Key Points To create multiple panels within one plot based on one or more factors, you can use one of the faceting functions: facet_wrap() or facet_grid(). To arrange multiple plots in a simple grid layout using the grid system, use the gridExtra package and the grid.arrange() function. To create complex layouts with shared axes and annotations using the ggplot2 system, you can use the cowplot package and the plot_grid() function. To arrange multiple plots with common features and themes using the ggplot2 system, you can use the ggpubr package and the ggarrange() function. To export the arranged plots to a file in various formats, you can use the ggsave() function. ggplot2 multiple plots in R

Read the Complete article :ggplot2 multiple plots in R

If you are working with data visualization in R, you have probably heard of ggplot2, one of the most popular and powerful packages for creating graphs. But what if you want to create multiple plots on the same page?

How can you arrange multiple plots using ggplot2 neat and organized?

In this article, I will show you how to produce multiple plots using ggplot2 and arrange them on a single page or multiple pages. You will learn to use different packages and functions to combine multiple ggplot in various layouts, such as grid, facet, or custom. You will also learn how to align the plot panels, add captions and annotations, and export the arranged plots to a file.

What is ggplot2?

ggplot2 is a popular R package for data visualization that implements the grammar of graphics. It allows you to create graphs by specifying your plot’s aesthetic mapping, geometric objects, statistical transformations, scales, coordinates, facets, and themes. Add labels, legends, titles, and other elements to your graph.

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

A scatter plot of sepal.length vs sepal.width for the iris dataset You can see that ggplot2 automatically creates a default theme, axes, and legend for your plot. You can customize these elements by adding more layers or modifying the existing ones.

How to Arrange Multiple Plots Using ggplot2?

One of the advantages of ggplot2 is that it allows you to create multiple plots from the same data or different data sources and arrange them on a single page or multiple pages. This can be useful for comparing different plots, showing different aspects of the same data, or creating a dashboard of graphs.

Depending on your needs and preferences, there are several ways to arrange multiple plots using ggplot2.

In this article, I will cover four main methods:

Using the facet_*() functions to create multiple panels within one plot Using the gridExtra package to arrange multiple plots in a grid layout Using the cowplot package to create complex layouts with shared axes and annotations Using the ggpubr package to arrange multiple plots with common features and themes Each method has advantages and disadvantages, so choose the one that best suits your purpose. Let’s see how each method works in detail.

How to Use the facet_*() Functions to Create Multiple Panels Within One Plot?

One of the simplest ways to create multiple plots using ggplot2 is to use one of the faceting functions: facet_wrap() or facet_grid(). These functions allow you to split your data into subsets based on one or more factors and create multiple panels (or facets) within one plot. Each panel shows a subset of the data with its axes and scales.

A scatter plot of sepal.length vs sepal.width for each species of iris You can see that ggplot2 creates three panels, one for each species (setosa, versicolor, and virginica). Each panel has its own x-axis and y-axis labels and scales. You can also see that ggplot2 adds a strip with the name of the factor (species) above each panel.

Change the number of rows or columns of panels

You can customize the appearance and behavior of the facets by using various arguments in the faceting functions. For example, you can change the number of rows or columns of panels by using the nrow or ncol arguments, respectively. You can also change the direction of the panels by using the dir argument. For example, the following code creates two rows of panels and arranges them horizontally:

A scatter plot of sepal.length vs sepal.width for each species of iris arranged in two rows Grid of Panels based on Two Factors You can also use facet_grid() to create a grid of panels based on two factors. For example, if you want to compare the scatter plots of sepal.length vs sepal.width for each combination of species and petal width, you can use facet_grid() as follows:

A scatter plot of sepal.length vs sepal.width for each combination of species and petal width You can see that ggplot2 creates a grid of 9 panels, one for each combination of species (setosa, versicolor, and virginica) and petal width ((0,1], (1,2], (2,3]). Each panel has its own x-axis and y-axis labels and scales. You can also see that ggplot2 adds strips with the names of the factors (species and petal.width) above and to the right of each panel. Customize the Appearance and Behavior of the Grid You can customize the appearance and behavior of the grid by using various arguments in the faceting function. For example, you can change the grid layout by using the layout argument. You can also change the alignment of the axes by using the scales argument. For example, the following code creates a grid with three columns and four rows and aligns the x-axes across all panels:

A scatter plot of sepal.length vs sepal.width for each combination of species and petal width arranged in a grid with three columns and four rows and aligned x-axes The faceting functions are convenient, and easy to create multiple panels within one plot. However, they are limited when creating more complex layouts or combining plots.

For example, you cannot use faceting functions to create plots with different geoms or data sources. You cannot add annotations or captions to individual panels or the plot. In these cases, you must use other methods to arrange multiple plots using ggplot2.