Wines Clusters
David Dominguez - A01570975
2024-03-03
Contexto
Estos datos son el resiltado de una análisis químico de vinos cultivados en la misma región de Italia derivados de tres cultivares diferentes.
El análisis determinó las cantidades de 13 componentes que se encuentran en cada uno de los tres tipos de vinos.
Paso 1. Instalar paquetes y llamar librerías
#install.packages("data.table")
library(cluster) # Librería para la realización de clusters
library(ggplot2) # Librería para la visualización de datos
library(data.table) #
library(factoextra) # librería para graficar cluster## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)Paso 2. Obtener los datos
df = read_csv("wine.csv")
# Conocer la naturalidad de los datos
summary(df)## 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. :1680.0Paso 3. Cantidad de grupos
df_escalada = scale(df)
grupos = 3 Paso 4. Generar los segmentos
segmentos = kmeans(df_escalada,grupos)
segmentos## K-means clustering with 3 clusters of sizes 62, 51, 65
##
## Cluster means:
## Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols
## 1 0.8328826 -0.3029551 0.3636801 -0.6084749 0.57596208 0.88274724
## 2 0.1644436 0.8690954 0.1863726 0.5228924 -0.07526047 -0.97657548
## 3 -0.9234669 -0.3929331 -0.4931257 0.1701220 -0.49032869 -0.07576891
## Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue
## 1 0.97506900 -0.56050853 0.57865427 0.1705823 0.4726504
## 2 -1.21182921 0.72402116 -0.77751312 0.9388902 -1.1615122
## 3 0.02075402 -0.03343924 0.05810161 -0.8993770 0.4605046
## OD280 Proline
## 1 0.7770551 1.1220202
## 2 -1.2887761 -0.4059428
## 3 0.2700025 -0.7517257
##
## Clustering vector:
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 2 3 3 3 3 3 3 3 3 3 3 3 1
## [75] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 2 3 3 1 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
##
## Within cluster sum of squares by cluster:
## [1] 385.6983 326.3537 558.6971
## (between_SS / total_SS = 44.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"Paso 5. Asignar el grupo al que pertenece cada observación
asignacion = cbind(df, cluster = segmentos$cluster)
#asignacionPaso 6. Graficar los clusters
fviz_cluster(segmentos, data = df_escalada)
Paso 7. Optimizar la cantidad de grupos
# La cantidad optima de grupos corresponde al punto más alto de la siguiente gráfica
set.seed(123)
optimizacion = clusGap(df_escalada, FUN = kmeans, nstart = 1, K.max = 10)
plot(optimizacion, xlab="Número de cluster k")
Paso 8. Análisis de clasificación del cluster
# Grouping by 'assignation$cluster' and calculating mean of all numeric variables
analisis <- asignacion %>%
group_by(asignacion$cluster) %>%
summarise(across(where(is.numeric), mean, na.rm = TRUE))## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `across(where(is.numeric), mean, na.rm = TRUE)`.
## ℹ In group 1: `asignacion$cluster = 1`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))# Display the result
print(analisis)## # A tibble: 3 × 15
## `asignacion$cluster` Alcohol Malic_Acid Ash Ash_Alcanity Magnesium
## <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 13.7 2.00 2.47 17.5 108.
## 2 2 13.1 3.31 2.42 21.2 98.7
## 3 3 12.3 1.90 2.23 20.1 92.7
## # ℹ 9 more variables: Total_Phenols <dbl>, Flavanoids <dbl>,
## # Nonflavanoid_Phenols <dbl>, Proanthocyanins <dbl>, Color_Intensity <dbl>,
## # Hue <dbl>, OD280 <dbl>, Proline <dbl>, cluster <dbl>Conclusion
la segmentacion o clusters es un algoritmno útil para identificar ell cultivar correspondiente a cada vino