Disability rights groups are using the court system to exact change. As a result, the enforcement of Section 508 and similar laws around the world has become a priority. When you generate SAS^® output, you need to protect your organization from litigation. This paper describes concrete steps that help you use SAS^® 9.4 Output Delivery System (ODS) to create SAS output that complies with accessibility standards. It also provides recommendations and code samples that are aligned with the accessibility standards defined by Section 508 and the Web Content Accessibility Guidelines (WCAG 2.0).

Two of the powerful features of ODS Graphics procedures is the ability to create forest plots and add inner margin tables to graphics output. The drawback, however, is that the syntax required by the programmer from PROC TEMPLATE is complex and tedious. A prompted application or even a parameterized stored process that connects PROC TEMPLATE code to a point-and-click application definitely makes life easier for coders in many industries who frequently create these types of graphic output.

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The new and highly anticipated SAS^® Output Delivery System (ODS) destination for Microsoft Excel is finally here! Available as a production feature in the third maintenance release of SAS^® 9.4 (TS1M3), this new destination generates native Excel (XLSX) files that are compatible with Microsoft Office 2010 or later. This paper is written for anyone, from entry-level programmers to business analysts, who uses the SAS^® System and Microsoft Excel to create reports. The discussion covers features and benefits of the new Excel destination, differences between the Excel destination and the older ExcelXP tagset, and functionality that exists in the ExcelXP tagset that is not available in the Excel destination. These topics are all illustrated with meaningful examples. The paper also explains how you can bridge the gap that exists as a result of differences in the functionality between the destination and the tagset. In addition, the discussion outlines when it is beneficial for you to use the Excel destination versus the ExcelXP tagset, and vice versa. After reading this paper, you should be able to make an informed decision about which tool best meets your needs.

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This paper demonstrates how to use the ODS destination for PowerPoint to create attractive presentations from your SAS^® output. Packed with examples, this paper gives you a behind-the-scenes tour of how ODS creates Microsoft PowerPoint presentations. You get an in-depth look at how to customize the ODS PowerPoint style templates that control the appearance of your presentation. With this information you can quickly turn your SAS output into an engaging and informative presentation. This paper is a follow-on to the SAS^® Global Forum 2013 paper A First Look at the ODS Destination for PowerPoint.

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SAS^® functions provide amazing power to your DATA step programming. Some of these functions are essential--they save you from writing volumes of unnecessary code. This talk covers a number of the most useful SAS functions. Some may be new to you, and they will change the way you program and approach common programming tasks. The majority of the functions discussed in this talk work with character data. Some functions search for strings, and others find and replace strings or join strings together. Still others measure the spelling distance between two strings (useful for fuzzy matching). Some of the newest and most amazing functions are not functions at all, but call routines. Did you know that you can sort values within an observation? Did you know that you can identify not only the largest or smallest value in a list of variables, but the second- or third- or nth-largest or smallest value? Knowledge of the functions described here will make you a much better SAS programmer.

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In health sciences and biomedical research we often search for scientific journal abstracts and publications within MEDLINE, which is a suite of indexed databases developed and maintained by the National Center for Biotechnology Information (NCBI) at the United States National Library of Medicine (NLM). PubMed is a free search engine within MEDLINE and has become one of the standard databases to search for scientific abstracts. Entrez is the information retrieval system that gives you direct access to the 40 databases with over 1.3 billion records within the NCBI. You can access these records by using the eight e-utilities (einfo, esearch, summary, efetch, elink, einfo, epost, and egquery), which are the NCBI application programming interfaces (APIs). In this paper I will focus on using three of the e-utilities to retrieve data from PubMed as opposed to the standard method of manually searching and exporting the information from the PubMed website. I will demonstrate how to use the SAS HTTP procedure along with the XML mapper to develop a SAS^® macro that generates all the required data from PubMed based on search term parameters. Using SAS to extract information from PubMed searches and save it directly into a data set allows the process to be automated (which is good for running routine updates) and eliminates manual searching and exporting from PubMed.

Over the last few years both Microsoft Excel file formats and the SAS^® interfaces to those Excel formats have changed. SAS^® has worked hard to make the interface between the two systems easier to use. Starting with Comma Separated Variable files and moving to PROC IMPORT and PROC EXPORT, LIBNAME processing, SQL processing, SAS^® Enterprise Guide^®, JMP^®, and then on to the HTML and XML tagsets like MSOFFICE2K, and EXCELXP. Well, there is now a new entry into the processes available for SAS users to send data directly to Excel. This new entry into the ODS arena of data transfer to Excel is the ODS destination called EXCEL. This process is included within SAS ODS and produces native format Excel files for version 2007 of Excel and later. It was first shipped as an experimental version with the first maintenance release of SAS^® 9.4. This ODS destination has many features similar to the EXCELXP tagsets.

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So you've heard about SAS^® arrays, but you're not sure when or why you would use them. This presentation provides some background on SAS arrays, from explaining what occurs during compile time to explaining how to use them programmatically. It also includes a discussion about how DO loops and macro variables can enhance array usability. Specific examples, including Fahrenheit-to-Celsius temperature conversion, salary adjustments, and data transposition and counting, demonstrate how you can use SAS arrays effectively in your own work and also provide a few caveats about their use.

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Hospital Episode Statistics (HES) is a data warehouse that contains records of all admissions, outpatient appointments, and accident and emergency (A&E) attendances at National Health Service (NHS) hospitals in England. Each year it processes over 125 million admitted patient, outpatient, and A&E records. Such a large data set gives endless research opportunities for researchers and health-care professionals. However, patient care data is complex and might be difficult to manage. This paper demonstrates the flexibility and power of SAS^® programming tools such as the DATA step, the SQL procedure, and macros to help to analyze HES data.

For some users, having an annotation facility is an integral part of creating polished graphics for their work. To meet that need, we created a new annotation facility for the ODS Graphics procedures in SAS^® 9.3. Now, with SAS^® 9.4, the Graph Template Language (GTL) supports annotation as well! In fact, GTL annotation facility has some unique features not available in the ODS Graphics procedures, such as using multiple sets of annotation in the same graph and the ability to bind annotation to a particular cell in the graph. This presentation covers some basic concepts of annotating that are common to both GTL and the ODS Graphics procedures. I apply those concepts to demonstrate some unique abilities of GTL annotation. Come see annotation in action!

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This presentation is an open-ended discussion about techniques for creating graphical reports using SAS^® ODS Graphics components. After some introductory comments, this presentation does not have any set content. Instead, the topics discussed are dictated by attendee questions. Come prepared to ask and get answers to your questions.

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This paper provides tools and guidelines to assess SAS^® skill level during the interview process. Included are interview questions for Base SAS^® developers, statisticians, and SAS administration candidates. Whether the interviewer has a deep understanding of or just some familiarity with these areas, the questions provided will allow discussions to uncover skills and usage experience for the skills your company requires.

The SAS^® Visual Analytics autoload feature loads any files placed in the autoload location into the public SAS^® LASR™ Analytic Server. But what if we want to autoload the tables into a private SAS LASR Analytic Server and we want to load database tables into SAS Visual Analytics and we also want to reload them every day so that the tables reflect any changes in the database? One option is to create a query in SAS^® Visual Data Builder and schedule the table for load into the SAS LASR Analytic Server, but in this case, a query has to be created for each table that has to be loaded. It would be rather efficient if all tables could be loaded simultaneously; that would make the job a lot easier. This paper provides a custom solution for autoloading the tables in one step, so that tables in SAS Visual Analytics don't have to be reloaded manually or multiple queries don't have to be rerun whenever there is a change in the source data. This solution automatically unloads and reloads all the tables listed in a text file every day so that the data available in SAS Visual Analytics is always up-to-date. This paper focuses on autoloading the database tables into the private SAS LASR Analytic server in SAS Visual Analytics hosted in a UNIX environment and uses the SASIOLA engine to perform the load. The process involves four steps: initial load, unload, reload, and schedule unload and reload. This paper provides the scripts associated for each step. The initial load needs to be performed only once. Unload and reload have to be performed periodically to refresh the data source, so these steps have to be scheduled. Once the tables are loaded, a description is also added to the tables detailing the name of the table, time of the load, and user ID performing the load, which is similar to the description added when loading the tables manually into the SAS LASR Analytic Server using SAS Visual Analytics Administrator.

