Lecture 7 Notes on SAS Conditional Statements

• Introduction to Conditional Statements in SAS

  • Lecture 7 focuses on the importance of conditional statements in data analysis, particularly in subsetting and manipulating data in SAS.

  • Conditional statements become crucial when dealing with missing data to refine the dataset for analysis.

• Subsetting Data with Conditional Statements

  • Fundamental to data manipulation is selecting specific subsets of data based on conditions, such as gender, age, or class standing (freshman, sophomore, etc.).

  • If-Then Statements: These are used to create temporary datasets with specified conditions.

  • Example:

    • Loading raw data: includes variables such as subject, numeric gender, and various exam scores.

    • Create a new temporary dataset for males only:
      sas DATA male_grade; SET grade; IF gender = 'f' THEN DELETE;

    • This operation deletes female observations, keeping only males.

• Retaining Conditions

  • The importance of coding is emphasized with clear conditional operations.

  • Illustration of Coding:

  • You can also use the not equal operator $=~$ or $<>$, plus comparisons such as greater than or less than, to filter your dataset further.

  • Example: sas DATA male_grade; SET grade; IF gender = 'male';

    • This retains only observations where gender equals male.

• Multiple Conditions

  • Conditional statements can incorporate multiple conditions utilizing logical operators (AND/OR).

  • Action blocks can execute more than one operation through DO-END structures.

• If-Then-Else Logic

  • An if-then-else structure is critical for assigning values based on conditions, e.g., grades based on final scores.

  • Example:

  • sas IF final < 65 THEN grade = 'F'; ELSE IF final >= 65 AND final < 75 THEN grade = 'C'; ELSE grade = 'A';

• Working with Missing Data

  • Special considerations for missing values as they can affect calculations and conditions.

  • SAS treats missing numeric values as less than any number, which can lead to unexpected conditions being true.

• Handling Complex Conditions

  • The significance of using parentheses for clarity when combining conditions is highlighted (e.g., more complex logical expressions can be delineated for better readability).

• Visualizing Dataset Updates

  • Regular validations of changes in datasets post-conditions application are crucial.

  • Example data manipulations are shown through iterations in SAS output (e.g., final grades, how missing data impact student grades).

• Closing Remarks

  • Effective data manipulation in SAS relies heavily on understanding conditional statements.

  • Insight into how SAS processes missed values, and how logical operations affect the output provides practical implications for data analysis.

• Basic If-Then Syntax:
  DATA new_dataset;

  • SET old_dataset;

  • IF condition THEN action;

  • This creates a new dataset based on a specified condition.

• If-Then-Else Syntax:
  IF condition1 THEN action1; ELSE IF condition2 THEN action2; ELSE action3;
  This structure allows for multiple conditions with respective actions.

• Retaining Conditions:
  IF condition THEN action;
  This filters observations in a dataset based on conditions.

• Deleting Observations:
  IF condition THEN DELETE;
  This removes observations that meet a specified condition from the dataset.

• Using Logical Operators:
  Use AND or OR to combine multiple conditions.
  Example:
  IF condition1 AND condition2 THEN action;

• Using Parentheses:
  Use parentheses to clarify complex conditions.
  Example:
  IF (condition1 AND condition2) OR condition3 THEN action;

• Working with Missing Values:
  Remember that SAS treats missing numeric values as less than any valid number, e.g., for conditions like IF numeric_var < number THEN ....

• Determining Length of Strings:
  Use LENGTH(variable), e.g., IF LENGTH(variable) > 5 THEN ... to check string lengths.

• Assigning or Modifying Variable Values:
  Use the format variable = new_value; within conditions to assign or modify values based on specific criteria.
  Example: IF condition THEN variable = 'new_value';

• If-Then Syntax:
  
  DATA male_grade; SET grade; IF gender = 'f' THEN DELETE;
  This code creates a new temporary dataset containing only male observations.

• Retaining Conditions Syntax:
  
  DATA male_grade; SET grade; IF gender = 'male';
  This code retains only observations where gender equals male.

• If-Then-Else Syntax:
  
  IF final < 65 THEN grade = 'F'; ELSE IF final >= 65 AND final < 75 THEN grade = 'C'; ELSE grade = 'A';
  This assigns letter grades based on final score conditions.

• Handling Missing Data:
  
  SAS treats missing numeric values as less than any number, which can affect conditional evaluations.

• Using Logical Operators:
  
  Combine conditions in a single statement using logical operators (AND, OR) for more complex filtering.

• Parentheses for Clarity:
  
  Use parentheses to delineate complex logical conditions clearly, e.g., IF (condition1 AND condition2) OR condition3 THEN ...

Ensure your understanding of these structures for your live coding exam. Practicing these syntax examples will aid in effectively utilizing conditional statements in your SAS coding tasks.