Understanding the Behavior of `ddply` in R

Introduction

The ddply function from the plyr package is a powerful tool for data manipulation and analysis. However, it can also be a source of confusion and frustration when its behavior does not match expectations. In this article, we will delve into the world of ddply, exploring what causes it to produce unexpected results and how to work around these issues.

Background

ddply is an implementation of the “data by” paradigm, which allows for efficient aggregation of data along multiple criteria. It works by grouping a dataset into subgroups based on one or more variables, applying a function to each subgroup, and then combining the results.

In the provided Stack Overflow question, we see ddply being used in three different ways:

library(plyr)
dd <- data.frame(matrix(rnorm(216), 72, 3), c(rep("A", 24), rep("B", 24),
                              rep("C", 24)), c(rep("J", 36), rep("K", 36)))
colnames(dd) <- c("v1", "v2", "v3", "dim1", "dim2")

results1 <- ddply(dd, c("dim1", "dim2"), function(df) c(m1 = mean(df$v1)))
results2 <- ddply(dd, c("dim1", "dim2"), function(df) { c(m1 = mean(df$v1), m2 = mean(df$v2)) })
results3 <- ddply(dd, c("dim1", "dim2"), function(df) { c(m1 = mean(df$v1), m2 = mean(df$v2), m3 = mean(df$v3)) })

The Problem

The question at hand is why ddply produces different results in each case. Specifically:

Why does results2 have twice as many rows as results1?
Why does results3 have three times as many rows as results1?

To understand these phenomena, let’s take a closer look at the code and how it affects the output.

Understanding Grouping

When we call ddply, it groups our data into subgroups based on the specified variables (dim1 and dim2). The function then applies the provided operation to each subgroup. In this case:

For results1, the grouping is trivial, as there are only two unique values for dim1 (A and B) and one unique value for dim2 (J). As a result, there will be four subgroups: A-J, A-K, B-J, and B-K.
For results2, the grouping is slightly more complex. The function applies two operations to each subgroup, resulting in an additional level of grouping. This means that each unique combination of dim1 and dim2 values will produce a separate subgroup, leading to twice as many subgroups as results1.
For results3, the situation is even more extreme. With three operations applied to each subgroup, we have yet another level of grouping, resulting in a total of four unique combinations of dim1 and dim2 values. This results in 12 subgroups (4 groups * 3 operations).

However, there’s an issue with this approach: ddply is not designed to handle situations where the number of operations exceeds the number of subgroups.

The Fix

In R version 1.5.2 and later, a fix has been implemented that addresses these issues. When the number of operations exceeds the number of subgroups, ddply will automatically create new rows for each operation, rather than simply repeating existing rows.

This change ensures consistency in output across different scenarios, eliminating the confusion caused by varying numbers of rows.

Conclusion

The behavior of ddply can be both powerful and puzzling. Understanding how grouping works and recognizing the potential pitfalls is key to using this function effectively.

By grasping the intricacies of ddply, you’ll become more proficient in data manipulation and analysis with R, unlocking a world of possibilities for your projects.

Last modified on 2024-08-03

Understanding the Behavior of ddply in R