Normality test for multiple variables

Data
iris
Shapiro-Wilk test
H0 : the variable follow a normal distribution
H1 : the variable NOT follow a normal distribution

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.5     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.0.2     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(rstatix)

## 
## Attaching package: 'rstatix'

## The following object is masked from 'package:stats':
## 
##     filter

iris %>%
  shapiro_test(Sepal.Length,Petal.Width)

## # A tibble: 2 × 3
##   variable     statistic            p
##   <chr>            <dbl>        <dbl>
## 1 Petal.Width      0.902 0.0000000168
## 2 Sepal.Length     0.976 0.0102

Petal.Width & Sepal.Length :
p-value < 0.05 Reject H0,the p values are statistically significant ( the variable NOT follow a normal distribution)

Q-Q plot using ggplot2

library(ggplot2)
library(qqplotr)

## 
## Attaching package: 'qqplotr'

## The following objects are masked from 'package:ggplot2':
## 
##     stat_qq_line, StatQqLine

p1<-ggplot(data = iris, mapping = aes(sample = Sepal.Length)) +
  stat_qq_band(fill="Purple",alpha = 0.15) +
  stat_qq_line(col="Purple") +
  stat_qq_point(col="black",size=1)+
  labs(title = "Sepal.Length")+theme_bw()

p2<-ggplot(data = iris, mapping = aes(sample = Petal.Width)) +
  stat_qq_band(fill="blue",alpha = 0.15) +
  stat_qq_line(col="blue") +
  stat_qq_point(col="black",size=1)+
  labs(title = "Petal.Width")+theme_bw()

Arrange in a grid using package gridExtra

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

#p1+p2
grid.arrange(p1,p2,ncol=2)

Density plot using ggplot2

p3<-ggplot(data=iris, aes(x = Sepal.Length)) + 
  geom_density(alpha = 0.15,fill="Purple")+
  labs(title = "Sepal.Length")+theme_bw()

p4<-ggplot(data=iris, aes(x = Petal.Width)) + 
  geom_density(alpha = 0.15,fill="blue")+
  labs(title = "Petal.Width")+theme_bw()

#p3+p4
grid.arrange(p3,p4,ncol=2)

Histogram plot using ggplot2

p5<-ggplot(data=iris, aes(x=Sepal.Length)) +
  geom_histogram(aes(x=Sepal.Length, y=..density..), bins=50,fill="Purple",alpha = 0.15) +
  stat_function(fun=dnorm, args = list(mean=mean(iris$Sepal.Length),
                                       sd=sd(iris$Sepal.Length)), color="Purple",size=1)+
  labs(title = "Sepal.Length")+theme_bw()

p6<-ggplot(data=iris, aes(x=Petal.Width)) +
  geom_histogram(aes(x=Petal.Width, y=..density..), bins=50,fill="blue",alpha = 0.15) +
  stat_function(fun=dnorm, args = list(mean=mean(iris$Petal.Width),
                                       sd=sd(iris$Petal.Width)), color="blue",size=1)+
  labs(title = "Petal.Width")+theme_bw()

#p5 + p6
grid.arrange(p5,p6,ncol=2)