BASE SAS vs CASL: A Comparative Analysis That Will Help You in Code Comparison and Transition
SAS vs. CASL #2: Load Datasets
Loading SAS Datasets into the SAS Work Library
The below example demonstrates how to load SAS datasets from the sashelp library into the work library, effectively creating copies of the datasets for temporary use. This allows you to work with these copies without altering the original data.
We’ll continue to use the datasets stored in the WORK library throughout this article to demonstrate the distinctions between BASE SAS and CASL across various examples.
/* load sas datasets directly into sas work lib */
data work.class; set sashelp.class;
run;
data work.cars; set sashelp.cars;
run;
data work.iris; set sashelp.iris;
run;
Output Log:
80 /* load sas datasets directly into sas work lib */
81
82 data work.class; set sashelp.class;
83 run;
NOTE: There were 19 observations read from the data set SASHELP.CLASS.
NOTE: The data set WORK.CLASS has 19 observations and 5 variables.
NOTE: DATA statement used (Total process time):
real time 0.00 seconds
cpu time 0.00 seconds
84 data work.cars; set sashelp.cars;
85 run;
NOTE: There were 428 observations read from the data set SASHELP.CARS.
NOTE: The data set WORK.CARS has 428 observations and 15 variables.
NOTE: DATA statement used (Total process time):
real time 0.00 seconds
cpu time 0.00 seconds
86 data work.iris; set sashelp.iris;
87 run;
NOTE: There were 150 observations read from the data set SASHELP.IRIS.
NOTE: The data set WORK.IRIS has 150 observations and 5 variables.
NOTE: DATA statement used (Total process time):
real time 0.00 seconds
cpu time 0.00 seconds
Loading SAS Datasets into the CAS Library
Similar to the previous example where copies of datasets are created in the work library, this code loads built-in datasets into the CAS (Cloud Analytic Services) environment for processing.
It connects to the CAS session named casauto and then loads three datasets, replacing any existing versions in CAS. You can find these three datasets with the same name but under CASUSER CAS library. This allows you to work with these datasets in the CAS environment, which is designed for high-performance, in-memory processing.
/* load sas datasets directly into CAS lib */
proc casutil;
cas casauto;
load data=sashelp.class replace;
load data=sashelp.cars replace;
load data=sashelp.iris replace;
run;
Output Log:
80 /* load sas datasets directly into CAS lib */
81 proc casutil;
NOTE: The UUID '105b1d4a-836b-3448-91a7-a1b5d0da05c2' is connected using session CASAUTO.
82 cas casauto;
WARNING: A session with the name CASAUTO already exists.
83 load data=sashelp.class replace;
NOTE: SASHELP.CLASS was successfully added to the "CASUSER(MayurJadhav)" caslib as "CLASS".
84 load data=sashelp.cars replace;
NOTE: SASHELP.CARS was successfully added to the "CASUSER(MayurJadhav)" caslib as "CARS".
85 load data=sashelp.iris replace;
NOTE: SASHELP.IRIS was successfully added to the "CASUSER(MayurJadhav)" caslib as "IRIS".
86 run;
SAS vs. CASL #3: Print Sample Data Values
Printing Sample Data Values in BASE SAS
This code prints the first 10 rows of a dataset named "class" from the "work" library, which we loaded in the previous example. It shows the values for the columns listed after the VAR statement: name, sex, age, height, and weight.
/* print sample data values */
proc print data=work.class (obs=10);
var name sex age height weight;
run;
Results:
Printing Sample Data Values in CASL
This code prints the first 10 rows of a dataset named "class" in the CAS (Cloud Analytic Services) environment from the CASUSER CAS library.
It connects to the CAS session named casauto, and then uses the table.fetch action to retrieve the values for the columns: name, sex, age, height, and weight. The to=10 option specifies that only the first 10 rows should be displayed. The output of this will be printed on the “RESULTS” tab.
/* print sample data values in CASL */
proc cas;
session casauto;
table.fetch /
format=true,
fetchvars = {"name", "sex", "age", "height", "weight"},
table="class",
to=10;
run;
quit;
Results:
SAS vs. CASL #4: Display Dataset Summary
Displaying a Summary of Dataset Contents in BASE SAS
The traditional BASE SAS proc contents procedure provides detailed structural and metadata information about a dataset named "class" located in the "work" library. It includes information such as variable names (columns), their types (numeric or character), and additional attributes like format and length.
This procedure provides an in-depth overview of the dataset's layout and characteristics, focusing solely on its structure and metadata, without displaying the actual data values contained within the dataset.
/* display table contents */
proc contents data=work.class;
run;
Results:
Displaying a Summary of Dataset Contents in CASL
To get the details of CAS dataset contents you need to use several CAS table actions such as caslibInfo, columninfo, recordCount, tableDetails, etc.
Retrieve CAS Table Information:
- table.caslibInfo;: Displays information about the CAS libraries available in the session.
- table.columninfo / table="class";: Provides details about the columns (variables) in the "class" table, such as their names, types, and lengths.
- table.recordCount / table="class";: Shows the number of records (rows) in the "class" table.
- table.tableDetails / table="class";: Gives comprehensive details about the structure and attributes of the "class" table, including metadata.
- table.tableInfo / table="class";: Offers general information about the "class" table, such as its name, location, and description.
