Exploring Hash Tables vs. SORT/DATA Step vs. PROC SQL - Richann Watson, Experis
There are often times when programmers need to merge multiple SAS data sets to combine data into one single
source data set. Like many other processes, there are various techniques to accomplish this using SAS software.
The most efficient method to use based on varying assumptions will be explored in this paper. We will describe the differences, advantages and disadvantages , and display benchmarks of using HASH tables, the SORT and DATA
step procedures, and the SQL procedure.
source data set. Like many other processes, there are various techniques to accomplish this using SAS software.
The most efficient method to use based on varying assumptions will be explored in this paper. We will describe the differences, advantages and disadvantages , and display benchmarks of using HASH tables, the SORT and DATA
step procedures, and the SQL procedure.
When ANY Function Will Just NOT Do - Richann Watson, Experis
Have you ever been working on a task and wondered whether there might be a SAS function that could save you some time? Let alone, one that might be able to do the work for you? Data review and validation tasks can be time-consuming efforts. Any gain in efficiency is highly beneficial, especially if you can achieve a standard level where the data itself can drive parts of the process. The ANY and NOT functions can help alleviate some of the manual work in many tasks such as data review of variable values, data compliance, data formats, and derivation or validation of a variable's data type. The list goes on. In this paper, we cover the functions and their details and use them in an example of handling date and time data and mapping it to ISO 8601 date and time formats.
About the Presenter
Richann Watson has been using SAS for 20 years. She is currently employed at Experis where she is a Delivery Specialist working for a large pharmaceutical company. She has worked in clinical research for 19 years with 6 of those years working with CDISC standards and has worked for 2 years in marketing research. She is also a member of the CDISC ADaM team, the ADaM Oncology subteam, the ADaM Breast Cancer and Prostate Cancer CFAST teams. In addition, she is the chairperson for the local SAS user group in her area, CinSUG and is actively involved with MWSUG as well.
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