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Surveys are a critical research component when trying to gather information about a population of people and their knowledge, attitudes, and experiences. Researchers often implement surveys after a population has participated in a class or educational program. Survey developers often create sets of items, which when analyzed as a group, can measure a construct that describes the underlying behavior, attribute, or characteristic of a study participant. After testing for the reliability and validity of individual items, the group of items can be combined to create a scale score that measures a predefined construct. For example, in education research, a construct can measure students' use of global reading strategies or teachers' confidence in literacy instruction. The number of items that compose a construct can vary widely. Construct scores can be used as outcome measures to assess the impact of a program on its participants. Our example is taken from a project evaluating a teacher's professional development program aimed at improving students' literacy in target subject classes. The programmer is tasked with creating such scores quickly. With an unlimited amount of time to spend, a programmer could operationalize the creation of these constructs by manually entering predefined formulas in the DATA step. More often, time and resources are limited. Therefore, the programmer is more efficient by automating this process by using macro programs. In this paper, we present a technique that uses an externally created key data set that contains the construct specifications and corresponding items of each construct. By iterating through macro variables created from this key data set, we can create construct score variables for varying numbers of items. This technique can be generalized to other processing tasks that involve any number of mathematical combinations of variables to create one single score.

Formats are powerful tools within the SAS^® System. They can be used to change how information is brought into SAS, to modify how it is displayed, and even to reshape the data itself. Base SAS^® comes with a great many predefined formats, and it is even possible for you to create your own specialized formats. This paper briefly reviews the use of formats in general and then covers a number of aspects of user-generated formats. Since formats themselves have a number of uses that are not at first apparent to the new user, we also look at some of the broader applications of formats. Topics include building formats from data sets, using picture formats, transformations using formats, value translations, and using formats to perform table lookups.

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You would think that training as a code breaker, similar to those who were employed during the Second World War, wouldn't be necessary to perform the routine SAS^® programming duties of your job, such as debugging code. However, if the author of the code doesn't incorporate good elements of style in his or her program, the task of reviewing code becomes arduous and tedious for others. Style touches upon very specific aspects of writing code--indention for code and comments, casing of keywords, variables, and data set names, and spacing between PROC and DATA steps. Paying attention to these specific issues enhances the reusability and lifespan of your code. By using style to make your code readable, you'll impress your superiors and grow as a programmer.

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With all the talk of big data and visual analytics, we sometimes forget how important, and often difficult, it is to get external data into SAS^®. In this paper, we review some common data sources such as delimited sources (for example, comma-separated values format [CSV]) as well as structured flat files and the programming steps needed to successfully load these files into SAS. In addition to examining the INFILE and INPUT statements, we look at some methods for dealing with bad data. This paper assumes only basic SAS skills, although the topic can be of interest to anyone who needs to read external files.

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As a SAS^® programmer, you probably spend some of your time reading and possibly creating specifications. Your job also includes writing and testing SAS code to produce the final product, whether it is Study Data Tabulation Model (SDTM) data sets, Analysis Data Model (ADaM) data sets, or statistical outputs such as tables, listings, or figures. You reach the point where you have completed the initial programming, removed all obvious errors and warnings from your SAS log, and checked your outputs for accuracy. You are almost done with your programming task, but one important step remains. It is considered best practice to check your SAS log for any questionable messages generated by the SAS system. In addition to messages that begin with the words WARNING or ERROR, there are also messages that begin with the words NOTE or INFO. This paper will focus on five different types of NOTE messages that commonly appear in the SAS log and will present ways to remove these messages from your log.

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Graphs are essential for many clinical and health care domains, including analysis of clinical trials safety data and analysis of the efficacy of the treatment, such as change in tumor size. Creating such graphs is a breeze with procedures from SAS^® 9.4 ODS Graphics. This paper shows how to create many industry-standard graphs such as Lipid Profile, Swimmer Plot, Survival Plot, Forest Plot with Subgroups, Waterfall Plot, and Patient Profile using Study Data Tabulation Model (SDTM) data with just a few lines of code.

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Managing and coordinating various aspects of a report can be challenging. This is especially true when the structure and composition of the report are data driven. For complex reports, the inclusion of attributes such as color, labeling, and the ordering of items complicates the coding process. Fortunately we have some powerful reporting tools in SAS^® that allow the process to be automated to a great extent. In the example presented in this paper, we are tasked with generating an Excel spreadsheet that ranks types of injuries within age groups. A given injury type is to be assigned a constant color regardless of its rank, and the labeling is to include not only the injury label but the actual count as well. Of course the user needs to be able to control such things as the age groups, color selection and order, and number of desired ranks.

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Suppose that you have a very large data set with some specific values in one of the columns of the data set, and you want to classify the entire data set into different comma-separated-values format (CSV) sheets based on the values present in that specific column. Perhaps you might use codes using IF/THEN and ELSE statement conditions in SAS^®, along with some OUTPUT statements. If you divide that data set into csv sheets, it is more frustrating to use the conventional, manual process of converting each of the separated data sets into csv files. This paper shows a comparative study of using the Macro command in SAS with the help of the proc Export statement and the Output Delivery System (ODS) command using proc tabulate. In these two processes, the whole tedious process is done automatically using the SAS code.

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SAS^® macros offer a very flexible way of developing, organizing, and running SAS code. In many systems, programming components are stored as macros in files and called as macros via a variety of means so that they can perform the task at hand. Consideration must be given to the organization of these files in a manner consistent with proper use and retention. An example might be the development of some macros that are company-wide in potential application, others that are departmental, and still others that might be for personal or individual user. Super-imposed on this structure in a factorial fashion are the need to have areas that are considered: (1) production - validated, (2) archived - retired and (3) developmental. Several proposals for accomplishing this are discussed as well as how this structure might interrelate with stored processes available in some SAS systems. The use of these macros in systems ranging from simple background batch processing to highly interactive SAS and BI processing are discussed. Specifically, the drivers or driver files are addressed. Pro's and con's to all approaches are considered.

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Within SAS^® literally millions of colors are available for use in our charts, graphs, and reports. We can name these colors using techniques that include color wheels, RGB (Red, Green, Blue) HEX codes, and HLS (Hue, Lightness, Saturation) HEX codes. But sometimes I just want to use a color by name. When I want purple, I want to be able to ask for purple, not CX703070 or H03C5066. But am I limiting myself to just one purple? What about light purple or pinkish purple? Do those colors have names or must I use the codes? It turns out that they do have names. Names that we can use. Names that we can select, names that we can order, names that we can use to build our graphs and reports. This paper shows you how to gather color names and manipulate them so that you can take advantage of your favorite purple, be it purple, grayish purple, vivid purple, or pale purplish blue.

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You might already know that SAS^® Studio tasks provide you with prompts to fill in the blanks in SAS^® code and help you navigate SAS syntax. But did you know that SAS Studio tasks are not only designed to allow you to modify them, but there is an entire common task model (CTM) provided for you to build your own? You can build basic utilities or complex reports. You can just run SAS code or you can have elaborate prompts to dynamically generate code. And since SAS Studio runs in a web browser, your tasks are browser-based and can easily be shared with others. In this paper, you learn how to take a typical SAS program and create a SAS Studio task from it. When the task is run, you see web-based prompts for the dynamic portions of the program and HTML output delivered to your browser.

This paper shows how to program a powerful Q-gram algorithm for measuring the similarity of two character strings. Along with built-in SAS^® functions--such as SOUNDEX, SPEDIS, COMPGED, and COMPLEV--Q-gram can be a valuable tool in your arsenal of string comparators. Q-gram is especially useful when measuring the similarity of strings that are intuitively identical, but which have a different word order, such as John Smith and Smith, John.

