We used the Iris dataset to create density plots and compare the petal length by species.
We used a simply method.
We created a density plot with the ggplot library, and then we used ggplotly to convert the plot to plotly.
library(datasets)
str(iris)
## '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 ...
library(ggplot2)
theme_set(theme_classic())
# Plot
g <- ggplot(iris, aes(x=Petal.Length, fill=Species))
myDensityPlotWithGGplot <- g + geom_density() +
labs(title="Density plot",
subtitle="Petal Length by Species",
caption="Source: Iris Set",
x="Petal Length",
fill="Species")
myDensityPlotWithGGplot
library(plotly)
## Warning: package 'plotly' was built under R version 4.0.4
##
## 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
myDensityPlotWithPLOTLY <- ggplotly(myDensityPlotWithGGplot)
myDensityPlotWithPLOTLY