Using Dataframes and Regex for Fuzzy Matching in R

Fuzzy Matching with Dataframes and Regex

Introduction

The problem presented in the question is a classic example of fuzzy matching, where we need to find matches between two datasets based on similarities. In this blog post, we’ll explore how to use dataframes as a regex reference to match string values.

Background

Fuzzy matching is a technique used in text processing and machine learning to find matches between strings that are similar but not identical. It’s commonly used in applications such as spell checking, autocomplete suggestions, and plagiarism detection.

The problem at hand involves creating a dataframe with the possible error codes and their meanings, and then using this dataframe to match against a long string of concatenated errors from another dataset.

Problem Statement

Given two datasets:

codes: a dataframe containing error codes and their corresponding meanings
errors: a dataframe containing the concatenated error strings

We want to create a new dataframe that contains the original error string, the matched error code, and its meaning. In other words, we want to find the first part of each error string that matches any of the values in the code_error column of the codes dataframe.

Solution Overview

To solve this problem, we’ll use a combination of R’s built-in functions and the agrep function from the stringr package. Here’s an overview of our approach:

Use the transform function to apply the sapply function to each value in the code_error column of the codes dataframe.
Use the agrep function to find all occurrences of each error code in the corresponding values from the errors$ERROR column.
Merge the resulting dataframes using the merge function.

Detailed Solution

Step 1: Create the Dataframes

codes <- tribble(~code_error, ~meaning,
                 "po_R83",   "No_call_bak",
                 "?OP",    "card_nofunds",
                 "HOTELARCH78",  "overbookings")

errors <- tribble(~ERROR,
                  "?OP_ERR7+JSU8.OIJK1",
                  "po_R83_io",
                  "IOS_NEVER:300SSSS",
                  "HOTELARCH78?123-")

Step 2: Apply `sapply` and `agrep`

transform(codes,
           ERROR = sapply(code_error, function(x) agrep(x, errors$ERROR, value = TRUE)))

The sapply function applies a function to each element of the code_error column. The function takes two arguments: the error code and the values from the errors$ERROR column.

The agrep function finds all occurrences of the error code in the values from the errors$ERROR column. It returns a logical vector indicating whether the error code is present or not.

Step 3: Merge the Dataframes

merge(
  transform(codes,
            ERROR = sapply(code_error, function(x) agrep(x, errors$ERROR, value = TRUE))),
  errors,
  all.x = TRUE
)

The transform function applies a transformation to each row of the original dataframe. The transformation replaces the values in the code_error column with the results from the agrep function.

The merge function merges two dataframes based on common columns. In this case, we merge the transformed dataframe with the original errors$ERROR column using all rows from both dataframes (all.x = TRUE).

Step 4: Output

               ERROR    code_error      meaning
1 ?OP_ERR7+JSU8.OIJK1         ?OP card_nofunds
2   HOTELARCH78?123- HOTELARCH78 overbookings
3   IOS_NEVER:300SSSS        <NA>          <NA>
4           po_R83_io      po_R83  No_call_bak

The resulting dataframe contains the original error string, the matched error code, and its meaning.

Conclusion

In this blog post, we explored how to use dataframes as a regex reference to match string values using R’s built-in functions and the agrep function from the stringr package. We created a new dataframe that contains the original error string, the matched error code, and its meaning by applying the sapply and agrep functions to each value in the code_error column of the codes dataframe.