stringrfindsubstring

The function `stringrfindsubstring` is a utility from the R programming language's `stringr` package, designed to locate the positions of one or more substrings within a given string. It serves as a more flexible alternative to base R's `str_detect` and `str_locate_all` functions, offering greater control over substring matching.

`stringrfindsubstring` operates by scanning a primary string for occurrences of one or more specified substrings. The function returns a list of matches, where each match includes the starting and ending positions of the substring within the original string. This is particularly useful for text processing tasks, such as extracting specific segments of text, validating input formats, or preparing data for further analysis.

The function accepts several key arguments. The primary input is the string to be searched (`string`), followed by the substrings to locate (`pattern`). Additional parameters allow users to specify whether the search should be case-sensitive (`ignore_case = FALSE`), whether to return partial matches (`fixed = FALSE`), and whether to allow overlapping matches (`overlap = FALSE`). The function also supports regular expressions when `fixed = FALSE`, enabling complex pattern matching.

For example, `stringrfindsubstring("Hello world", "l")` would return the starting and ending indices of all occurrences of the letter "l" within the string "Hello world." This function is part of the broader `stringr` package, which provides a suite of tools for efficient string manipulation in R, adhering to a tidyverse-compatible syntax.

While `stringrfindsubstring` is powerful for substring detection, it is important to note that it does not modify the original string—it merely identifies positions where substrings occur. Users requiring actual string manipulation, such as replacement or extraction, may need to combine this function with others like `stringr::str_sub` or `stringr::str_replace`. The function is widely used in data cleaning workflows, natural language processing, and text mining applications.