Data Cleaning in SAS: Tips and Techniques

Introduction

Data cleaning is a critical step in the data preparation process. Raw data is often messy, incomplete, or inconsistent, and cleaning it ensures accurate and reliable analysis. SAS offers a wide range of tools and functions that make data cleaning efficient and effective.

In this post, you’ll learn essential data cleaning techniques in SAS, including handling missing values, removing duplicates, formatting variables, and more.

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1. Identifying and Handling Missing Values

Missing data can lead to biased results. SAS represents missing numeric values as . and character missing values as a blank space ('').

Check for missing values:

PROC MEANS DATA=your_dataset N NMISS;
RUN;

Replace missing values:

DATA cleaned_data;
  SET your_dataset;
  IF Age = . THEN Age = 0;
  IF Name = '' THEN Name = 'Unknown';
RUN;

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2. Removing Duplicate Records

Duplicate data can affect the accuracy of your results.

Identify duplicates:

PROC SORT DATA=your_dataset OUT=check_dups NODUPKEY;
  BY ID;
RUN;

Remove complete duplicates:

PROC SORT DATA=your_dataset NODUPRECS OUT=cleaned_data;
RUN;

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3. Standardizing Text Variables

Standardizing case and removing unwanted spaces improves consistency.

Use SAS functions:

DATA standardized;
  SET your_dataset;
  Name_clean = STRIP(UPCASE(Name));
RUN;

• STRIP() removes leading and trailing spaces
• UPCASE(), LOWCASE() or PROPCASE() standardize text case

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4. Filtering Out Invalid Values

Sometimes variables contain invalid or out-of-range data.

Example: Remove ages less than 0 or more than 120

DATA cleaned_data;
  SET your_dataset;
  IF 0 <= Age <= 120;
RUN;

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5. Converting Data Types (INPUT and PUT)

Mismatch between numeric and character types can cause issues.

Convert character to numeric:

Age_num = INPUT(Age_char, 8.);

Convert numeric to character:

Age_char = PUT(Age_num, 8.);

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6. Replacing Values with IF or ARRAY Logic

You can recode or transform values using conditional logic.

Example:

DATA recoded;
  SET your_dataset;
  IF Gender = 'M' THEN Gender = 'Male';
  ELSE IF Gender = 'F' THEN Gender = 'Female';
RUN;

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7. Handling Outliers

Use PROC UNIVARIATE to detect outliers in numeric variables.

PROC UNIVARIATE DATA=your_dataset;
  VAR Salary;
RUN;

Then, you can treat outliers by capping, removing, or flagging them.

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8. Validating Cleaned Data

Use PROC FREQ or PROC MEANS to validate the cleaned dataset.

PROC FREQ DATA=cleaned_data;
  TABLES Gender Age_Group;
RUN;

PROC MEANS DATA=cleaned_data;
  VAR Salary Age;
RUN;

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Best Practices for Data Cleaning in SAS

• Always keep a backup of your raw data
• Document every cleaning step
• Use descriptive variable names (e.g., Name_clean, Age_flag)
• Combine steps using macros for reusable workflows

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Conclusion

Clean data is the foundation of trustworthy analysis. Using SAS, you can efficiently handle missing values, fix data quality issues, and standardize your datasets. By mastering these data cleaning techniques, you’re well on your way to becoming a proficient SAS programmer and data analyst.

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Mastering PROC MEANS in SAS: A Complete Guide for Statistical Summaries

When working with data in SAS, one of the most powerful and frequently used procedures is PROC MEANS. Whether you're summarizing your data before analysis or generating descriptive statistics for reporting, PROC MEANS provides a flexible and efficient way to get the job done.

In this post, we’ll dive deep into what PROC MEANS is, how to use it, and explore examples and tips to make the most of it in your data analysis workflow.

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🔍 What is PROC MEANS?

PROC MEANS is a SAS procedure used to calculate descriptive statistics such as:

• Mean
• Standard Deviation
• Minimum
• Maximum
• Sum
• Count (N)

It provides both default and customizable statistical summaries and can be used with grouping variables, class statements, and output datasets for further analysis.

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📌 Basic Syntax

PROC MEANS DATA=dataset-name <options>;
   VAR variable-list;
RUN;

Example:

proc means data=sashelp.class;
   var age height weight;
run;

This command summarizes the age, height, and weight variables from the sashelp.class dataset using default statistics.

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⚙️ Key Options in PROC MEANS

• N – Count of non-missing values
• MEAN – Arithmetic mean
• STD – Standard deviation
• MIN – Minimum value
• MAX – Maximum value
• SUM – Sum of values
• MEDIAN – Median
• QRANGE – Interquartile range
• MAXDEC= – Controls the number of decimal places

Example with Options:

proc means data=sashelp.class mean std min max maxdec=2;
   var height weight;
run;

This will return the mean, standard deviation, minimum, and maximum of height and weight rounded to 2 decimal places.

