Creating Contour Plots with ggplot2: A Step-by-Step Guide

Introduction to ggplot2 and Contour Plots

In this article, we will explore the world of ggplot2, a powerful data visualization library in R. Specifically, we will delve into creating contour plots using ggplot2.

Contour plots are a type of plot that displays values on a 3D surface, where each point represents the value at a specific coordinate (x, y). These plots are commonly used to visualize implicit functions, such as decision boundaries trained with neural networks. In this article, we will show you how to create contour plots using ggplot2 and display an implicit function.

Background: Contour Plots and ggplot2

Contour plots are a type of 3D surface plot that displays values on a 2D plane. Each point on the plot represents the value at a specific coordinate (x, y), where x and y are the input variables. These plots are commonly used to visualize functions, such as decision boundaries trained with neural networks.

ggplot2 is a powerful data visualization library in R that provides a wide range of tools for creating high-quality plots. One of its features is support for contour plots, which can be created using the geom_contour layer.

Understanding Contour Plots

A contour plot displays values on a 3D surface, where each point represents the value at a specific coordinate (x, y). These plots are commonly used to visualize implicit functions, such as decision boundaries trained with neural networks.

In general, a contour plot consists of three main components:

X and Y axes: These represent the input variables, x and y.
Z-axis: This represents the output variable or function value.
Contour lines: These are the lines that connect points on the plot with the same function value.

Creating Contour Plots using ggplot2

To create a contour plot using ggplot2, you can use the geom_contour layer. Here is an example of how to create a simple contour plot:

ggplot(data, aes(x=x,y=y)) + geom_point(aes(color = group)) + scale_shape_manual(values=c(1,16))

In this code, we are creating a scatter plot with points colored based on their groups. We can then add a contour layer using the geom_contour layer:

ggplot(data, aes(x=x,y=y)) + 
  geom_point(aes(color = group)) + 
  scale_shape_manual(values=c(1,16)) + 
  geom_contour(aes(z=value), breaks=0)

However, in this case, we can’t see the contour lines because breaks=0 means there will be no contours. We need to change it.

Reshaping Contour Data into a ggplot2 Format

The data for creating contour plots is usually in the form of x and y coordinates, with corresponding function values. To create contour plots using ggplot2, we need to reshape this data into a format that can be used by the library.

We can do this using the melt function from the reshape2 package:

library(reshape2)
mm <- melt(cc$z)
dimnames(mm)[[1]] <- c("Var1", "Var2")

Here, we are melting the z values into a new data frame with two variables: Var1 and Var2. This will be used to create the contour plot.

Adding Contour Lines using ggplot2

Now that we have our data reshaped, we can add contour lines to our plot:

ggplot(data, aes(x=x,y=y)) + 
  geom_point(aes(color = group)) + 
  scale_shape_manual(values=c(1,16)) + 
  geom_contour(data=mm, aes(x=Var1,y=Var2,z=value), breaks=0, colour="black")

In this code, we are adding a contour layer using the geom_contour layer. We pass in our melted data frame and specify that we want to display the contours.

And there you have it! You now know how to create a contour plot using ggplot2.

Example Code

Here is the complete example code for creating a contour plot using ggplot2:

# Install necessary libraries
install.packages("ggplot2")
install.packages("reshape2")

# Load necessary libraries
library(ggplot2)
library(reshape2)

# Create data for contour plot
data <- data.frame(
  x = runif(100, min=1, max=4),
  y = runif(100, min=1, max=4),
  group = sample(c("red", "blue"), size=100, replace=True),
  value = rnorm(100)
)

# Create contour plot
g0 <- ggplot(data, aes(x=x,y=y)) + 
  geom_point(aes(color = group)) + 
  scale_shape_manual(values=c(1,16))

# Generate data for contour layer
cc <- emdbook::curve3d(
  1.91*(1/(1+exp(-(23.50+12.64*x-24.54*y))))-
  1.95*(1/(1+exp(-(73.51-12.36*x-10.01*y)))) + 0.98,
  xlim=c(2,4), ylim=c(2,4), sys3d="none"
)

# Reshape contour data
mm <- melt(cc$z)
dimnames(mm)[[1]] <- c("Var1", "Var2")

# Add contour lines to plot
g0 + geom_contour(data=mm, aes(x=Var1,y=Var2,z=value), breaks=0, colour="black")

Conclusion

In this article, we explored the world of ggplot2 and created a contour plot using the library. We discussed how to create a contour layer, reshape contour data into a ggplot2 format, and add contour lines to our plot.

Contour plots are a powerful tool for visualizing implicit functions, such as decision boundaries trained with neural networks. By learning how to create contour plots using ggplot2, you can gain valuable insights into your data and create high-quality visualizations that communicate your message effectively.

Last modified on 2023-08-05