Wednesday, September 15, 2021

Good time to Strip()

There is a time and a place to use the strip() function to remove both leading and trailing spaces.

Consider the below code that is an example of how to convert a numeric value to a character. If you use the put() function it will indeed convert from a number to a string. The anyalnum() function is used to populate the position column which returns a value of 6 meaning there are 5 leading spaces before you encounter the 1 in 123.

data results;
  num = 123;
  char = put(123, 8.);
  position = anyalnum(char);

So while SAS does not write any errors, warning or notes related to the above code you can very well have issues when attemping to join the data to other tables that have character values. The assumption is that character columns are left justified. To correct the issue, you can use the strip() function or the -l format modifier as follows:

 data results;
   num = 123;
   char1 = strip(put(123, 8.));
   char2 = put(123, 8.-l);       /* -l is used to left align */
   position1 = anyalnum(char1);
   position2 = anyalnum(char2);

Friday, August 27, 2021

Last Column of DSN

You can use the double dash ( -- ) shorthand to drop a list of columns in a data set based on names. I needed to do this for a list of data sets starting at a certain column to the very last column, but the last column name changed between data sets.

The below macro utilizes low level SAS functions to return the name of the last column. So now I can do something like this:

data x;
  set sashelp.class(drop = age -- %lastcolumn(sashelp.class));

%macro lastcolumn(dsn);
  %local dsid retval;

  %let dsid = %sysfunc(open(&dsn));
  %if &dsid %then %do;
     %let retval = %sysfunc(varname(&dsid, %sysfunc(attrn(&dsid, nvars))));
     %let dsid = %sysfunc(close(&dsid));


Thursday, August 19, 2021

My Snippets - SQL Snapshot

Now that SAS Studio is becomming more prevalant, I am moving away from Enterprise Guide into SAS Studio. A nice feature of SAS Studio is the ability to add your own code snippets.

While working on a project, I often find myself having to trace thru a multitude of data sets and track the number of rows and columns at each step. I typically keep all those data sets in the WORK library. I have created the below code snippet to support that type of activity.

The steps to save this snippet are to write the SQL statement as shown below then either right mouse click and select "Add to My Snippets" or click on the icon circled in red. Provide a name then click the Save button. Now you can easily recall that code snippet by clicking on Snippets, My Snippets, snap (short for snapshot).

Below is an example output from running the snap snippet. Notice that the output is sorted by the last modified date so it follows the flow of my program.

Thursday, July 15, 2021

Free Beer

Ok, so maybe the title got you a bit excited but how do you feel about free SODA as in SAS OnDemand for Academics? SAS OnDemand for Academics is a free cloud based version of SAS that uses SAS Studio in your web browser as an editor. This means that there is no software to download and install and that with your credentials you can use SAS anywhere you have an internet connection.

To get started you just need to create a SAS profile for a free account with SAS on this page. If you already have a profile you can use your email address and password to logon. Otherwise, click on the Don't have a SAS Profile link shown in the below image. You will need to verify that your email is valid by acknowledging a verfication email.

After you have logged in, you will see a screen similar to the image below. Notice in the lower right that there is a progress bar used to display how much of your 5GB of free disk space you have used. To get started, click on the SAS Studio link circled in red below.

Once you start SAS Studio, you will see an image similar to that shown below. There is way too much to cover with a blank editor so suggest users look at the SAS Studio documentation for more information.

One quick example is to right click on Files (Home) under Server Files and Folders and create a new folder (I called mine data). Next click on that new folder then on the Upload icon (or right mouse click, Upload Files...). Now that your file is in the cloud, right mouse click and select Import Data. Follow the wizard and if that was an Excel file the data just got loaded into a SAS data set in the cloud. From there do whatever analysis you desire usign the familiar SAS syntax.

The important thing to keep in mind is that all your code and data will live in the cloud there is nothing on your own computer. Hope you enjoyed this post and drink up!

Friday, June 25, 2021

Creating Sample Data

Sample data plays important roles in testing and benchmarking applications. We need to be mindful of personally identifiable information or PII to not reveal actual user data. SAS does provide procedures such as surveyselect to create extended sample data from existing data but that can be risky if PII is not properly addressed. The use of SAS supplied test data such as SASHELP.CLASS or SASHELP.BASEBALL and the like are just too small to be of any value, especially when benchmarking.

As a result, I have created a macro named sampledata that creates fictious data of any size you like in terms of rows. You can modify the code to your desires to expand the number of columns but this gives you a very good start and explains the use of random numbers when applied to the SAS byte() function.

It is important to understand the American Standard Code for Information Interchange or ASCII character set. As it turns out my very first blog post from 10 years ago was on the creation of the printable ASCII values - see below (click to see a clearer image).

