Understanding the Problem and Requirements

The problem presented involves replacing characters in a string based on positions specified in another variable. The replacement should be done without searching for the character itself, but rather by position.

Given a data frame xo with two variables: locus and sequence. Each row of sequence contains a sequence of characters followed by occurrences of ‘R’ that need to be removed. Another variable positions_of_Ns_to_remove specifies the positions where these replacements should take place. However, this latter variable contains comma-separated values for each sequence.

Solution Overview

The solution involves creating a custom function to remove characters at specified positions from a string and applying it to the relevant rows in the data frame using the tidyverse functions mutate, str_split, and group_by.

Step 1: Define Custom Function

To achieve the desired outcome, we define a function called remove_pos that takes two arguments: a string and an integer n. It calculates the length of the string (nchar(string)), sorts the positions in descending order (since we start removing from the beginning at each position n), and then iteratively removes characters up to position i-1 and after position i, where i is our specified position.

Step 2: Split and Process Positions

We need to split the positions_of_Ns_to_remove variable into individual positions because they are listed comma-separated but each sequence needs its own replacements. We do this with str_split(positions_of_Ns_to_remove, ",").

Step 3: Apply Custom Function

Next, we group the data by locus and apply the custom function to each row’s sequence, replacing it only if there are non-NA values in positions_of_Ns_to_remove. We use str_split again because our sequence might contain multiple occurrences of ‘R’, but we want to replace at specific positions.

Step 3: Solution Implementation

library(stringr)
library(dplyr)

# Custom function to remove characters from string at specified positions
remove_pos <- function(string, n) {
  # Ensure n is an integer and ordered in descending order for accurate removal
  n <- as.integer(n)
  
  # Calculate the length of the string
  len <- nchar(string)
  
  # Initialize output with original string
  output <- string
  
  # Remove characters up to position i-1 followed by those after position i
  for (i in n) {
    output <- paste0(
      str_sub(output, start = 1L, end = i - 1L),
      str_sub(output, start = i + 1, end = len)
      )
  }
  
  # Return the modified string
  return(output)
}

# Data processing and transformation using tidyverse functions
xo %>% 
  mutate(positions = str_split(positions_of_Ns_to_remove, ",")) %>% 
  group_by(locus, n=row_number()) %>% 
  mutate(
    new_seq = ifelse(!is.na(positions_of_Ns_to_remove), 
                     remove_pos(sequence, unlist(positions)), 
                     sequence)
  ) %>% 
  select(-positions) %>% 
  ungroup()

Conclusion

The approach described involves using a custom function to replace characters at specified positions in strings while working with data frames that have multiple sequences and positions of removal. This method leverages R’s string manipulation functions (str_sub) alongside tidyverse tools for efficient data processing, ensuring an elegant solution to the problem without relying on loops or complicated string search operations.

Additional Considerations

• The use of dplyr packages like mutate, group_by, and ungroup is crucial for the efficient transformation of data based on specified conditions.
• Custom functions in R can greatly enhance productivity, especially when dealing with complex logic that would be cumbersome to implement through base R or other methods.

This solution provides a clear path forward for similar problems involving string manipulation based on position rather than character content.

Last modified on 2025-02-14