Análisis Exploratorio de Datos

Análisis Exploratorio de Toyota

En el presente análisis se analizarán los datos de Toyota Corolla, los cuales pueden descargarse desde:

https://www.kaggle.com/datasets/tolgahancepel/toyota-corolla

Cargar la base de datos

# Cargar la data
BD <- read.csv("D:/Met_Mora/Version_04/ToyotaCorolla.csv",
               stringsAsFactors = FALSE)

# Verificar la estructura
str(BD)

## 'data.frame':    1436 obs. of  10 variables:
##  $ Price    : int  13500 13750 13950 14950 13750 12950 16900 18600 21500 12950 ...
##  $ Age      : int  23 23 24 26 30 32 27 30 27 23 ...
##  $ KM       : int  46986 72937 41711 48000 38500 61000 94612 75889 19700 71138 ...
##  $ FuelType : chr  "Diesel" "Diesel" "Diesel" "Diesel" ...
##  $ HP       : int  90 90 90 90 90 90 90 90 192 69 ...
##  $ MetColor : int  1 1 1 0 0 0 1 1 0 0 ...
##  $ Automatic: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ CC       : int  2000 2000 2000 2000 2000 2000 2000 2000 1800 1900 ...
##  $ Doors    : int  3 3 3 3 3 3 3 3 3 3 ...
##  $ Weight   : int  1165 1165 1165 1165 1170 1170 1245 1245 1185 1105 ...

# Resumen estadístico
summary(BD)

##      Price            Age              KM           FuelType        
##  Min.   : 4350   Min.   : 1.00   Min.   :     1   Length:1436       
##  1st Qu.: 8450   1st Qu.:44.00   1st Qu.: 43000   Class :character  
##  Median : 9900   Median :61.00   Median : 63390   Mode  :character  
##  Mean   :10731   Mean   :55.95   Mean   : 68533                     
##  3rd Qu.:11950   3rd Qu.:70.00   3rd Qu.: 87021                     
##  Max.   :32500   Max.   :80.00   Max.   :243000                     
##        HP           MetColor        Automatic             CC      
##  Min.   : 69.0   Min.   :0.0000   Min.   :0.00000   Min.   :1300  
##  1st Qu.: 90.0   1st Qu.:0.0000   1st Qu.:0.00000   1st Qu.:1400  
##  Median :110.0   Median :1.0000   Median :0.00000   Median :1600  
##  Mean   :101.5   Mean   :0.6748   Mean   :0.05571   Mean   :1567  
##  3rd Qu.:110.0   3rd Qu.:1.0000   3rd Qu.:0.00000   3rd Qu.:1600  
##  Max.   :192.0   Max.   :1.0000   Max.   :1.00000   Max.   :2000  
##      Doors           Weight    
##  Min.   :2.000   Min.   :1000  
##  1st Qu.:3.000   1st Qu.:1040  
##  Median :4.000   Median :1070  
##  Mean   :4.033   Mean   :1072  
##  3rd Qu.:5.000   3rd Qu.:1085  
##  Max.   :5.000   Max.   :1615

# Dimensiones
dim(BD)

## [1] 1436   10

# Nombres de variables
names(BD)

##  [1] "Price"     "Age"       "KM"        "FuelType"  "HP"        "MetColor" 
##  [7] "Automatic" "CC"        "Doors"     "Weight"

Análisis visual de las variables

BDN <- c()
BDC <- c()

columnas <- ncol(BD)

# Guardar configuración gráfica actual
op <- par(no.readonly = TRUE)

# Configuración de gráficos
par(
  mfrow = c(2, ceiling(columnas/2)),
  mar = c(3,3,3,1)
)

for(i in 1:columnas){

  if(is.numeric(BD[[i]])){

    hist(
      BD[[i]],
      main = paste("Histograma de", names(BD)[i]),
      xlab = names(BD)[i],
      col = "skyblue",
      border = "white"
    )

    BDN <- c(BDN, i)

  }else{

    pie(
      table(BD[[i]], useNA = "ifany"),
      main = paste("Distribución de", names(BD)[i])
    )

    BDC <- c(BDC, i)

  }

}