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Presentations:CWilliams Papers and Presentations
Papers for Christianna Williams
- A Row Is a Row Is a Row, or Is it? A Hands-on Guide to Transposing Your Data
Categories: CWilliams Papers and Presentations, DATA Step, Presentations, SAS Global Forum 2013, TRANSPOSE Procedure
Sometimes life would be easier for the busy SAS programmer if information stored across multiple rows were all accessible in one observation, using additional columns to hold that data. Sometimes it makes more sense to turn a short, wide data set into a long, skinny one -- convert columns into ..→
- PROC COMPARE: Worth Another Look!
Categories: COMPARE Procedure, CWilliams Papers and Presentations, Presentations, SESUG 2011
PROC COMPARE is one of those workhorse procedures in the Base SAS® closet that deserves to be dusted off, tuned up and pressed into service again! With well-chosen PROC COMPARE options and statements, you can compare pairs of SAS datasets at multiple levels without the need for ..→
- Queries, Joins and Where Clauses, Oh My! Demystifying PROC SQL
Categories: CWilliams Papers and Presentations, Presentations, SAS Global Forum 2012, SQL Procedure
Subqueries, inner joins, outer joins, HAVING expressions, set operators…just the terminology of PROC SQL might intimidate SAS® programmers accustomed to getting the DATA step to do our bidding for data manipulation. Nonetheless, even DATA step die-hards must grudgingly acknowledge ..→
- SYMPUT and SYMGET: Getting DATA Step Variables and Macro Variables to Share
Categories: CWilliams Papers and Presentations, NESUG 2004, SYMGET Function, SYMPUT and SYMPUTX Routines
Because one of the most powerful incentives to use the SAS® macro language is to allow SAS programs to be more data-driven, it is critical for the DATA step and the macro facility to "talk" to each other. The SYMPUT routine and the SYMGET function provide two mechanisms to facilitate this ..→
- Using PROC FORMAT for Data Validation and Cleanup
Categories: CWilliams Papers and Presentations, FORMAT Procedure, Presentations, SAS Global Forum 2007
Sometimes one is faced with a large and potentially “dirty” data set that needs to be checked for invalid data. The data set may contain character fields that have been entered in varied and non-standard ways. However, the set of valid data values for some of the variables may itself be large ..→