SCSUG 2013 Papers - Index

29 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	A Dose of SAS to Brighten Your Health Care Blues	Gabriela Cantu, Christopher Klekar	The development and adoption of electronic health records (EHR) is creating exciting times in healthcare research. Today, copious amounts of EHR and secondary data lie at the tips of the researcher's fingers and robust data management techniques are essential in transforming the data to maximize its potential. Data derived from the EHR is critical for grant applications, pilot studies and is becoming the main source of data for research projects. The role of the data manager becomes crucial to research projects utilizing EHR and secondary data in order to maximum it's analysis potential. Data managers encounter roadblocks when dealing with healthcare data and using some SAS tips and tricks on bulky, cumbersome datasets can transform them into clean and simpl e ones that are analysis ready	proc step, proc sql, proc transpose, longitudinal data, data transformation	source	14
2	Best Practices in Enterprise Data Governance	Scott Gidley	Data governance combines the disciplines of data quality, data management, data policy management, business process management, and risk management into a methodology that ensures important data assets are formally managed throughout an enterprise. SAS has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise's overall data management efficiency. This paper discusses data governance best practices. It explains where and how SAS capabilities (such as the Business Data Network, Reference Data Management, Data Federation, Data Quality, Data Management, and Master Data Management) can be used to ensure that data governance initiatives remain successful, continue to deliver overall return on investment, and gain buy-in across the enterprise	data governance, data cleaning, reference data	source	9
3	Big Data, Fast Processing Speeds	Gary Ciampa	As data sets continue to grow, it is important for programs to be written very efficiently to make sure no time is wasted processing data. This paper covers various techniques to speed up data processing time for very large data sets or databases, including `PROC SQL`, `DATA STEP`, indexes and SAS macros. Some of these procedures may result in just a slight speed increase, but when you process 500 million records per day, even a 10% increase is very good. The paper includes actual time comparisons to demonstrate the speed increases using the new techniques	proc sql, proc step, grid computing, cpu time, missing value, group by, sas programmer, data step, where statement, sas programming, macro definition, macro variable	source	16
4	Clinical Data Dashboards for Centralized Monitoring Using SAS and Microsoft SharePoint	Jagan Mohan Achi, Randy Rak			source	9
5	Demystifying PROC SQL Join Algorithms	Kirk Paul Lafler	When it comes to performing `PROC SQL` joins, users supply the names of the tables for joining along with the join conditions, and the `PROC SQL` optimizer determines which of the available join algorithms to use for performing the join operation. Attendees learn about the different types of join algorithms and explore nested loop (brute-force), sort-merge, index, and hash join methods along with selected options to control processing	proc sql, where statement, proc step, inner join, data merging, right join	source	6
6	Finding Duplicates with First. and Last.	Clarence W. Jackson	Duplicated data, records, and other information is a pain. Duplicated information takes up space, can cause inaccurate measures, and cost money that can be used for something else. Finding those duplicated values in data, records, and other sources is easy using SAS' "FIRST." and "LAST." Expressions. This paper will explain how SAS can be used to help find those dups	sas expression, dataset	source	4
7	Getting Connected - The Power of Using Hash Objects	Philip Burtch	The Hash Object and the Hash Iterator Object¹, two `DATA STEP` component objects, offer a simple and powerful solution for many complex tasks. This paper will describe the hash object and show how to merge a number of data sets using hash objects, base SAS, and object dot notation. It will also illustrate how the program data vector (PDV) enables the interaction of data set variables and the data variables within the objects. The hash object is an in-memory solution allowing direct access via keys during execution. It is fast and can contain an unlimited number of key items and data items. The only limitation is available memory. As memory size increases and the constraints of available memory lessen, the usefulness of hash objects can only increase	hash table, do loop, data step, proc sql, pdv, proc step, hash iterator	source	15
8	Hypertension Management in Hypertensive Adults with Diabetes in Primary Care setting	Pratik P. Rane, Archita Bhansali, Ruta Sawant, Rajender R. Aparasu		logistic distribution, proc step, proc surveyfreq, regression analysis	source	12
9	Improving Your Relationship with SAS EG: Tips from SAS Tech Support	Jennifer Bjurstrom	SAS Enterprise Guide has proven to be a very beneficial tool for both novice and experienced SAS users. Because it is such a powerful tool, SAS Enterprise Guide has risen in popularity over the years. As a result, SAS Technical Support consultants field many calls from users who want to know the best way to use the application to accomplish a task or to obtain the results they want. This paper encompasses many of those tips that SAS Technical Support has provided to customers over the years. These tips are designed to improve your proficiency with SAS Enterprise Guide in the areas of connection and configuration, workflow preferences, logging, data source management, project files, operating-system commands, and custom tasks	sas enterprise guide, sas base, sql pass-through, excel, implicit sql pass-through, record count, sas programmer, proc sql, dataset	source	20