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The DATA step and DS2 both offer the user a built-in general purpose hash object that has become the go-to tool for many data analysis problems. However, there are occasions where the best solution would require a custom object specifically tailored to the problem space. The DS2 Package syntax allows the user to create custom objects that can form linked structures in memory. With DS2 Packages it is possible to create lists or tree structures that are custom tailored to the problem space. For data that can describe a great many more states than actually exist, dynamic structures can provide an extremely compact way to manipulate and analyze the data. The SAS^® In-Database Code Accelerator allows these custom packages to be deployed in parallel on massive data grids.

This presentation describes a technique for dealing with the precision problems inherent in datetime values containing nanosecond data. Floating-point values cannot store sufficient precision for this, and this limitation can be a problem for transactional data where nanoseconds are pertinent. Methods discussed include separation of variables and using the special GROUPFORMAT feature of the BY statement with MERGE in the DATA step.

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Do you know how many different ways SAS^® Studio can run your programs with SAS^® Grid Manager? SAS Studio is the latest and coolest interface to the SAS^® software. As such, we want to use it in most situations, including sites that leverage SAS Grid Manager. Are you new to SAS and want to be guided by a modern GUI? SAS Studio is here to help you. Are you a code fanatic, who wants total control of how your program runs to harness the full power of SAS Grid Manager? Sure, SAS Studio is for you, too. This paper covers all the different SAS Studio editions. You can learn how to connect each of them to SAS Grid Manager and discover best practices for harnessing a high-performance SAS analytics environment, while avoiding potential pitfalls.

This paper covers developing SAS^® Studio repositories. SAS Studio introduced a new way of developing custom tasks using an XML markup specification and the Apache Velocity templating language. SAS Studio repositories build on this framework and provide a flexible way to package custom tasks and snippets. After tasks and snippets are packaged into a repository, they can be shared with users inside your organization or outside your organization. This paper uses several examples to help you create your first repository.

As SAS^® programmers, we often develop listings, graphs, and reports that need to be delivered frequently to our customers. We might decide to manually run the program every time we get a request, or we might easily schedule an automatic task to send a report at a specific date and time. Both scenarios have some disadvantages. If the report is manual, we have to find and run the program every time someone request an updated version of the output. It takes some time and it is not the most interesting part of the job. If we schedule an automatic task in Windows, we still sometimes get an email from the customers because they need the report immediately. That means that we have to find and run the program for them. This paper explains how we developed an on-demand report platform using SAS^® Enterprise Guide^®, SAS^® Web Application Server, and stored processes. We had developed many reports for different customer groups, and we were getting more and more emails from them asking for updated versions of their reports. We felt we were not using our time wisely and decided to create an infrastructure where users could easily run their programs through a web interface. The tool that we created enables SAS programmers to easily release on-demand web reports with minimum programming. It has web interfaces developed using stored processes for the administrative tasks, and it also automatically customizes the front end based on the user who connects to the website. One of the challenges of the project was that certain reports had to be available to a specific group of users only.

Discover how to document your SAS^® programs, data sets, and catalogs with a few lines of code that include SAS functions, macro code, and SAS metadata. Do you start every project with the best of intentions to document all of your work, and then fall short of that aspiration when deadlines loom? Learn how your programs can automatically update your processing log. If you have ever wondered who ran a program that overwrote your data, SAS has the answer! And If you don't want to be tracing back through a year's worth of code to produce a codebook for your client at the end of a contract, SAS has the answer!

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In any organization where people work with data, it is extremely unlikely that there will be only one way of doing things. Things like divisional silos and differences in education, training, and subject matter often result in a diversity of tools and levels of expertise. One situation that frequently arises is that of 'code versus click.' Let's call it the difference between code-based, 'power user' data tools and simpler, purely graphic point-and-click tools such as Microsoft Excel. Even though the work itself might be quite similar, differences in analysis tools often mean differences in vocabulary and experience, and it can be difficult to convert users of one tool to another. This discussion will highlight the potential challenges of SAS^® adoption in an Excel-based workplace and propose strategies to gain new SAS advocates in your organization.

Dynamic interactive visual displays known as dashboards are most effective when they show essential graphs, tables, statistics, and other information where data is the star. The first rule for creating an effective SAS^® dashboard is to keep it simple. Striking a balance between content and style, a dashboard should be void of clutter so as not to distract from or obscure the information displayed. The second rule of effective dashboard design is to display data that meets one or more business or organizational objectives. To accomplish this, the elements in a dashboard should convey a format easily understood by its intended audience. Attendees learn how to create dynamic interactive user- and data-driven dashboards, graphical and table-driven dashboards, statistical dashboards, and drill-down dashboards with a purpose by using Base SAS^® programming techniques including the DATA step, PROC FORMAT, PROC PRINT, PROC MEANS, PROC SQL, ODS, statistical graphics, and HTML.

Data-driven decision making is critical for any organization to thrive in this fiercely competitive world. The decision-making process has to be accurate and fast in order to stay a step ahead of the competition. One major problem organizations face is huge data load times in loading or processing the data. Reducing the data loading time can help organizations perform faster analysis and thereby respond quickly. In this paper, we compared the methods that can import data of a particular file type in the shortest possible time and thereby increase the efficiency of decision making. SAS^® takes input from various file types (such as XLS, CSV, XLSX, ACCESS, and TXT) and converts that input into SAS data sets. To perform this task, SAS provides multiple solutions (such as the IMPORT procedure, the INFILE statement, and the LIBNAME engine) to import the data. We observed the processing times taken by each method for different file types with a data set containing 65,535 observations and 11 variables. We executed the procedure multiple times to check for variation in processing time. From these tests, we recorded the minimum processing time for the combination of procedure and file type. From our analysis of processing times taken by each importing technique, we observed that the shortest processing times for CSV and TXT files, XLS and XLSX files, and ACCESS files are the INFILE statement, the LIBNAME engine, and PROC IMPORT, respectively.

This session illustrates how to quickly create rates over a specified period of time, using the MEANS and EXPAND procedures. For example, do you want to know how to use the power of SAS^® to create a year-to-date, rolling 12-month, or monthly rate? At Kaiser Permanente, we use this technique to develop Emergency Department (ED) use rates, ED admit rates, patient day rates, readmission rates, and more. A powerful function of PROC MEANS, given a database table with several dimensions and one or more facts, is to perform a mathematical calculation on fact columns across several different combinations of dimensions. For example, if a membership database table exists with the dimensions member ID, year-month, line of business, medical office building, and age grouping, PROC MEANS can easily determine and output the count of members by every possible dimension combination into a SAS data set. Likewise, if a hospital visit database table exists with the same dimensions and facts, PROC MEANS can output the number of hospital visits by the dimension combinations into a second SAS data set. With the power of PROC EXPAND, each of the data sets above, once sorted properly, can have columns added, which calculate total members and total hospital visits by a time dimension of the analyst's choice. Common time dimensions used for Kaiser Permanente's utilization rates are monthly, rolling 12-months, and year-to-date. The resulting membership and hospital visit data sets can be joined with a MERGE statement, and simple division produces a rate for the given dimensions.

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SAS^® Embedded Process offers a flexible, efficient way to leverage increasing amounts of data by injecting the processing power of SAS^® directly where the data lives. SAS Embedded Process can tap into the massively parallel processing (MPP) architecture of Hadoop for scalable performance. Using SAS^® In-Database Technologies for Hadoop, you can run scoring models generated by SAS^® Enterprise Miner™ or, with SAS^® In-Database Code Accelerator for Hadoop, user-written DS2 programs in parallel. With SAS Embedded Process on Hadoop you can also perform data quality operations, and extract and transform data using SAS^® Data Loader. This paper explores key SAS technologies that run inside the Hadoop parallel processing framework and prepares you to get started with them.

