Primer Notebook

Primer ejercicio haciendo reportes desde R.

Para las letras en negrita se usa "**"
Para las letras cursivas se usa "*"


Estas son letras en negrita
Esta es letra cursiva


Para configurar un texto como titulo se usa “#”

Para un subtitulo de usa “##”

Para un subtitulo de tercer nivel se usa “###”
Para un subtitulo de cuarto nivel se usa “####”


Agregar imagenes a un documento en R


Para agregar imagenes se usa la sintaxis:

![texto_alternativo](ubicacion_de_la_imagen){width=width height=height}
Insercion de imagen en Notebook

Para lineas horizontales poner 3 asteriscos "***".


Ejecutar scripts de R


La región donde se ejecuta el código de R se denomina “chunck”

# install.packages('devtools')

library(devtools)
## Loading required package: usethis
# devtools::install_github("XanderHorn/autoEDA")

library(autoEDA)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.2     ✔ dplyr   1.0.7
## ✔ tidyr   1.1.3     ✔ stringr 1.4.0
## ✔ readr   1.4.0     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
# Analisis univariado (una sola variable)

iris %>% str()
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
overview_1 <-  autoEDA(x = iris ) # en lugar de iris escribimos el nombre de nuestra base de datos
## Loading required package: RColorBrewer
## autoEDA | Setting color theme 
## autoEDA | Removing constant features 
## autoEDA | 0 constant features removed 
## autoEDA | 0 zero spread features removed 
## autoEDA | Removing features containing majority missing values 
## autoEDA | 0 majority missing features removed 
## autoEDA | Cleaning data 
## autoEDA | Correcting sparse categorical feature levels 
## autoEDA | Performing univariate analysis 
## autoEDA | Visualizing data

# Cada uno de los parámetro para el EDA automático (Analisis exploratorio de datos) es personalizable 


overview_1$Feature
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
overview_1$FeatureClass
## [1] "numeric"   "numeric"   "numeric"   "numeric"   "character"
overview_1$FeatureType
## [1] "Continuous"  "Continuous"  "Continuous"  "Continuous"  "Categorical"
overview_1$PercentageMissing
## [1] 0 0 0 0 0
overview_1$PercentageUnique
## [1] 23.33 15.33 28.67 14.67  2.00
overview_1$ConstantFeature
## [1] "No" "No" "No" "No" "No"
overview_1$LowerOutliers
## [1] 0 1 0 0 0
overview_1$UpperOutliers
## [1] 0 3 0 0 0
overview_1$ImputationValue
## [1] "5.8"    "3"      "4.35"   "1.3"    "SETOSA"
overview_1$FirstQuartile
## [1] 5.1 2.8 1.6 0.3 0.0
overview_1$Median
## [1] 5.80 3.00 4.35 1.30 0.00
overview_1$UpperOutlierValue
## [1]  8.35  4.05 10.35  4.05  0.00
overview_1$LowerOutlierValue
## [1]  3.15  2.05 -3.65 -1.95  0.00
overview_1
##        Feature Observations FeatureClass FeatureType PercentageMissing
## 1 Sepal.Length          150      numeric  Continuous                 0
## 2  Sepal.Width          150      numeric  Continuous                 0
## 3 Petal.Length          150      numeric  Continuous                 0
## 4  Petal.Width          150      numeric  Continuous                 0
## 5      Species          150    character Categorical                 0
##   PercentageUnique ConstantFeature ZeroSpreadFeature LowerOutliers
## 1            23.33              No                No             0
## 2            15.33              No                No             1
## 3            28.67              No                No             0
## 4            14.67              No                No             0
## 5             2.00              No                No             0
##   UpperOutliers ImputationValue MinValue FirstQuartile Median Mean   Mode
## 1             0             5.8      4.3           5.1   5.80 5.84      5
## 2             3               3      2.0           2.8   3.00 3.06      3
## 3             0            4.35      1.0           1.6   4.35 3.76    1.4
## 4             0             1.3      0.1           0.3   1.30 1.20    0.2
## 5             0          SETOSA      0.0           0.0   0.00 0.00 SETOSA
##   ThirdQuartile MaxValue LowerOutlierValue UpperOutlierValue
## 1           6.4      7.9              3.15              8.35
## 2           3.3      4.4              2.05              4.05
## 3           5.1      6.9             -3.65             10.35
## 4           1.8      2.5             -1.95              4.05
## 5           0.0      0.0              0.00              0.00