Mis primeros analisis de datos en R

En el presente analisis de detalan las estadisticas descriptivas en la base de datos propuesta por Kaggle.

ToyotaCorolla <- read.csv("C:/Users/frank/OneDrive/Escritorio/DIPLOMADO/Statistics Programming for Business Analytics/Nueva carpeta/ToyotaCorolla.csv")

dim(ToyotaCorolla)
## [1] 1436   10
length(ToyotaCorolla)
## [1] 10
colnames(ToyotaCorolla)
##  [1] "Price"     "Age"       "KM"        "FuelType"  "HP"        "MetColor" 
##  [7] "Automatic" "CC"        "Doors"     "Weight"
names(ToyotaCorolla)
##  [1] "Price"     "Age"       "KM"        "FuelType"  "HP"        "MetColor" 
##  [7] "Automatic" "CC"        "Doors"     "Weight"
summary(ToyotaCorolla)
##      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
str(ToyotaCorolla)
## '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 ...

Analisis de datos por tipo de variable

Se analizaron los datos usando FOR:

BDN <- NULL
BDC <- NULL
columna <- ncol(ToyotaCorolla)
par(mfrow = c(2, 5))

for (i in 1:columna) {
  if (is.numeric(ToyotaCorolla[ , i])) {
    texto <- paste("Análisis del atributo", colnames(ToyotaCorolla)[i])
    hist(ToyotaCorolla[ , i], main = texto, xlab = colnames(ToyotaCorolla)[i])
    BDN <- c(BDN, i)
  } else {
    texto2 <- paste("Análisis del atributo", colnames(ToyotaCorolla)[i])
    pie(table(ToyotaCorolla[ , i]), main = texto2)
    BDC <- c(BDC, i)
  }
}

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.