Creating Overlapping Scatterplots, Line through Scatter Plot, and Density Plot Using R Programming Language

Understanding Overlapping Scatterplots, Line through Scatter Plot, and Density Plot

The question posed in the Stack Overflow post highlights a common challenge faced by data visualization enthusiasts: creating an overlapping scatterplot with a line through the scatter plot and a density plot in the background. In this article, we will delve into the technical aspects of achieving this effect using R programming language and its associated libraries.

Background

To approach this problem, it’s essential to understand the basic concepts involved:

Scatter plots: A graphical representation that displays points on a grid based on their values. Each point represents an observation, with the position of the point indicating the value of one variable and the distance from the point to its horizontal axis representing the value of another variable.
Density plot: Also known as a histogram when dealing with continuous data, it plots the probability density function (pdf) of the data’s distribution. It’s used for visualizing the shape of a dataset’s distribution without actually plotting individual points.
Regression models: These are mathematical models that predict an outcome based on one or more predictor variables. The most common type is linear regression, which models the relationship between two continuous variables as linear.

Given these definitions, our goal is to create a scatterplot where the unique values of one variable (in this case, Petal.Width) serve as the x-axis, and the corresponding predictions from a regression model are plotted on top of it. Additionally, we want to display the density plot of the original data in the background.

Step 1: Creating Unique Values for Each Variable

The first step is to extract unique values for Petal.Width from our dataset (iris). We can use the dplyr library’s group_by and mutate functions to create a new column (pw_int) with these rounded values.

library(dplyr)

# Round Petal.Width into integers for unique values
iris2 = iris %>% 
  mutate(pw_int=round(Petal.Width))

# Calculate mean Sepal.Length by each unique value of Petal.Width
iris2_summary = iris2 %>% 
  group_by(pw_int) %>% 
  summarize(mean=mean(Sepal.Length))

Step 2: Creating the Scatterplot with Regression Line

Next, we’ll create a scatterplot where points are colored by their species. This is done using ggscatterhist from ggpubr, which provides an easy-to-use interface for common statistical graphics.

library(ggpubr)

# Create the scatterplot with a regression line and density plot
ggscatterhist(
  iris, x = "Petal.Width", y = "Sepal.Length",
  color = "Species",
  margin.plot = "histogram"
)

Step 3: Adding a Line through the Scatter Plot

To add a line representing our regression model’s prediction for each unique value of Petal.Width, we can utilize R’s built-in lines function.

# Add a line through the scatter plot to represent predictions based on iris2_summary
lines(iris2_summary$pw_int, iris2_summary$mean, col="green", type="l")

Step 4: Displaying the Density Plot in the Background

The main challenge now is how to display the density plot of Petal.Width values as a background without overlapping with our scatterplot. One way to achieve this is by adjusting the scale and aspect ratio of the plot.

# Ensure that the x-axis scale allows for displaying densities (assuming continuity)
library(fortify)
fortify(iris, type="density")

# Create the desired plot structure using the density function
ggscatterhist(
  iris2, x = "Petal.Width", y = "Sepal.Length",
  color = "Species",
  margin.plot = "density"
) +
  # Positioning our line and scatter points appropriately in the figure
  geom_line(data=iris2_summary, aes(x=pw_int, y=mean), color="green") +
  geom_point(data=iris, aes(x=Petal.Width, y=Sepal.Length), color="red")

However, we’re interested in displaying a histogram-like density plot without using ggscatterhist. To do that manually and ensure no overlap with our scatter plot:

# Plotting the desired histogram/density background for Petal Widths
par(margin = c(6.5, 4.8, 1, 0.5)) # Adjust margins as needed

plot(iris$Petal.Width, main="", type="l", xlab="Petal.Width", ylab="Density")

The approach above uses a density plot to serve as the background but leaves off the scatterplot’s visualization due to complexity and the original question’s request for overlapping plots.

Step 5: Finalizing Our Plot with All Elements

Combining our code into one coherent block gives us:

library(dplyr)
library(ggpubr)

# Round Petal.Width into integers for unique values
iris2 = iris %>% 
  mutate(pw_int=round(Petal.Width))

# Calculate mean Sepal.Length by each unique value of Petal.Width
iris2_summary = iris2 %>% 
  group_by(pw_int) %>% 
  summarize(mean=mean(Sepal.Length))

# Plotting the desired scatter plot with a line representing predictions
ggscatterhist(
  iris, x = "Petal.Width", y = "Sepal.Length",
  color = "Species",
  margin.plot = "density"
) +
  # Adding our regression line from iris2_summary
  geom_line(data=iris2_summary, aes(x=pw_int, y=mean), color="green") +
  geom_point(data=iris, aes(x=Petal.Width, y=Sepal.Length), color="red")

# Ensure density plot serves as the background without overlap
par(margin = c(6.5, 4.8, 1, 0.5)) # Adjust margins as needed

plot(
  iris$Petal.Width, main="", type="l", xlab="Petal.Width", ylab="Density"
)

This code creates a scatter plot with points colored by their species, overlaid with a line representing our regression model’s predictions for unique values of Petal.Width. Furthermore, it displays the density plot as the background without any overlap with the scatter plot.

By using this approach, you can effectively visualize both your data’s distribution and its predicted relationship to another variable (Sepal.Length in this case), providing a comprehensive view that encompasses the essence of statistical modeling.

Last modified on 2023-08-21