Plotting Every Third Column in a Data Frame Function

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In this post, we’ll delve into plotting every third column of a data frame using the ggtern library and some creative use of data manipulation techniques.

Introduction to ggtern

The ggtern package provides a set of functions for creating ternary plots. Ternary plots are useful for visualizing three-dimensional data in two dimensions by reducing it to two dimensions using an orthogonal projection. This can be particularly helpful when dealing with high-dimensional data or when visualizing relationships between variables.

Background

For this example, we’ll use the tidyverse package, which provides a set of tools for data manipulation and visualization. We’ll also leverage the pivot_longer function to transform our data into a more suitable format for plotting.

Step 1: Creating the Data Frame

Let’s start by creating our sample data frame using rDirichlet.acomp. This function generates random Dirichlet distributions with specified parameters. We’ll create six sets of distributions, each with three variables (x, y, and z).

library(ggtern)
library(compositions)

dummy <- data.frame(
  t1 = rDirichlet.acomp(100, alpha = c(x=15,y=7,z=3)),
  t2 = rDirichlet.acomp(100, alpha = c(x=15,y=7,z=3)),
  t3 = rDirichlet.acomp(100, alpha = c(x=15,y=7,z=3)),
  t4 = rDirichlet.acomp(100, alpha = c(x=15,y=7,z=3)),
  t5 = rDirichlet.acomp(100, alpha = c(x=15,y=7,z=3)),
  t6 = rDirichlet.acomp(100, alpha = c(x=15,y=7,z=3))
)

Step 2: Creating the Regex Vector

We’ll create a regex vector to extract the column names from our data frame.

regex <- paste0("^t", 1:6)

This regex pattern matches any string that starts with “t” followed by a digit between 1 and 6. This will allow us to extract the column names of interest.

Step 3: Looping Over the Regex Vector

We’ll loop over the regex vector, use grep to extract each set of three columns, and then plot them using ggtern.

for (i in seq_along(regex)) {
  cols <- grep(regex[i], names(dummy), value = TRUE)
  if (length(cols) == 3) {
    ggtern(
      data = dummy, mapping = aes(x=x, y=y, z=z),
      facet_wrap(~var)
    ) +
      geom_point()
  }
}

However, this approach is not very scalable. We can do better by using pivot_longer.

Step 4: Using Pivot Longer

We’ll use pivot_longer to transform our data into a long format, where each row represents a single observation.

library(tidyr)

dummy_long <- dummy %>% 
  pivot_longer(everything(), names_pattern="(t\\d).([x-z])", 
              names_to = c("var", ".value"))

In this code:

We use pivot_longer to reshape our data from wide format (with separate columns for each observation) to long format.
The everything() function tells pivot_longer to include all variables in the transformation.
The names_pattern argument is used to specify a regular expression pattern that matches the variable names. In this case, we match any string that starts with “t” followed by a digit between 1 and 6 and then one of the characters x, y, or z.
We use names_to = c("var", ".value") to specify that the original column name should become the “var” variable, and the value from the transformed variable should become the “.value” variable.

Step 5: Plotting the Data

Finally, we can plot our data using ggtern.

ggtern(dummy_long, aes(x=x, y=y, z=z)) +
  geom_point() +
  facet_wrap(~var)

This code creates a ternary plot where each row represents a single observation.

Conclusion

We’ve demonstrated how to create a function that plots every third column of a data frame using ggtern. By leveraging the pivot_longer function, we can transform our data into a more suitable format for plotting. This approach provides an efficient way to visualize relationships between variables in high-dimensional data.

Example Use Cases

Data Analysis: When working with large datasets, you may need to analyze relationships between variables. Using ggtern, you can plot every third column of your data frame and examine patterns or correlations.
Machine Learning: In machine learning models, you often work with high-dimensional data. By plotting every third column using ggtern, you can gain insights into the relationships between variables and improve model performance.

References

Last modified on 2023-05-21