Reusable SAS Code: Master Macros for Maximum Efficiency

In this lesson, you'll learn how to standardize numeric values across an entire dataset using the replace_numeric_values macro. Whether you're replacing missing values, outdated figures, or inconsistent entries, this macro automates the process by scanning all numeric variables and applying your specified changes in one step. It's a fast, flexible, and consistent way to clean numeric data without manual edits.

In this walkthrough, we break down the replace_numeric_values macro line by line to show exactly how it works. You’ll learn how each part of the macro contributes to scanning numeric variables, identifying target values, and replacing them across the dataset. This hands-on explanation will help you understand not just how to use the macro—but how to build and adapt similar logic in your own projects.

In this lesson, we walk through two practical examples using the replace_numeric_values macro. You’ll see how to apply it to real datasets, modify input parameters, and observe the macro in action. These examples reinforce the macro’s flexibility and demonstrate how it can be used to quickly clean and standardize numeric data in different scenarios.

This lesson introduces the replace_character_values macro, designed to efficiently update character values across an entire dataset. Whether you're fixing inconsistent labels, standardising codes, or replacing outdated text, this macro makes the process quick and repeatable. You'll learn what the macro does, why it's useful, and how it can simplify routine data cleaning tasks.

Here, we break down the replace_character_values macro step by step. You’ll explore how the macro identifies and replaces character values across all relevant variables in your dataset. This tutorial highlights the macro logic, key syntax, and parameter roles—giving you a solid understanding of how it works under the hood and how you can adapt it for your own use.

Watch the macro in action through two real-world examples. You'll see how to apply replace_character_values to clean and standardise character data efficiently. These practical use cases demonstrate how input parameters can be adjusted to handle different scenarios, helping you build confidence in applying the macro to your own datasets.

In this lesson, we introduce the import macro, which simplifies the process of importing raw files into SAS. Whether you're working with CSVs, Excel files, or other delimited formats, this macro helps automate and standardize data import routines, reducing manual steps and ensuring consistent results across projects.

Here, we go through the import macro line by line. You’ll learn how it dynamically handles file paths, input types, and dataset naming conventions. This walkthrough will give you a clear understanding of how the macro works, how to control its behaviour using parameters, and how it can be adapted to different import needs.

In this lesson, we demonstrate how to use the import macro with real examples. You’ll see how to import different types of files and structure your inputs effectively. These examples showcase the macro’s flexibility and how it can be used to streamline repetitive import tasks in your day-to-day workflow.

This lesson introduces the export macro, designed to automate the process of exporting SAS datasets to commonly used formats such as CSV or Excel. Whether you're sharing data with stakeholders or saving results for external tools, this macro helps ensure your exports are quick, consistent, and easy to manage.

In this walkthrough, we examine the export macro line by line. You'll learn how it handles dataset selection, file path construction, and output format control. This breakdown helps you understand each component of the macro and how to customize it for your own export routines.

Watch the export macro in action with practical examples. We demonstrate how to export datasets with different settings, showing how the macro can adapt to various business requirements. These examples reinforce the value of automating exports and show how easily the macro can fit into your workflows.

In this lesson, we introduce the check_exists macro—an essential tool for verifying the presence of datasets or files before executing further logic. By automating these checks, the macro adds a layer of robustness to your workflows and helps you catch potential issues early, ensuring smoother and more reliable code execution.

This walkthrough breaks down the check_exists macro line by line. You’ll learn how it cleans the input path, identifies whether it’s checking for a file or a dataset, and logs the outcome. We explain the logic behind each step so you can understand how the macro validates existence and handles both success and failure cases.

Here, we demonstrate how to use the check_exists macro through practical examples. You’ll see how to check for the presence of datasets and external files in different locations, and how the macro behaves in each scenario. These examples highlight its usefulness in creating defensive, error-resistant code.

In this lesson, we introduce the fiscal_year macro, which simplifies fiscal date management in your SAS programs. Whether you're working with financial, tax, or accounting years, this macro helps you quickly calculate the start and end dates of a given fiscal year—automating a task that’s crucial for reporting and planning.

In this walkthrough, we unpack the logic behind the fiscal_year macro step by step. You'll learn how the macro handles input formatting, calculates key date ranges, and sets global macro variables that can be reused throughout your session. A great way to build confidence in date-based logic and automation.

This lesson puts the fiscal_year macro into practice with real examples. You’ll see how to apply it for different fiscal years and how the generated start and end dates can be used across reporting tasks. These examples highlight how the macro reduces manual effort and ensures consistency in time-based analysis.

In this lesson, we explore the custom_fiscal_year macro, which allows you to define fiscal years that don’t follow a standard calendar. By entering your own start and end dates, this macro helps tailor your analysis to match your organization's or region’s financial reporting schedule.

We break down the custom_fiscal_year macro line by line, explaining how it processes inputs and calculates fiscal year boundaries. You’ll learn how each parameter contributes to generating precise date ranges, and how these values are made globally available for consistent use throughout your SAS session.

This lesson shows the macro in action with real-world scenarios where fiscal years differ from the standard calendar. You'll see how to define unique start and end points and use the results in your analysis. These examples highlight the macro’s flexibility and its ability to streamline date calculations for non-standard fiscal years.

In this lesson, we introduce the business_day macro, which helps you automatically identify business days, weekends, and public holidays within your dataset. This is especially useful for time-sensitive analysis, reporting deadlines, or calculating working days in a given period.

We walk through the logic behind the business_day macro, showing how it reformats dates, determines weekdays, and checks against a holiday calendar. You’ll learn how the macro categorizes each date and creates helpful new variables that enhance time-based analysis.

This lesson demonstrates the macro in action using real examples. You'll see how to apply the macro to different datasets, how it handles public holidays, and how to interpret the new business day indicators it creates. A practical tool for analysts dealing with scheduling, compliance, or daily activity metrics.

In this lesson, we introduce the business_day_time macro, designed to determine if a given datetime falls within defined business days and hours. This is essential for analyses that require precision on when work actually happens, beyond just identifying business dates.

We break down the business_day_time macro, explaining how it processes datetime variables, extracts weekdays and hours, and checks against public holidays and specified business hours. You’ll gain insight into how the macro categorizes datetimes for accurate business hour analysis.

This lesson puts the macro to work with practical examples, showing how to apply it to datasets with varying business hour definitions. You’ll see how it flags datetimes outside business hours or on holidays, providing a powerful tool for detailed time-sensitive reporting and analysis.