Clustering_vinos

Contexto

Estos datos son el resultado de un análisis químico de vinos cultivados en la misma región de Italia, pero derivados de tres cultivares diferentes.

El análisis determinó las cantidades de 13 componentes que se encuentran en cada uno de los tres cultivares.

Paso 1: Instalar paquetes y llamar librerías

#install.packages("cluster") # Análisis de agrupamiento"
library(cluster)
#install.packages("ggplot2")# Graficar
library(ggplot2)
#install.packages("data.table") #Manejo de muchos datos
library(data.table)
#install.packages("factoextra") #Gráfica optimización de número de clusters
library(factoextra)

Paso 2: Obtener los datos

df1 <- read.csv("~/Desktop/Concentración IA/wine.csv")

Paso 3: Entender los datos

summary(df1)

##     Alcohol        Malic_Acid         Ash         Ash_Alcanity  
##  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      
##  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      
##  Min.   : 278.0  
##  1st Qu.: 500.5  
##  Median : 673.5  
##  Mean   : 746.9  
##  3rd Qu.: 985.0  
##  Max.   :1680.0

str(df1)

## 'data.frame':    178 obs. of  13 variables:
##  $ Alcohol             : num  14.2 13.2 13.2 14.4 13.2 ...
##  $ Malic_Acid          : num  1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
##  $ Ash                 : num  2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
##  $ Ash_Alcanity        : num  15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...
##  $ Magnesium           : int  127 100 101 113 118 112 96 121 97 98 ...
##  $ Total_Phenols       : num  2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ...
##  $ Flavanoids          : num  3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 2.98 3.15 ...
##  $ Nonflavanoid_Phenols: num  0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ...
##  $ Proanthocyanins     : num  2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ...
##  $ Color_Intensity     : num  5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ...
##  $ Hue                 : num  1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ...
##  $ OD280               : num  3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ...
##  $ Proline             : int  1065 1050 1185 1480 735 1450 1290 1295 1045 1045 ...

Paso 4: Escalar los datos

# Sólo si los datos no están en la misma escala.
datos_escalados <- scale(df1)

Paso 5: Determinar número de grupos

# Siempre es un valor inicial "cualquiera", luegop se optimiza.
plot(df1)

grupos <- 3

Paso 6: Generar los grupos

clusters <- kmeans(df1,grupos)
clusters

## K-means clustering with 3 clusters of sizes 62, 69, 47
## 
## Cluster means:
##    Alcohol Malic_Acid      Ash Ash_Alcanity Magnesium Total_Phenols Flavanoids
## 1 12.92984   2.504032 2.408065     19.89032 103.59677      2.111129   1.584032
## 2 12.51667   2.494203 2.288551     20.82319  92.34783      2.070725   1.758406
## 3 13.80447   1.883404 2.426170     17.02340 105.51064      2.867234   3.014255
##   Nonflavanoid_Phenols Proanthocyanins Color_Intensity       Hue    OD280
## 1            0.3883871        1.503387        5.650323 0.8839677 2.365484
## 2            0.3901449        1.451884        4.086957 0.9411594 2.490725
## 3            0.2853191        1.910426        5.702553 1.0782979 3.114043
##     Proline
## 1  728.3387
## 2  458.2319
## 3 1195.1489
## 
## Clustering vector:
##   [1] 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 3 3 1 1 3 3 1 3 3 3 3 3 3 1 1
##  [38] 3 3 1 1 3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 2 1 2 2 1 2 2 1 1 1 2 2 3
##  [75] 1 2 2 2 1 2 2 1 1 2 2 2 2 2 1 1 2 2 2 2 2 1 1 2 1 2 1 2 2 2 1 2 2 2 2 1 2
## [112] 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 1 1 1 2 2 2 1 1 2 2 1 1 2 1
## [149] 1 2 2 2 2 1 1 1 2 1 1 1 2 1 2 1 1 2 1 1 1 1 2 2 1 1 1 1 1 2
## 
## Within cluster sum of squares by cluster:
## [1]  566572.5  443166.7 1360950.5
##  (between_SS / total_SS =  86.5 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

Paso 7: Graficar los grupos

fviz_cluster(clusters, data=df1)