# Line Chart With Two Y-Axis In Ggplot2

Wednesday, September 27th 2023. | Chart Templates

When it comes to visualizing data, line charts are a popular choice as they allow us to track changes over time. However, there are situations where we may want to compare two different variables on the same chart, but they have different scales. In such cases, using two y-axes can be a useful technique. In this article, we will explore how to create a line chart with two y-axes using ggplot2, a powerful data visualization package in R.

## What is ggplot2?

ggplot2 is an R package developed by Hadley Wickham that provides a flexible and powerful framework for creating data visualizations. It is based on the grammar of graphics, which allows users to build complex plots by combining different layers and mappings.

### Why use two y-axes?

There are situations where we may have two variables that are measured on different scales but are still related. In such cases, using two y-axes can help us visualize the relationship between these variables more effectively. By using two y-axes, we can plot the variables on different scales while still maintaining their relative positions on the chart.

For example, let’s say we want to compare the temperature and rainfall data for a particular location over time. Temperature is typically measured in degrees Celsius or Fahrenheit, while rainfall is measured in millimeters or inches. These two variables have different scales, and plotting them on the same chart without considering their scales may result in misleading visualizations. By using two y-axes, we can plot temperature on one axis and rainfall on the other, allowing us to see the relationship between these two variables more clearly.

## Creating a Line Chart with Two Y-Axes

Now let’s dive into the process of creating a line chart with two y-axes in ggplot2. We will use a sample dataset that contains temperature and rainfall data for a fictional location over a period of time.

The first step is to install and load the necessary packages. We will need the ggplot2 package for creating the chart and the scales package for adding the second y-axis.

``install.packages("ggplot2") install.packages("scales") library(ggplot2) library(scales) ``

Next, we need to import our data into R and prepare it for visualization. We will assume that our data is stored in a CSV file called “weather_data.csv” and contains two columns: “temperature” and “rainfall”.

``data <- read.csv("weather_data.csv") ``

Once we have our data, we can start creating the line chart. We will use the ggplot() function to initialize the chart and the geom_line() function to add the lines.

``ggplot(data, aes(x = date)) + geom_line(aes(y = temperature, color ="Temperature")) + geom_line(aes(y = rainfall * 10, color ="Rainfall")) + scale_color_manual(values = c("Temperature" ="red", "Rainfall" ="blue")) + xlab("Date") + ylab("Temperature / Rainfall") ``

In the code above, we specify the x-axis variable as "date" and the y-axis variables as "temperature" and "rainfall". We use the geom_line() function to add the lines to the chart, and the aes() function to specify the colors for each line. We also use the scale_color_manual() function to assign specific colors to each line.

By default, both y-axes will have the same scale, which may not be ideal if the variables have different ranges. To add a second y-axis with a different scale, we can use the sec_axis() function from the scales package.

``ggplot(data, aes(x = date)) + geom_line(aes(y = temperature, color ="Temperature")) + geom_line(aes(y = rainfall * 10, color ="Rainfall")) + scale_color_manual(values = c("Temperature" ="red", "Rainfall" ="blue")) + xlab("Date") + ylab("Temperature / Rainfall") + scale_y_continuous(sec.axis = sec_axis(~./10, name ="Rainfall")) ``

In the code above, we use the scale_y_continuous() function to define the second y-axis. The ~./10 syntax specifies that the second axis should be equal to the first axis divided by 10. We also provide a name for the second axis using the name parameter.

With these steps, we have successfully created a line chart with two y-axes in ggplot2. We can further customize the chart by adding labels, titles, legends, and adjusting the axis limits and scales to suit our needs.

## Sample Line Charts with Two Y-Axes

### 1. Temperature and Humidity

In this example, we will compare the temperature and humidity data for a specific location over time. The temperature is measured in degrees Celsius, while the humidity is measured as a percentage.

``ggplot(data, aes(x = date)) + geom_line(aes(y = temperature, color ="Temperature")) + geom_line(aes(y = humidity, color ="Humidity")) + scale_color_manual(values = c("Temperature" ="red", "Humidity" ="blue")) + xlab("Date") + ylab("Temperature / Humidity") ``

### 2. Sales and Profit

In this example, we will compare the sales and profit data for a fictional business over a period of time. The sales data is measured in dollars, while the profit data is measured as a percentage.

``ggplot(data, aes(x = date)) + geom_line(aes(y = sales, color ="Sales")) + geom_line(aes(y = profit * 100, color ="Profit")) + scale_color_manual(values = c("Sales" ="red", "Profit" ="blue")) + xlab("Date") + ylab("Sales / Profit") ``

### 3. Stock Price and Volume

In this example, we will compare the stock price and trading volume data for a particular company over time. The stock price is measured in dollars, while the trading volume is measured in millions of shares.

``ggplot(data, aes(x = date)) + geom_line(aes(y = price, color ="Stock Price")) + geom_line(aes(y = volume / 1000000, color ="Volume")) + scale_color_manual(values = c("Stock Price" ="red", "Volume" ="blue")) + xlab("Date") + ylab("Stock Price / Volume") ``

### 4. Website Traffic and Conversion Rate

In this example, we will compare the website traffic and conversion rate data for a fictional e-commerce website. The website traffic is measured in number of visits, while the conversion rate is measured as a percentage.

``ggplot(data, aes(x = date)) + geom_line(aes(y = traffic, color ="Website Traffic")) + geom_line(aes(y = conversion_rate * 100, color ="Conversion Rate")) + scale_color_manual(values = c("Website Traffic" ="red", "Conversion Rate" ="blue")) + xlab("Date") + ylab("Website Traffic / Conversion Rate") ``

### 5. Population and GDP

In this example, we will compare the population and GDP data for different countries over a period of time. The population is measured in millions, while the GDP is measured in billions of dollars.

``ggplot(data, aes(x = date)) + geom_line(aes(y = population / 1000000, color ="Population")) + geom_line(aes(y = gdp / 1000000000, color ="GDP")) + scale_color_manual(values = c("Population" ="red", "GDP" ="blue")) + xlab("Date") + ylab("Population / GDP") ``

## Frequently Asked Questions (FAQ)

### Q: Can I add more than two y-axes to a line chart in ggplot2?

A: No, ggplot2 does not support adding more than two y-axes to a line chart. Adding more y-axes can make the chart more complex and harder to interpret.

### Q: How do I change the colors of the lines in the chart?

A: You can change the colors of the lines by using the scale_color_manual() function and specifying the desired colors. For example, scale_color_manual(values = c("Temperature" ="red", "Rainfall" ="blue")) will set the color of the temperature line to red and the color of the rainfall line to blue.

### Q: Can I customize the labels and titles of the axes?

A: Yes, you can customize the labels and titles of the axes by using the xlab() and ylab() functions. For example, xlab("Date") will set the label

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