Course 2: SAS Advanced Programming Certificate

Welcome to the SAS Advanced Programming Certificate Program! This course builds on foundational SAS skills, diving into advanced techniques for data management and manipulation. Over one week, you will master data step processing, data cleaning, transformation, merging, appending, and advanced manipulation techniques using SAS. Designed for learners with basic SAS knowledge, this course equips you with the skills to handle complex data processing tasks efficiently.

Objective: By the end of the course, learners will be proficient in advanced SAS programming techniques, including reading and processing raw data, cleaning and transforming data, combining data sets, and using arrays and loops for complex manipulations, enabling robust data analysis and reporting.

Scope: The course covers data step execution, data cleaning with missing values and outliers, data transformation with variable creation and transposition, merging and appending data sets, and advanced manipulation with arrays and DO loops, using practical examples and hands-on exercises.

Day 1: Data Step Processing

Introduction: The DATA step is the heart of SAS programming, providing the foundation for reading, manipulating, and creating data sets. This session introduces you to the core concepts of data step processing, including reading raw data, using the INFILE and INPUT statements, and understanding the data step execution process.

Learning Objective: By the end of this session, you will be able to read raw data from external files using the INFILE statement, define variables and read data values using the INPUT statement, and understand the data step execution process, including the program data vector (PDV).

Scope of the Lesson: This lesson covers reading raw data using the INFILE statement (including specifying file paths and options), defining variables and reading data values using the INPUT statement (including different input styles and formats), and understanding the data step execution process (including the compilation phase, execution phase, and the program data vector).

Background Information: The DATA step is a powerful tool for data management and manipulation in SAS. Understanding how to read raw data, define variables, and control the data step execution process is essential for writing efficient and effective SAS programs.

Hands-On Example:

* Example 1: Reading data from a TXT file using the INFILE and INPUT statements;
DATA work.raw_data;
  INFILE "C:\data\mydata.txt";
  INPUT var1 var2 var3;
RUN;

* Example 2: Specifying a delimiter in the INFILE statement;
DATA work.delimited_data;
  INFILE "C:\data\mydata.csv" DELIMITER=',';
  INPUT var1 var2 var3;
RUN;

* Example 3: Using list input to read data;
DATA work.list_input;
  INFILE "C:\data\mydata.txt";
  INPUT var1 $ var2 var3;
RUN;

* Example 4: Using formatted input to read data;
DATA work.formatted_input;
  INFILE "C:\data\mydata.txt";
  INPUT var1 :$10. var2 :8.2 var3 :date9.;
RUN;

* Example 5: Understanding the program data vector (PDV);
/* The PDV is an area in memory where SAS builds the data set */
/* Variables are created and assigned values in the PDV */

Interpretation: Example 1 demonstrates reading data from a TXT file into a SAS data set named "raw_data" using the INFILE and INPUT statements. Example 2 specifies a delimiter in the INFILE statement to read data from a CSV file. Example 3 uses list input to read data, specifying the $ format for character variables. Example 4 uses formatted input to read data, specifying formats for character, numeric, and date variables. Example 5 explains the concept of the program data vector (PDV), which is a key component of the data step execution process.

Supplemental Information:

SAS INFILE Statement: SAS INFILE Statement
SAS INPUT Statement: SAS INPUT Statement
SAS Documentation: SAS Documentation
SAS Communities: SAS Communities

Discussion Points:

What are the key components of the DATA step, and what is the purpose of each component?
What are the different input styles available in SAS, and when is it appropriate to use each style?
How does the program data vector (PDV) work, and how does it affect the data step execution process?
What are some common errors that can occur when reading raw data, and how can you prevent them?
How can you use options in the INFILE and INPUT statements to control the reading process?

Day 2: Data Cleaning Techniques

Introduction: Data cleaning is a critical step in the data management process, ensuring that your data is accurate, consistent, and ready for analysis. In this session, you will learn how to identify and handle missing values, detect and correct outliers, and standardize data formats using SAS. Mastering these techniques will help you produce reliable results and maintain data integrity.

Learning Objective: By the end of this session, you will be able to identify and handle missing values, detect and correct outliers, and standardize data formats using SAS programming techniques. These skills will enable you to clean and prepare your data for accurate analysis.

