.88 5.700000 0.810
## 140 0.60 0.53 0.81 4.920000 0.890
## 141 0.50 0.53 0.75 4.600000 0.770
## 142 0.50 0.37 0.64 5.600000 0.700
## 143 0.52 0.50 0.55 4.350000 0.890
## 144 0.80 0.47 1.02 4.400000 0.910
## 145 0.78 0.29 1.14 8.210000 0.650
## 146 0.55 0.43 1.30 4.000000 0.600
## 147 0.34 0.40 0.68 4.900000 0.580
## 148 0.65 0.47 0.86 7.650000 0.540
## 149 0.76 0.45 1.25 8.420000 0.550
## 150 1.39 0.34 1.14 9.400000 0.570
## 151 1.57 0.22 1.25 8.600000 0.590
## 152 1.36 0.24 1.26 10.800000 0.480
## 153 1.28 0.26 1.56 7.100000 0.610
## 154 0.83 0.61 1.87 10.520000 0.560
## 155 0.58 0.53 1.40 7.600000 0.580
## 156 0.63 0.61 1.55 7.900000 0.600
## 157 0.83 0.48 1.56 9.010000 0.570
## 158 0.58 0.63 1.14 7.500000 0.670
## 159 1.31 0.53 2.70 13.000000 0.570
## 160 1.10 0.52 2.29 11.750000 0.570
## 161 0.92 0.50 1.04 7.650000 0.560
## 162 0.56 0.50 0.80 5.880000 0.960
## 163 0.60 0.60 0.96 5.580000 0.870
## 164 0.70 0.40 0.94 5.280000 0.680
## 165 0.68 0.41 1.03 9.580000 0.700
## 166 0.47 0.52 1.15 6.620000 0.780
## 167 0.92 0.43 1.46 10.680000 0.850
## 168 0.66 0.40 0.97 10.260000 0.720
## 169 0.84 0.39 1.54 8.660000 0.740
## 170 0.96 0.27 1.11 8.500000 0.670
## 171 0.49 0.40 0.73 5.500000 0.660
## 172 0.51 0.48 0.64 9.899999 0.570
## 173 0.70 0.44 1.24 9.700000 0.620
## 174 0.61 0.52 1.06 7.700000 0.640
## 175 0.75 0.43 1.41 7.300000 0.700
## 176 0.69 0.43 1.35 10.200000 0.590
## 177 0.68 0.53 1.46 9.300000 0.600
## 178 0.76 0.56 1.35 9.200000 0.610
## od280.od315_of_diluted_wines proline target
## 1 3.92 1065 0
## 2 3.40 1050 0
## 3 3.17 1185 0
## 4 3.45 1480 0
## 5 2.93 735 0
## 6 2.85 1450 0
## 7 3.58 1290 0
## 8 3.58 1295 0
## 9 2.85 1045 0
## 10 3.55 1045 0
## 11 3.17 1510 0
## 12 2.82 1280 0
## 13 2.90 1320 0
## 14 2.73 1150 0
## 15 3.00 1547 0
## 16 2.88 1310 0
## 17 2.65 1280 0
## 18 2.57 1130 0
## 19 2.82 1680 0
## 20 3.36 845 0
## 21 3.71 780 0
## 22 3.52 770 0
## 23 4.00 1035 0
## 24 3.63 1015 0
## 25 3.82 845 0
## 26 3.20 830 0
## 27 3.22 1195 0
## 28 2.77 1285 0
## 29 3.40 915 0
## 30 3.59 1035 0
## 31 2.71 1285 0
## 32 2.88 1515 0
## 33 2.87 990 0
## 34 3.00 1235 0
## 35 2.87 1095 0
## 36 3.47 920 0
## 37 2.78 880 0
## 38 2.51 1105 0
## 39 2.69 1020 0
## 40 3.53 760 0
## 41 3.38 795 0
## 42 3.00 1035 0
## 43 3.56 1095 0
## 44 3.00 680 0
## 45 3.35 885 0
## 46 3.33 1080 0
## 47 3.44 1065 0
## 48 3.33 985 0
## 49 2.75 1060 0
## 50 3.10 1260 0
## 51 2.91 1150 0
## 52 3.37 1265 0
## 53 3.26 1190 0
## 54 2.93 1375 0
## 55 3.20 1060 0
## 56 3.03 1120 0
## 57 3.31 970 0
## 58 2.84 1270 0
## 59 2.87 1285 0
## 60 1.82 520 1
## 61 1.67 680 1
## 62 1.59 450 1
## 63 2.46 630 1
## 64 2.87 420 1
## 65 2.23 355 1
## 66 2.30 678 1
## 67 3.18 502 1
## 68 3.48 510 1
## 69 1.93 750 1
## 70 3.07 718 