Understanding R Plots and the Issue of Collapsing Y-Axes

As a data analyst or scientist working with R, you’ve likely encountered various types of plots, from simple scatterplots to complex heatmaps. However, sometimes your plots may not display as expected, and that’s where this post comes in – to help you understand why your R plot might be collapsing the y-axis and provide solutions to rectify the issue.

What is a Coordinate System in R?

In R, a coordinate system is used to map the data points onto a graphical plane. There are several types of coordinate systems available in R, including the default “equal” scale and custom scales. The “equal” scale ensures that both the x and y axes have the same unit length, making it easier to compare distances between data points.

Using coord_equal() in R

The coord_equal() function is used to set the coordinate system for a plot to be equal on both axes. This means that both the x and y axes will have the same length, regardless of the actual values in the data. However, when working with data that has different scales, such as time and distance, this can lead to misleading visualizations.

The Problem with coord_equal() for NMDS Plots

In the context of Non-Metric Multidimensional Scaling (NMDS), a technique used for dimensionality reduction, using coord_equal() can cause issues. NMDS plots are designed to show the relationship between different variables or features in a dataset. However, when both axes have the same unit length, it can be difficult to interpret the results.

Rotating Data in R

One way to overcome this issue is by rotating the data so that it occupies a diagonal instead of a single axis. This is achieved using rotation matrices, which are used to transform the data points from their original space to a new coordinate system where one dimension lies on the x-axis and another dimension lies on the y-axis.

How to Rotate Data in R

To rotate the data, you can use the rot_mat function, which generates a 2x2 rotation matrix based on the angle of rotation. The rotation matrix is then multiplied with the original data points to transform them into the new coordinate system.

Here’s an example code snippet that demonstrates how to rotate data in R:

# Set seed for reproducibility
set.seed(123)

# Generate random data points
v <- c(rnorm(18), rnorm(18)+2)
matrix(ncol = 6) %>% 
  metaMDS(autotransform = FALSE, distance = "euclidean")

# Create a rotation matrix to rotate the data by 45 degrees
rot_mat <- matrix(c(sqrt(2)/2, sqrt(2)/2, -sqrt(2)/2, sqrt(2)/2), ncol=2)

# Rotate the data points using the rotation matrix
v$points %*% rot_mat %>%
  as.data.frame() %>%
  setNames(c("MDS1", "MDS2"))

# Create a plot of the rotated data points
ggplot(v$points) +
  geom_point(aes(x=MDS1, y=MDS2)) +
  coord_equal()

Alternative Solutions

In some cases, you may not need to rotate the entire dataset. Instead, you can consider dropping certain dimensions from your NMDS output without losing too much information. This is achieved by using techniques such as principal component analysis (PCA) or hierarchical clustering.

Another alternative solution is to use geom_density() instead of geom_point(). geom_density() creates a kernel density estimate of the data points, which can provide valuable insights into the underlying distribution of the data.

Here’s an example code snippet that demonstrates how to drop dimensions from NMDS output using PCA:

# Set seed for reproducibility
set.seed(123)

# Generate random data points
v <- c(rnorm(18), rnorm(18)+2)
matrix(ncol = 6) %>% 
  metaMDS(autotransform = FALSE, distance = "euclidean", k = 1)

# Perform PCA on the NMDS output
pca_v <- prcomp(v$points[, 1:3])

# Create a plot of the PCA data points
ggplot(pca_v) +
  geom_density(aes(x = pc1, color = rep(c("A", "B"), each=3)))

Conclusion

In conclusion, when working with R plots, it’s essential to understand how coordinate systems work and how they can impact the visualization of your data. By using techniques such as rotation matrices and PCA, you can overcome issues with collapsing y-axes and create more interpretable visualizations. Remember to explore different alternatives and choose the one that best suits your needs.

Last modified on 2024-09-07