A Comprehensive Guide to SAS Data Sets with Examples

Disclaimer: This content is provided for informational purposes only and does not intend to substitute financial, educational, health, nutritional, medical, legal, etc advice provided by a professional.

A Comprehensive Guide to SAS Data Sets with Examples

Are you interested in learning SAS and exploring the world of data science and analytics? In this comprehensive guide, we will delve into the concept of SAS data sets and provide you with real-world examples to help you understand how they work. Whether you are a beginner or an experienced professional, this guide will equip you with the knowledge and skills needed to work with SAS data sets effectively.

What Is a SAS Data Set?

A SAS data set represents a SAS file that is stored in a SAS library. It is a structured collection of data that SAS creates and processes. Think of it as a table or spreadsheet that contains rows and columns of data. Each row represents an observation, while each column represents a variable or attribute.

Parts of the SAS Data Set

A SAS data set is composed of two main parts: the SAS descriptor portion and the data portion. The descriptor portion contains metadata about the data set, such as the names and attributes of variables. The data portion stores the actual data values.

Importing External Data Sets

One of the most common tasks in SAS is importing external data sets. SAS provides various methods and procedures to import data from different sources, including CSV files, Excel spreadsheets, and databases. These methods allow you to bring in data from external sources and convert them into SAS data sets for further analysis.

Variable (Or Column)

In a SAS data set, a variable represents a column or attribute. It can be numeric or character-based, depending on the type of data it represents. Variables can also have additional attributes, such as labels and formats, to provide additional information about the data.

Rows (Or Observation)

Rows in a SAS data set are often referred to as observations. Each row represents a unique set of data values for each variable. Observations can be filtered, sorted, and manipulated to extract meaningful insights from the data set.

SAS Built-in Data Sets

SAS provides a wide range of built-in data sets that you can use for practice and analysis. These data sets cover various domains, including finance, healthcare, marketing, and more. By working with these data sets, you can gain hands-on experience and learn how to apply SAS techniques to real-world scenarios.

PROC Import

PROC Import is a SAS procedure that allows you to import data from external sources. It provides a simple and efficient way to read data from different file formats, such as CSV, Excel, and database files. PROC Import automatically detects the data structure and creates a SAS data set based on the imported data.

Using INFILE - Get External File

Another method to import external data sets is by using the INFILE statement. This statement allows you to specify the location and format of the external file and read its contents into a SAS data set. It provides more flexibility and control over the import process, allowing you to handle complex file structures and data formats.

Example 1: Reading External File Data

Let's start with a simple example of reading data from an external file using PROC Import. Suppose we have a CSV file named 'sales.csv' that contains sales data for different products. We can use the following code to import the data into a SAS data set:

PROC Import DATAFILE='sales.csv' OUT=SalesData DBMS=CSV REPLACE; RUN;

This code imports the data from the 'sales.csv' file and creates a SAS data set named 'SalesData'. The DBMS=CSV option specifies that the file format is CSV, and the REPLACE option replaces any existing data set with the same name.

Example 2: Reading Instream Data Lines

In some cases, you may need to read data directly from the program code without using an external file. This can be done using instream data lines. Here's an example:

DATA ExampleData; INPUT ID Name $ Age; DATALINES; 1 John 25 2 Jane 30 3 Mark 35 ; RUN;

This code creates a SAS data set named 'ExampleData' with three variables: ID, Name, and Age. The INPUT statement specifies the variables and their formats. The DATALINES statement is used to input the data directly within the program code.

Example 3: Reading Instream Data Lines with Missing Values

Sometimes, the data may contain missing values. SAS provides a way to handle missing values during the data input process. Here's an example:

DATA ExampleData; INPUT ID Name $ Age; DATALINES; 1 John 25 2 Jane . 3 Mark 35 ; RUN;

In this example, the second observation has a missing value for the 'Age' variable. SAS automatically assigns a missing value indicator (.) for the missing value, allowing you to perform calculations and analyses without errors.

Example 4: Using Multiple Input Files in Instream Data

SAS also allows you to read data from multiple input files within the same data step. This can be useful when you have data split across different files or when you want to combine data from different sources. Here's an example:

DATA CombinedData; INFILE 'file1.dat' 'file2.dat' 'file3.dat'; INPUT ID Name $ Age; RUN;

This code reads data from three different files: 'file1.dat', 'file2.dat', and 'file3.dat'. The INPUT statement specifies the variables, and the INFILE statement specifies the input files. SAS will automatically read the data from each file and combine them into a single SAS data set named 'CombinedData'.

Conclusion

In this guide, we have explored the concept of SAS data sets and provided you with real-world examples to help you understand how they work. We have covered various topics, including importing external data sets, working with variables and observations, and using PROC Import and the INFILE statement. By applying the knowledge and examples from this guide, you will be well-equipped to work with SAS data sets and perform data analysis and manipulation tasks effectively.