1
## 71 1.82 870 1
## 72 3.16 410 1
## 73 2.78 472 1
## 74 3.50 985 1
## 75 3.13 886 1
## 76 2.14 428 1
## 77 2.48 392 1
## 78 2.52 500 1
## 79 2.31 750 1
## 80 3.13 463 1
## 81 3.12 278 1
## 82 3.14 714 1
## 83 2.72 630 1
## 84 2.01 515 1
## 85 3.08 520 1
## 86 3.16 450 1
## 87 2.26 495 1
## 88 3.21 562 1
## 89 2.75 680 1
## 90 3.21 625 1
## 91 2.27 480 1
## 92 2.65 450 1
## 93 2.06 495 1
## 94 3.30 290 1
## 95 2.96 345 1
## 96 2.63 937 1
## 97 2.26 625 1
## 98 2.74 428 1
## 99 2.77 660 1
## 100 2.83 406 1
## 101 2.96 710 1
## 102 2.77 562 1
## 103 3.38 438 1
## 104 2.44 415 1
## 105 3.57 672 1
## 106 3.30 315 1
## 107 3.17 510 1
## 108 2.42 488 1
## 109 3.02 312 1
## 110 3.26 680 1
## 111 2.81 562 1
## 112 2.78 325 1
## 113 2.50 607 1
## 114 2.31 434 1
## 115 3.19 385 1
## 116 2.87 407 1
## 117 3.33 495 1
## 118 2.96 345 1
## 119 2.12 372 1
## 120 3.05 564 1
## 121 3.39 625 1
## 122 3.69 465 1
## 123 3.12 365 1
## 124 3.10 380 1
## 125 3.64 380 1
## 126 3.28 378 1
## 127 2.84 352 1
## 128 2.44 466 1
## 129 2.78 342 1
## 130 2.57 580 1
## 131 1.29 630 2
## 132 1.42 530 2
## 133 1.36 560 2
## 134 1.29 600 2
## 135 1.51 650 2
## 136 1.58 695 2
## 137 1.27 720 2
## 138 1.69 515 2
## 139 1.82 580 2
## 140 2.15 590 2
## 141 2.31 600 2
## 142 2.47 780 2
## 143 2.06 520 2
## 144 2.05 550 2
## 145 2.00 855 2
## 146 1.68 830 2
## 147 1.33 415 2
## 148 1.86 625 2
## 149 1.62 650 2
## 150 1.33 550 2
## 151 1.30 500 2
## 152 1.47 480 2
## 153 1.33 425 2
## 154 1.51 675 2
## 155 1.55 640 2
## 156 1.48 725 2
## 157 1.64 480 2
## 158 1.73 880 2
## 159 1.96 660 2
## 160 1.78 620 2
## 161 1.58 520 2
## 162 1.82 680 2
## 163 2.11 570 2
## 164 1.75 675 2
## 165 1.68 615 2
## 166 1.75 520 2
## 167 1.56 695 2
## 168 1.75 685 2
## 169 1.80 750 2
## 170 1.92 630 2
## 171 1.83 510 2
## 172 1.63 470 2
## 173 1.71 660 2
## 174 1.74 740 2
## 175 1.56 750 2
## 176 1.56 835 2
## 177 1.62 840 2
## 178 1.60 560 2Entender la base de datos/span>
summary(datos)## alcohol malic_acid ash alcalinity_of_ash
## Min. :11.03 Min. :0.740 Min. :1.360 Min. :10.60
## 1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210 1st Qu.:17.20
## Median :13.05 Median :1.865 Median :2.360 Median :19.50
## Mean :13.00 Mean :2.336 Mean :2.367 Mean :19.49
## 3rd Qu.:13.68 3rd Qu.:3.083 3rd Qu.:2.558 3rd Qu.:21.50
## Max. :14.83 Max. :5.800 Max. :3.230 Max. :30.00
## magnesium total_phenols flavanoids nonflavanoid_phenols
## Min. : 70.00 Min. :0.980 Min. :0.340 Min. :0.1300
## 1st Qu.: 88.00 1st Qu.:1.742 1st Qu.:1.205 1st Qu.:0.2700
## Median : 98.00 Median :2.355 Median :2.135 Median :0.3400
## Mean : 99.74 Mean :2.295 Mean :2.029 Mean :0.3619
## 3rd Qu.:107.00 3rd Qu.:2.800 3rd Qu.:2.875 3rd Qu.:0.4375
## Max. :162.00 Max. :3.880 Max. :5.080 