Scope of the Lesson: This lesson covers identifying and handling missing values using SAS functions and conditional logic, detecting and correcting outliers using statistical methods and data step programming, and standardizing data formats using SAS functions and formats.

Background Information: Data cleaning is essential for ensuring the quality of your data. Missing values can distort analysis, outliers can skew results, and inconsistent formats can make data difficult to use. SAS provides a variety of tools and functions to help you clean and standardize your data efficiently.

Hands-On Example:

* Example 1: Identifying and handling missing numeric values;
DATA work.cleaned;
  SET work.raw_data;
  IF MISSING(var1) THEN var1 = 0;
RUN;

* Example 2: Detecting and correcting outliers;
DATA work.no_outliers;
  SET work.cleaned;
  IF var2 > 100 THEN var2 = 100;
RUN;

* Example 3: Standardizing date formats;
DATA work.standardized;
  SET work.no_outliers;
  FORMAT date_var date9.;
RUN;

* Example 4: Replacing missing character values;
DATA work.char_cleaned;
  SET work.standardized;
  IF name = "" THEN name = "Unknown";
RUN;

* Example 5: Using PROC UNIVARIATE to identify outliers;
PROC UNIVARIATE DATA=work.cleaned;
  VAR var2;
RUN;

Interpretation: Example 1 demonstrates how to identify missing numeric values in "var1" and replace them with zero, ensuring that missing data does not affect calculations. Example 2 shows how to detect and correct outliers in "var2" by capping values above 100, which helps prevent skewed analysis. Example 3 standardizes the format of a date variable using the DATE9. format, making dates consistent across the data set. Example 4 replaces missing character values in the "name" variable with "Unknown," ensuring that all records have a value. Example 5 uses PROC UNIVARIATE to identify outliers in "var2," providing statistical summaries and visualizations to help spot unusual values.

Supplemental Information:

SAS Data Cleaning Techniques Guide: SAS Data Cleaning Techniques
Video Tutorial: SAS Data Cleaning Techniques
SAS Data Management Documentation: SAS Data Management
SASCrunch Best Practices: SAS Data Cleaning Best Practices
SAS Communities: Data Management Forum

Discussion Points:

What are the most common data quality issues you encounter, and how do you address them?
How do you decide whether to replace, remove, or impute missing values?
What are the risks of not addressing outliers in your data?
How can standardizing data formats improve the quality and usability of your data?
What are some best practices for documenting your data cleaning process?

Day 3: Data Transformation Techniques

Introduction: Data transformation is the process of converting data from one format or structure into another. This session introduces you to various data transformation techniques in SAS, including creating new variables, recoding existing variables, and transposing data sets. By mastering these techniques, you will be able to prepare your data for advanced analysis and reporting.

Learning Objective: By the end of this session, you will be able to create new variables using calculations and conditional logic, recode existing variables to create new categories or values, and transpose data sets to change the orientation of your data. These skills will enable you to transform your data for specific analytical needs.

Scope of the Lesson: This lesson covers creating new variables using arithmetic operations and SAS functions, recoding existing variables using IF-THEN/ELSE statements and the RENAME statement, and transposing data sets using PROC TRANSPOSE.

Background Information: Data transformation is a crucial step in the data analysis process. Creating new variables can provide additional insights, recoding variables can simplify analysis, and transposing data sets can change the perspective of your data. SAS provides a variety of tools and functions to help you transform your data efficiently.

Hands-On Example:

* Example 1: Creating a new variable using arithmetic operations;
DATA work.transformed;
  SET work.raw_data;
  new_var = var1 + var2;
RUN;

* Example 2: Creating a new variable using conditional logic;
DATA work.conditional;
  SET work.transformed;
  IF var3 > 10 THEN category = "High";
  ELSE category = "Low";
RUN;

* Example 3: Recoding an existing variable;
DATA work.recoded;
  SET work.conditional;
  IF category = "High" THEN category_code = 1;
  ELSE category_code = 0;
RUN;

* Example 4: Transposing a data set;
PROC TRANSPOSE DATA=work.recoded OUT=work.transposed;
  VAR var1 var2 var3;
RUN;

