Dual Y axis with R and ggplot2



This post describes how to build a dual Y axis chart using R and ggplot2. It uses the sec.axis attribute to add the second Y axis. Note that this kind of chart has major drawbacks. Use it with care.

Line chart Section About line chart

Visualizing 2 series with R and ggplot2


Let’s consider a dataset with 3 columns:

  • date
  • first serie to display: fake temperature. Range from 0 to 10.
  • second serie: fake price. Rangee from 0 to 100.

One could easily build 2 line charts to study the evolution of those 2 series using the code below.

But even if strongly unadvised, one sometimes wants to display both series on the same chart, thus needing a second Y axis.

# Libraries
library(ggplot2)
library(dplyr)
library(patchwork) # To display 2 charts together
library(hrbrthemes)

# Build dummy data
data <- data.frame(
  day = as.Date("2019-01-01") + 0:99,
  temperature = runif(100) + seq(1,100)^2.5 / 10000,
  price = runif(100) + seq(100,1)^1.5 / 10
)

# Most basic line chart
p1 <- ggplot(data, aes(x=day, y=temperature)) +
  geom_line(color="#69b3a2", size=2) +
  ggtitle("Temperature: range 1-10") +
  theme_ipsum()
  
p2 <- ggplot(data, aes(x=day, y=price)) +
  geom_line(color="grey",size=2) +
  ggtitle("Price: range 1-100") +
  theme_ipsum()

# Display both charts side by side thanks to the patchwork package
p1 + p2

Adding a second Y axis with sec.axis(): the idea


sec.axis() does not allow to build an entirely new Y axis. It just builds a second Y axis based on the first one, applying a mathematical transformation.

In the example below, the second Y axis simply represents the first one multiplied by 10, thanks to the trans argument that provides the ~.*10 mathematical statement.

Note that because of that you can’t easily control the second axis lower and upper boundaries. We’ll see a trick below in the tweaking section.

# Start with a usual ggplot2 call:
ggplot(data, aes(x=day, y=temperature)) +
  
  # Custom the Y scales:
  scale_y_continuous(
    
    # Features of the first axis
    name = "First Axis",
    
    # Add a second axis and specify its features
    sec.axis = sec_axis( trans=~.*10, name="Second Axis")
  ) +
  
  theme_ipsum()

Show 2 series on the same line chart thanks to sec.axis()


We can use this sec.axis mathematical transformation to display 2 series that have a different range.

Since the price has a maximum value that is 10 times biggeer than the maximum temperature:

  • the second Y axis is like the first multiplied by 10 (trans=~.*10).
  • the value be display in the second variable geom_line() call must be divided by 10 to mimic the range of the first variable.

# Value used to transform the data
coeff <- 10

ggplot(data, aes(x=day)) +
  
  geom_line( aes(y=temperature)) + 
  geom_line( aes(y=price / coeff)) + # Divide by 10 to get the same range than the temperature
  
  scale_y_continuous(
    
    # Features of the first axis
    name = "First Axis",
    
    # Add a second axis and specify its features
    sec.axis = sec_axis(~.*coeff, name="Second Axis")
  )

Dual Y axis customization with ggplot2


A feew usual tricks to make the chart looks better:

  • ipsum theme to remove the black background and improve the general style
  • add a title
  • customize the Y axes to pair them with their related line.

# Value used to transform the data
coeff <- 10

# A few constants
temperatureColor <- "#69b3a2"
priceColor <- rgb(0.2, 0.6, 0.9, 1)

ggplot(data, aes(x=day)) +
  
  geom_line( aes(y=temperature), size=2, color=temperatureColor) + 
  geom_line( aes(y=price / coeff), size=2, color=priceColor) +
  
  scale_y_continuous(
    
    # Features of the first axis
    name = "Temperature (Celsius °)",
    
    # Add a second axis and specify its features
    sec.axis = sec_axis(~.*coeff, name="Price ($)")
  ) + 
  
  theme_ipsum() +

  theme(
    axis.title.y = element_text(color = temperatureColor, size=13),
    axis.title.y.right = element_text(color = priceColor, size=13)
  ) +

  ggtitle("Temperature down, price up")

Barplot with overlapping line chart


It is totally possible to usee the same tricks with other geoms.

Here is an example displaying a line chart on top of a barplot.

# Value used to transform the data
coeff <- 10

# A few constants
temperatureColor <- "#69b3a2"
priceColor <- rgb(0.2, 0.6, 0.9, 1)

ggplot(head(data, 80), aes(x=day)) +
  
  geom_bar( aes(y=temperature), stat="identity", size=.1, fill=temperatureColor, color="black", alpha=.4) + 
  geom_line( aes(y=price / coeff), size=2, color=priceColor) +
  
  scale_y_continuous(
    
    # Features of the first axis
    name = "Temperature (Celsius °)",
    
    # Add a second axis and specify its features
    sec.axis = sec_axis(~.*coeff, name="Price ($)")
  ) + 
  
  theme_ipsum() +

  theme(
    axis.title.y = element_text(color = temperatureColor, size=13),
    axis.title.y.right = element_text(color = priceColor, size=13)
  ) +

  ggtitle("Temperature down, price up")

Related chart types


Scatter
Heatmap
Correlogram
Bubble
Connected scatter
Density 2d



❤️ 10 best R tricks ❤️

👋 After crafting hundreds of R charts over 12 years, I've distilled my top 10 tips and tricks. Receive them via email! One insight per day for the next 10 days! 🔥