Do you create complex reports using PROC REPORT? Are you confused by the COMPUTE BLOCK feature of PROC REPORT? Are you even aware of it? Maybe you already produce reports using PROC REPORT, but suddenly your boss needs you to modify some of the values in one or more of the columns. Maybe your boss needs to see the values of some rows in boldface and others highlighted in a stylish yellow. Perhaps one of the columns in the report needs to display a variety of fashionable formats (some with varying decimal places and some without any decimals). Maybe the customer needs to see a footnote in specific cells of the report. Well, if this sounds familiar then come take a look at the COMPUTE BLOCK of PROC REPORT. This paper shows a few tips and tricks of using the COMPUTE DEFINE block with conditional IF/THEN logic to make your reports stylish and fashionable. The COMPUTE BLOCK allows you to use data DATA step code within PROC REPORT to provide customization and style to your reports. We'll see how the Census Bureau produces a stylish demographic profile for customers of its Special Census program using PROC REPORT with the COMPUTE BLOCK. The paper focuses on how to use the COMPUTE BLOCK to create this stylish Special Census profile. The paper shows quick tips and simple code to handle multiple formats within the same column, make the values in the Total rows boldface, trafficlighting, and how to add footnotes to any cell based on the column or row. The Special Census profile report is an Excel table created with ODS tagsets.ExcelXP that is stylish and fashionable, thanks in part to the COMPUTE BLOCK.

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SAS^® for Windows is extremely powerful software, not only for analyzing data, but also for organizing and maintaining output and permanent data sets. By using pipes and operating system (x) commands within a SAS session, you can easily and effectively manage files of all types stored on your local network.

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U.S. stock exchanges (currently there are 12) are tracked in real time via the Consolidated Tape System (CTS) and the Consolidated Quotation System (CQS). CQS contains every updated quote (buyer's bid price and seller's offer price) from each exchange, covering some 8,500 stock tickers. This is the basis by which brokers can honor their obligation to investors, mandated by the U.S. Securities and Exchange Commission, to execute transactions at the best price, that is, at the National Best Bid and Offer (NBBO). With the advent of electronic exchanges and high-frequency trading (timestamps are published to the microsecond), data set size has become a major operational consideration for market researchers re-creating NBBO values (over 1 billion quotes requiring 80 gigabytes of storage for a normal trading day). This presentation demonstrates a straightforward use of hash tables for tracking constantly changing quotes for each ticker/exchange combination, in tandem with an efficient means of determining changes in NBBO with every new quote.

In this era of data analytics, you are often faced with a challenge of joining data from multiple legacy systems. When the data systems share a consistent merge key, such as ID or SSN, the solution is straightforward. However, what do you do when there is no common merge key? If one data system has a character value ID field, another has an alphanumeric field, and the only common fields are the names or addresses or dates of birth, a standard merge query does not work. This paper demonstrates fuzzy matching methods that can overcome this obstacle and build your master record through Base SAS^® coding. The paper also describes how to leverage the SAS^® Data Quality Server in SAS^® code.

Collection of customer data is often done in status tables or snapshots, where, for example, for each month, the values for a handful of variables are recorded in a new status table whose name is marked with the value of the month. In this QuickTip, we present how to construct a table of last occurrence times for customers using a DATA step merge of such status tables and the colon (':') wildcard. If the status tables are sorted, this can be accomplished in four lines of code (where RUN; is the fourth). Also, we look at how to construct delta tables (for example, from one time period to another, or which customers have arrived or left) using a similar method of merge and colons.

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When attempting to match names and addresses from different files, we often run into a situation where the names are similar, but not exactly the same. Sometimes there are additional words in the names, sometimes there are different spellings, and sometimes the businesses have the same name but are located thousands of miles apart. The files that contain the names might have numeric keys that cannot be matched. Therefore, we need to use a process called fuzzy matching to match the names from different files. The SAS^® function COMPGED, combined with SAS character-handling functions, provides a straightforward method of applying business rules and testing for similarity.

Color is an important aspect of data visualization and provides an analyst with another tool for identifying data trends. But is the default option the best for every case? Default color scales may be familiar to us; however, they can have inherent flaws that skew our perception of the data. The impact of data visualizations can be considerably improved with just a little thought toward choosing the correct colors. Selecting an appropriate color map can be difficult, and you may decide that it may be easier to generate your own custom color scale. After a brief introduction to the red, green, and blue (RGB) color space, we discuss the strengths and weaknesses of some widely used color scales, as well as what to keep in mind when designing your own. A simple technique is presented, detailing how you can use SAS^® to generate a number of color scales and apply them to your data. Using just a few graphics procedures, you can transform almost any complex data into an easily digestible set of visuals. The techniques used in this discussion were developed and tested using SAS^® Enterprise Guide^® 5.1.

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SAS^® Output Delivery System (ODS) Graphics started appearing in SAS^® 9.2. When first starting to use these tools, the traditional SAS/GRAPH^® software user might come upon some very significant challenges in learning the new way to do things. This is further complicated by the lack of simple demonstrations of capabilities. Most graphs in training materials and publications are rather complicated graphs that, while useful, are not good teaching examples. This paper contains many examples of very simple ways to get very simple things accomplished. Over 20 different graphs are developed using only a few lines of code each, using data from the SASHELP data sets. The use of the SGPLOT, SGPANEL, and SGSCATTER procedures are shown. In addition, the paper addresses those situations in which the user must alternatively use a combination of the TEMPLATE and SGRENDER procedures to accomplish the task at hand. Most importantly, the use of ODS Graphics Designer as a teaching tool and a generator of sample graphs and code are covered. The emphasis in this paper is the simplicity of the learning process. Users will be able to take the included code and run it immediately on their personal machines to achieve an instant sense of gratification.

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In today's world of torrential data, plotting large amounts of data has its own challenges. The SGPLOT procedure in ODS Graphics offers a simple yet powerful tool for your graphing needs. In this paper we present some graphs that work better for large data sets. We create heat maps, box plots, and parallel coordinate plots that visualize large data. You can make your graphs resilient to your growing data with ODS Graphics!

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There are many ways to customize data presentation and graphics for an area as specialized as oncology. This paper touches on various approaches to presenting oncological data and on ways that each of the graphics can be customized and individualized based on the needs of a study. The topics range from using SGPLOTs and SGPANEL, which are a simple but visually effective way to present stratified information, to using a complex integration of a series of HIGHLOW plots to depict duration of patient responses on a clinical trial. We also take a detailed look at how to generate specialized and customized waterfall plots used to visualize tumor growth patterns, as well as linear graphics that display a different approach to viewing the same endpoint. In addition to specifying the syntax to graph the data, instruction will be given on how to construct data sets in order to achieve the desired graphics.

SAS^® provides some powerful, flexible tools for creating reports, like the REPORT and TABULATE procedures. With the advent of the Output Delivery System (ODS), you have almost total control over how the output from those procedures looks. But there are still times where you need (or want) just a little more and that's where the Report Writing Interface (RWI) can help. The Report Writing Interface is just a fancy way of saying you're using the ODSOUT object in a DATA step. This object allows you to lay out the page, create tables, embed images, add titles and footnotes, and more--all from within a DATA step, using whatever DATA step logic you need. Also, all the style capabilities of ODS are available to you so that the output created by your DATA step can have fonts, sizes, colors, backgrounds, and borders to make your report look just like you want. This presentation quickly covers some of the basics of using the ODSOUT object and then walks through some of the techniques to create four real-world examples. Who knows, you might even go home and replace some of your PROC REPORT code--I know I have!

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SAS^® Federated Query Language (FedSQL) is a SAS proprietary implementation of the ANSI SQL:1999 core standard capable of providing a scalable, threaded, high-performance way to access relational data in multiple data sources. This paper explores the FedSQL language in a three-step approach. First, we introduce the key features of the language and discuss the value each feature provides. Next, we present the FEDSQL procedure (the Base SAS^® implementation of the language) and compare it to PROC SQL in terms of both syntax and performance. Finally, we examine how FedSQL queries can be embedded in DS2 programs to merge the power of these two high-performance languages.

