🔄 Mastering IF-THEN-ELSE and DO Loops in SAS – Complete Guide with Examples

🔰 Introduction

Conditional logic and looping are core concepts in any programming language. In SAS, IF-THEN-ELSE statements and DO loops provide powerful tools for controlling the flow of your Data Step code. This guide explains how to use these tools effectively with real-world examples.

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🧩 1. IF-THEN-ELSE in SAS

The IF-THEN-ELSE statement allows you to execute specific code based on conditions.

✅ Syntax:

IF condition THEN action;
ELSE IF condition THEN action;
ELSE action;

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📌 Example 1: Simple IF-THEN-ELSE

data class_flag;
  set sashelp.class;
  if age < 13 then group = 'Child';
  else if age < 18 then group = 'Teen';
  else group = 'Adult';
run;

Explanation:
Classifies students into Child, Teen, or Adult groups based on age.

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🧪 More IF-THEN-ELSE Examples in SAS

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📌 Example 1: Assign Grades Based on Scores

data grades;
  input name $ score;
  if score >= 90 then grade = 'A';
  else if score >= 80 then grade = 'B';
  else if score >= 70 then grade = 'C';
  else if score >= 60 then grade = 'D';
  else grade = 'F';
datalines;
John 85
Sara 92
Alex 67
Nina 74
Bob 58
;
run;

---

📌 Example 2: Handle Missing Values in Conditions

data test_missing;
  input id age;
  if age = . then status = 'Missing';
  else if age < 18 then status = 'Minor';
  else status = 'Adult';
datalines;
1 25
2 .
3 17
;
run;

---

📌 Example 3: Create Flags for Categorical Variables

data product_flag;
  input product $ category $;
  if category = 'Electronics' then flag = 1;
  else flag = 0;
datalines;
Laptop Electronics
Shoes Apparel
Phone Electronics
Watch Accessories
;
run;

---

📌 Example 4: Nested IF-THEN-ELSE Logic

data nested_logic;
  input city $ temp;
  if city = 'Delhi' then do;
    if temp > 40 then warning = 'Heatwave';
    else warning = 'Normal';
  end;
  else warning = 'Check city';
datalines;
Delhi 45
Delhi 30
Mumbai 32
;
run;

---

📌 Example 5: Use with Multiple Variables

data risk_check;
  input age income;
  if age < 25 and income < 30000 then risk = 'High';
  else if age >= 25 and income < 30000 then risk = 'Medium';
  else risk = 'Low';
datalines;
22 25000
28 28000
35 60000
;
run;

---

📌 Example 6: Case-Insensitive Character Comparison

data department;
  input empname $ dept $;
  if upcase(dept) = 'HR' then team = 'Human Resources';
  else team = 'Other';
datalines;
John HR
Alex finance
Sara hr
;
run;

---

📌 Example 7: Assign Labels to Numeric Ranges

data salary_bracket;
  input empid salary;
  if salary < 30000 then bracket = 'Low';
  else if 30000 <= salary < 60000 then bracket = 'Medium';
  else bracket = 'High';
datalines;
101 25000
102 32000
103 60000
104 58000
;
run;

---

📌 Example 8: IF Without ELSE (Slower)

Data no_else;
  set sashelp.class;
  if age < 12 then group = 'Preteen';
  if age >= 12 then group = 'Teen';
run;

💡 Best Practices:

• Use IF-THEN/ELSE instead of multiple IF statements for better performance.
• When checking for missing values, use IF var = ..

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🔄 2. DO Loops in SAS

DO loops are used to repeat code a specified number of times or while a condition is true.

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✅ 2.1 DO Loop Syntax

do index = start to end;
   /* repeated statements */
end;

---

📌 Example 2: Basic DO Loop

data loop_example;
  do i = 1 to 5;
    square = i**2;
    output;
  end;
run;

Explanation:
Generates a dataset with numbers 1 to 5 and their squares.

