Introducción

Cuantitativas: Las variables cuantitativas son aquellas medibles numericamente, pueden ser continuas o exactas.

Cualitativas: Las variables cualitativas son subjetivas y sus datos pueden ser interpretados con distintas formas. Expresan niveles, posibilidades y gamas.

Tipos de variable

Tabla 1. Tipos :
Cualitativas Cuantitativas
Nominales Discretas
Ordinales Continuas

Librería

Indica un llamado a los paquetes intalados para llevar a cabo funciones del documento.

library(readr)
library(skimr)
library(prettydoc)
library(dplyr)
library(janitor)
library(ggplot2)
mtcars = mtcars
head(mtcars,3)
##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
tail(mtcars,4)
##                 mpg cyl disp  hp drat   wt qsec vs am gear carb
## Ford Pantera L 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
## Ferrari Dino   19.7   6  145 175 3.62 2.77 15.5  0  1    5    6
## Maserati Bora  15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
## Volvo 142E     21.4   4  121 109 4.11 2.78 18.6  1  1    4    2

Modificación de tablas:

skim(mtcars)
Data summary
Name mtcars
Number of rows 32
Number of columns 11
_______________________
Column type frequency:
numeric 11
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
mpg 0 1 20.09 6.03 10.40 15.43 19.20 22.80 33.90 ▃▇▅▁▂
cyl 0 1 6.19 1.79 4.00 4.00 6.00 8.00 8.00 ▆▁▃▁▇
disp 0 1 230.72 123.94 71.10 120.83 196.30 326.00 472.00 ▇▃▃▃▂
hp 0 1 146.69 68.56 52.00 96.50 123.00 180.00 335.00 ▇▇▆▃▁
drat 0 1 3.60 0.53 2.76 3.08 3.70 3.92 4.93 ▇▃▇▅▁
wt 0 1 3.22 0.98 1.51 2.58 3.33 3.61 5.42 ▃▃▇▁▂
qsec 0 1 17.85 1.79 14.50 16.89 17.71 18.90 22.90 ▃▇▇▂▁
vs 0 1 0.44 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
am 0 1 0.41 0.50 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▆
gear 0 1 3.69 0.74 3.00 3.00 4.00 4.00 5.00 ▇▁▆▁▂
carb 0 1 2.81 1.62 1.00 2.00 2.00 4.00 8.00 ▇▂▅▁▁ 
summary(mtcars)
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
mtcars$cyl = as.factor(mtcars$cyl)
mtcars$vs = as.factor(mtcars$vs)
mtcars$am = as.factor(mtcars$am)
mtcars$gear = as.factor(mtcars$gear)
ggplot(datasets::mtcars) +
 aes(x = carb) +
 geom_histogram(bins = 30L, fill = "#112446") +
 theme_minimal()

library(ggplot2)