Great question! Let's break this down step by step—first how to create those side-by-side cluster plots with shared data, then the key differences between gridExtra and cowplot for this task.

Step 1: Generate Shared Data & Individual Cluster Plots #

First, let's use a concrete example with ggplot2 (the go-to tool for this kind of visualization). We’ll take a dataset, add cluster assignments from different algorithms, then create a reusable function to generate plots.

Sample Data Preparation #

We’ll use the classic Iris dataset and add results from K-Means, Hierarchical, and DBSCAN clustering:

library(ggplot2)
library(dplyr)
library(dbscan)

set.seed(123) # For reproducibility
cluster_data <- iris %>%
  mutate(
    kmeans = as.factor(kmeans(select(., -Species), centers = 3)$cluster),
    hierarchical = as.factor(cutree(hclust(dist(select(., -Species))), k = 3)),
    dbscan = as.factor(dbscan(select(., -Species), eps = 0.5, minPts = 5)$cluster)
  )

Reusable Plot Function #

Create a function to generate a plot for any cluster column—this avoids repeating code and ensures consistency:

make_cluster_plot <- function(data, cluster_col, plot_title) {
  ggplot(data, aes(x = Sepal.Length, y = Sepal.Width, color = .data[[cluster_col]])) +
    geom_point(size = 2) +
    labs(title = plot_title, color = "Cluster ID") +
    theme_minimal()
}

# Generate individual plots
cluster_plots <- list(
  make_cluster_plot(cluster_data, "kmeans", "K-Means Clustering"),
  make_cluster_plot(cluster_data, "hierarchical", "Hierarchical Clustering"),
  make_cluster_plot(cluster_data, "dbscan", "DBSCAN Clustering")
)

Step 2: Arranging Plots with gridExtra vs cowplot #

Now let’s cover how to combine these plots, then break down the key differences between the two packages.

Using gridExtra #

gridExtra is built on R’s core grid system, making it flexible for mixing plot types:

library(gridExtra)
grid.arrange(grobs = cluster_plots, ncol = 3, top = "Cluster Result Comparison")

Using cowplot #

cowplot is optimized for ggplot2, with built-in alignment and styling helpers:

library(cowplot)
plot_grid(
  plotlist = cluster_plots,
  ncol = 3,
  labels = c("A", "B", "C"),
  label_size = 12,
  title = "Cluster Result Comparison"
)

Key Differences Between gridExtra and cowplot #

Let’s break down the critical distinctions that matter for your clustering visualization task:

• Underlying Focus

  • gridExtra is a general-purpose grid layout tool—it works with ggplot2, lattice, and even base R plots. It’s great if you need to mix plot systems in one layout.
  • cowplot is purpose-built for ggplot2. All its features are tailored to streamline workflows with ggplot, so it feels more intuitive if you’re only working with ggplot plots.

• Alignment & Consistency

  • gridExtra requires manual adjustment (via the align parameter or arrangeGrob()) to get perfectly aligned axes between plots. If your plots have different scales or labels, alignment can be finicky.
  • cowplot::plot_grid() automatically aligns plot panels or axes by default. This is a huge plus for side-by-side cluster comparisons, where consistent axes make it easier to spot differences.

• Theme Handling

  • gridExtra leaves your plot themes untouched—your plots retain whatever styling you set (e.g., ggplot’s default theme, theme_minimal()).
  • cowplot applies its own clean, minimal default theme to plots unless you specify theme = NULL. This is handy if you want all your comparison plots to have a unified look without manually setting themes for each one.

• Extra Features

  • gridExtra includes tableGrob() to add summary tables (like cluster metrics) directly to your layout, which is useful for pairing visualizations with quantitative data.
  • cowplot has ggplot-specific helpers: get_legend() lets you extract a legend from one plot and reuse it across all (great for saving space), draw_plot_label() adds custom annotations, and rel_widths/rel_heights make it easy to adjust plot sizes in complex layouts.

• Ease of Use

  • For ggplot-only workflows, cowplot is more straightforward. Tasks like adding labels, aligning plots, and adjusting sizes require less code.
  • gridExtra is more verbose for ggplot-specific tasks but offers greater flexibility for mixed-plot scenarios.

Final Recommendation #

If your entire pipeline uses ggplot2 and you prioritize easy alignment, unified styling, and ggplot-specific tools, go with cowplot. If you need to mix plot types (e.g., a base R histogram alongside your ggplot cluster plots) or work with low-level grid objects, gridExtra is the better choice.

内容的提问来源于stack exchange，提问作者A Duv