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SAS Data Sets

About SAS dataset

SAS data set is a SAS file, which holds data. Data should be in the form of a SAS data set to get processed. A SAS data set contains data value organized as a table of observations and variables to process by SAS.
Rows are defined as Observations and Columns are defines as Variables in SAS data set.

Rules for SAS Data sets:

Rules for Valid SAS files /data set Names:

  • 1-32 characters long
  • Must begin with letter or underscore
  • Can continue with any combination of numbers, letters or underscore.

Learn more about SAS in this insightful blog now!
SAS data sets consists of two parts:

  • Descriptor portion
  • Data portion

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Descriptor portion:

Descriptor portion of SAS data set contains attributes of the SAS data set and variables. It includes:

  • Number of observations
  • Observation length
  • Data and time that data set was created / last modified
  • Name of the datasets
  • Other factors

Descriptor portion for variables contain attributes such as name, type, length, format, label and other.

Data Portion:

It’s a collection of data value. Arranged in the form of table.

Observations:

Rows are called observations in SAS data set. It is a collection of data values that usually relate to a single object in SAS Datasets
Variables:
Columns are called variables in SAS. It is a collection of values that describe a particular characteristic.

Missing Value:

If a data is unknown for a particular observation, a missing value is recorded

  • “.” (Dot/Period) indicate missing value of a numeric value
  • “ “ (Blank) indicate missing value of a character value

Null Data Sets

If you want to execute a DATA step but do not want to create a SAS data set, specify keyword _NULL_  as data set name;
data _NULL_  ;

Automatic Naming:

If you do not specify a SAS data set name or using _NULL_ in DATA statement, SAS automatically creates a data set with the names DATA1, DATA2 and so on to WORK or USER library. This is called DATAn naming convention; data;

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About the Author

Technical Research Analyst - Big Data Engineering

Abhijit is a Technical Research Analyst specialising in Big Data and Azure Data Engineering. He has 4+ years of experience in the Big data domain and provides consultancy services to several Fortune 500 companies. His expertise includes breaking down highly technical concepts into easy-to-understand content.