Arranging with different heights
The third combination of these plots would be arranging them in different heights in multiple rows and columns in the same graph window. The different height orientation arranges the plots in different lines according to heights. Basically, the first two plots are arranged horizontally and the last plot here is placed below the second plot.
We use the add(+) operator to arrange the first two plots horizontally. Then we use the slash (/) operator for the third plot to place it on a new line below the second plot.
Example: Arranging plots with different heights
R
library(ggplot2) library(patchwork) data(iris) head(iris) gfg1 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_bar(stat = "identity") gfg2 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_point() gfg3 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_line() # Arrangement of plots according to # different heights gfg_comb_3 <- gfg1 + gfg2 / gfg3 gfg_comb_3 |
Output :
Different heights orientation
Draw Composition of Plots Using the patchwork Package in R
In this article, we will discuss how to draw a composition of plots using the patchwork package in R programming language. The patchwork package makes it easier to plot different ggplots in a single graph. We will require both ggplot2 packages for plotting our data and the patchwork package to combine the different ggplots.
Let us first draw all the plots normally and independently.
Example: Plotting the dataset in a bar plot
R
library(ggplot2) library(patchwork) data(iris) head(iris) # Plotting the bar chart gfg1 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_bar(stat = "identity") gfg1 |
Output :
Bar plot
Example: Plotting the dataset in a scatterplot
R
library(ggplot2) library(patchwork) data(iris) head(iris) # Scatterplot gfg2 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_point() gfg2 |
Output :
Scatterplot
Example: Plotting the dataset in a line plot
R
library(ggplot2) library(patchwork) data(iris) head(iris) # Line plot gfg3 <- ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) + geom_line() gfg3 |
Output :
Line plot
Now let us look at how these three plots can be combined in various orientations.
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