10	Introduction to SAS Hash Objects	Christopher W. Schacherer	The SAS hash object is an incredibly powerful technique for integrating data from two or more datasets based on a common key. The current work describes the basic methodology for defining, populating, and utilizing a hash object to perform lookups within the `DATA STEP` and provides examples of situations in which the performance of SAS programs is improved by their use. Common problems encountered when using hash objects are explained, and tools and techniques for optimizing hash objects within your SAS program are demonstrated	hash table, cpu time, length statement, temporary array, proc sql, key, set statement, proc step, memory usage, proc means, where statement, execution phase, reference data	source	26
11	Leveraging Metadata with the SAS Macro Language	Keith G. Curtis		call symputx, proc step, dataset, macro facility, macro call, macro variable, macro definition	source	15
12	Macro utility to compare multiple SAS datasets	Krish Krishnan	This paper describes an efficient method to compare pairs of SAS data sets. The utility presented here will compare one or more pairs of SAS data sets and will generate a condensed user friendly report outlining the findings. The summary report generated by th is utility lists above and beyond the 16 comparison results that can be obtained from the return codes stored in the automatic macro SYSINFO from `PROC COMPARE`. Maximum efficiency from this macro can be achieved if the business process involves independent validation of analysis data sets, tables, listings or any other outputs that requires creation of permanent SAS data sets by both production and independent QC programmer. The macro can be modified by end user to accommodate other scenarios (e.g. to check only the data structure compliance of current data transfer to previous data transfer, print the entire `PROC COMPARE` output by removing the NOPRINT option etc.)	proc compare, proc step, macro parameter, dataset, macro quoting function, sysrc, data cleaning	source	8
13	One problem - Multiple solutions; various ways of removing duplicates from dataset using SAS	Jaya Dhillon	In real world, analysts seldom come across data which is in ready to use format. Data cleaning is a critical aspect and one of the major problem s faced is duplicate records within a dataset . For instance, we may want to have all unique Employee ID 's in our dataset or we may want unique transactions from a list of customer transactions. This paper will cover various options available for treating duplicates records. Usage of options such as NODUPKEY in combination with DUPOUT, how NOUNIQUEKEY, NODUPRECS and NODUPKEY are different from each other, will be some of the points discussed here	sas log, cpu time, out=	source	6
14	Practical Array Usage with String Search	Charles Basham	As a Base SA S Developer, we find ourselves seeking ways of applying SAS to real world scenarios. Gathering transactional data an d processing it in search of specific scenarios and trends is one such business case that is routinely required. This paper demonstrates a technique leveraging `PROC TRANSPOSE`, `DATA STEP` Arrays, and String concatenation to capture transactional data in s earch of specific criteria. Identifying transactions that meet business conditions or trends will most likely be an ongoing need	proc transpose, proc step, sample	source	5
15	Preparing Interaction Variables for Logistic Regression	Bruce S. Lund	Interactions between two (or more) variables often add predictive power to a binary logistic regression model beyond what the original variables offer alone. In the simplest case, if X1 and X2 are zero-one valued variables, then their interaction variable is X1_X2 = X1*X2. However, X1_X2, in combination with X1 and X2, use 3 degrees of freedom. A nominal variable XC with four values can be defined from X1 and X2 with values XC = compress(X1 \|\| X2). Perhaps a collapsing of the four levels of XC to three values (having 2 d.f.) would provide nearly as much predictive power as the saturated model X1, X2, X1_X2 while providing more predictive power than X1, X2 alone. In this paper this question is answered for interactions of nominal or numeric X1 and X2, each with 2 or more levels. First, the user creates XC. Then a "best-collapse" algorithm optimally collapses the levels of XC until a stopping point is reached that provides a trade-off between degrees of freedom and predictive power	proc logistic, degrees of freedom, logistic distribution, proc step, likelihood function, main effect, logistic regression, stratified sampling, regression analysis, macro definition	source	14
16	SAS Macro Programming Tips and Techniques	Kirk Paul Lafler	The SAS Macro Language is a powerful feature for extending the capabilities of the SAS System. This presentation highlights a collection of techniques for constructing reusable and effective macros tools. Attendees are introduced to the techniques associated with building functional macros that process statements containing SAS code; design reusable macro techniques; create macros containing keyword and positional parameters; utilize defensive programming tactics and techniques; build a library of macro utilities; interface the macro language with the `SQL` procedure; and develop efficient and portable macro language code	macro variable, macro facility, autocall macro, proc step, macro definition, macro function, macro parameter, %macro parameters, dictionary tables, options statement, proc sql, macro loop, &variable	source	9
