AnĂ¡lisis Exploratorio de Vinos
Roxana Noelia
28 de diciembre de 2019
Funciones principales
dim()
Con la funcion dim() vemos las dimensiones del dataset
library(rattle.data)
dim(wine)## [1] 178 14head()
Con la funciĂ³n head() vemos los primeros casos del dataset wines
head(wine)## Type Alcohol Malic Ash Alcalinity Magnesium Phenols Flavanoids Nonflavanoids
## 1 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28
## 2 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26
## 3 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30
## 4 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24
## 5 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39
## 6 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34
## Proanthocyanins Color Hue Dilution Proline
## 1 2.29 5.64 1.04 3.92 1065
## 2 1.28 4.38 1.05 3.40 1050
## 3 2.81 5.68 1.03 3.17 1185
## 4 2.18 7.80 0.86 3.45 1480
## 5 1.82 4.32 1.04 2.93 735
## 6 1.97 6.75 1.05 2.85 1450summary()
Con la funciĂ³n summary() vemos el resumen estadĂstico del dataset.
summary(wine)## Type Alcohol Malic Ash Alcalinity
## 1:59 Min. :11.03 Min. :0.740 Min. :1.360 Min. :10.60
## 2:71 1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210 1st Qu.:17.20
## 3:48 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 Phenols Flavanoids Nonflavanoids
## 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 Hue Dilution
## 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. :1680.0str()
Con el comando str(), vemos que tipos de datos componen el dataset, si son nĂºmeros, enteros o factores componen el dataset.
str(wine)## 'data.frame': 178 obs. of 14 variables:
## $ Type : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ Alcohol : num 14.2 13.2 13.2 14.4 13.2 ...
## $ Malic : 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 ...
## $ Alcalinity : 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 ...
## $ 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 ...
## $ Nonflavanoids : 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 : 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 ...
## $ Dilution : 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 ...glimpse()
Con la funciĂ³n glimpse() de dataset dplyr podemos ver quĂ© tipos de datos componen el dataset
library(dplyr)
glimpse(wine)## Observations: 178
## Variables: 14
## $ Type <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ Alcohol <dbl> 14.23, 13.20, 13.16, 14.37, 13.24, 14.20, 14.39, 14...
## $ Malic <dbl> 1.71, 1.78, 2.36, 1.95, 2.59, 1.76, 1.87, 2.15, 1.6...
## $ Ash <dbl> 2.43, 2.14, 2.67, 2.50, 2.87, 2.45, 2.45, 2.61, 2.1...
## $ Alcalinity <dbl> 15.6, 11.2, 18.6, 16.8, 21.0, 15.2, 14.6, 17.6, 14....
## $ Magnesium <int> 127, 100, 101, 113, 118, 112, 96, 121, 97, 98, 105,...
## $ Phenols <dbl> 2.80, 2.65, 2.80, 3.85, 2.80, 3.27, 2.50, 2.60, 2.8...
## $ Flavanoids <dbl> 3.06, 2.76, 3.24, 3.49, 2.69, 3.39, 2.52, 2.51, 2.9...
## $ Nonflavanoids <dbl> 0.28, 0.26, 0.30, 0.24, 0.39, 0.34, 0.30, 0.31, 0.2...
## $ Proanthocyanins <dbl> 2.29, 1.28, 2.81, 2.18, 1.82, 1.97, 1.98, 1.25, 1.9...
## $ Color <dbl> 5.64, 4.38, 5.68, 7.80, 4.32, 6.75, 5.25, 5.05, 5.2...
## $ Hue <dbl> 1.04, 1.05, 1.03, 0.86, 1.04, 1.05, 1.02, 1.06, 1.0...
## $ Dilution <dbl> 3.92, 3.40, 3.17, 3.45, 2.93, 2.85, 3.58, 3.58, 2.8...
## $ Proline <int> 1065, 1050, 1185, 1480, 735, 1450, 1290, 1295, 1045...ggpairs()
La funciĂ³n ggpairs de GGally nos permite ver de un vistazo todas las variables y sus relaciones con las demĂ¡s en el mismo dataset.
library(GGally)
ggpairs(wine, aes(colour = Type, alpha = 0.4))
Estudio de las caracterĂsticas (features)
Vemos si hay correlaciones entre las variables
library(dplyr)
wine1<-select(wine, -Type)
library(corrplot)
var.corr<-cor(wine1)
corrplot(var.corr, method="circle", type="upper", order="hclust")
Para mĂ¡s informaciĂ³n consultar: http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram
AnĂ¡lisis de Componentes Principales
En primer lugar calculamos las componentes principales a lo largo de las 2 primeras componentes principales.
library(ade4)
library(factoextra)
res.pca<-dudi.pca(wine1, scannf = FALSE, nf=2)Visualizar las componentes principales
Para mĂ¡s informaciĂ³n http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/119-pca-in-r-using-ade4-quick-scripts/
fviz_eig(res.pca)
Visualizar las variables
fviz_pca_var(res.pca,
col.var = "contrib", # Color by contributions to the PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
Para realizar un biplot
fviz_pca_biplot(res.pca, repel = TRUE,
col.var = "#2E9FDF", # Variables color
)