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🧮 Grouping Data: Using the CLASS Statement

If you want to group data by one or more categorical variables, use the CLASS statement.

proc means data=sashelp.class mean std;
   class sex;
   var height weight;
run;

This breaks down the statistics for height and weight by gender (sex variable).

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💾 Saving the Output: The OUTPUT Statement

To save the summary statistics to a new dataset for further use, use the OUTPUT statement.

proc means data=sashelp.class n mean std maxdec=2;
   var age height weight;
   output out=summary_stats
      n= n_age n_height n_weight
      mean= mean_age mean_height mean_weight
      std= std_age std_height std_weight;
run;

This stores count, mean, and standard deviation of each variable in a new dataset called summary_stats.

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🧑‍💻 Advanced Example: Multiple Grouping and Custom Output

proc means data=sashelp.class mean std;
   class sex age;
   var height weight;
   output out=multi_summary
      mean= mean_height mean_weight
      std= std_height std_weight;
run;

Here, we group the summary statistics by both sex and age, and save the mean and standard deviation of height and weight to the multi_summary dataset.

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💡 Tips and Best Practices

• Use CLASS instead of BY unless your data is already sorted. BY requires pre-sorted data.
• Use MAXDEC= to keep results readable. This is especially useful for reports.
• Store results with OUTPUT for further analysis or exporting.
• Combine PROC MEANS with PROC PRINT to display or filter specific outputs from your summary dataset.

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Pro TIP (Frequently asked Interview Question)

🆚 PROC MEANS vs. PROC SUMMARY

You might wonder: what’s the difference between PROC MEANS and PROC SUMMARY?

They are functionally similar. The key difference is:

• PROC MEANS displays output by default.
• PROC SUMMARY does not produce output unless the PRINT option is specified.

proc summary data=sashelp.class print;
   class sex;
   var height weight;
   output out=summary_data mean= std=;
run;

SAS ETL: A Complete Guide to Extract, Transform, and Load in SAS

SAS ETL - We sometime heard this Term "ETL" from big companies, Job posted for SAS ETL Developer, so Lets see what is SAS ETL exactly.

"ETL" Means - Extract Transform and Load - Its a type of Data Integration tool which is used to blend data from different Sources.

In the world of data analytics, ETL (Extract, Transform, Load) plays a crucial role in preparing data for analysis and reporting. In this blog post, we will explore how ETL processes are implemented in SAS (Statistical Analysis System)—a powerful tool widely used in data management, analytics, and business intelligence.

Whether you're a beginner or a data professional, understanding SAS ETL is essential for managing and transforming large volumes of data efficiently.

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🔍 What is ETL?

ETL stands for:

• Extract: Retrieving raw data from multiple sources (databases, Excel files, text files, web sources, etc.)
• Transform: Cleaning, standardizing, and reshaping data to suit your business needs.
• Load: Importing the processed data into a target system such as a data warehouse or analytical platform.

SAS provides robust tools and procedures to handle all three stages with precision and scalability.

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📥 Extracting Data Using SAS

The data extraction phase involves connecting to different data sources using SAS libraries, PROC IMPORT, or LIBNAME statements. SAS can extract data from:

• Excel and CSV files
• SQL databases (Oracle, MySQL, SQL Server, etc.)
• Cloud-based storage or Hadoop
• Web sources via APIs

Example:

libname mydb odbc dsn='SalesDB' user='admin' password='xyz123';

This command connects SAS to a SQL database and allows direct access to tables for extraction.

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🔄 Transforming Data in SAS

The transformation step is where the real magic happens. You can use SAS procedures, data step programming, and functions to clean and prepare data:

• Filtering and sorting (PROC SORT, WHERE clauses)
• Merging datasets (MERGE, PROC SQL JOIN)
• Creating calculated fields
• Handling missing values
• Aggregating data using PROC MEANS, PROC SUMMARY, or PROC SQL

Example:

data sales_clean;
  set sales_raw;
  if sales > 0;
  revenue = quantity * price;
run;

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📦 Loading Data in SAS

The final step is loading the transformed data into a target destination for reporting or visualization. In SAS, you can:

• Save datasets using the DATA step
• Export to Excel/CSV using PROC EXPORT
• Load into relational databases via LIBNAME or PROC SQL

Example:

proc export data=sales_clean
  outfile="C:\Reports\Cleaned_Sales.csv"
  dbms=csv
  replace;
run;

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