17	Shortcuts to Save Time While Working with SAS	Ruth Marisol Rivera Barragan	Have you asked to yourself things like these? Can I clear the log and output windows with a shortcut? How can I review the log faster in an interactive session? Is there a button that can do it for me? If you answered "Yes" to at least one of these questions, continue reading… This paper will give you a list of Shortcuts that will make your experience working with SAS faster, for example: How to make your log and output windows clear before you run your program, just by hitting an F key. How to customize buttons to check your log interactively. How to find a specific value on a dataset, without to scroll down for all the variables available, just by using the correct operator in the where expression. * And some other interesting topics that really will help you to save time!	sas log, sas output	source	12
18	Some Techniques for Integrating SAS Output with Microsoft Excel Using Base SAS	Vincent DelGobbo	This paper explains some techniques to integrate your SAS output with Microsoft `EXCEL`. The techniques that are presented in this paper require Base SAS9.1.3 SP4 and above, and can be used regardless of the platform on which SAS is installed. You can even use them on a mainframe! Creating and delivering your workbooks on- demand and in real time using SAS server technology is discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented	excel, xls, sas base, proc export, sas output, proc print, title statement, proc step, dataset, sas log, sample, ods html, sas 9, format statement, output delivery system, by group processing	source	19
19	Strategies and Techniques for Debugging SAS Program Errors and Warnings	Kirk Paul Lafler	As a SAS user, you've probably experienced first-hand more than your share of program code bugs, and realize that debugging SAS program errors and warnings can, at times, be a daunting task. This paper and presentation explores the world of SAS errors and warnings, provides important information about syntax errors, input and output data sources, system-related default specifications, and logic scenarios specified in program code. Attendees will learn how to apply effective techniques to understand, fix, and resolve errors and warnings, allowing program code to work as intended	sas log, options statement, data cleaning, hash table, proc step, mprint	source	13
20	The Complexities of an Address	Barry E. Mullins	Addresses can come in many forms that are messy, unorganized and not standardized. Trying to match people by their addresses could take days to manually match, but with SAS can be done quickly. However, without standardizing the addresses, SAS can miss matches or even give you false positives. This paper will discuss the steps to standardizing and fuzzy matching addresses. I will show examples of standardizing addresses and parsing out the pieces using PRXCHANGE and TRANWRD. Also, I will discuss the importance of removing common words, like street, to help increase the accuracy of matching addresses to one another using COMPGED. The examples are depicted in SAS EG 4.2	type i error, macro definition	source	7
21	The Concepts and Practice of Analysis with SAS Enterprise Guide	Christopher W. Schacherer	SAS Enterprise Guide continues to gain acceptance among SAS programmers as an enterprise solution for a wide range of reporting and analytic applications. However, in order for organizations to realize maximum benefit from their investment in Enterprise Guide, subject-matter experts and a new generation of analysts who have not been trained as SAS programmers need to be trained in the use of this tool. The current work provides a framework for this training by explaining the relationship between Enterprise Guide and traditional SAS programming, introducing the basic Enterprise Guide concepts necessary to function in this environment, and presenting examples of common tasks that will help these users become immediately productive	sas enterprise guide, proc step, out=, sas programmer, sas programming, dataset, raw data, sas library, excel, libname statement, insurance claims, proc freq, sql server, proc sql, relational database, work library, bar chart, sas administrator, proc sort, format statement, substr function, sas output, user-defined format, numeric variable, proc means	source	48
22	The Good, The Bad, and The Ugly	Toby Dunn, Kirk Paul Lafler	The SAS System has all the tools users need to read data from a variety of external sources. This has been, perhaps, one of the most important and powerful features since its introduction in the mid-1970s. The cornerstone of this power begins with the INFILE and INPUT statements, the use of a single- and double-trailing @ sign, and the ability to read data using a predictable form or pattern. This paper will provide insights into the INFILE statement, the various styles of INPUT statements, and provide numerous examples of how data can be read into SAS with the `DATA STEP`	input statement, raw data, infile statement, dataset, missing value, sas log, trailing @	source	12
