Rolling Joins in R with data.table: Understanding the Behavior and Workarounds

Introduction

When working with data tables in R, one common operation is the rolling join. This allows us to perform an inner join between two data tables where each row of one table is matched with every row of another table that has a corresponding key value. In this post, we will delve into how the data.table package handles rolling joins and explore some potential pitfalls and workarounds.

Why Rolling Joins Matter

In many real-world scenarios, we need to analyze data over time or across different groups. A rolling join is particularly useful when working with time-series data or aggregating values from multiple sources. By joining two tables based on common keys, we can create new columns that contain information about the corresponding data points in each table.

Setting Up the Problem

To illustrate the issue at hand, let’s consider a simple example:

# Create data tables A and B
A <- data.table(id = c(1, 2, 2, 3),
                dod = as.Date(c('2022-08-01', '2022-01-01', '2022-01-01', '2022-03-01')),
                sex = c('M', 'F', 'M', 'F'))

B <- data.table(id = c(1, 2, 2, 3, 4, 5),
               pay_date = as.Date(c('2022-12-01', '2022-01-01', '2022-01-01', '2022-07-01', '2022-08-01', '2022-10-01')),
               prem = c(100, 150, 120, 80, 160, 180))

# Assign a new column roll_date to A and pay_date to B
A[, roll_date := dod]
B[, roll_date := pay_date]

The Expected Output

Our goal is to create an output table where each row contains the corresponding values from tables A and B. We expect the output to have 6 rows, with columns for id, pay_date, prem, and roll_date.

# Desired output
output <- data.table(id = c(1, 2, 2, 3, 4, 5),
                    pay_date = c('2022-12-01', '2022-01-01', '2022-01-01', '2022-07-01', NA, NA),
                    prem = c(100, 150, 120, 80, NA, NA),
                    roll_date = c('2022-12-01', '2022-01-01', '2022-01-01', '2022-07-01', NA, NA))

# Expected output
output

The Issue: Extra Rows Created

When we run the rolling join A[B, on = .(id, roll_date), roll = T], R creates an unexpected number of rows in the output. Specifically, it includes all combinations of the key values where id == 2 & roll_date == '2022-01-01'. This results in two extra rows being created.

A Solution with Duplicated()

One possible solution to this issue is to use the duplicated() function to remove duplicate rows from table A before performing the rolling join. Here’s an example:

# Remove duplicates from A using duplicated()
non_duplicate_A <- A[!duplicated(id, roll_date)]

# Perform rolling join on non-duplicate A with B
output <- non_duplicate_A[B, on = .(id, roll_date), roll = T]

The Problem with the Solution: Loss of Non-First Rows

While this solution removes duplicate rows from table A, it also loses some of the original data. Specifically, if there are multiple rows in A where id == 2 & roll_date != '2022-01-01', these rows will be removed.

Alternative Solutions: Using Outer Joins or Subsets

Another approach to this problem is to use an outer join instead of the default inner join. This allows us to include all rows from both tables, even if there are no matching key values.

# Use outer join with B and A
outer_join_output <- A[B, on = .(id, roll_date), roll = "all"]

# Alternatively, use subset() to exclude non-matching rows in B
subsetted_B <- B[B$id %in% A$id & B$roll_date == A$roll_date, ]

# Use subsetted B with A for the rolling join
subset_join_output <- A[subsetted_B, on = .(id, roll_date), roll = T]

Conclusion

In conclusion, when working with data tables in R and using the data.table package, we can encounter unexpected behavior with rolling joins. In this post, we explored an example where a simple rolling join resulted in extra rows being created. We also examined potential workarounds, including removing duplicates from one of the tables, using outer joins or subsets to exclude non-matching rows, and exploring alternative approaches like aggregating values over time.

By understanding how data tables handle rolling joins, we can develop more effective strategies for working with these operations in R and improve our overall data analysis workflow.

Last modified on 2024-04-15