As SAS^® programmers we often want our code or program logic to be driven by the data at hand, rather than be directed by us. Such dynamic code enables the data to drive the logic or sequence of execution. This type of programming can be very useful when creating lists of observations, variables, or data sets from ever-changing data. Whether these lists are used to verify the data at hand or are to be used in later steps of the program, dynamic code can write our lists once and ensure that the values change in tandem with our data. This Quick Tip paper will present the concepts of creating data-driven lists for observations, variables, and data sets, the code needed to execute these tasks, and examples to better explain the process and results of the programs we will create.

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In SAS^® LASR™ Analytic Server, data can reside in three types of environments: client hard disk (for example, a laptop), the Hadoop File System (HDFS) and the memory of the SAS LASR Analytic Server. Moving the data efficiently among these is critical for getting insights from the data on time. In this paper, we illustrate all the possible ways to move the data, including 1) moving data from client hard disk to HDFS; 2) moving data from HDFS to client hard disk; 3) moving data from HDFS to SAS LASR Analytic Server; 4) moving data from SAS LASR Analytic Server to HDFS; 5) moving data from client hard disk to SAS LASR Analytic Server; and 6) moving data from SAS LASR Analytic Server to client hard disk.

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PERL is a good language to work with text in in the case of SAS^® with character variables. PERL was much used in website programming in the early days of the World Wide Web. SAS provides some PERL functions in DATA step statements. PERL is useful for finding text patterns or simply strings of text and returning the strings or positions in a character variable of the string. I often use PERL to locate a string of text in a character variable. Knowing this location, I know where to start a substr function. The poster covers a number of PERL functions available in SAS^® 9.2 and later. Each function is explained with written text, and then a brief code demonstration shows how I use the function. The examples are jointly explained based on SAS documentation and my best practices.

Although hashing methods such as SHA256 and MD5 are already available in SAS^®, other methods may be needed by SAS users. This presentation shows how hashing and methods can be implemented using SAS DATA steps and macros, with judicious use of the bitwise functions (BAND, BOR, and so on) and by referencing public domain sources.

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Importing metadata can be a time-consuming, painstaking, and fraught task when you have multiple SAS packages to deal with. Fortunately SAS^® software provides a batch facility to import (and export) metadata by using the command line, thereby saving you from many a mouse click and potential repetitive strain injury when you use the Metadata Import Wizard. This paper aims to demystify the SAS batch import tool. It also introduces a template-driven process for programmatically building a batch file that contains the syntax needed to import metadata and then automatically executing the batch import scripts.

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One of the tedious but necessary things that SAS^® programmers must do is to trace and monitor SAS runs: counting observations and columns, checking performance, drawing flowcharts, and diagramming data flows. This is required in order to audit results, find errors, find long-running steps, identify large data files, and to simply understand what a particular job does. SAS^® Enterprise Guide^® includes functionality to help with some of this, on some SAS jobs. This paper presents an innovative custom tool that automatically produces flowcharts--showing all SAS steps in a job, file counts in and out, variable counts, recommendations and generated code for performance improvements, and more. The system was written completely in SAS using SAS source programs, SAS logs, PROC SCAPROC output, and more as input. This tool can eliminate much of the manual work needed in job optimization, space issues, debugging, change management, documentation, and job check-out. As has been said many times before: We have computers, let's use them.

An interactive SAS^® environment is preferred for developing programs as it gives the flexibility of instantly viewing the log in the log window. The programmer must review the log window to ensure that each and every single line of a written program is running successfully without displaying any messages defined by SAS that are potential errors. Reviewing the log window every time is not only time consuming but also prone to manual error for any level of programmer. Just to confirm that the log is free from error, the programmer must check the log. Currently in the interactive SAS environment there is no way to get an instant notification about the generated log from the Log window, indicating whether there have been any messages defined by SAS that are potential errors. This paper introduces an instant approach to analyzing the Log window using the SAS macro %ICHECK that displays the reports instantly in the same SAS environment. The report produces a summary of all the messages defined by SAS in the Log window. The programmer does not need to add %ICHECK at the end of the program. Whether a single DATA step, a single PROC step, or the whole program is submitted, the %ICHECK macro is automatically executed at the end of every submission. It might be surprising to you to learn how a compiled macro can be executed without calling it in the Editor window. But it is possible with %ICHECK, and you can develop it using only SAS products. It can be used in a Windows or UNIX interactive SAS environment without requiring any user inputs. With the proposed approach, there is a significant benefit in the log review process and a 100% gain in time saved for all levels of programmers because the log is free from error. Similar functionality can be introduced in the SAS product itself.

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Microsoft Visual Basic Scripting Edition (VBScript) and SAS^® software are each powerful tools in their own right. These two technologies can be combined so that SAS code can call a VBScript program or vice versa. This gives a programmer the ability to automate SAS tasks; traverse the file system; send emails programmatically via Microsoft Outlook or SMTP; manipulate Microsoft Word, Microsoft Excel, and Microsoft PowerPoint files; get web data; and more. This paper presents example code to demonstrate each of these capabilities.

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High-quality effective graphs not only enhance understanding of the data but also facilitate regulators in the review and approval process. In recent SAS^® releases, SAS has made significant progress toward more efficient graphing in ODS Statistical Graphics (SG) procedures and Graph Template Language (GTL). A variety of graphs can be quickly produced using convenient built-in options in SG procedures. With graphical examples and comparison between SG procedures and traditional SAS/GRAPH^® procedures in reporting clinical trial data, this paper highlights several key features in ODS Graphics to efficiently produce sophisticated statistical graphs with more flexible and dynamic control of graphical presentation including: 1) Better control of axes in different scales and intervals; 2) Flexible ways to control graph appearance; 3) Plots overlay in single-cell or multi-cell graphs; 4) Enhanced annotation; 5) Classification panel of multiple plots with individualized labeling.

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You are a skilled SAS^® programmer. You can code circles around those newbies who point and click in SAS^® Enterprise Guide^®. And yet& there are tasks you struggle with on a regular basis, such as Is the name of that data set DRUG or DRUGS? and What intern wrote this code? It's not formatted well at all and is hard to read. In this seminar you learn how to program, yes program, more efficiently. You learn the benefits of autocomplete and inline help, as well as how to easily format the code that intern wrote that you inherited. In addition, you learn how to create a process flow of a program to identify any dead ends, i.e., data sets that get created but are not used in that program.

From stock price histories to hospital stay records, analysis of time series data often requires use of lagged (and occasionally lead) values of one or more analysis variables. For the SAS^® user, the central operational task is typically getting lagged (lead) values for each time point in the data set. While SAS has long provided a LAG function, it has no analogous lead function--an especially significant problem in the case of large data series. This paper reviews the LAG function, in particular the powerful, but non-intuitive implications of its queue-oriented basis. The paper demonstrates efficient ways to generate leads with the same flexibility as the LAG function, but without the common and expensive recourse to data re-sorting. It also shows how to dynamically generate leads and lags through use of the hash object.

Are you still using TRIM, LEFT, and vertical bar operators to concatenate strings? It's time to modernize and streamline that clumsy code by using the string concatenation functions introduced in SAS^®9. This paper is an overview of the CAT, CATS, CATT, and CATX functions introduced in SAS^®9, and the new CATQ function added in SAS^® 9.2. In addition to making your code more compact and readable, this family of functions offers some new tricks for accomplishing previously cumbersome tasks.

For SAS^® Enterprise Guide^® users, sometimes macro variables and their values need to be brought over to the local workspace from the server, especially when multiple data sets or outputs need to be written to separate files in a local drive. Manually retyping the macro variables and their values in the local workspace after they have been created on the server workspace would be time-consuming and error-prone, especially when we have quite a number of macro variables and values to bring over. Instead, this task can be achieved in an efficient manner by using dictionary tables and the CALL SYMPUT routine, as illustrated in more detail below. The same approach can also be used to bring macro variables and their values from the local to the server workspace.

