#Create a scatterplot of sepal length with sepal width, facet by species. Run a regression on the plot using geom_smooth.

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.2

data<-iris
x<-ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width))+ 
  geom_point(mapping = NULL, col="BLue", size=1)+
  geom_smooth(method="lm", col="firebrick") + 
  labs(title="Sepal length with Sepal width", 
       subtitle="From iris dataset", 
       y="Sepal.Width", 
       x="Sepal.Length")+
  facet_wrap(~Species, nrow=2)+
  scale_colour_brewer(palette = "Set1")+
  theme_classic() + labs(subtitle="Classic Theme")+
  theme(legend.position="None")
print(x)

## `geom_smooth()` using formula 'y ~ x'

library(plotly)

## Warning: package 'plotly' was built under R version 4.0.2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

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

## The following object is masked from 'package:graphics':
## 
##     layout

ggplotly(x)

## `geom_smooth()` using formula 'y ~ x'

## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

#Create a histogram of sepal length, change the bin size as convenient. Add a frequency polygon.

library(ggplot2)
y<-ggplot(data = iris, aes(Sepal.Length)) + 
  geom_histogram(col="green") + 
  geom_freqpoly(stat = "bin", col= "red", size= 1) + 
  stat_bin(bins=30, binwidth = 0.10)+ 
  labs(title="Histogram of Sepal.Length", 
       subtitle="From iris dataset", 
       x="Sepal.Length")
y

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

library(plotly)
ggplotly(y)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#Create a Boxplot of petal length with species

library(ggplot2)
z<-ggplot(data=iris, aes(Petal.Length, Species))+
  geom_boxplot(varwidth= TRUE, 
               fill = "white", notch = TRUE, colour = "blue", 
               outlier.colour = "red", outlier.shape = 4) +
  coord_flip()+theme_bw()
z

library(plotly)
ggplotly(z)

#Use par mfrow to create 2 histograms for petal length and petal width, side by side. Comment on the distributions.

par(mfrow=c(1,2))
plot(iris$Petal.Length, col="blue")
plot(iris$Petal.Width, col="firebrick")