CAS-Action! Grouping Frequency Tables - Part 3


Welcome back to my SAS Users blog series CAS Action! - a series on fundamentals. In my previous part 1 and part 2 posts I reviewed how to use the simple.freq CAS action to generate frequency distributions for one or more columns and how to save the results. In this post I will show you how to group the results of the freq action.

In this example, I will use the CAS language (CASL) to execute the freq CAS action. Be aware, instead of using CASL, I could execute the same action with Python, R and more with some slight changes to the syntax for the specific language. Refer to the documentation for syntax in other languages.

Load the demonstration data into memory

I'll start by executing the loadTable action to load the WARRANTY_CLAIMS_0117.sashdat file from the Samples caslib into memory. By default the Samples caslib should be available in your SAS Viya environment. I'll load the table to the Casuser caslib and then I'll clean up the CAS table by renaming and dropping columns to make the table easier to use. For more information how to rename columns check out my previous post. Lastly I'll execute the fetch action to preview 5 rows.

proc cas;
   * Specify the input/output CAS table *;
   casTbl = {name = "WARRANTY_CLAIMS", caslib = "casuser"};
   * Load the CAS table into memory *;
    table.loadtable / 
        path = "WARRANTY_CLAIMS_0117.sashdat", caslib = "samples",
        casOut = casTbl + {replace=TRUE};
* Rename columns with the labels. Spaces replaced with underscores *;
   *Store the results of the columnInfo action in a dictionary *;
   table.columnInfo result=cr / table = casTbl;
   * Loop over the columnInfo result table and create a list of dictionaries *;
   listElementCounter = 0;
   do columnMetadata over cr.ColumnInfo;
	listElementCounter = listElementCounter + 1;
	convertColLabel = tranwrd(columnMetadata['Label'],' ','_');
	renameColumns[listElementCounter] = {name = columnMetadata['Column'], rename = convertColLabel, label=""};
   * Rename columns *;
   keepColumns = {'Campaign_Type', 'Platform','Trim_Level','Make','Model_Year','Engine_Model',
                  'Vehicle_Assembly_Plant','Claim_Repair_Start_Date', 'Claim_Repair_End_Date'};
   table.alterTable / 
	name = casTbl['Name'], caslib = casTbl['caslib'], 
	keep = keepColumns;
   * Preview CAS table *;
   table.fetch / table = casTbl, to = 5;

The results above show a preview of the warranty_claims CAS table.

Add a grouping column

What if you want a frequency distribution of each Model_Year by Make? You can easily do that with the freq action. The key is adding the groupBy sub parameter when referencing the CAS table. Then use the grouped CAS table in the freq action and specify the column to analyze. In this example, the CAS table is grouped by Model_Year and the freq action specifies the Make column.

proc cas;
   * Reference the CAS table and group by Model_Year *;
   casTbl = {name = "WARRANTY_CLAIMS", 
             caslib = "casuser",
	     groupby = "Model_Year"};
   * Model_Year by Make frequency *;
   simple.freq / table = casTbl, input = 'Make';


Partial results

The above results show that the freq action returns a separate table for each distinct Model_Year. While this is great information, what if I want a single table with the Model_Year by Make?

Saving the results as a CAS table

One option is saving the results as a CAS table. This works similarly to my previous post CAS-Action! Saving Frequency Tables - Part 2. Simply add the casOut parameter to the freq action. I'll add a label to the new CAS table to give it a description and then preview the new CAS table with the fetch action.

proc cas;
   * Reference the CAS table and group by Model_Year *;
   casTbl = {name = "WARRANTY_CLAIMS", 
             caslib = "casuser",
	     groupby = "Model_Year"};
   * Specify the output CAS table information *;
   outputTbl = {name = "yearByMake", caslib = "casuser"};
   * Get a frequency of Model_Year by Make and create a CAS table *;
   simple.freq / 
	table = casTbl, 
	input = 'Make',
	casOut = outputTbl || {label = "Year by Make frequency table"};
   * Preview the CAS table *;
   table.fetch / table = outputTbl;

The results above show the freq action with the casOut parameter returns information about the newly created CAS table, and the fetch action returns a preview of the new CAS table. Notice the analysis is grouped by Model_Year and is consolidated into a single table.

