Collapsing a Matrix in R: A Step-by-Step Guide

Introduction

In this article, we will explore how to collapse a matrix in R while obtaining the minimum and maximum values of some columns. We’ll start by examining the problem, then discuss potential solutions using aggregate(), followed by an exploration of more suitable alternatives.

Background

The provided R data frame contains information about protein structures, including Uniprot IDs, chain names, and sequence positions. The goal is to collapse this matrix down into a simplified format, where each row represents a collapsed group based on the first three columns (Uniprots and Chain). This process should result in the minimum and maximum values for two of these columns being reported.

Exploring Using aggregate()

One potential approach involves utilizing aggregate(), a function from base R that allows grouping data by one or more variables. Here’s how we might attempt to use it:

require(dplyr)

# Create a sample data frame (identical to the original)
dat <- read.table("data.txt",
                 header = TRUE,
                 row.names = 1,
                 stringsAsFactors = FALSE)

# Use aggregate() to group by Uniprots and Chain, then calculate min and max of resSeq
dat %>% 
    group_by(Uniprots, Chain) %>%
    summarize(resSeq_start = min(resSeq),
              resSeq_end   = max(resSeq))

However, the use of aggregate() in this context is not very efficient because it will produce a new data frame where each Uniprot/Chain combination appears as a separate row. This approach would result in an enormous number of rows for the given data.

Exploring Using dplyr’s group_by() and summarize()

Fortunately, the dplyr package provides an elegant way to accomplish this task by using the group_by() function followed by summarize(). Let’s take a look:

require(dplyr)

# Create a sample data frame (identical to the original)
dat <- read.table("data.txt",
                 header = TRUE,
                 row.names = 1,
                 stringsAsFactors = FALSE)

# Use dplyr to group by Uniprots and Chain, then calculate min and max of resSeq
dat %>% 
    group_by(Uniprots, Chain) %>%
    summarize(resSeq_start = min(resSeq),
              resSeq_end   = max(resSeq))

This method is significantly more efficient than using aggregate() alone because it produces the desired output in a much more compact format.

Additional Considerations

One important note to keep in mind when dealing with this type of data manipulation is that Uniprot/Chain combinations are not always unique. For instance, a single Uniprot might have multiple chains assigned to it.

When using dplyr for such tasks, consider how these duplicate entries will impact your results. In the provided example, we’re able to leverage group_by() and summarize() effectively to sidestep this issue, but in more complex scenarios, additional steps may be necessary to handle duplicate combinations.

Advanced Solution: Handling Duplicate Uniprot/Chain Combinations

In some cases, it’s possible that duplicate Uniprot/Chain pairs might need to be handled. For example, if the same resSeq value appears multiple times within a single Uniprot/Chain group, how should this be represented?

To handle such scenarios effectively in R, you could consider grouping by both Uniprots and Chain, then applying summarize() on these groups:

require(dplyr)

# Create a sample data frame (identical to the original)
dat <- read.table("data.txt",
                 header = TRUE,
                 row.names = 1,
                 stringsAsFactors = FALSE)

# Group by both Uniprots and Chain, then calculate min and max of resSeq
dat %>% 
    group_by(Uniprots, Chain) %>%
    summarise(resSeq_start = min(resSeq),
              resSeq_end   = max(resSeq))

By examining the output closely, you can see whether duplicate entries are correctly accounted for within each Uniprot/Chain pair.

Conclusion

In conclusion, we’ve explored how to collapse a matrix in R using dplyr. By leveraging the group_by() and summarize() functions provided by this package, we’re able to elegantly produce the desired output without encountering performance issues related to duplicate Uniprot/Chain combinations.

Whether your specific task involves data frames with multiple chains or rows with overlapping values, these techniques offer an excellent foundation for tackling similar challenges in the future.

Last modified on 2025-04-24