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The SQL procedure is extremely powerful when it comes to summarizing and aggregating data, but it can be a little daunting for programmers who are new to SAS^® or for more experienced programmers who are more familiar with using the SUMMARY or MEANS procedure for aggregating data. This hands-on workshop demonstrates how to use PROC SQL for a variety of summarization and aggregation tasks. These tasks include summarizing multiple measures for groupings of interest, combining summary and detail information (via several techniques), nesting summary functions by using inline views, and generating summary statistics for derived or calculated variables. Attendees will learn how to use a variety of PROC SQL summary functions, how to effectively use WHERE and HAVING clauses in constructing queries, and how to exploit the PROC SQL remerge. The examples become progressively more complex over the course of the workshop as you gain mastery of using PROC SQL for summarizing data.

When analyzing data with SAS^®, we often encounter missing or null values in data. Missing values can arise from the availability, collectibility, or other issues with the data. They represent the imperfect nature of real data. Under most circumstances, we need to clean, filter, separate, impute, or investigate the missing values in data. These processes can take up a lot of time, and they are annoying. For these reasons, missing values are usually unwelcome and need to be avoided in data analysis. There are two sides to every coin, however. If we can think outside the box, we can take advantage of the negative features of missing values for positive uses. Sometimes, we can create and use missing values to achieve our particular goals in data manipulation and analysis. These approaches can make data analyses convenient and improve work efficiency for SAS programming. This kind of creative and critical thinking is the most valuable quality for data analysts. This paper exploits real-world examples to demonstrate the creative uses of missing values in data analysis and SAS programming, and discusses the advantages and disadvantages of these methods and approaches. The illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

Across the languages of SAS^® are many golden nuggets--functions, formats, and programming features just waiting to impress your friends and colleagues. While learning SAS for over 30 years, I have collected a few of these nuggets, and I offer a dozen more of them to you in this presentation. I presented the first dozen in a similar paper at SAS Global Forum 2015.

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We asked for it, and once again, SAS delivers! Now production in SAS^® 9.4M3, the ODS EXCEL statement provides users with an extremely easy way to export output to Microsoft Excel. Using existing ODS techniques that you may already be familiar with (like predefined styles, traffic-lighting, and custom formatting) in tandem with common Excel features (like formulas, frozen headers and footers, and page setup options), you can create a publication-ready document in a snap. As a matter of fact, the new ODS EXCEL statement is such a handy tool, you may find yourself using it for more than large-scope production programs. This statement is so straightforward that it may be a good choice for outputting quick ad hoc requests as well. Join me on my adventure as I explore how to use the ODS EXCEL statement to create institutional research reports. I offer tips and techniques you can use to make your output excel lent, too.

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Writing music with SAS^® is a simple process. Including a snippet of music in a program is a great way to signal completion of processing. It's also fun! This paper illustrates a method for translating music into SAS code using the CALL SOUND routine.

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A new ODS destination for creating Microsoft Excel workbooks is available starting in the third maintenance release of SAS^® 9.4. This destination creates native Microsoft Excel XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation you learn step-by-step techniques for quickly and easily creating attractive multi-sheet Excel workbooks that contain your SAS^® output. The techniques can be used regardless of the platform on which SAS software 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 presentations by this author, this presentation contains new and revised material not previously presented.

In the consumer credit industry, privacy is key and the scrutiny increases every day. When returning files to a client, we must ensure that they are depersonalized so that the client cannot match back to any personally identifiable information (PII). This means that we must locate any values for a variable that occur on a limited number of records and null them out (i.e., replace them with missing values). When you are working with large files that have more than one million observations and thousands of variables, locating variables with few unique values is a difficult task. While the FREQ procedure and the DATA step merging can accomplish the task, using first./last. BY variable processing to locate the suspect values and hash objects to merge the data set back together might offer increased efficiency.

The SAS^® macro language is an efficient and effective way to handle repetitive processing tasks. One such task is conducting the same DATA steps or procedures for different time periods, such as every month, quarter, or year. SAS^® dates are based on the number of days between January 1st, 1960, and the target date value, which, while very simple for a computer, is not a human-friendly representation of dates. SAS dates and macro processing are not simple concepts themselves, so mixing the two can be particularly challenging, and it can be very easy to end up working with bad dates! Understanding how macros and SAS dates work individually and together can greatly improve the efficiency of your coding and data processing tasks. This paper covers the basics of SAS macro processing, SAS dates, and the benefits and difficulties of using SAS dates in macros, to ensure that you never have a bad date again.

Many databases include default values that are set inappropriately. For example, a top-level Yes/No question might be followed by a group of check boxes, to which a respondent might indicate multiple answers. When creating the database, a programmer might choose to set a default value of 0 for each box that is not checked. One should interpret these default values with caution, however, depending on whether the top-level question is answered Yes or No or is missing. A similar scenario occurs with a Yes/No question where No is the default value if the question is not answered (but actually the value should be missing). These default values might be scattered throughout the database; there might be no pattern to their occurrence. Records without valid information should be omitted from statistical analysis. Programmers should understand the difference between missing values and invalid values (that is, incorrectly set defaults) because it is important to handle these records differently. Choosing the best method to omit records with invalid values can be difficult. Manual corrections are often prohibitively time-consuming. SAS^® offers a useful approach: a combined DATA step and SQL procedure. This paper provides a step-by-step method to accomplish the task.

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In today's fast paced work environment, time management is crucial to the success of the project. Sending requests to SAS^® programmers to run reports every time you need to get the most current data can be a stretch sometimes on an already strained schedule. Why bother to contact the programmer? Why not build the execution of the SAS program into the report itself so that when the report is launched, real-time data is retrieved and the report shows the most recent data. This paper demonstrates that by opening an existing SAS report in Microsoft Word or Microsoft Excel, the data in the report refreshes automatically. Simple Visual Basic for Applications (VBA) code is written in Word or Excel. When an existing SAS report is opened, this VBA code calls the SAS programs that create the report from within a Microsoft Office product and overwrites the existing report data with the most current data.

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This paper highlights many of the major capabilities of the DATASETS procedure. It discusses how it can be used as a tool to update variable information in a SAS^® data set; to provide information on data set and catalog contents; to delete data sets, catalogs, and indexes; to repair damaged SAS data sets; to rename files; to create and manage audit trails; to add, delete, and modify passwords; to add and delete integrity constraints; and more. The paper contains examples of the various uses of PROC DATASETS that programmers can cut and paste into their own programs as a starting point. After reading this paper, a SAS programmer will have practical knowledge of the many different facets of this important SAS procedure.

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The DATA step has served SAS^® programmers well over the years. Now there is a new, exciting, and powerful alternative: the DS2 procedure. By introducing an object-oriented programming environment, PROC DS2 enables users to effectively manipulate complex data and efficiently manage programming through additional data types, programming structure elements, user-defined methods, and shareable packages, as well as threaded execution. This hands-on workshop was developed based on our experiences with getting started with PROC DS2 and learning to use it to access, manage, and share data in a scalable and standards-based way. It will help SAS users of all levels easily get started with PROC DS2 and understand its basic functionality by practicing with its features.

Inspired by Christianna William's paper on transitioning to PROC SQL from the DATA step, this paper aims to help SQL programmers transition to SAS^® by using PROC SQL. SAS adapted the Structured Query Language (SQL) by means of PROC SQL back with SAS^®6. PROC SQL syntax closely resembles SQL. However, there are some SQL features that are not available in SAS. Throughout this paper, we outline common SQL tasks and how they might differ in PROC SQL. We also introduce useful SAS features that are not available in SQL. Topics covered are appropriate for novice SAS users.