23	The SQL Tuning Checklist: Making Slow Database Queries a Thing of the Past	Jeffrey Bailey	The SQL query had been running for 36 hours before it was killed. The project team is panicking. The customer is upset. What can you, a SAS programmer, do to get out of this dilemma? Something has to be done, but what? Chances are you have been there. You have felt the pain. You have lived through the helplessness and fear of imminent failure. Fortunately, it is possible to get out of this mess. This paper will show you the steps (and the mindset) required to solve this all-too-common problem. This paper includes examples from DB2, Greenplum, Oracle, and Teradata	proc sql, proc step, datepart, sas log, where statement	source	13
24	Type, Point and Click - A Practical Guide to SAS Enterprise Guide Prompts	Patricia Hettinger	Your SAS Enterprise Guide project is set up. Processes have been created, linking code in the desired order. Now if only you didn't have to keep changing your code every time your input file name changed or your date range or …. This paper gives an overview of the prompts available in SAS Enterprise 5.1 and ways to automate your project. We will also touch on the general nature of macro variables, and the various quoting and unquoting functions. We will also explore the many additional prompt features available in stored processes	sas enterprise guide, macro variable	source	17
25	Uncovering Patterns in Textual Data for SAS Visual Analytics and SAS Text Analytics	Meera Venkataramani	SAS Visual Analytics is a powerful tool for exploring data to uncover patterns and hidden opportunities. The challenge with the growing amount of data is that the majority is unstructured, often in the form of customer feedback, survey responses, social media conversation, blogs, news articles, and other unstructured text. By integrating SAS Visual Analytics with SAS Text Analytics, you can uncover patterns in both structured and unstructured data, while enriching and visualizing your data with customer sentiment and categorical flags, and uncovering root causes that primarily exist within unstructured data. This paper highlights a case study that provides greater insight into unstructured data and demonstrates advanced visualization, while enhancing time to value by leveraging SAS Visual Analytics high-performance, in-memory technology and SAS advanced text analytics capabilities	unstructured data, root cause analysis	source	11
26	Using PROC STANDARD and PROC SCORE to impute missing multivariate values	Paul Montagna	One of the most frustrating aspects of multivariate analysis is that one missing value in one column will cause the entire sample row to be deleted. This means that all the other values of the sample are lost, and one is not able to classify that sample. There are at least two ways to deal with this problem, what I call the "pre-analysis method" and the "post analysis method." There are many approaches to impute a missing value prior to analyses so the samples are retained. One simple method is to imput e the missing value "before" the multivariate analysis using `PROC STANDARD` to replace missing values based on a statistical characteristics or `PROC REG` to use a predictive approach. Alternatively, one could impute the missing value " after" the multivariat e analysis using the multivariate coefficients as input to `PROC SCORE`, thus being able to classify the sample. Either approach has its strengths and weaknesses	missing value, sample, proc step, multivariate analysis, proc score, regression analysis	source	8
27	Using SAS to Build Customer Level Datasets for Predictive Modeling	Scott M. Shockley	If you are using operational data to build datasets at the customer level, you're faced with the challenge of condensing a plethora of raw transactions into a data set that summarizes each customer, one row per customer. You will probably have to use multiple tables with different levels of granularity. Some of the data will change over time, and some of it won't. If the focus of your research is to predict events like customer defection, then changes over time will be a major consideration and make this process even more difficult. The goal of this paper is to guide readers through the process of transforming raw data into a data set for predictive modeling that accurately represents a customer and the factors that could possibly impact the outcome being predicted. The paper will use specific examples like how to calculate derived variables based on complex conditions related to time	do loop, data step, proc step, proc sort, proc means, logistic regression, proc transpose, numeric variable, churn, dataset, logistic distribution, regression analysis	source	14
28	Whirlwind Tour Around SAS Visual Analytics	Rick Styll	SAS brings a revolutionary approach for analytical based data visualization, exploration and reporting. Come and join us for a whirlwind tour through SAS Visual Analytics 6.1 inspired by customer experiences and use cases. We start with an overview of the technology concept behind SAS VA and enter into the Hub, leading to various destinations during the tour. From Hub we will do multiple trips out to applications starting with the Data Builder followed by the Explorer, Designer and end the journey with Mobile integration. There will be a sneak peek into world of Microsoft Office and don't worry, SAS coders are not left behind. Final stop will be Enterprise Guide showing you how you can leverage the technology behind SAS Visual Analytics through SAS code	excel, analytics platform, sas enterprise guide, sas visual analytics	source	11
29	Zero My Hero!	Anna Vincent		proc report, proc step, class variable, proc tabulate, proc format, output delivery system, by group processing	source	12

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