🔄 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;

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📌 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;

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📌 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;

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📌 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;

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📌 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;

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✅ 2.3 DO UNTIL Loop

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

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🧠 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;

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📌 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

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✅ 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

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✅ 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.

📊 Mastering PROC DATASETS in SAS: Efficient Data Management Made Easy

PROC DATASETS is a powerful yet underutilized procedure in SAS that allows users to manage and manipulate SAS data sets efficiently without reading or rewriting data. From renaming variables to deleting datasets and changing labels, PROC DATASETS offers a faster, resource-optimized alternative to using traditional DATA steps.

In this blog, you'll learn:

• What is PROC DATASETS?
• Key features and use cases
• Syntax and options
• Practical examples with screenshots
• Tips for using PROC DATASETS effectively

🔍 What is PROC DATASETS in SAS?

PROC DATASETS is a SAS procedure designed to manage data sets and other SAS files such as indexes, catalogs, and views. It provides a non-destructive way of editing metadata and managing data libraries.

Instead of rewriting the entire dataset (as is done in a DATA step), PROC DATASETS makes metadata-level changes, which significantly improves performance, especially with large datasets.

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✅ Key Features of PROC DATASETS

Feature	Description
Rename variables	Easily rename variables in a dataset
Delete datasets	Delete one or multiple datasets from a library
Modify labels & formats	Add or change variable labels and formats
Append data	Combine datasets efficiently
Manage indexes	Create or delete indexes for faster access
Change dataset attributes	Modify dataset-level properties like labels or sorted variables

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🧾 Syntax of PROC DATASETS

PROC DATASETS LIBRARY=libref <options>;
   MODIFY dataset-name;
      RENAME oldname=newname;
      LABEL varname = 'New Label';
      FORMAT varname format.;
   RUN;

   DELETE dataset1 dataset2;
   APPEND BASE=target DATA=source;
RUN;
QUIT;

---

💡 Practical Examples

1️⃣ Renaming Variables in a Dataset

proc datasets library=work;
   modify sales_data;
      rename sales_amt = total_sales;
run;
quit;

📝 This renames the variable sales_amt to total_sales without rewriting the data.

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2️⃣ Deleting Multiple Datasets

proc datasets library=work nolist;
   delete temp1 temp2 temp3;
run;
quit;

🗑️ Use this to remove unused datasets from your workspace.

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3️⃣ Changing Labels and Formats

proc datasets library=work;
   modify employees;
      label emp_id = "Employee ID"
            salary = "Monthly Salary";
      format salary dollar10.2;
run;
quit;

🎯 Modify how variables are displayed in reports without altering data values.

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4️⃣ Appending Data Efficiently

proc datasets library=work;
   append base=all_data data=new_data;
run;
quit;

⚡ APPEND is faster than using SET in a DATA step for combining large datasets.

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🧠 Best Practices & Tips

• Always use NOLIST to suppress unnecessary log output.
• Use CONTENTS to verify changes after modifications:

proc contents data=work.sales_data;
run;

• Combine multiple actions within a single MODIFY statement for efficiency.
• Always QUIT the procedure to release memory and avoid unexpected behavior.

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📌 When to Use PROC DATASETS

Scenario	Recommended?
Renaming/deleting datasets	✅ Yes
Modifying labels and formats	✅ Yes
Changing actual data values	❌ No
Creating new derived variables	❌ No

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🔎 Final Thoughts

PROC DATASETS is a hidden gem for SAS programmers looking to optimize their workflows. By focusing on metadata management and library-level operations, it significantly boosts performance and simplifies tasks that would otherwise require verbose code.