Max. :0.6600
## proanthocyanins color_intensity hue od280.od315_of_diluted_wines
## Min. :0.410 Min. : 1.280 Min. :0.4800 Min. :1.270
## 1st Qu.:1.250 1st Qu.: 3.220 1st Qu.:0.7825 1st Qu.:1.938
## Median :1.555 Median : 4.690 Median :0.9650 Median :2.780
## Mean :1.591 Mean : 5.058 Mean :0.9574 Mean :2.612
## 3rd Qu.:1.950 3rd Qu.: 6.200 3rd Qu.:1.1200 3rd Qu.:3.170
## Max. :3.580 Max. :13.000 Max. :1.7100 Max. :4.000
## proline target
## Min. : 278.0 Min. :0.0000
## 1st Qu.: 500.5 1st Qu.:0.0000
## Median : 673.5 Median :1.0000
## Mean : 746.9 Mean :0.9382
## 3rd Qu.: 985.0 3rd Qu.:2.0000
## Max. :1680.0 Max. :2.0000Escalar la base de datos
datos_escalados <- datos
datos_escalados <- subset(datos_escalados, select = -target)
datos_escalados <- scale(datos)Escalar la base de datos
grupos <- 3 #Inicio con cualquier valor, luego verifica
segmentos <- kmeans(datos_escalados,grupos)Escalar la base de datos
asignacion <- cbind(datos, cluster = segmentos$cluster)Escalar la base de datos
fviz_cluster(segmentos, data = datos)
Escalar la base de datos
set.seed(123)
optimizacion <- clusGap(datos_escalados, FUN = kmeans, nstart = 1, K.max=10)
plot(optimizacion, xlab= "Numero de cluster k")
Escalar la base de datos
promedio <- aggregate(asignacion, by=list(asignacion$cluster), FUN=mean)
promedio## Group.1 alcohol malic_acid ash alcalinity_of_ash magnesium
## 1 1 13.15163 3.344490 2.434694 21.43878 99.02041
## 2 2 13.71148 1.997049 2.453770 17.28197 107.78689
## 3 3 12.25412 1.914265 2.239118 20.07941 93.04412
## total_phenols flavanoids nonflavanoid_phenols proanthocyanins color_intensity
## 1 1.678163 0.7979592 0.4508163 1.163061 7.343265
## 2 2.842131 2.9691803 0.2891803 1.922951 5.444590
## 3 2.248971 2.0733824 0.3629412 1.601324 3.064706
## hue od280.od315_of_diluted_wines proline target cluster
## 1 0.6859184 1.690204 627.5510 1.97959184 1
## 2 1.0677049 3.154754 1110.6393 0.03278689 2
## 3 1.0542059 2.788529 506.5882 1.00000000 3table(asignacion$cluster)##
## 1 2 3
## 49 61 68Actividad mapa de México
#install.packages("sf") #Analisis de datos espaciales
library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE#install.packages("rnaturalearth") #Limites geograficos
library(rnaturalearth)
#install.packages("rnaturalearthdata") #Datos geograficos
library(rnaturalearthdata)##
## Attaching package: 'rnaturalearthdata'## The following object is masked from 'package:rnaturalearth':
##
## countries110#install.packages("devtools") #instalar paquetes de fuentes externas
library(devtools)## Loading required package: usethisdevtools::install_github("ropensci/rnaturalearthhires")## Skipping install of 'rnaturalearthhires' from a github remote, the SHA1 (153b0ea5) has not changed since last install.