SAS^® software provides many DATA step functions that search and extract patterns from a character string, such as SUBSTR, SCAN, INDEX, TRANWRD, etc. Using these functions to perform pattern matching often requires you to use many function calls to match a character position. However, using the Perl regular expression (PRX) functions or routines in the DATA step improves pattern-matching tasks by reducing the number of function calls and making the program easier to maintain. This talk, in addition to discussing the syntax of Perl regular expressions, demonstrates many real-world applications.

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A bubble map is a useful tool for identifying trends and visualizing the geographic proximity and intensity of events. This session describes how to use readily available map data sets in SAS^® along with PROC GEOCODE and PROC GMAP to turn a data set of addresses and events into a bubble map of the United States with scaled bubbles depicting the location and intensity of events.

Representational State Transfer (REST) is being used across the industry for designing networked applications to provide lightweight and powerful alternatives to web services such as SOAP and Web Services Description Language (WSDL). Since REST is based entirely on HTTP, SAS^® provides everything you need to make REST calls and to process structured and unstructured data alike. This paper takes a look at how some enhancements in the third maintenance release of SAS^® 9.4 can benefit you in this area. Learn how the HTTP procedure and other SAS language features provide everything you need to simply and securely use REST.

Creating web applications and web services can sometimes be a daunting task. With the ever changing facets of web programming, it can be even more challenging. For example, client-side web programming using applets was very popular almost 20 years ago, only to be replaced with server-side programming techniques. With all of the advancements in JavaScript libraries, it seems client-side programming is again making a comeback. Amidst all of the changing web technologies, surprisingly one of the most powerful tools I have found that has provided exceptional capabilities across all types of web development techniques has been SAS/IntrNet^®. Traditionally seen as a server-side programming tool for generating complete web pages based on SAS^® content, SAS/IntrNet, coupled with jQuery AJAX or AJAX alone, also has the ability to provide client-side web programming techniques, as well as provide RESTful web service implementations. I hope to show that with the combination of these tools including the JSON procedure from SAS^® 9.4, simple yet powerful web services or dynamic content rich web pages can be created easily and rapidly.

This paper explores some proven methods used to automate complex SAS^® Enterprise Guide^® projects so that the average Joe can run them with little or no prior experience. There are often times when a programmer is requested to extract data and dump it into Microsoft Excel for a user. Often these data extracts are very similar and can be run with previously saved code. However, the user quite often has to wait for the programmer to have the time to simply run the code. By automating the code, the programmer regains control over their data requests. This paper discusses the benefits of establishing macro variables and creating stored procedures, among other tips

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High-quality documentation of SAS^® code is standard practice in multi-user environments for smoother group collaborations. One of the documentation items that facilitate program sharing and retrospective review is a header section at the beginning of a SAS program highlighting the main features of the program, such as the program's name, its creation date, the program's aims, the programmer's identification, and the project title. In this header section, it is helpful to keep a list of the inputs and outputs of the SAS program (for example, SAS data sets and files that the program used and created). This paper introduces SAS-IO, a browser-based HTML/JavaScript tool that can automate production of such an input/output list. This can save the programmers' time, especially when working with long SAS programs.

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SAS^® users are always surprised to discover their programs contain bugs (or errors). In fact, when asked, users will emphatically stand by their programs and logic by saying they are error free. But, the vast number of experiences, along with the realities of writing code, say otherwise. Errors in program code can appear anywhere, whether accidentally introduced by developers or programmers, when writing code. No matter where an error occurs, the overriding sentiment among most users is that debugging SAS programs can be a daunting and humbling task. This presentation explores the world of SAS errors, providing essential information about the various error types. Attendees learn how errors are created, their symptoms, identification techniques, and how to apply effective techniques to better understand, repair, and enable program code to work as intended.

Spontaneous combustion describes combustion that occurs without an external ignition source. With the right combination of fire tetrahedron components--including fuel, oxidizer, heat, and chemical reaction--it can be a deadly yet awe-inspiring phenomenon, and differs from traditional combustion that requires a fire source, such as a match, flint, or spark plugs (in the case of combustion engines). SAS^® code as well often requires a 'spark' the first time it is run or run within a new environment. Thus, SAS^® programs might operate correctly in an organization's original development environment, but might fail in its production environment until folders are created, SAS^® libraries are assigned, control tables are constructed, or configuration files are built or modified. And, if software portability is problematic within a single organization, imagine the complexities that exist when SAS code is imported from a blog, white paper, textbook, or other external source into one's own infrastructure. The lack of software portability and the complexities of initializing new code often compel development teams to build code from scratch rather than attempting to rehabilitate or customize existent code to run in their environment. A solution is to develop SAS code that flexibly builds and validates its environment and required components during execution. To that end, this text describes techniques that increase the portability, reusability, and maintainability of SAS code by demonstrating self-extracting, spontaneously combustible code that requires no spark.

The third maintenance release of SAS^® 9.4 was a huge release with respect to the interoperability between SAS^® and Hadoop, the industry standard for big data. This talk brings you up-to-date with where we are: more distributions, more data types, more options. Come and learn about the exciting new developments for blending your SAS processing with your shared Hadoop cluster. Grid processing. Check. SAS data sets on HDFS. Check.

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Just as there are many ways to solve any problem in any facet of life, most SAS^® programming problems have more than one potential solution. Each solution has tradeoffs; a complex program might execute very quickly but prove challenging to maintain, while a program designed for ease of use might require more resources for development, execution, and maintenance. Too often, it seems like those tasked to produce the results are advised on delivery date and estimated development time in advance, but are given no guidelines for efficiency expectations. This paper provides ways for improving the efficiency of your SAS^® programs. It suggests coding techniques, provides guidelines for their use, and shows the results of experimentation to compare various coding techniques, with examples of acceptable and improved ways to accomplish the same task.

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Whether it is a question of security or a question of centralizing the SAS^® installation to a server, the need to phase out SAS in the PC environment has never been so common. On the surface, this type of migration seems very simple and smooth. However, migrating SAS from a PC environment to a SAS server environment (SAS^® Enterprise Guide^®) is really easy to underestimate. This paper presents a way to set up the winning conditions to achieve this goal without falling into the common traps. Based on a successful conversion with a previous employer, I have identified a high-level roadmap with specific objectives that will guide people in this important task.

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Are you frustrated with manually setting options to control your SAS^® Display Manager sessions but become daunted every time you look at all the places you can set options and window layouts? In this paper, we look at various files SAS accesses when starting, what can (and cannot) go into them, and what takes precedence after all are executed. We also look at the SAS Registry and how to programmatically change settings. By the end of the paper, you will be comfortable in knowing where to make the changes that best fit your needs.

The report looks simple enough--a bar chart and a table, like something created with GCHART and REPORT procedures. But there are some twists to the reporting requirements that make those procedures not quite flexible enough. It's often the case that the programming tools and techniques we envision using for a project or are most comfortable with aren't necessarily the best to use. Fortunately, SAS^® can provide many ways to get results. Rather than procedure-based output, the solution here was to mix 'old' and 'new' DATA step-based techniques to solve the problem. Annotate data sets are used to create the bar chart and the Report Writing Interface (RWI) is used to create the table. Without a whole lot of additional code, you gain an extreme amount of flexibility.

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This paper compares different solutions to a data transpose puzzle presented to the SAS^® User Group at the United States Census Bureau. The presented solutions range from a SAS 101 multi-step solution to an advanced solution using techniques that are not widely known, which yields run-time savings of 85 percent!

