Cody’s Guide to Data Cleaning in SAS: Practical Methods and Examples

Introduction

Data is the foundation of modern analytics, but raw datasets are rarely perfect. Missing values, duplicates, and inconsistencies can distort results, leading to unreliable insights. That’s why Cody's data cleaning techniques using sas is one of the most critical steps in the data analysis process.

In this guide, inspired by Cody’s classic approach to SAS programming, we’ll walk through practical methods and examples of data cleaning in SAS. Whether you’re a beginner or a seasoned analyst, these techniques will help you prepare high-quality data for accurate analysis.

 Why Data Cleaning Matters

Data cleaning ensures that datasets are:

• Accurate → Free from errors and inconsistencies.

• Complete → Handling missing values appropriately.

• Consistent → Standardized for analysis and reporting.

• Reliable → Suitable for building trustworthy models and reports.

Without proper data cleaning, even the most advanced statistical techniques can produce misleading results.

 Common Data Issues in SAS

When working with raw data in SAS, analysts often encounter:

1. Missing values

2. Duplicate observations

3. Inconsistent formatting (dates, text, numeric fields)

4. Outliers

5. Incorrect variable types

Cody’s structured approach in SAS emphasizes addressing each of these systematically.

Practical Methods of Data Cleaning in SAS

1. Handling Missing Data

Missing values can bias results. SAS provides functions like NMISS and CMISS to count missing values.

Example:

proc means data=work.sales n nmiss;

   var revenue cost;

run;

This code checks the number of missing values in numeric variables.

To replace missing values, you can use conditional statements:

data sales_clean;

   set work.sales;

   if revenue = . then revenue = 0;

run;

2. Removing Duplicate Observations

Duplicates inflate counts and skew analysis. SAS’s PROC SORT can help identify and remove them.

Example:

proc sort data=work.customers nodupkey out=customers_clean;

   by customer_id;

run;

This ensures only one record per customer_id remains.

3. Standardizing Data Formats

Inconsistent data (like dates or text) needs standardization.

Example – Date Conversion:

data orders_clean;

   set work.orders;

   order_date_clean = input(order_date, mmddyy10.);

   format order_date_clean date9.;

run;

This converts raw text dates into a standardized SAS date format.

4. Detecting and Handling Outliers

Outliers may indicate errors or extreme cases. Using PROC UNIVARIATE, you can detect them.

Example:

proc univariate data=work.sales;

   var revenue;

run;

You can then decide whether to cap, transform, or remove outliers depending on the context.

5. Validating Variable Types

Sometimes numeric values are stored as text. You can convert them using the INPUT function.

Example:

data data_clean;

   set work.raw_data;

   amount_num = input(amount_char, 8.);

run;

This ensures the variable is properly recognized for statistical analysis.

 Cody’s Best Practices for SAS Data Cleaning

• Document every step: Use comments in SAS code for traceability.

• Check frequencies: Use PROC FREQ to detect unexpected categories.

• Keep raw data intact: Always create cleaned datasets separately.

• Automate checks: Write macros for repetitive cleaning tasks.

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 Real-World Example: Cleaning a Customer Dataset

Suppose you have a customer dataset with missing values, duplicates, and inconsistent date formats.

Step 1 – Identify Missing Values:

proc means data=work.customers n nmiss;

run;

Step 2 – Remove Duplicates:

proc sort data=work.customers nodupkey out=customers_unique;

   by customer_id;

run;

Step 3 – Standardize Dates:

data customers_clean;

   set customers_unique;

   dob_clean = input(dob, mmddyy10.);

   format dob_clean date9.;

run;

By following these steps, the dataset becomes analysis-ready.

Why SAS is Ideal for Data Cleaning

• Robust functions for handling missing and duplicate data.

• Flexible formatting for dates, text, and numeric conversions.

• Automation through macros for large-scale cleaning.

• Integration with advanced analytics once data is clean.

For anyone working in data analytics, business intelligence, or research, mastering SAS data cleaning ensures efficient and reliable workflows.

 Final Thoughts

Cody’s systematic approach to data cleaning in SAS remains a gold standard for analysts. By handling missing data, removing duplicates, standardizing formats, and validating variable types, you can ensure your datasets are both accurate and trustworthy.

In an era where data-driven decisions define business success, clean data is the foundation of meaningful insights.