#Paqueterias o librerias
#Para este protocolo se utilizaran las paqueterias readr o readxl y janitor

library(readxl)
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(skimr)

readr: es el paquete o libreria que se utiliza para leer una tabla con formato .cvs janitor: es la libreria que fuinciona para limpiar o modificar

En el siguiente recuadro se utiliza la funcion read_csv para cargar los datos que se van a utilizar

Previsualizacion

Modificacion del contenido de la tabla

head: sirve para visualizar los primeros renglones de la tabla tail: es para visualizar los ultimos renglones

data("mtcars")
mtcars = mtcars 
head(mtcars,6)
##                    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
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
tail(mtcars,6)
##                 mpg cyl  disp  hp drat    wt qsec vs am gear carb
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
## Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
## Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2
Data summary
Name mtcars
Number of rows 32
Number of columns 11
_______________________
Column type frequency:
numeric 11
________________________
Group variables None

Variable type: numeric

Data summary
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 ▇▂▅▁▁
Name mtcars
Number of rows 32
Number of columns 11
_______________________
Column type frequency:
factor 3
numeric 8
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
cyl 0 1 FALSE 3 8: 14, 4: 11, 6: 7
vs 0 1 FALSE 2 0: 18, 1: 14
carb 0 1 FALSE 6 2: 10, 4: 10, 1: 7, 3: 3

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 ▃▇▅▁▂
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 ▃▇▇▂▁
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 ▇▁▆▁▂