Saving the results as a SAS data set

Instead of saving the results back to the CAS server, you can save them as a SAS data set. I did an example in my previous post CAS-Action! Saving Frequency Tables - Part 2. However, when you save the results of an action summarized by groups, you need to combine each individual group into a single result table. To do that you need to use the COMBINE_TABLES function on the dictionary returned from the CAS server. The COMBINE_TABLES function will combine each individual table in a dictionary and return a single result table. Then you can save the new table by using the SAVERESULT statement. Lastly, I'll view the SAS data set using the PRINT procedure.

proc cas;
   * Reference the CAS table and group by Model_Year *;
   casTbl = {name = "WARRANTY_CLAIMS", 
             caslib = "casuser",
	     groupby = "Model_Year"};
   * Specify the output CAS table information *;
   outputTbl = {name = "yearByMake", caslib = "casuser"};
   * Get a frequency of Model_Year by Make and store the results in a dictionary *;
   simple.freq result=freq_cr / 
	table = casTbl, 
	input = 'Make';
   * Combine all the tables in the dictionary and create a result table *;
   freqTbl = combine_tables(freq_cr);
   * Save the result table as a SAS data set *;
   saveresult freqTbl dataout=work.yearByMake;
* Preview the SAS data set *;
proc print data=work.yearByMake;

The results above show the new SAS data set. Once you save the results of the CAS server as a SAS data set, you can use familiar SAS knowledge to continue processing the data on the compute server.

Plot the results of the freq action

Now, let's explore and visualize this data. You can use the SGPLOT procedure to visualize the summarized results from the CAS server that you saved as a SAS data set.

title height=14pt justify=left color=charcoal "Total Number of Warranty Claims by Model Year and Car Make";
title2 "";
proc sgplot data=work.yearByMake
	vline Model_Year / 
			group = CharVar 
	format Frequency comma16.;
	keylegend / position=topleft title='Car Makes';
	label Frequency='Warranty Claims';
	xaxis display=(nolabel);

The visualization above shows the Zeus car Make is the primary cause of warranty claims in all years, and in the years 2016, 2017 and 2018 had a huge increase in warranty claims.


Using the groupBy sub parameter when referencing a CAS table enables you to easily group the results of a CAS action. When using the groupBy parameter:

  • the action will return separate result tables for each distinct grouped value
  • the casOut parameter in the action to creates a single CAS table with all the groups
  • the COMBINE_TABLES function combines each distinct group by result table in the dictionary to create a single result table, and then saves that as a SAS data set

Additional resources

freq action
SAVERESULT statement
Plotting a Cloud Analytic Services (CAS) In-Memory Table
SAS® Cloud Analytic Services: CASL Programmer’s Guide 
SAS® Cloud Analytic Services: Fundamentals
CAS Action! - a series on fundamentals
Getting Started with Python Integration to SAS® Viya® - Index


About Author

Peter Styliadis

Technical Training Consultant

Peter Styliadis is a Technical Training Consultant at SAS on the Foundations team in the Education division. The team focuses on course development and customer training. Currently Peter spends his time working with SAS Programming, Structured Query Language (SQL), SAS Visual Analytics, Programming in Viya and Python. Peter is from the Rochester/Buffalo New York area and does not miss the cold weather up north. After work, Peter can be found spending time with his wife, child and three dogs, as well as hiking and spending time at home learning something new. Prior to joining SAS, Peter worked at his family's restaurant (think My Big fat Greek Wedding), worked in customer service, then became a High School Math and Business teacher. To connect with Peter, feel free to connect on LinkedIn.

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