Manipulating Labels, Legends, Spacing in Parallel Coordinate Plots with grid.arrange

In the realm of data visualization, parallel coordinate plots have gained significant attention for effectively showcasing complex relationships between multiple variables. The grid.arrange function from the gridExtra package provides a convenient way to arrange multiple graphs into a single figure. However, when dealing with parallel coordinate plots, additional considerations come into play regarding labels, legends, and spacing.

In this article, we will delve into the intricacies of working with parallel coordinate plots using grid.arrange. We’ll explore various techniques for customizing labels, legends, and spacing to create visually appealing and informative plots.

Understanding Parallel Coordinate Plots

A parallel coordinate plot is a type of visualization that displays multiple variables in a single graph. Each variable is represented by a line or curve that is drawn on the chart. The key feature of parallel coordinate plots is that all lines are drawn simultaneously, allowing for direct comparisons between different data points.

In the context of our problem, we’re dealing with a custom function plotClusterPar that generates multiple parallel coordinate subplots in a single figure. Each subplot features four variables (a, b, c, and d), which are used to create lines on the chart.

Customizing Labels

One common requirement when working with parallel coordinate plots is to remove or customize labels for individual subplots. By default, each subplot displays its own x-axis label, which can be distracting when dealing with multiple plots. To address this issue, we need to use the panel.layout argument in grid.arrange to specify a custom layout.

## Customizing Labels

To remove x-axis labels from individual subplots, we can use the following approach:

```r
library(gridExtra)
library(GGally)

plotClusterPar = function(cNum){
  plot_i = vector("list", length=cNum)
  for (i in 1:cNum){
    x = data.frame(a=runif(100,0,1),b=runif(100,0,1),c=runif(100,0,1),d=runif(100,0,1))
    plot_i[[i]] = 
      ggparcoord(x, columns=c(1:4), scale="globalminmax", alphaLines = 0.9) +
      ylab("Count") +
      theme(panel.grid.major.x = element_blank(),
            panel.grid.minor.x = element_blank())
  }
  
  p = do.call("grid.arrange", 
             c(plot_i, ncol=1,
                main = textGrob("Main Title", vjust = 1, gp = gpar(fontface = "bold", cex = 1.5)),
                left = textGrob("Global Y-axis Label", rot = 90, vjust = 1)))
}

By adding the panel.grid.major.x and panel.grid.minor.x elements to the theme, we effectively remove x-axis labels from each subplot.

Customizing Legends

Another crucial aspect of parallel coordinate plots is the legend. By default, each subplot displays its own legend, which can become cluttered when dealing with multiple plots. To address this issue, we can use the legend.position argument in grid.arrange to specify a custom legend position.

## Customizing Legends

To create a single legend for all subplots, we can modify our code as follows:

```r
library(gridExtra)
library(GGally)

plotClusterPar = function(cNum){
  plot_i = vector("list", length=cNum)
  for (i in 1:cNum){
    x = data.frame(a=runif(100,0,1),b=runif(100,0,1),c=runif(100,0,1),d=runif(100,0,1))
    plot_i[[i]] = 
      ggparcoord(x, columns=c(1:4), scale="globalminmax", alphaLines = 0.9) +
      ylab("Count") +
      theme(panel.grid.major.x = element_blank(),
            panel.grid.minor.x = element_blank())
  }
  
  p = do.call("grid.arrange", 
             c(plot_i, ncol=1,
                main = textGrob("Main Title", vjust = 1, gp = gpar(fontface = "bold", cex = 1.5)),
                left = textGrob("Global Y-axis Label", rot = 90, vjust = 1),
                legend.position = "bottom"))
}

By setting the legend.position to "bottom", we create a single legend that spans across all subplots.

Customizing Spacing

Finally, let’s discuss how to customize spacing between subplots. By default, grid.arrange uses a fixed spacing between plots. To adjust this spacing, we can use the strip argument in grid.arrange.

## Customizing Spacing

To add some breathing room between subplots, we can modify our code as follows:

```r
library(gridExtra)
library(GGally)

plotClusterPar = function(cNum){
  plot_i = vector("list", length=cNum)
  for (i in 1:cNum){
    x = data.frame(a=runif(100,0,1),b=runif(100,0,1),c=runif(100,0,1),d=runif(100,0,1))
    plot_i[[i]] = 
      ggparcoord(x, columns=c(1:4), scale="globalminmax", alphaLines = 0.9) +
      ylab("Count") +
      theme(panel.grid.major.x = element_blank(),
            panel.grid.minor.x = element_blank())
  }
  
  p = do.call("grid.arrange", 
             c(plot_i, ncol=1,
                main = textGrob("Main Title", vjust = 1, gp = gpar(fontface = "bold", cex = 1.5)),
                left = textGrob("Global Y-axis Label", rot = 90, vjust = 1),
                legend.position = "bottom",
                strip = list(top = unit(10, "pt"),
                            bottom = unit(10, "pt"))))
}

By adding the strip argument with custom top and bottom spacing, we create a more visually appealing layout.

Conclusion

In this article, we explored various techniques for customizing labels, legends, and spacing in parallel coordinate plots using grid.arrange. By applying these strategies, you can create visually appealing and informative plots that effectively communicate your data insights. Whether you’re working with multiple subplots or a single plot, mastering the art of layout customization will take your data visualization skills to the next level.

Remember to experiment with different layouts and techniques to find the perfect balance between visual appeal and data clarity. Happy plotting!

Last modified on 2024-09-19