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✅ 2.2 DO WHILE Loop

data do_while;
  x = 1;
  do while (x < 5);
    square = x**2;
    output;
    x + 1;
  end;
run;

---

✅ 2.3 DO UNTIL Loop

data do_until;
  x = 1;
  do until (x > 5);
    cube = x**3;
    output;
    x + 1;
  end;
run;

---

🧠 Combining IF and DO Loops

data even_numbers;
  do i = 1 to 10;
    if mod(i, 2) = 0 then output;
  end;
run;

Explanation:
Generates only even numbers between 1 and 10 using both DO loop and IF condition.

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⚠️ Common Mistakes to Avoid

Mistake	Fix
Missing OUTPUT in DO loop	Always include output; if needed
Forgetting semicolons	End each SAS statement with ;
Using multiple IF instead of IF-THEN-ELSE	Can slow performance

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🔚 Conclusion

Mastering IF-THEN-ELSE and DO loops in SAS empowers you to create dynamic, flexible, and readable data processing routines. Whether you're classifying data, creating new variables, or iterating through logic, these tools are fundamental to writing efficient SAS programs.

🔄 Mastering PROC TRANSPOSE in SAS: Convert Rows to Columns and Vice Versa

PROC TRANSPOSE is a powerful SAS procedure used to reshape data by converting rows into columns or columns into rows. Whether you're preparing datasets for reporting or statistical analysis, PROC TRANSPOSE can simplify your task with just a few lines of code.

In this blog, you'll learn:

• What is PROC TRANSPOSE?
• When and why to use it
• Syntax and options
• Multiple real-world examples
• Tips and tricks for efficient use

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🔍 What is PROC TRANSPOSE in SAS?

PROC TRANSPOSE is used to pivot data—turning variables (columns) into observations (rows), or vice versa. It's especially useful for:

• Summarizing repeated measures
• Restructuring long or wide datasets
• Preparing data for visualizations or modeling

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📚 Basic Syntax of PROC TRANSPOSE

proc transpose data=input_data out=output_data <options>;
   by variable(s);         * Optional: groups data;
   id variable;            * Optional: names for new columns;
   var variable(s);        * Variables to transpose;
run;

---

📌 Key Options Explained

Option	Description
BY	Groups data before transposing
VAR	Specifies variables to transpose
ID	Uses values of a variable as new column names
NAME=	Renames the default _NAME_ column
LABEL=	Renames the default _LABEL_ column

---

✅ Example 1: Transposing Without BY or ID

🔹 Input Data

data sales;
   input Quarter $ Sales;
   datalines;
Q1 100
Q2 120
Q3 140
Q4 160
;

🔹 Transpose Code

proc transpose data=sales out=sales_transposed;
   var Sales;
run;

🔹 Output

NAME	COL1	COL2	COL3	COL4
Sales	100	120	140	160

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✅ Example 2: Transposing with ID to Use Column Names

proc transpose data=sales out=sales_wide;
   id Quarter;
   var Sales;
run;

🔹 Output

NAME	Q1	Q2	Q3	Q4
Sales	100	120	140	160

---

✅ Example 3: Transpose with BY Grouping

🔹 Input Data

data student_scores;
   input Student $ Subject $ Score;
   datalines;
John Math 85
John English 78
John Science 92
Anna Math 88
Anna English 91
Anna Science 84
;

🔹 Transpose Code

proc sort data=student_scores;
   by Student;
run;

proc transpose data=student_scores out=scores_wide;
   by Student;
   id Subject;
   var Score;
run;

🔹 Output

Student	English	Math	Science
John	78	85	92
Anna	91	88	84

---

✅ Example 4: Transposing Multiple Variables

data patient_data;
   input ID $ Visit $ Height Weight;
   datalines;
P1 Visit1 170 65
P1 Visit2 171 66
P2 Visit1 160 60
P2 Visit2 161 61
;

🔹 Code

proc sort data=patient_data;
   by ID;
run;

proc transpose data=patient_data out=trans_height prefix=Height_;
   by ID;
   id Visit;
   var Height;
run;

proc transpose data=patient_data out=trans_weight prefix=Weight_;
   by ID;
   id Visit;
   var Weight;
run;

data final_transposed;
   merge trans_height trans_weight;
   by ID;
run;

🔹 Output

ID	Height_Visit1	Height_Visit2	Weight_Visit1	Weight_Visit2
P1	170	171	65	66
P2	160	161	60	61

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🧠 Tips for Using PROC TRANSPOSE

• Always SORT your data before using BY.
• Use the PREFIX= option to create meaningful column names.
• Combine multiple transpositions for complex reshaping.
• Use NAME= and LABEL= to rename the default variables _NAME_ and _LABEL_.

