# Kernel density plot

**Note:** This entry revolves specifically around Kernel density plots. For more general information on quantitative data visualisation, please refer to Introduction to statistical figures.

## Kernel density plots

This entry aims to introduce kernel density plot and its visualization using R’s ggplot2 package. **Density plot is used to plot the distribution of a single quantitative variable.** It allows to see which score of a variable is more frequent and which score is relatively rare. The x-axis represents the values of the variable whereas the y-axis represents its density. The area under the curve equates to 1.

Packages used : gapminder, ggplot2

# Install and load the gapminder and ggplot2 packages install.packages("gapminder") library(gapminder) library(ggplot2) #A glimpse of the gapminder dataset head(gapminder)

#?gapminder #View(gapminder) #Using the basic plot function of R to view the distribution of GDP per capita plot(density(gapminder$gdpPercap))

**Bandwidth** determines the smoothing and detail of a variable. The bandwidth can be changed in the `aes`

parameter of `gemo_density()`

function. The default bandwidth can be viewed as:

bw.nrd0(gapminder$lifeExp) #Output: [1] 2.624907

A basic density plot of life expectancy with ggplpot2() over the years can be viewed as:

ggplot(gapminder, aes(x = lifeExp))+ geom_density(fill = "red", bw = 1)+ labs(title = "Life expectancy over the years")

Representation of life expectancy for every continent can be further seen with using the "continent" variable for the fill parameter.

ggplot(gapminder, aes(x = lifeExp))+ geom_density(aes(fill = continent, color = continent), alpha = 0.5)+ scale_fill_discrete(name = "Continent")+ scale_color_discrete(name = "Continent")+ labs(title = "Life expectancy over the years")

### Faceting

With facetting, the variable can be split into groups and viewed side-by-side for a better comparison. The code for viewing the plot below is the following:

ggplot(gapminder, aes(x = lifeExp))+ geom_density(aes(fill = continent, color = continent),alpha = 0.5)+ scale_fill_discrete(name = "Continent")+ scale_color_discrete(name = "Continent")+ labs(title = "Life Expectancy over the years")+ facet_wrap(continent ~.)

**Refernces:**

- Lecture slides.
- "Histograms and Density Plots in Python" by Will Koehrson
- Kabacoff, R. (2018). Data visualization with R. EEUU: Wesleyan University.

The author of this entry is Archana Maurya.