How To Make Density Plots with ggplot2 in R?

In this article, we are going to see how to make Density plots with ggtplot2 in R Programming language.

The probability density function of a continuous variable is estimated using a data visualization approach called a density plot, sometimes referred to as a kernel density plot or kernel density estimation plot. It offers a rounded picture of the data’s underlying distribution.

The probability density function (PDF) is estimated using the observed data points in the theory underlying a density plot. Each data point is the center of a kernel function, usually a Gaussian (normal) kernel. The density estimate’s shape and width are determined by the kernel function.

To create a density plot in R using ggplot2, we use the geom_density() function of the ggplot2 package.

Syntax: ggplot( aes(x)) + geom_density( fill, color, alpha)

Parameters:

• fill: background color below the plot
• color: the color of the plotline
• alpha: transparency of graph

Creating a basic Density Plot with ggplot2

Here we are going to create a density plot.

R

# create dataframe
set.seed(1234)
df <- data.frame(value =round(c(rnorm(200,
                                      mean=100,
                                      sd=7))))

# import libraries ggplot2
library(ggplot2)

# create density plot
 ggplot(df, aes(x=value)) + geom_density()

Output:

Density Plots with ggplot2 in R

Color and line type Customization

To change color, we use the color property of the geom_density() function.

R

# create dataframe
set.seed(1234)
df <- data.frame(value =round(c(rnorm(900,
                                      mean=60,
                                      sd=21))))

# import libraries ggplot2
library(ggplot2)

# create density plot with desired colours
 ggplot(df, aes(x=value)) + geom_density(color="green",linetype="dashed")

Output:

Density Plots with ggplot2 in R

To change the background color, we use the fill property of the geom_density() function.

R

# create dataframe
set.seed(1234)
df <- data.frame(value =round(c(rnorm(900,
                                      mean=60,
                                      sd=21))))

# import libraries ggplot2
library(ggplot2)

# create density plot with desired colours
 ggplot(df, aes(x=value)) + geom_density(fill="#77bd89",
                                         color="#1f6e34",
                                         alpha=0.8)

Output:

Density Plots with ggplot2 in R

Kernel selection:

With kernel parameters, the kernel that is being utilized can also be modified. There are other alternatives that can be selected, including “Gaussian” (the default), “rectangular,” “triangular,” “epanechnikov,” “biweight,” “cosine,” and “optcosine.”.

R

set.seed(1234)
df <- data.frame(value = round(c(rnorm(900, mean = 60, sd = 21))))

# Load the required libraries
library(ggplot2)

# Create the density plot with kernel selection
ggplot(df, aes(x = value, y = ..density..)) +
  geom_density(aes(fill = "Density"), alpha = 0.5,
               kernel = "rectangular") +
  scale_fill_manual(values = "lightgreen",
                    guide = guide_legend(override.aes = list(color = NA))) +
  labs(title = "Density Plot with Kernel Selection",
       x = "Value",
       y = "Density") +
  theme_minimal()

Output:

Density Plots with ggplot2 in R

Combine histogram and density plots:

R

set.seed(1234)
df <- data.frame(value = round(c(rnorm(900, mean = 60, sd = 21))))

# Load the required libraries
library(ggplot2)

# Create the combined histogram and density plot
ggplot(df, aes(x = value)) +
  geom_histogram(aes(y = ..density..),
                 bins = 30,
                 fill = "lightblue",
                 color = "black") +
  geom_density(alpha = 0.5, fill = "lightgreen") +
  labs(title = "Combined Histogram and Density Plot",
       x = "Value",
       y = "Density") +
  theme_minimal()

Output:

Density Plots with ggplot2 in R