## Use `force = TRUE` to force installationdatosmex <- read.csv("~/Documents/Clases/Semestre 6/R clase eeee/mexico2024.csv")
datosmex## Estado Población PIB.per.cápita Esperanza.de.vida
## 1 Aguascalientes 1.4 142703 75.4
## 2 Baja California 3.7 152317 76.2
## 3 Baja California Sur 0.8 151590 76.5
## 4 Campeche 0.9 481697 75.1
## 5 Chiapas 5.5 44387 74.0
## 6 Chihuahua 3.8 141532 75.8
## 7 Distrito Federal 9.2 316761 76.2
## 8 Coahuila 3.2 166389 75.9
## 9 Colima 0.7 128953 75.6
## 10 Durango 1.8 101500 75.2
## 11 Guanajuato 6.2 104393 74.1
## 12 Guerrero 3.5 59922 73.5
## 13 Hidalgo 3.1 78891 74.8
## 14 Jalisco 8.3 133857 75.2
## 15 México 17.4 85184 74.5
## 16 Michoacán 4.8 82199 74.3
## 17 Morelos 2.0 87849 74.9
## 18 Nayarit 1.3 83135 74.7
## 19 Nuevo León 5.8 225862 75.8
## 20 Oaxaca 4.1 57239 73.8
## 21 Puebla 6.6 80424 73.9
## 22 Querétaro 2.3 160301 75.5
## 23 Quintana Roo 1.9 128903 75.6
## 24 San Luis Potosà 2.8 119143 74.8
## 25 Sinaloa 3.0 117150 75.5
## 26 Sonora 3.0 179296 75.5
## 27 Tabasco 2.4 180564 74.6
## 28 Tamaulipas 3.5 129590 75.3
## 29 Tlaxcala 1.3 65899 74.5
## 30 Veracruz 8.1 86373 73.8
## 31 Yucatán 2.3 107364 74.9
## 32 Zacatecas 1.6 87211 74.6
## Tasa.de.pobreza Tasa.de.alfabetización
## 1 27.2 98.0
## 2 13.4 98.5
## 3 13.3 98.6
## 4 40.9 96.5
## 5 67.4 88.7
## 6 26.3 97.5
## 7 28.4 99.0
## 8 20.2 98.2
## 9 18.0 98.3
## 10 32.7 96.8
## 11 43.4 95.0
## 12 60.4 86.5
## 13 43.5 94.5
## 14 29.8 97.8
## 15 42.7 97.1
## 16 46.0 94.0
## 17 45.0 94.8
## 18 35.0 95.5
## 19 16.0 98.5
## 20 58.4 89.0
## 21 59.4 94.2
## 22 27.6 98.1
## 23 30.2 97.0
## 24 35.5 95.8
## 25 30.0 96.8
## 26 34.5 96.8
## 27 50.9 94.0
## 28 30.9 96.5
## 29 48.4 94.5
## 30 58.1 93.5
## 31 41.1 95.4
## 32 46.8 94.0summary(datosmex)## Estado Población PIB.per.cápita Esperanza.de.vida
## Length:32 Min. : 0.700 Min. : 44387 Min. :73.50
## Class :character 1st Qu.: 1.875 1st Qu.: 84672 1st Qu.:74.50
## Mode :character Median : 3.050 Median :118146 Median :75.00
## Mean : 3.947 Mean :133393 Mean :75.00
## 3rd Qu.: 4.975 3rd Qu.:151772 3rd Qu.:75.53
## Max. :17.400 Max. :481697 Max. :76.50
## Tasa.de.pobreza Tasa.de.alfabetización
## Min. :13.30 Min. :86.50
## 1st Qu.:28.20 1st Qu.:94.42
## Median :35.25 Median :96.50
## Mean :37.54 Mean :95.61
## 3rd Qu.:46.20 3rd Qu.:97.85
## Max. :67.40 Max. :99.00datosmex_escalados <- datosmex
datosmex_escalados <- subset(datosmex_escalados, select = -Estado)
datosmex_escalados <- scale(datosmex_escalados)grupos <- 4
segmentos <- kmeans(datosmex_escalados,grupos)asignacion <- data.frame(Estado = datosmex$Estado, cluster = segmentos$cluster)fviz_cluster(segmentos, data = datosmex_escalados)
set.seed(123)
optimizacion <- clusGap(datosmex_escalados, FUN = kmeans, nstart = 1, K.max=10)
plot(optimizacion, xlab= "Numero de cluster k")
promedio <- aggregate(asignacion, by=list(asignacion$cluster), FUN=mean)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NApromedio## Group.1 Estado cluster
## 1 1 NA 1
## 2 2 NA 2
## 3 3 NA 3
## 4 4 NA 4table(asignacion$cluster)##
## 1 2 3 4
## 2 10 6 14mexico <- rnaturalearth::ne_states(country = "Mexico", returnclass = "sf")
mexico <- mexico %>%
rename(Estado = name)
mexico_clusters <- left_join(mexico, asignacion, by = "Estado")ggplot(mexico_clusters) +
geom_sf(aes(fill = factor(cluster)), color = "black") +
scale_fill_manual(values = c("green", "yellow", "red", "purple")) +
labs(title = "Clusters de Población por Estado en México") +
theme_minimal()
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