How to Use str_match in R (With Examples)

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The str_match function in R, part of the stringr package, is a powerful tool employed for string manipulation, especially when working with patterns and regular expressions. This function is crucial for extracting matched groups from a string based on a specified pattern, serving as a versatile utility for text processing and data extraction.

Syntax of str_match

The syntax of the str_match function is as follows:

str_match(string, pattern)
  • string: The input character vector.
  • pattern: The regular expression pattern to match.

The function returns a character matrix where the first column represents the complete match, and the subsequent columns represent the captured groups.

Basic Usage of str_match

Example 1: Simple Pattern Match

Let’s start with a simple example of extracting digits from a string.


string <- "The price is $100"
match <- str_match(string, "\\$(\\d+)")
# Output: [,1]   [,2] 
#        "$100" "100"

Here, the whole match “$100” is in the first column, and the captured group “100” (the digits) is in the second column.

Example 2: Extracting Multiple Groups

If you are working with strings that have multiple groups to extract, str_match becomes very handy.

string <- "Date: 2023-09-25, Time: 14:30"
match <- str_match(string, "Date: (\\d{4}-\\d{2}-\\d{2}), Time: (\\d{2}:\\d{2})")
# Output: [,1]                    [,2]        [,3]   
#        "Date: 2023-09-25, Time: 14:30" "2023-09-25" "14:30"

Advanced Applications and Examples

Using str_match with Data Frames

When dealing with data frames, str_match can be instrumental in extracting valuable information from string columns.

# Creating a data frame
df <- data.frame(description = c("Price: $200, Quantity: 5", "Price: $150, Quantity: 3"))

# Extracting price and quantity using str_match
df$match <- str_match(df$description, "Price: \\$(\\d+), Quantity: (\\d+)")

# Creating new columns for price and quantity
df$price <- df$match[,2]
df$quantity <- df$match[,3]

# Output:
#               description                     match price quantity
# 1 Price: $200, Quantity: 5 Price: $200, Quantity: 5   200        5
# 2 Price: $150, Quantity: 3 Price: $150, Quantity: 3   150        3

Extracting Email Addresses

If you are working with a text dataset containing email addresses, you can use str_match to extract them efficiently.

string <- "Contact us at for more information."
match <- str_match(string, "([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,})")
print(match[,1]) # Output: ""

Real-world Scenarios and Implications

Analyzing Log Files

For those dealing with log analysis, str_match can be a valuable tool to extract and analyze specific patterns from log entries.

log_entry <- "ERROR [2023-09-25 14:30:50] - Connection timeout"
match <- str_match(log_entry, "(ERROR) \\[(\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2})] - (.+)")
# Output: [,1]                                 [,2]               [,3]   
#        "ERROR [2023-09-25 14:30:50] - Connection timeout" "ERROR" "2023-09-25 14:30:50" "Connection timeout"

Web Scraping

When dealing with web scraping, str_match helps in extracting specific pieces of information from the scraped content.

html_content <- "<div class='price'>$100</div>"
match <- str_match(html_content, "<div class='price'>\\$(\\d+)</div>")
print(match[,2]) # Output: "100"

Considerations and Best Practices

  1. Regular Expressions: Mastery of regular expressions is crucial for effective use of str_match as it relies on regex patterns to match and extract strings.
  2. Performance Consideration: For large datasets, it’s important to consider the performance and runtime of str_match.
  3. Pattern Complexity: The complexity of the pattern should be managed carefully. Extremely complicated patterns can be hard to understand and maintain.
  4. Multiple Matches: str_match only returns the first match. To get all matches, consider using str_match_all.


The str_match function in R is a versatile and powerful tool for string manipulation, allowing users to match and extract specific patterns from character strings with precision. Its applications range from simple text processing to advanced data extraction in various domains such as log analysis and web scraping.

With a solid understanding of regular expressions and careful consideration of patterns and performance, str_match can be instrumental in unveiling insights and information hidden within textual data, enabling a more sophisticated and refined approach to text analysis in R. Whether it is extracting dates, times, email addresses, or analyzing log files, str_match proves to be an invaluable asset in the repertoire of any data analyst or scientist working with R.

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