La base de datos de calidad de vinos describe el comportamiento de 12 atributos y de 1599 registros. Se descargo desde PAIDEIA https://paideia.pucp.edu.pe/programas/mod/assign/view.php?id=351234
La base de datos esta en formato CSV y fue importado al RStudio.Adicionalmente se realizo un resumen de cada variable
library(readr)
BD2<- read_csv("C:/Users/Hola/Downloads/winequality-red.csv")
## Rows: 1599 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (12): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(BD2)
## fixed acidity volatile acidity citric acid residual sugar
## Min. : 4.60 Min. :0.1200 Min. :0.000 Min. : 0.900
## 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090 1st Qu.: 1.900
## Median : 7.90 Median :0.5200 Median :0.260 Median : 2.200
## Mean : 8.32 Mean :0.5278 Mean :0.271 Mean : 2.539
## 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420 3rd Qu.: 2.600
## Max. :15.90 Max. :1.5800 Max. :1.000 Max. :15.500
## chlorides free sulfur dioxide total sulfur dioxide density
## Min. :0.01200 Min. : 1.00 Min. : 6.00 Min. :0.9901
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00 1st Qu.:0.9956
## Median :0.07900 Median :14.00 Median : 38.00 Median :0.9968
## Mean :0.08747 Mean :15.87 Mean : 46.47 Mean :0.9967
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00 3rd Qu.:0.9978
## Max. :0.61100 Max. :72.00 Max. :289.00 Max. :1.0037
## pH sulphates alcohol quality
## Min. :2.740 Min. :0.3300 Min. : 8.40 Min. :3.000
## 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50 1st Qu.:5.000
## Median :3.310 Median :0.6200 Median :10.20 Median :6.000
## Mean :3.311 Mean :0.6581 Mean :10.42 Mean :5.636
## 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :4.010 Max. :2.0000 Max. :14.90 Max. :8.000
A continuación se realiza una función para poder evaluar cada atributo de variable e identificar si es númerico o tipo carácter.A los datos númericos se les reportara con un histograma y a los datos tipo caracter se les reportara con un diagrama de pie
dimension <- dim(BD2)
par(mfrow=c(2,ceiling(dimension[2]/2)))
for(j in 1:dimension[2]){
x<- unlist(BD2[,j])
if(is.numeric(x)==TRUE) {
texto <- paste('analisis del atributo', colnames(BD2)[j])
hist(x, col=j, main = texto)
} else {
texto <- paste('analisis del atributo 1', colnames(BD2)[j])
pie(x, main = texto)
}
}