/* display CAS table contents */
proc cas;
session casauto;
table.caslibInfo;
table.columninfo / table="class";
table.recordCount / table="class";
table.tableDetails / table="class";
table.tableInfo / table="class";
run;
quit;
Results:
SAS vs. CASL #5: Data Handling (Filtering, Grouping, and Sorting)
Filtering, Grouping, and Sorting Variables in BASE SAS
The following code shows the basic data handling operations such as filtering the data, grouping and sorting data based on selected variables.
This code retrieves specific columns (name, sex, age, height, weight) from the "class" dataset, filters it to include only females (sex="F"), groups the data by "name" and "age", and then sorts the grouped data in descending order by "name" and "age". It demonstrates how PROC SQL procedure are used in the data handling tasks like filtering, grouping, and sorting within SAS.
/* data handling: filtering, grouping, and sorting by variables */
proc sql outobs=10;
select name, sex, age, height, weight from work.class
where sex="F"
group by name, age
order by name desc, age desc;
quit;
Results:
Filtering, Grouping, and Sorting Variables in CASL
Similar to the proc sql procedure in traditional SAS, in CAS you can use the table.fetch action to perform a wide range of data handling operations, from basic to advanced tasks. This action allows you to retrieve data from CAS tables based on specified criteria, filter results, aggregate data, sort rows, and limit the number of returned rows, among other functionalities.
The below code connects to CAS, specifies conditions and variables to fetch from the "class" table, retrieves data where sex is female, sorts it by name and age in descending order, fetches the top 10 rows, describes the fetched result, and prints the data.
The following code snippet demonstrates its usage:
- classtbl.name ="class"; specifies that the table to be queried is named "class".
- classtbl.where = "sex = 'F'"; sets a condition to retrieve rows where the sex column equals 'F' (indicating females).
- table.fetch result=r_var/ ... ; performs the fetch action to retrieve data and result saved in the r_var variable.
- table=classtbl, specifies the table from which data is fetched.
- to=10; limits the fetch to the first 10 rows.
- describe r_var; provides a description of the fetched result (r_var).
- print r_var; prints the fetched data (r_var).
/* data handling in CASL: filtering, grouping, and sorting by variables */
proc cas;
session casauto;
classtbl.name ="class";
classtbl.where = "sex = 'F'";
fvars = {"name", "sex", "age", "height", "weight"};
table.fetch result=r_var/ /* results of the fetch action are saved in the "r_var" variable */
format=false,
fetchvars = fvars,
index=false,
sortby={
{name="name", order="descending"},
{name="age", order="descending"}
},
table=classtbl,
to=10;
describe r_var;
print r_var;
run;
quit;
Results:
SAS vs. CASL #6: Generate Descriptive Statistics
Calculating Descriptive Statistics in BASE SAS
This code generates descriptive statistics for a dataset named "class" and organizes the results by the "sex" variable. First, it sorts the dataset by gender and then calculates descriptive statistics such as minimum, maximum, mean, standard deviation, etc for each gender group. The results are saved in a new dataset named work.summary_stats.
/* Generate descriptive statistics */
proc sort data=class out=classbysex;
by sex;
run;
proc means data=classbysex max mean min n nmiss std stderr;
by sex;
output out=summary_stats
;
run;
Results:
Calculating Descriptive Statistics in CASL
You can generate the same descriptive statistics from the CAS table using the simple.summary CAS action. It generates descriptive statistics for numeric variables such as the sample mean, sample variance, sample size, sum of squares, and more.
The following code generates descriptive statistics for a dataset named "class" in the CAS environment, organizing the results by the "sex" variable. The simple.summary action calculates various descriptive statistics mentioned after subSet= option , including maximum, mean, minimum, count, number of missing values, standard deviation, and standard error for each gender group.
/* Generate descriptive statistics in CASL*/
proc cas;
tbl1.name = "class";
tbl1.groupBy = "sex";
simple.summary /
table = tbl1
subSet = {"MAX", "MEAN", "MIN", "N", "NMISS", "STD", "STDERR"};
run;
quit;
Results:
Conclusion
In conclusion, this comparative analysis between BASE SAS and CASL provides a comprehensive exploration of their respective strengths and applications in data analytics. I hope this head-to-head comparison with demonstrated examples would help you select the appropriate toolset based on specific analytical needs and infrastructure requirements.
This article guide you how to transform your BASE SAS code into CASL but you could learn more about “When to CASL and not to CASL, SAS programming in SAS Viya”.
Whether you choose to leverage the advanced capabilities of CASL or retain some functionalities of BASE SAS, this choice will greatly impact how well you can understand and use your data to gain valuable insights.
References:
- https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/caslpg/titlepage.htm
- https://support.sas.com/resources/papers/proceedings20/4454-2020.pdf
- https://www.lexjansen.com/phuse-us/2022/as/PRE_AS09.pdf
- https://communities.sas.com/t5/SAS-Communities-Library/CASL-It-s-like-Base-SAS-with-the-MACRO-Built-...
- https://www.pharmasug.org/proceedings/2018/AD/PharmaSUG-2018-AD23.pdf
- https://learnsascode.com/base-sas-vs-casl-a-simple-comparison/
- https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/proccas/n19orokqs3mwaen16qfcx986gmfm.htm
- https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/pgmdiff/p06ibhzb2bklaon1a86ili3wpil9.htm