As you can see, numeric values 0 - 9 equate to decimal 48 to 57 in ASCII. If you use the SAS byte() function on ASCII 57 as BYTE(57) it will return a 9. Similarly upper case alphabet characters A - Z are represented as ASCII values 65 - 90. Finally, lowercase a - z are the same as ASCII 97 - 122.

Another critical consideration to create the data is to understand that SAS uses numbers to reprsent dates. SAS dates start with zero (0) on January 1, 1960 and increase or decrease from that base value. You will see a fake date of birth (dob) value has been created for each row. I chose a 60 year time span from 1930 - 1990 based on the SAS date methodology - see below. A span of 30 years in SAS is 365.25 * 30 = 10957 rounded. In order to expand this to a 60 year time period, I used the RAND() function and if the return value was less than 0.5 the value was multiplied by a positive 1 otherwise it was multiplied by a negative 1.

The most important piece of this code is the use of the RAND() function. CALL steaminit() is used to specify a starting seed value used by the RAND() function. If a positive number is used in STREAMINIT() the sequence is repeatable. The RAND() function will return a fractional value between 0 and 1 that you can then multiply by whatever value you like. You can see that in the case of both the firstname and lastname values that each are 16 characters in length so I used byte(int(97 + 26 * rand('uniform')) to generate lower case characters (the range A - Z is 26) and lower case ASCII values start at 97. An outer loop was used to create a name that can be up to 16 characters in length. I set a minimum length of 3 so the loop will be used anywhere between 3 and 16 times to create the names.

The distribution of sex ('F' / 'M') is set at 50.8% female. This ratio was taken from the Census bureau. The SAS IFC() function came in handy here.

I wanted to add some reality to this fake data and did that by using the SASHELP.ZIPCODE data set and augmented it by adding in an incrementing key value (1 to number of rows). This makes it easy to randomly extract a zip code from the hash object. I used the hash object reference of H along with its num_items attribute as h.num_items to return the total number of rows loaded into the hash object. Once a mathc is made (it has to in this scenario) then the associated satellite values are copied into the program data vector (PDV). The value column can be whatever you like but here it is set to a random value between a low side of 30,000 up to 1,000,000 so it could be the value of a house.

The results of running this code via %sampledata() will generate the output shown below. After the image, you will find the entire source code used to create the output.

%macro sampledata(
    rows   = 1000000
  , outdsn = work.sampledata
  , seed   = 123

  /* dsn of zip codes with incrementing key */
  data work.zipcodes;
    key + 1;
    set sashelp.zipcode(keep = zip city statecode areacode);

  data &outdsn(keep = id -- value);
      id        length =   8
      firstname length = $16
      lastname  length = $16
      sex       length =  $1
      city      length = $35
      statecode length =  $2
      zip       length =   8
      phone     length = $10
      dob       length =   4 format = mmddyy10.
      age       length =   3
      value     length =   8 format = dollar12.
      areacode  length =   8

    dcl hash h(dataset: "work.zipcodes");
    h.definedata('zip', 'city', 'statecode', 'areacode');
    /* seed for random function */
    call streaminit(&seed);   

    do id = 1 to &rows;
      /* firstname can be between 3 and 16 characters long */
      t_firstlen = max(3, ceil(16 * rand('uniform')));
      do t_i = 1 to t_firstlen;
        if t_i = 1 then firstname = byte(int(65 + 26 * rand('uniform')));
        else substr(firstname, t_i, 1) = byte(int(97 + 26 * rand('uniform')));
      t_lastlen  = max(3, ceil(16 * rand('uniform')));
      do t_i = 1 to t_lastlen;
        if t_i = 1 then lastname = byte(int(65 + 26 * rand('uniform')));
        else substr(lastname, t_i, 1) = byte(int(97 + 26 * rand('uniform')));
      /* Females 50.8% per census: */
      sex = ifc(rand('uniform') >= 0.508, 'F', 'M');
      key = ceil(h.num_items * rand('uniform'));
      if h.find() = 0 then do;
        phone = put(areacode, 3.);
        do t_i = 4 to 10;
          substr(phone, t_i, 1) = byte(int(48 + 10 * rand('uniform')));

             /* (365.25 * 30) = 10957 or 30 years of days */
      dob   = ceil(10957 * rand('uniform')) * ifn(rand('uniform') < .5, 1, -1);
      age   = int(yrdif(dob, date(), 'actual'));
      value = max(1000000 * rand('uniform'), 30000);

    stop;  /* terminates this data step */

Thursday, June 3, 2021

Recursive Query with PROC SQL

The SAS implementation of SQL is not as robust as most relational databases. One example where SAS falls short is in the area of recursive queries. I know that SQL Server and PostgreSQL handle recursive queries via the use of common table expressions or CTEs using the WITH statement. Hopefully SAS will add this feature in a future release since CTEs were included in the SQL:1999 standard.