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🔎 When to Use PROC TRANSPOSE

Use Case	PROC TRANSPOSE?
Convert long to wide format	✅ Yes
Convert wide to long format	✅ Yes (reverse)
Reshape repeated measures	✅ Yes
Change actual data values	❌ No
Merge multiple reshaped tables	✅ Yes

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📈 Conclusion

PROC TRANSPOSE is an essential tool in any SAS programmer’s toolkit. It simplifies the process of reshaping data for reporting, analysis, and modeling. With a good understanding of BY, ID, and VAR options, you can handle almost any data transformation challenge.

How to Create Data Groups Using PROC FORMAT in SAS: Real-World Examples

Problem/Requirement -

Lets Suppose, We have Data in Numbers which could be range from 100 to 10000
now we want to create Groups of Each 100 Values, like below - 

Lets See with the Example | The Power of the FORMAT Procedure - 

/*This code will Generate some Numbers between 100 and 10000 to Get the Practice Dataset */

Data A;

Num=100;

Do while(Num<10000);

output;

Num=Num+54;

end;

Run;

Proc Print Data=A;

Run;

Now we want to Group it in as per given in Requirement 

/* This Code will Genrate FMTName (Format Name), Start(Starting Number of Group), End(Ending Number of Group), Label(Group name for Numbers between Start and End Values)

*/

Data B;

Retain FMTName "Group";

Start=100;

i=1;

Do until(End>=10000);

End=Start+99;

Label=compbl("Group"||i);

Output;

Start=End+1;

i=i+1;

end;

drop i;

run;

Proc Print Data=B;

Run;

Now if we need to create a User Defined Format -

/* This wil create a format for Above created B Dataset with Format Name "Group", PROC FORMAT with the CNTLIN= option is to build the formats from the imported control data set */

Proc Format CNTLIN=B;

Run; 

/*Lets see how it will print A Dataset if we apply Newly create Group format to Variable Num */

Proc Print Data=A;
Format Num Group.;

run; 

/* If we want Actual Number and the Group name both the things then we can do it like this */

Data C;

Set A;

GroupName=Put(Num,Group.);

run;

/*Here we created a new Variable Group name and Copy value of Num with Group format, For this we used PUT Function */

Proc Print Data=C;

Run;

I Hope you like this Article, please comment if you have any doubt or question.

Thank you

🏅 Ultimate Guide to SAS Certifications: Base, Advanced, Data Scientist & Clinical Trials Programmer

Are you planning to build a career in SAS Programming, Analytics, or Clinical Research? Getting SAS certified is a powerful way to validate your skills and enhance job prospects across industries like healthcare, finance, retail, and pharma.

In this detailed guide, we’ll cover everything you need to know about the top SAS certifications:

• Base SAS Certification
• Advanced SAS Certification
• SAS Certified Data Scientist
• Clinical Trials Programmer Certification

Let’s dive in!

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🧑‍💻 Base SAS Programming Specialist Certification

✅ Who Should Take This?

Beginners or professionals who want to start their journey in SAS programming. It’s a foundational certification focusing on data step programming, importing/exporting data, basic reporting, and debugging.

📚 Key Topics Covered:

• SAS programming basics (DATA step, PROC step)
• Reading and writing data
• Combining datasets
• Conditional logic
• Basic statistical procedures
• Data formatting and functions

📝 Exam Details:

• Exam Code: A00-231
• Format: 40-45 multiple-choice and short-answer questions
• Duration: 135 minutes
• Passing Score: 725/1000
• Fee: $180 (may vary by location)

📌 Recommended Preparation:

• Official SAS e-learning: SAS Base Programming Learning Path
• Books: The Little SAS Book by Delwiche & Slaughter
• Practice exams and sample questions available on the SAS portal.

