SAS (Statistical Analysis System) is a software suite used for advanced analytics, business intelligence, data management, and predictive analytics. Originally developed for data manipulation and statistical analysis, SAS has evolved to include a wide array of functionalities, including data mining, forecasting, and operations research. It is widely utilized across various industries—especially healthcare, finance, and academia—due to its powerful analytical capabilities and user-friendly interface.
SAS was developed in the 1960s at North Carolina State University by a group of researchers led by Anthony James Barr. The initial purpose was to analyze agricultural data to support statistical projects. The first version of SAS was written in assembler language and later transitioned to a more user-friendly interface in the form of a statistical package.
In the 1970s, SAS began to gain traction outside academia as companies recognized its potential for commercial applications. The first SAS Institute was founded in 1976, which has since expanded into a global company providing software solutions and analytics services. As the demand for data analytics grew, SAS diversified its offerings to include business intelligence tools, data integration solutions, and advanced analytics capabilities.
Today, SAS is a leader in the field of analytics, offering a comprehensive software suite that encompasses a wide range of statistical techniques and methodologies. With the rise of big data and machine learning, SAS has adapted by incorporating artificial intelligence (AI) and machine learning (ML) capabilities into its platform. Its software is heavily relied upon for compliance and risk management in highly regulated industries, such as pharmaceuticals and finance.
The core of SAS programming is based on data steps and procedure (PROC) steps. Data steps are used for data manipulation, while PROC steps are utilized for analysis.
data mydata;
input name $ age salary;
datalines;
John 30 50000
Jane 25 60000
;
run;
SAS supports two types of variables: numeric and character. Numeric variables can store numbers, while character variables can store text strings.
data example;
name = "Alice";
age = 28;
run;
SAS allows the use of arrays for efficient data manipulation.
data array_example;
array nums(3) x1 x2 x3;
do i = 1 to 3;
nums(i) = i * 10;
end;
run;
SAS provides a range of built-in functions for data transformation, statistics, and string manipulation.
data example;
x = abs(-5); /* Absolute value */
y = length("SAS"); /* Length of string */
run;
SAS allows formatting of data values using formats, enhancing the presentation of output.
data formatted;
value = 12345.678;
formatted_value = put(value, dollar8.2); /* Formats as $12,345.68 */
run;
Adding labels to variables can improve the readability of output.
data labeled;
x = 1;
label x = "Variable X Label";
run;
SAS supports conditional statements for data manipulation.
data conditional;
set mydata;
if age > 30 then status = "Senior";
else status = "Junior";
run;
SAS provides syntax for merging multiple datasets based on common keys.
data merged;
merge dataset1 dataset2;
by ID;
run;
SAS includes macro programming capabilities for dynamic code generation.
%macro example(data);
data &data;
set &data;
run;
%mend example;
SAS provides built-in procedures for creating graphical representations of data.
proc sgplot data=mydata;
scatter x=age y=salary;
run;
SAS Enterprise Guide is a widely used graphical user interface (GUI) for SAS that allows users to build projects using a point-and-click method. Other popular environments include SAS Studio and Base SAS, which offer a more code-centric approach. SAS Viya is a newer cloud-based analytics platform that supports SAS programming as well.
To build a SAS project, users typically write scripts in an IDE or a text editor, which are then executed to perform data transformations and analyses. The typical workflow involves writing the data step, followed by one or more PROC steps to analyze or visualize the data. The output can be exported to various formats, including CSV, Excel, and RTF.
SAS is predominantly used in industries requiring rigorous data analysis, including:
When comparing SAS to relevant programming languages:
In terms of source-to-source translation, there are tools like "SASTransformer," which can facilitate the conversion of SAS code to R, Python, or SQL. However, each language has unique syntax and libraries that may not have direct equivalents, requiring careful consideration during translation.