In the mean time, I have emulated an existing article "Learn PostgreSQL Recursive Query By Example". Please reference that article for additional details and to compare the different techniques. Credit must be given to my co-worker and excellent SAS programmer, Dave Devoll for the concept he used recently.

data employees;
  infile datalines dsd;
    employee_id : 4.
    full_name : $32.
    manager_id : 4.
1, 'Michael North', .
2, 'Megan Berry', 1
3, 'Sarah Berry', 1
4, 'Zoe Black', 1
5, 'Tim James', 1
6, 'Bella Tucker', 2
7, 'Ryan Metcalfe', 2
8, 'Max Mills', 2
9, 'Benjamin Glover', 2
10, 'Carolyn Henderson', 3
11, 'Nicola Kelly', 3
12, 'Alexandra Climo', 3
13, 'Dominic King', 3
14, 'Leonard Gray', 4
15, 'Eric Rampling', 4
16, 'Piers Paige', 7
17, 'Ryan Henderson', 7
18, 'Frank Tucker', 8
19, 'Nathan Ferguson', 8
20, 'Kevin Rampling', 8 

The very short macro function code for %isblank can be found here. The initial query in the below code only returns a single row for employee_id = 2, Megan Berry. After that, a test is made to ensure that at least one row was returned. If not, an error message will be generated and the macro will terminate.

If all is good, the output that was written to the lev0 data set is copied to a data set named results. This is done so it can be used to append subsequent query results. The next SQL statement uses a sub-query to reference back to the prior data set using %eval(&iter -1). If that query returned rows (&sqlobs > 0) then it is append to the results data set and is repeated until no rows are returned.

%macro recursive(id =, iter = );
  %if %isblank(&id) %then %do;
    %put %str(E)RROR: Must pass in a PK value.;

  %if %isblank(&iter) %then %do;
    proc sql;
      create table lev0 as
        select    employee_id
                , full_name
                , manager_id
        from      employees
        where     employee_id = &id

    %if &sqlobs = 0 %then %do;
      %put %str(E)RROR: No entries found using ID = &id;

    %if %sysfunc(exist(results)) %then %do;
      /* Clear out old version should it exist */
      proc delete data = results;

    data results;
      set lev0;

    %let iter = 1;

  proc sql;
    create table lev&iter as
      select      employee_id
                , full_name
                , manager_id
      from        employees
      where       manager_id in(
        select    distinct employee_id
        from      lev%eval(&iter - 1))

  %if &sqlobs %then %do;
    proc append
      base = results
      data = lev&iter
    %recursive(id = &id, iter = %eval(&iter + 1 ));


%recursive(id = 2);

Monday, December 7, 2020

DOW Sort

Sorting data in ascending or descending order is a very easy and straightforward process.  However, there are times when a simple sort does not suffice.  Consider a scenario of submitting a large number of processes in a SAS grid environment that has a limit on the number of active parallel processes.

In that scenario, you will want to mix tasks that take a long time with those quicker running tasks.  There is normally a key driver that can be used to identify longer running processes.  The number of rows and columns are prime candidates to sort the data.

This example uses the available SASHELP.CLASS data set and sorts it by age and height in ascending order.  The ability to read SAS data sets in random order via the POINT= option in a SET statement make this relatively easy.  The idea is to read the highest or last row with the first X rows that are lower in value.  The technique used is a variant of the DOW Loop created and popularized by SAS Gurus Don Henderson, Paul Dorfman and Ian Whitlock.

The below data set is sorted by age and height.

The following data set shows the result of reading the last row then the top 5 rows.  After that the second to the last row is output followed by rows 6 through 10



      Author: Tom Bellmer

     Created: 12/07/2020 @ 1:25:37 PM

SAS Version: SAS 9.4 (TS1M6)

          OS: LIN X64

     Purpose: Places subrows under the highest value via a DOW loop

       Notes: Assumes the input data set is sorted in ascending order

              will be used to stuff low value rows below high for RSUBMITS

              to reduce expected time in each parallel session


                             Modifications in descending order

FL-YYYYMMDD                             Description

----------- --------------------------------------------------------------------


         1    1    2    2    3    3    4    4    5    5    6    6    7    7    8




%macro sort_dow(

    dsnin   =

  , subrows = 5

  , dsnout  = sort_dow



  %if not %sysfunc(exist(&dsnin)) %then %do;

    %put %str(E)RROR: Invalid input data set: &dsnin..;




  data &dsnout;

    if 0 then set &dsnin nobs = totobs;


    do until(totobs = i);

      set &dsnin point = totobs;

      totobs = totobs - 1;


      if totobs = i then stop;


      do _n_ = 1 to &subrows;

        i + 1;

        set &dsnin point = i;


        if totobs = i then stop;







/*EOF: */




proc sort data = sashelp.class out = class;

  by age height;




    dsnin   = class

  , subrows = 5

  , dsnout  = sort_dow