Extracting the First Three Alphabetic Characters in SAS: A Comparative Guide

When working with "Agreement" numbers that contain both alphabetic and numeric characters, there are several approaches you can use in SAS to extract the first three alphabetic characters. Below, I’ll discuss three common methods: substr with findc, substr with compress, and substr with left. Each method has its own advantages and disadvantages, depending on the structure of your data and your specific needs.

Approach 1: substr with findc

char_part = substr(Agreement, 1, findc(Agreement, '0123456789')-1);

Explanation:
This approach finds the position of the first numeric character in the Agreement string using findc, and then extracts all characters before that position using substr. It works well if the alphabetic part of the "Agreement" is always at the beginning and is directly followed by numbers.

Pros:

• Simplicity: Easy to understand and implement if the format of the string is consistent (alphabetic characters followed by numeric characters).
• Efficiency: Quickly identifies and extracts the desired portion without processing the entire string.

Cons:

• Limited Flexibility: Assumes that the alphabetic portion is always at the start and directly followed by numeric characters.
• Not Suitable for Mixed Formats: Does not handle cases where alphabetic characters appear after numeric characters.

Best Used When: You have a string where the alphabetic prefix is always followed by numbers, and you want to extract everything before the first numeric digit.

Approach 2: substr with compress

want = substr(compress(agreement, "", "ka"), 1, 3);

Explanation:
This method uses the compress function to remove all non-alphabetic characters from the Agreement string, then extracts the first three characters from the resulting string using substr. The "ka" argument in compress tells SAS to keep only alphabetic characters.

Pros:

• Flexibility: Extracts the first three alphabetic characters regardless of their position in the string.
• Robustness: Works well with various formats, including strings with interspersed alphabetic and numeric characters.

Cons:

• Performance: Slightly more processing-intensive as it needs to examine and filter the entire string before extracting the first three characters.
• Potential Overkill: Might be unnecessary if the format is simple and consistent, where alphabetic characters always come first.

Best Used When: Your data might have mixed formats or you need to ensure that only alphabetic characters are extracted, no matter where they appear in the string.

Approach 3: substr with left

newcol = substr(left(agreement), 1, 3);

Explanation:
This approach first removes leading spaces from the Agreement string using left, and then extracts the first three characters using substr. It is straightforward and assumes that the first three characters (after removing spaces) are the ones you need.

Pros:

• Simplicity: Very easy to implement and understand. No need to worry about character types or positions if the string format is simple.
• Performance: Efficient for consistent and clean data where the first three characters are the desired ones.

Cons:

• Assumption-Dependent: This method assumes that the first three characters after removing spaces are correct, which might not always be the case.
• No Character Filtering: Does not differentiate between alphabetic and numeric characters, so it will extract whatever is in the first three positions.

Best Used When: The format is consistent, with the first three characters after any leading spaces being the ones you need, and there’s no concern about numeric or other characters appearing first.

Comparison and Recommendation

Flexibility: Approach 2 (substr with compress) is the most flexible, handling various formats and ensuring only alphabetic characters are extracted. This makes it the best choice when the data format is not consistent or when alphabetic characters may appear in different parts of the string.

Simplicity and Performance: Approach 3 (substr with left) is the simplest and fastest, suitable for cases where the data format is known and consistent. It's ideal for straightforward tasks where the first three characters are always correct.

Targeted Extraction: Approach 1 (substr with findc) is optimal when you know that the string format always has alphabetic characters at the start, immediately followed by numbers. It effectively extracts everything before the first numeric digit, making it a good choice for this specific pattern.

Conclusion:

• If you need a quick and simple extraction, and you’re confident about the string format, Approach 3 is ideal.
• For more complex or mixed formats, where you need to ensure that only the first three alphabetic characters are extracted, Approach 2 is the best option.
• If you have a consistent pattern where alphabetic characters are followed by numbers, Approach 1 might be the most efficient.

Choosing the right approach depends on the specific characteristics of your dataset and the exact requirements of your task.