* Example 5: Using SAS functions to transform data;
DATA work.function_data;
  SET work.raw_data;
  log_var = LOG(var1);
RUN;

Interpretation: Example 1 demonstrates creating a new variable "new_var" by adding "var1" and "var2," which is useful for combining existing data. Example 2 creates a new variable "category" based on the value of "var3" using conditional logic, categorizing data into "High" and "Low." Example 3 recodes the "category" variable into a numeric "category_code" variable, which can be useful for statistical analysis. Example 4 transposes the "recoded" data set, changing the orientation of the data from wide to long. Example 5 uses the LOG function to create a new variable "log_var," which can be useful for normalizing data.

Supplemental Information:

SAS Data Transformation Techniques Guide: SAS Data Transformation Techniques
Video Tutorial: SAS Data Transformation Techniques
SAS Data Management Documentation: SAS Data Management
SASCrunch Best Practices: SAS Data Transformation Best Practices
SAS Communities: Data Management Forum

Discussion Points:

What are the most common data transformation tasks you perform, and why?
How do you decide when to create a new variable versus recode an existing variable?
What are the benefits of transposing a data set, and when is it appropriate to do so?
How can SAS functions help you transform your data more efficiently?
What are some best practices for documenting your data transformation process?

Day 4: Merging and Appending Data

Introduction: Combining data from multiple sources is a common and essential task in data management. In this session, you will learn how to merge and append data sets in SAS, allowing you to integrate information from different tables for comprehensive analysis. Mastering these techniques will help you efficiently manage and prepare your data for further processing.

Learning Objective: By the end of this session, you will be able to merge data sets using the MERGE statement and BY variables, append data sets using the SET and PROC APPEND statements, and understand the differences between merging and appending. These skills will enable you to combine data from multiple sources effectively.

Scope of the Lesson: This lesson covers merging data sets using the MERGE statement and BY variables, appending data sets using the SET statement and PROC APPEND, handling common issues such as variable mismatches and missing values, and understanding the implications of different combining methods.

Background Information: Merging combines data sets side-by-side based on common variables, while appending stacks data sets on top of each other. SAS provides several methods for combining data, each with its own use cases and considerations. Understanding these methods is crucial for accurate and efficient data integration.

Hands-On Example:

* Example 1: Merging two data sets by a common variable;
DATA work.merged;
  MERGE work.data1(IN=a) work.data2(IN=b);
  BY id;
RUN;

* Example 2: Appending two data sets using the SET statement;
DATA work.appended;
  SET work.data1 work.data2;
RUN;

* Example 3: Appending data sets using PROC APPEND;
PROC APPEND BASE=work.data1 DATA=work.data2 FORCE;
RUN;

* Example 4: Handling variable mismatches during append;
DATA work.data2_fixed;
  SET work.data2;
  IF missing(new_var) THEN new_var = .;
RUN;
DATA work.appended_fixed;
  SET work.data1 work.data2_fixed;
RUN;

* Example 5: Identifying the source of each observation after merging;
DATA work.merged_flag;
  MERGE work.data1(IN=a) work.data2(IN=b);
  BY id;
  source = (a*1) + (b*2);
RUN;

Interpretation: Example 1 demonstrates merging two data sets, "data1" and "data2," by a common variable "id" using the MERGE statement and BY variable. Example 2 shows how to append two data sets using the SET statement, stacking the observations from "data1" and "data2." Example 3 uses PROC APPEND to add the observations from "data2" to "data1," with the FORCE option to handle variable differences. Example 4 addresses variable mismatches by ensuring both data sets have the same variables before appending. Example 5 adds a "source" variable to identify the origin of each observation after merging, which is useful for tracking data provenance.

Supplemental Information:

SAS Combining Data Sets Guide: SAS Combining Data Sets
Video Tutorial: SAS Merge and Append Techniques
SAS Data Management Documentation: SAS Data Management
SASCrunch Best Practices: SAS Data Combining Best Practices
SAS Communities: Data Management Forum

Discussion Points:

What are the key differences between merging and appending data sets in SAS?
When would you use the MERGE statement versus the SET statement?
How do you handle variable mismatches when combining data sets?
What are some best practices for tracking the source of data after merging or appending?
What are the potential risks of combining data sets incorrectly?

