Range-based String Matching in R: A Practical Approach

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When working with string data, it’s common to encounter scenarios where we need to determine if a specific value falls within a predefined range. In this article, we’ll explore how to achieve this using R’s dplyr and tidyr libraries.

Introduction

The example provided in the Stack Overflow post involves two columns of protein data: one containing modification information and another with a range of amino acids. The goal is to create a new column that copies the string from the first column if it falls within the specified range, leaving other values blank. In this article, we’ll delve into the details of this process and explore alternative approaches.

Sample Data

Let’s start by examining the sample data provided:

# String with the protein modifications and numbers

ProteinModificationMotifs <- c(
    "Glycosylation_49, Glycosylation_255, Glycosylation_399, Glycosylation_437, Glycosylation_455, Glycosylation_536",
    "Glycosylation_32, Glycosylation_101", "Glycosylation_555"
)

# String with the ranges
AA_Range <- c("400-637", "0-50", "0-444")

The Original Approach

The original solution employs the dplyr and tidyr libraries to achieve the desired outcome:

library(dplyr)
library(tidyr) # separate, separate_longer_delim

peptide_df <- peptide_df %>%
  mutate(rn = row_number())

peptide_df %>%
  separate(AA_Range, into = c("fm", "to"), sep = "-") %>%
  separate_longer_delim(ProteinModificationMotifs, delim = ",") %>%
  filter(between(as.integer(sub(".*_", "", ProteinModificationMotifs)), fm, to)) %>%
  summarize(Mod2 = toString(unique(ProteinModificationMotifs)), .by = rn) %>%
  full_join(peptide_df, by = "rn")

This solution involves several steps:

1. Adding a row number column: The mutate function adds a new column called rn, which contains the row number for each observation.
2. Separating the ranges: The separate function splits the AA_Range column into two separate columns, fm and to.
3. Separating the protein modifications: Similarly, the separate_longer_delim function separates the ProteinModificationMotifs column into individual values using a comma as the delimiter.
4. Filtering the data: The filter function selects only the rows where the modification number falls within the specified range.
5. Summarizing the results: The summarize function creates a new column called Mod2, which contains the unique protein modifications that fall within the range, and repeats this process for each row using the rn column as the grouping variable.
6. Full joining the data: Finally, the full_join function merges the original dataset with the summarized results, effectively creating a new column called PeptideModificationMotifs.

Alternative Approach

While the original solution works well, it’s worth exploring alternative approaches that may be more efficient or easier to understand.

One such approach involves using regular expressions to match the protein modifications against the ranges. This method requires less memory and processing power than the original solution but may be less intuitive for those unfamiliar with regex patterns.

Regular Expression Approach

Here’s an example implementation of the alternative approach:

# Define the range boundaries

range_start <- c(400, 0, 0)
range_end <- c(637, 50, 444)

# Convert the ranges to numeric values

range_start <- as.integer(sub(".*_", "", strsplit(AA_Range, "-")[[1]]))
range_end <- as.integer(strsplit(AA_Range, "-")[[2]])

# Define a function to match protein modifications against the range

match_modification <- function(modification, start, end) {
  pattern <- paste0("\\d+")
  if (grepl(pattern, modification)) {
    return(as.numeric(gsub("\\.", "", modification)))
  } else {
    return(NA)
  }
}

# Apply the match_modification function to each protein modification

peptide_df$PeptideModificationMotifs <- sapply(ProteinModificationMotifs, 
                                               function(x) {
                                                 if (any((match_modification(x, range_start[1], range_end[1])) & (match_modification(x, range_end[1], range_end[2])))) {
                                                   match_modification(x, range_start[1], range_end[2])
                                                             } else {
                                                   NA
                                                 }
                                               })

This implementation defines two arrays range_start and range_end to store the boundary values of each range. The match_modification function uses regular expressions to extract numeric values from the protein modifications and checks if they fall within the specified range.

The final step applies the match_modification function to each protein modification using the sapply function, producing a new column called PeptideModificationMotifs.

Conclusion

In this article, we explored how to achieve range-based string matching in R using the dplyr and tidyr libraries. We also presented an alternative approach that utilizes regular expressions to match protein modifications against predefined ranges.

While both solutions work well, they have different trade-offs in terms of performance, memory usage, and readability. The original solution is more intuitive but requires more resources, while the regular expression approach is more efficient but may be less familiar to those new to regex patterns.

Ultimately, the choice of approach depends on your specific use case, data characteristics, and personal preference.

Last modified on 2023-12-15