*Copyright @ www.statschoice.com;

/********************************************************************************

What is the word meaning of 'explicit'?

********************************************************************************/

/********************************************************************************

stated clearly and in detail, leaving no room for confusion or doubt.

********************************************************************************/

/********************************************************************************

When does a SAS data step write an observation to the output dataset?

********************************************************************************/

/********************************************************************************

SAS data step writes the contents of the Program Data Vector (PDV) as an observation to the output dataset when it encounters a step boundary
In SAS, a 'run' statement or a 'data' statement or a 'proc' statement is considered as a step boundary

********************************************************************************/

/********************************************************************************

What does an (explicit) output statement do?

********************************************************************************/

/********************************************************************************

An output statement is used for requesting SAS to write the contents of PDV to the output dataset as an observation
As we are explicitly requesting SAS to write an observation, we call it an 'explicit' output statement
We can provide multiple output statements within a data step
When at least one explicit output statement is used in a data step SAS no longer writes the observation to the output dataset at the step boundary

********************************************************************************/

/********************************************************************************

Different step boundary scenarios

********************************************************************************/

/********************************************************************************

Run statement as step boundary

********************************************************************************/

data class01;
     set sashelp.class;
  run;

/********************************************************************************

Proc statement as step boundary for the data step

********************************************************************************/

data class02;
     set sashelp.class;
   
  proc sort data=class02;
     by age;
  run;

/********************************************************************************

Data statement as step boundary for the first data step

********************************************************************************/

/********************************************************************************

data statement of the step creating class04 dataset works as a step boundary for the data step creating class03 dataset

********************************************************************************/

data class03;
     set sashelp.class;
   
  data class04;
     set sashelp.class;
  run;

/********************************************************************************

What happens when a step boundary is not present for a data step

********************************************************************************/

/********************************************************************************

When a data step code which does not have a step boundary is submitted for execution, SAS does not execute the code and shows a message on the top as shown in screenshot
Same is applicable for any proc step, SAS does not execute a step unless a step is encountered

********************************************************************************/

/********************************************************************************

A data step without a step boundary

********************************************************************************/

data class05;
     set sashelp.class;

/********************************************************************************

A proc step without a step boundary

********************************************************************************/

proc sort data=class05;
     by name;

/********************************************************************************

Replacing implicit output instruction with explicit output statement

********************************************************************************/

/********************************************************************************

Basics of explicit output statement

********************************************************************************/

/********************************************************************************

Implicit output, writing an observation to the output dataset, happens at the step boundary
Implicit output instruction can be overwritten by placing an explicit output statement as the last statement in a data step
The below data steps produce the same result: the first one works with implicit output, and the second one with explicit output

********************************************************************************/

data class_i;
     set sashelp.class;
     agemon=age*12;
  run;
   
  data class_e;
     set sashelp.class;
     agemon=age*12;
     output;
  run;

NOTE: There were 19 observations read from the data set WORK.CLASS05.
  NOTE: The data set WORK.CLASS05 has 19 observations and 5 variables.
  NOTE: PROCEDURE SORT used (Total process time):
        real time           0.03 seconds
        cpu time            0.01 seconds
   
   
  589685  data class_i;
  589686     set sashelp.class;
  589687     agemon=age*12;
  589688  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS_I has 19 observations and 6 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds
   
   
  589689
  589690  data class_e;
  589691     set sashelp.class;
  589692     agemon=age*12;
  589693     output;
  589694  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS_E has 19 observations and 6 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds

/********************************************************************************

Multiple explicit output statements

********************************************************************************/

/********************************************************************************

SAS data step processes one observation at a time
SAS data step uses PDV to build an observation in memory before writing it to output dataset
SAS writes the contents of PDV as an observation to the output dataset whenever it encounters an output statement

In the below example, for each observation of input dataset, SAS writes the contents of the PDV to the output dataset twice, once because of the first output statement and second time because of the second output statement
The output dataset, class01 will contain twice the number of observations of sashelp.class as each observation of the input dataset is written twice to the output dataset because of the two output statements
Similary, the output dataset class02 will contain thrice the number observations of sashelp.class as each observation is written thrice because of the three output statements

********************************************************************************/

data class;
     set sashelp.class;
  run;
   
  data class01;
     set sashelp.class;
     output;
     output;
  run;
   
  data class02;
     set sashelp.class;
     output;
     output;
     output;
  run;

