R - Dual Y Axis with Free Scale - GGPLOT

The use of dual y-axes in plotting can be a powerful tool for visualizing data that has different scales or units. In this article, we will explore how to create a dual y-axis plot using the ggplot2 package in R, specifically focusing on achieving free scales for both axes.

Background and Introduction

In a standard plot, there is only one y-axis, which can be limiting when working with data that has different scales or units. To address this issue, we use the sec.axis function within the theme() layer of our ggplot2 code. However, as shown in the Stack Overflow question, simply applying sec.axis does not provide the desired outcome.

The goal is to create a dual y-axis plot where one axis has a free scale (i.e., no fixed limits or transformations) and the other axis has its own scales and limits. This requires understanding of how ggplot2 handles axes, scales, and transformations.

Understanding Axes and Scales

In ggplot2, axes are created using the aes() function within the geom_point() or geom_line() layer of our code. The y-axis is determined by the variable mapped to the y aesthetic. If we have multiple variables mapped to the same axis, we can use the group aesthetic to specify which group of points should be plotted on that axis.

Scales are used to define the range and behavior of a plot’s axes. In ggplot2, scales can be specified using the scale_y_continuous() function within the theme() layer. These scales can include various transformations such as logarithmic or linear scaling, as well as fixed limits.

Creating Dual Y-Axis Plots

To create a dual y-axis plot with free scales for both axes, we will employ the following steps:

1. Use scale_y_continuous() to define one axis with a free scale.
2. Create a secondary y-axis using scale_y_reverse().
3. Define the limits and behavior of the secondary y-axis using its own scale.

Step-by-Step Implementation

Below is an example code snippet demonstrating how to create a dual y-axis plot:

library(tidyverse)

# Sample data
Year <- c(2010:2014)
OilPrice <- c(60:64)
GasPrice <- c(6789, 6802, 6580, 6890, 7020)
DATASET <- data.frame(Year, OilPrice, GasPrice) %>% 
  as_tibble(DATASET) %>% 
  gather(key = "Prices", value = "value", -Year)

# Create the plot
ggplot(DATASET, aes(Year, value, color=Prices)) + 
  geom_point() + 
  geom_line(aes(group=Prices)) +
  labs(
    title = "Prices evolution over time",
    y="Prices") + 
  theme(
    # Define one axis with a free scale
    legend.position = "bottom",
    
    # Create a secondary y-axis
    panel.background = element_rect(fill = NA),
    panel.grid.major = element_line(colour = "black"),
    panel.grid.minor = element_line(colour = "lightgray")
  ) + 
  # Define the limits and behavior of the secondary y-axis
  scale_y_reverse(name = "Gas Prices",
                   breaks = c(6500, 6800, 7000),
                   minor_breaks = c(100),
                   major_breaks = c(500, 1000)) +
  # Customize the appearance of the secondary axis
  theme(axis.text.y = element_text(angle = -45, vjust = .5), 
        axis.ticks.y = element_line(colour = "black"))

Explanation

The provided code snippet demonstrates how to create a dual y-axis plot with one axis having a free scale and the other axis having its own scales and limits. Here’s a breakdown of each step:

1. We start by defining our sample data, including two variables OilPrice and GasPrice, along with their corresponding years.
2. Next, we use the gather() function to transform our data into a long format, making it easier to work with in ggplot2.
3. We then create the plot using ggplot(), specifying the x-axis as “Year”, y-axis as “value” (with color mapping to the “Prices” variable), and adding points and lines for visualization.
4. Within the theme() layer, we define one axis with a free scale by removing the panel background, grid majors, and minor breaks, which gives us an empty canvas for our secondary y-axis.
5. We then create the secondary y-axis using scale_y_reverse(), specifying its name (“Gas Prices”), breaks (including both major and minor), and customizing its appearance through theme() layer settings.
6. The resulting plot displays two y-axes: one with a free scale on the left, representing oil prices, and another with fixed scales on the right, representing gas prices.

Conclusion

By following these steps and employing ggplot2’s various features, we can create complex dual y-axis plots that provide valuable insights into data with disparate scales or units. The key takeaway from this article is understanding how to define and customize axes, scales, and transformations within ggplot2 to achieve the desired visualizations for your specific use case.

In conclusion, the R programming language offers a wide range of powerful tools for data visualization using ggplot2, including creating dual y-axis plots with free scales. This article has provided an in-depth exploration of this topic, covering key concepts and techniques for achieving the desired outcomes in plotting data with different scales or units.

Last modified on 2024-03-10