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🧠 Advanced Programming Professional Certification

✅ Who Should Take This?

SAS users who already understand base programming and want to demonstrate expertise in macros, advanced functions, and optimizing code.

📚 Key Topics Covered:

• Creating and using SAS macros
• Advanced DATA step programming
• Error handling and debugging
• PROC SQL and advanced querying
• Creating reusable code

📝 Exam Details:

• Exam Code: A00-232
• Format: 10–15 coding questions
• Duration: 2 hours
• Passing Score: 725
• Fee: $180

📌 Recommended Preparation:

• SAS Advanced Programming Learning Path:
• SAS Advanced Certification Prep
• Practice with real-world datasets
• Use SAS Studio or SAS OnDemand for Academics (free tool)

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🧪 Clinical Trials Programmer Professional Certification

✅ Who Should Take This?

Designed for professionals working in clinical research and pharmaceutical domains. You’ll need knowledge of both SAS programming and CDISC standards (SDTM, ADaM).

📚 Key Topics Covered:

• Clinical trials data lifecycle
• Generating TLFs (Tables, Listings, and Figures)
• SDTM and ADaM data models
• FDA submission standards
• Clinical data cleaning and validation

📝 Exam Details:

• Exam Code: A00-282
• Format: 50–60 multiple-choice and short-answer questions
• Duration: 2 hours
• Passing Score: 68%
• Fee: $180

📌 Recommended Preparation:

• Clinical Trials Certification Details
• CDISC standards overview: https://www.cdisc.org
• Use pharma-focused datasets to practice transformations and reporting

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🧬 SAS Certified Data Scientist

✅ Who Should Take This?

Mid to senior-level analysts or data professionals who want to master data wrangling, advanced analytics, machine learning, and model deployment using SAS.

🧩 It’s a bundled certification path that includes:

• SAS Certified Advanced Analytics Professional
• SAS Certified AI & Machine Learning Professional

📚 Key Topics Covered:

• Data exploration, preparation, and visualization
• Predictive modeling and machine learning
• Forecasting and optimization
• Neural networks and natural language processing
• Model deployment and model monitoring

📝 Exam Details:

• Total Exams: 5
• Learning path includes hands-on case studies and labs
• Duration: ~6-9 months with regular practice
• Each exam: 2 hours, scenario-based

💡 Learn More:

• Official SAS Path: SAS Certified Data Scientist
• Free learning access via: SAS Skill Builder Portal

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🎯 Which SAS Certification Should You Choose?

Career Goal	Recommended Certification
SAS Programmer	Base or Advanced SAS
Data Analyst	Advanced SAS or Data Scientist
Clinical Research	Clinical Trials Programmer
AI & Machine Learning	SAS Certified Data Scientist
Entry-Level Professional	Base SAS Certification

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💼 Job Opportunities After SAS Certification

Earning a SAS certification opens doors to roles like:

• SAS Programmer / Analyst
• Clinical SAS Programmer
• Data Scientist
• Business Intelligence Analyst
• Biostatistician

Top companies hiring SAS professionals include IQVIA, Accenture, Pfizer, Novartis, Deloitte, and Infosys.

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🎓 Free Tools to Learn SAS

1. SAS OnDemand for Academics – Free online SAS environment
2. SAS Skill Builder for Students
3. Free E-Learning Resources

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🔍 Conclusion

Whether you’re beginning your SAS journey or aiming to become a data science expert, SAS Certifications are a globally recognized badge of your expertise. Choose the one aligned with your career goals and get started with SAS’s official resources and training paths.

✨ Stay consistent, practice with real datasets, and join communities like SAS Support or Reddit's r/SAS to boost your confidence.

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🔗 Quick Certification Links Recap:

• 🎓 Base SAS Certification (A00-231)
• 📈 Advanced SAS Programming (A00-232)
• 🧬 SAS Certified Data Scientist
• 🩺 Clinical Trials Programmer

Labels :

• Base SAS Programming Specialist – Beginner level
• Advanced SAS Programming Professional – Intermediate level
• Clinical Trials Programmer – Intermediate to Advanced (industry-specific)
• SAS Certified Data Scientist – Advanced level (data science & AI)

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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