597188  data class;
  597189     set sashelp.class;
  597190  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           0.01 seconds
        cpu time            0.01 seconds
   
   
  597191
  597192  data class01;
  597193     set sashelp.class;
  597194     output;
  597195     output;
  597196  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS01 has 38 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds
   
   
  597197
  597198  data class02;
  597199     set sashelp.class;
  597200     output;
  597201     output;
  597202     output;
  597203  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS02 has 57 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds

/********************************************************************************

Mentioning the name of the output dataset on the output statement

********************************************************************************/

/********************************************************************************

We can optionally mention the name of the output dataset in the output statement
Below three data steps produce same output

The first data step is using the explicit output statement. (The instruction is interpreted by SAS as 'Write the observation to the dataset specified on the data statement')
The second data step is using the explicit output statement along with the name of output dataset. (The instruction is interpreted by SAS as 'Write the observation to the class02 dataset specified on the data statement'). Remember that more than one dataset name can be specified on the data statement, which means more than one dataset can be created within a single data step
The third data step is using implicit output instruction present at the step boundary

********************************************************************************/

data class01;
     set sashelp.class;
     output;
  run;
   
  data class02;
     set sashelp.class;
     output class02;
  run;
   
  data class03;
     set sashelp.class;
  run;

604696  data class01;
  604697     set sashelp.class;
  604698     output;
  604699  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS01 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.00 seconds
        cpu time            0.00 seconds
   
   
  604700
  604701  data class02;
  604702     set sashelp.class;
  604703     output class02;
  604704  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS02 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.00 seconds
   
   
  604705
  604706  data class03;
  604707     set sashelp.class;
  604708  run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.CLASS03 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds

/********************************************************************************

Creating multiple datasets in a single data step

********************************************************************************/

/********************************************************************************

We can create multiple datasets in a single data step by specifying the names of the output datasets on the data statement
In the below example, all 4 data steps create 2 datasets, each output dataset as a copy of sashelp.class

In the first data step, implicit output writes each observation of input dataset to all the datasets specified on data statement
In the second data step, the explicit output statement writes each observation of input dataset to both copy11 and copy12 datasets as no specific output dataset name is mentioned on output statement
In the third data step, we are asking the explicit output statement to write each observation to both copy21 and copy22 datasets by explcitly mentioning the names of the output datasets
In the fourth data step, we are instructing the first output statement to write the observation to only copy31 dataset(so, observation will not be written to copy32 because of this output statement) and we are instructing the second output statement to write observation only to copy32 dataset (so, observation will not be written to copy31 because of this output statement)
Examine the log to check the number of observations being written to each output dataset

********************************************************************************/

data copy1 copy2;
     set sashelp.class;
  run;
   
  data copy11 copy12;
     set sashelp.class;
     output;
  run;
   
  data copy21 copy22;
     set sashelp.class;
     output copy21 copy22;
  run;
   
  data copy31 copy32;
     set sashelp.class;
     output copy31;
     output copy32;
  run;

612203   data copy1 copy2;
  612204      set sashelp.class;
  612205   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY1 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY2 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds
   
   
  612206
  612207   data copy11 copy12;
  612208      set sashelp.class;
  612209      output;
  612210   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY11 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY12 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.00 seconds
        cpu time            0.00 seconds
   
   
  612211
  612212   data copy21 copy22;
  612213      set sashelp.class;
  612214      output copy21 copy22;
  612215   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY21 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY22 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.00 seconds
   
   
  612216
  612217   data copy31 copy32;
  612218      set sashelp.class;
  612219      output copy31;
  612220      output copy32;
  612221   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY31 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY32 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds

/********************************************************************************

Test your understanding: How many number of observations will be present in each output dataset?