Day 5: Advanced Data Manipulation

Introduction: This session delves into advanced data manipulation techniques in SAS, enabling you to handle complex data processing tasks efficiently. We will explore using arrays, DO loops, and conditional logic for sophisticated data transformations. By mastering these techniques, you will be able to tackle challenging data management scenarios and streamline your SAS programming.

Learning Objective: By the end of this session, you will be able to use arrays to process multiple variables efficiently, implement DO loops for iterative processing, and apply conditional logic for complex data transformations. These skills will enable you to perform advanced data manipulation tasks in SAS.

Scope of the Lesson: This lesson covers using arrays to perform operations on multiple variables, implementing DO loops for iterative processing, applying conditional logic using IF-THEN/ELSE statements, and combining these techniques for complex data transformations.

Background Information: Advanced data manipulation techniques allow you to write more efficient, flexible, and maintainable SAS code. Arrays simplify the processing of multiple variables, DO loops automate repetitive tasks, and conditional logic enables you to handle complex data transformations.

Hands-On Example:

* Example 1: Using arrays to process multiple variables;
DATA work.array_example;
  SET work.raw_data;
  ARRAY vars var1-var5;
  DO i = 1 TO DIM(vars);
    vars(i) = vars(i) * 2;
  END;
RUN;

* Example 2: Implementing a DO loop for iterative processing;
DATA work.do_loop_example;
  SET work.raw_data;
  DO i = 1 TO 10;
    new_var = i * var1;
    OUTPUT;
  END;
RUN;

* Example 3: Applying conditional logic using IF-THEN/ELSE statements;
DATA work.conditional_example;
  SET work.raw_data;
  IF var1 > 10 THEN result = "High";
  ELSE IF var1 > 5 THEN result = "Medium";
  ELSE result = "Low";
RUN;

* Example 4: Combining arrays and conditional logic;
DATA work.combined_example;
  SET work.raw_data;
  ARRAY vars var1-var5;
  DO i = 1 TO DIM(vars);
    IF vars(i) > 10 THEN vars(i) = 10;
  END;
RUN;

* Example 5: Using DO WHILE loop;
DATA work.dowhile_example;
  SET work.raw_data;
  i = 1;
  DO WHILE (i <= 5);
    new_var = i * var1;
    OUTPUT;
    i + 1;
  END;
RUN;

Interpretation: Example 1 demonstrates using arrays to multiply variables var1 through var5 by 2, simplifying the processing of multiple variables. Example 2 implements a DO loop to create multiple observations based on iterative calculations, which is useful for generating sequences. Example 3 applies conditional logic to categorize data based on the value of var1, assigning "High," "Medium," or "Low" to the result variable. Example 4 combines arrays and conditional logic to cap the values of variables var1 through var5 at 10, ensuring that no value exceeds a certain limit. Example 5 uses a DO WHILE loop to create multiple observations based on a condition, which is useful for generating data until a certain criterion is met.

Supplemental Information:

SAS Advanced Programming Techniques Guide: SAS Advanced Programming
Video Tutorial: SAS Advanced Data Manipulation
SAS Data Management Documentation: SAS Data Management
SASCrunch Best Practices: SAS Advanced Data Manipulation Best Practices
SAS Communities: Data Management Forum

Discussion Points:

What are the benefits of using arrays for data manipulation in SAS?
How can DO loops automate repetitive tasks in SAS programming?
What are some common use cases for conditional logic in data transformation?
How can you combine arrays, DO loops, and conditional logic for complex data transformations?
What are some best practices for writing efficient and maintainable SAS code for advanced data manipulation?

Daily Quiz

Practice Lab

Select an environment to practice coding exercises. Use SAS OnDemand for Academics for a free SAS programming environment.

Exercise

Download the following files to support your learning:

Grade

Day 1 Score: Not completed

Day 2 Score: Not completed

Day 3 Score: Not completed

Day 4 Score: Not completed

Day 5 Score: Not completed

Overall Average Score: Not calculated

Overall Grade: Not calculated

Generate Certificate

Click the button below to generate your certificate for completing the course.

Readme