Renaming All Variables in a SAS Data Set Using the SASHELP VIEWS

*Create a temporary dataset... DSN;
data dsn;
a=1;
b=2;
c=3;
d=4;
e=5;
f=6;
run;


%macro test(lib,dsn);

*/1)*/ data _null_;
set sashelp.vtable(where=(libname="&LIB" and memname="&DSN"));
call symput('nvars',nvar);
run;

*/2)*/ data dsn;
set sashelp.vcolumn(where=(libname="&LIB" and memname="&DSN"));
call symput(cats("var",_n_),name);
run;

*/3)*/ proc datasets library=&LIB;
modify &DSN;
rename
%do i = 1 %to &nvars;
&&var&i=Rename_&&var&i.
%end;
;
quit;
run;
%mend;

%test(WORK,DSN);

After submitting the above program... the output looks like this....

Output:
Rename_a Rename_b Rename_c Rename_d Rename_e Rename_f
1 2 3 4 5 6

Here is a way I know of.. to rename all the variables in the dataset;

It can be done using the SASHELP views as follows:

1) The 1st step of the program determines the total number of variables inside the dataset with the help of SASHELP.Vtable, Data _null_ step used to do the same …. A macro variable NVAR (Number of Variables) will be created after this step which can be accessed in the following steps.

2) What the 2nd step does is….assigns a unique IDNUM to all the variables of the dataset with the help of SASHELP.Vcolumn.

3) What the 3rd step does is … it uses Proc Datasets with the MODIFY statement to rename all the variables in the dataset. The DO LOOP is been used to resolve the 6 macro variables in a macro of a temporary dataset.

Clean-Up: Delete datasets in the work library:

It is better always to clean-up/empty the work directory before we run the next set of SAS code. This is VERY helpful in situations where the “working” files created tend to use up a large amount of memory, once the logic of the program has been checked, KILLing the working files will result in a more efficient program. Another important reason to issue the above statement at the end of a program is when programs are run in batch, this will clean up the working library to be sure any “old” files are not left around to be erroneously used1.

PROC DATASETS procedure offers an elegant solution to do just.
Remember that there is no need of knowing any dataset names when we are emptying the work directory.

Here is the simple syntax:
proc datasets lib=work kill nolist memtype=data;
quit;

We have specified lib=work, because we are cleaning up the work directory.
KILL option removes all the datasets that are happened to be in the work directory.
NOLIST option tells SAS, printing the details isn’t required.
Since our main objective here to remove the datasets and not the catalogs, we need to specify MEMTYPE=DATA.
To remove datasets and catalogs…use. MEMTYPE=ALL.
What if we need to delete only some datasets and not all….
Proc DATASETS procedure has a solution for it also….datasets..
The following code only deletes 2 datasets, named AE and DEMO.
proc datasets lib=work nolist;
delete AE DEMO;
quit;
run;
CAUTION:
The KILL option deletes the SAS files immediately after you submit the statement.
Source: sugi28 proceedings..page 190-28.pdf

Comparing Two Methods for Removing Formats and Informats in SAS: DATA Step vs. PROC DATASETS

Comparing Two Approaches to Removing Formats and Informats in SAS

When working with SAS datasets, there are times when you need to remove formats and informats that have been previously assigned to variables. Two primary approaches can be used for this task:

• Using the DATA Step
• Using the PROC DATASETS Procedure

This article compares and contrasts these two approaches to help you determine which method is most appropriate for your needs.

Approach 1: Using the DATA Step

The DATA step is a versatile and commonly used method for removing formats and informats. By assigning variables to a null format or informat, you can effectively remove these attributes from your dataset.

Example:

data mydata_clean;
    set mydata;
    format _all_;
    informat _all_;
run;

In this example, the mydata dataset is processed in the DATA step, and all formats and informats are removed. The resulting dataset mydata_clean is a new dataset without any formats or informats.

Advantages:

• Flexibility: The DATA step allows you to remove formats and informats from specific variables or all variables in the dataset.
• Control: You can perform additional data manipulation or transformation while removing formats, all within the same DATA step.
• Simplicity: The syntax is straightforward and familiar to most SAS users.

Disadvantages:

• Data Duplication: The DATA step creates a new dataset, which can be inefficient when working with large datasets, as it requires additional storage space.
• Processing Time: For very large datasets, the process of creating a new dataset can be time-consuming.

Approach 2: Using the PROC DATASETS Procedure

The PROC DATASETS procedure provides another method for removing formats and informats. Unlike the DATA step, this procedure can modify the dataset in place, avoiding the need to create a new dataset.

Example:

proc datasets library=work nolist;
    modify mydata;
    format _all_;
    informat _all_;
    quit;

In this example, the dataset mydata is modified directly in the WORK library. All formats and informats are removed from the dataset without creating a new dataset.

Advantages:

• Efficiency: Since the dataset is modified in place, this approach can be more efficient in terms of both processing time and storage space.
• Scalability: PROC DATASETS is well-suited for handling large datasets because it avoids data duplication.
• Batch Processing: The procedure can be easily integrated into larger batch processes where multiple datasets need to be modified.

Disadvantages:

• Limited Control: Unlike the DATA step, PROC DATASETS does not allow for additional data transformations or manipulations during the removal of formats.
• Less Familiarity: Some SAS users may be less familiar with PROC DATASETS, making it slightly less intuitive than the DATA step.

Comparison Summary

Both approaches have their strengths and weaknesses, and the choice between them depends on the specific needs of your task:

• Use the DATA Step if you need to perform additional data manipulation while removing formats, or if you prefer a method that is simple and easy to understand.
• Use PROC DATASETS if you are working with large datasets and want to avoid data duplication, or if you need to modify datasets in place for efficiency.

Conclusion

Removing formats and informats is a common task in SAS, and understanding the advantages and limitations of both the DATA step and PROC DATASETS will help you choose the most appropriate method for your specific situation. By mastering both techniques, you can ensure that your data processing tasks are both efficient and effective.