********************************************************************************/

/********************************************************************************

Answer can be found by examining the log section
FYI, sashelp.class has 19 observations

********************************************************************************/

data copy11 copy12 copy13;
     set sashelp.class;
     output copy11 copy12;
     output copy13;
  run;
   
  data copy21 copy22 copy23;
     set sashelp.class;
     output copy21 copy22;
  run;
   
  data copy31 copy32 copy33;
     set sashelp.class;
     output copy31 copy32;
     output;
  run;

619706   data copy11 copy12 copy13;
  619707      set sashelp.class;
  619708      output copy11 copy12;
  619709      output copy13;
  619710   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY11 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY12 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY13 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.01 seconds
   
   
  619711
  619712   data copy21 copy22 copy23;
  619713      set sashelp.class;
  619714      output copy21 copy22;
  619715   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY21 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY22 has 19 observations and 5 variables.
  NOTE: The data set WORK.COPY23 has 0 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.03 seconds
        cpu time            0.01 seconds
   
   
  619716
  619717   data copy31 copy32 copy33;
  619718      set sashelp.class;
  619719      output copy31 copy32;
  619720      output;
  619721   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.COPY31 has 38 observations and 5 variables.
  NOTE: The data set WORK.COPY32 has 38 observations and 5 variables.
  NOTE: The data set WORK.COPY33 has 19 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.03 seconds
        cpu time            0.01 seconds

/********************************************************************************

Using Explicit Output Statement to Create Data Subsets

********************************************************************************/

/********************************************************************************

We can use conditional processing to write only specific observations to the output dataset of interest

********************************************************************************/

data class;
     set sashelp.class;
  run;
   
  data cars;
     set sashelp.cars;
  run;
   
  data males females;
     set sashelp.class;
     if sex="F" then output females;
     if sex="M" then output males;
  run;
   
  data preteen teen;
     set sashelp.class;
     if age lt 13 then output preteen;
     else if 13 le age le 19 then output teen;
  run;
   
  data sedan sports others;
     set sashelp.cars;
     if upcase(type)="SEDAN" then output sedan;
     else if upcase(type)="SPORTS" then output sports;
     else output others;
  run;

619733   data class;
  619734      set sashelp.class;
  619735   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
   
   
  619736
  619737   data cars;
  619738      set sashelp.cars;
  619739   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           0.01 seconds
        cpu time            0.00 seconds
   
   
  619740
  619741   data males females;
  619742      set sashelp.class;
  619743      if sex="F" then output females;
  619744      if sex="M" then output males;
  619745   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.MALES has 10 observations and 5 variables.
  NOTE: The data set WORK.FEMALES has 9 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.03 seconds
        cpu time            0.03 seconds
   
   
  619746
  619747   data preteen teen;
  619748      set sashelp.class;
  619749      if age lt 13 then output preteen;
  619750      else if 13 le age le 19 then output teen;
  619751   run;
   
  NOTE: There were 19 observations read from the data set SASHELP.CLASS.
  NOTE: The data set WORK.PRETEEN has 7 observations and 5 variables.
  NOTE: The data set WORK.TEEN has 12 observations and 5 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.01 seconds
        cpu time            0.00 seconds
   
   
  619752
  619753   data sedan sports others;
  619754      set sashelp.cars;
  619755      if upcase(type)="SEDAN" then output sedan;
  619756      else if upcase(type)="SPORTS" then output sports;
  619757      else output others;
  619758   run;
   
  NOTE: There were 428 observations read from the data set SASHELP.CARS.
  NOTE: The data set WORK.SEDAN has 262 observations and 15 variables.
  NOTE: The data set WORK.SPORTS has 49 observations and 15 variables.
  NOTE: The data set WORK.OTHERS has 117 observations and 15 variables.
  NOTE: DATA statement used (Total process time):
        real time           0.03 seconds
        cpu time            0.01 seconds

/********************************************************************************

Creating additional rows for counting 'Total' observations

********************************************************************************/

/********************************************************************************

Requirement is to get the number of students in each age group and also report total number of students in a new dataset
We can duplicate each student row with a value of 999 in age value and then run a proc freq on age variable to get age wise and total student counts in a single step

********************************************************************************/

data class01;
     set sashelp.class;
     output;
     age=999;
     output;
  run;
   
  proc freq data=class01;
     tables age/out=age_counts01;
  run;

/********************************************************************************

Lengthy approach for the above task

********************************************************************************/

/********************************************************************************

First get the number of subjects in each age group using proc freq and then create and then add them to find total students

********************************************************************************/

proc freq data=sashelp.class;
     tables age/out=counts01;
  run;
   
  data counts02;
     set counts01 end=last;
     by age;
     total+count;
     output;
     if last then do;
        age=999;
        count=total;
        output;
     end;
  run;