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Are you living in Heartbreak Hotel because your boss wants different statistics in the SAME column on your report? Need a currency symbol in one cell of your pre-summarized data, but a percent sign in another cell? Large blocks of text on your report have you all shook up because they wrap badly on your report? Have you hit the wall with PROC PRINT? Well, rock out of your jailhouse with ODS, DATA step, and PROC REPORT. Are you living in Heartbreak Hotel because your boss wants different statistics in the SAME column on your report? Need a currency symbol in one cell of your pre-summarized data, but a percent sign in another cell? Large blocks of text on your report have you all shook up because they wrap badly on your report? Have you hit the wall with PROC PRINT? Well, rock out of your jailhouse with ODS, DATA step, and PROC REPORT. This paper is a sequel to the popular 2008 paper Creating Complex Reports. The paper presents a nuts-and-bolts look at more complex report examples gleaned from SAS^® Community Forum questions and questions from students. Examples will include use of DATA step manipulation to produce PROC REPORT and PROC SGPLOT output as well as examples of ODS LAYOUT and the new report writing interface. And PROC TEMPLATE makes a special guest appearance. Even though the King of Rock 'n' Roll won't be there for the presentation, perhaps we'll hear his ghost say Thank you very much, I always wanted to know how to do that at the end of this presentation.

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VBA has been described as a glue language, and has been widely used in exchanging data between Microsoft products such as Excel and Word or PowerPoint. How to trigger the VBA macro from SAS^® via DDE has been widely discussed in recent years. However, using SAS to send parameters to a VBA macro was seldom reported. This paper provides a solution for this problem. Copying Excel tables to PowerPoint using the combination of SAS and VBA is illustrated as an example. The SAS program rapidly scans all Excel files that are contained in one folder, passes the file information to VBA as parameters, and triggers the VBA macro to write PowerPoint files in a loop. As a result, a batch of PowerPoint files can be generated by just one mouse-click.

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Like a good pitcher and catcher in baseball, ODS layout and the ODS destination for PowerPoint are a winning combination in SAS^® 9.4. With this dynamic duo, you can go straight from performing data analysis to creating a quality presentation. The ODS destination for PowerPoint produces native PowerPoint files from your output. When you pair it with ODS layout, you are able to dynamically place your output on each slide. Through code examples this paper shows you how to create a custom title slide, as well as place the desired number of graphs and tables on each slide. Don't be relegated to the sidelines--increase your winning percentage by learning how ODS layout works with the ODS destination for PowerPoint.

Like all skilled tradespeople, SAS^® programmers have many tools at their disposal. Part of their expertise lies in knowing when to use each tool. In this paper, we use a simple example to compare several common approaches to generating the requested report: the TABULATE, TRANSPOSE, REPORT, and SQL procedures. We investigate the advantages and disadvantages of each method and consider when applying it might make sense. A variety of factors are examined, including the simplicity, reusability, and extensibility of the code in addition to the opportunities that each method provides for customizing and styling the output. The intended audience is beginning to intermediate SAS programmers.

The SAS^® procedure PROC TREE sketches parent-child lineage--also known as trees--from hierarchical data. Hierarchical relationships can be difficult to flatten out into a data set, but with PROC TREE, its accompanying ODS table TREELISTING, and some creative yet simple handcrafting, a de-lineage of parent-children variables can be derived. Because the focus of PROC TREE is to provide the tree structure in graphical form, it does not explicitly output the depth of the tree, although the depth is visualized in the accompanying graph. Perhaps unknown to most, the path length variable, or simply the height of the tree, can be extracted from PROC TREE merely by capturing it from the ODS output, as demonstrated in this paper.

Time flies. Thirteen years have passed since the first introduction of the hash method by SAS^®. Dozens of papers have been written offering new (sometimes weird) applications for hash. Even beyond look-ups, we can use the hash object for summation, splitting files, sorting files, fuzzy matching, and other data processing problems. In SAS, almost any task can be solved by more than one method. For a class of problems where hash can be applied, is it the best tool to use? We present a variety of problems and provide several solutions, using both hash tables and more traditional methods. Comparing the solutions, you must consider computing time as well as your time to code and validate your responses. Which solution is better? You decide!

This SAS^® test begins where the SAS^® Advanced Certification test leaves off. It has 25 questions to challenge even the most experienced SAS programmers. Most questions are about the DATA step, but there are a few macro and SAS/ACCESS^® software questions thrown in. The session presents each question on a slide, then the answer on the next. You WILL be challenged.

I like to think that SAS^® error messages come in three flavors, IMPORTANT, INTERESTING, and IRRELEVANT. SAS calls its messages NOTES, WARNINGS, and ERRORS. I intend to show you that not all NOTES are IRRELEVANT nor are all ERRORS IMPORTANT. This paper walks through many different scenarios and explains in detail the meaning and impact of messages presented in the SAS log. I show you how to locate, classify, analyze, and resolve many different SAS message types. And for those brave enough, I go on to teach you how to both generate and suppress messages sent to the SAS log. This paper presents an overview of messages that can often be found in a SAS Log window or output file. The intent of this presentation is to familiarize you with common messages, the meaning of the messages, and how to determine the best way to react to the messages. Notice I said react , not necessarily correct. Code examples and log output will be presented to aid in the explanations.

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Specifying colors based on group value is a quite popular practice in visualizing data, but it is not so easy to do, especially when there are multiple group values. This paper explores three different methods to dynamically assign colors to plots based on their group values. They are combining EVAL and IFN functions in the plot statements; bringing the DISCRETEATTRMAP block into the plot statements; and using the macro from the SAS^® sample 40255.

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SAS^® DS2 is a powerful new object-oriented programming language that was introduced with SAS^® 9.4. Having been designed for advanced data manipulation, it enables the programmer not only to use a much wider range of data types than with the traditional Base SAS^® language but it also allows for the creation of custom methods and packages. These packages are analogous to classes in other object-oriented languages such as C# or Ruby and can be used to massively improve your programming effectiveness. This paper demonstrates a number of techniques that can be used to take full advantage of DS2's object-oriented user-defined package capabilities. Examples include creating new data types, storing and reusing packages, using simple packages as building blocks in the creation of more complex packages, a technique to overcome DS2's lack of support for inheritance and building lightweight packages to facilitate method overloading and make parameter passing simpler.

Do you need a macro for your program? This paper provides some guidelines, based on user experience, and explores whether it's worth the time to create a macro (for example, a parameter-driven macro or just a simple macro variable). This paper is geared toward new users and experienced users who do not use macros.

Sometimes data are not arranged the way you need them to be for the purpose of reporting or combining with other data. This presentation examines just such a scenario. We focus on the TRANSPOSE procedure and its ability to transform data to meet our needs. We explore this method as an alternative to using longhand code involving arrays and OUTPUT statements in a SAS^® DATA step.

If data sets are being merged and a variable occurs in more than one of the data sets, the last value (from the variable in the right-most data set listed in the MERGE statement) is kept. It is critical, therefore, to know the name and sources of any common variables. This paper reviews simple techniques for noting overwriting in the log, summarizing the names and sources of all variables, and identifying common variables before merging files.

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This session describes the construction of a program that converts any set of relational tables into a single flat file, using Base SAS^® in SAS^® 9.3. The program gets the information it needs from the data tables themselves, with a minimum of user configuration. It automatically detects one-to-many relationships and creates sets of replicate variables in the flat file. The program illustrates the use of macro programming and the SQL, DATASETS, and TRANSPOSE procedures, among others. The output is sent to a Microsoft Excel spreadsheet using the Output Delivery System (ODS).

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UNC General Administration's Division of Institutional Research (UNCGA IR) collects student-level data on enrollment, graduation, courses, grades, financial aid, facilities, and personnel from the 16 public universities. The university system is transitioning from data collection and processing of flat files submitted via SAS/IntrNet^® to an Oracle Data Mart that retrieves the data from the campuses' source systems. After collecting the data, either via flat files or data mart, SAS^® data sets are created and used for analysis and reporting for public and private universities, policy makers, elected officials, and the general public. After a complete turnover in programming staff, UNCGA IR decided to make some major upgrades and improvements to a server and system that were quickly becoming unsupported and outdated. This presentation covers the following topics: a background of our office functions, including our SAS environment; the decision-making process to upgrade to a SAS^® BI and SAS^® Visual Analytics environment, using SAS^® 9.3 and eventually SAS^® 9.4; the factors in determining which processes should be moved to a PHP interface, to SAS^® Stored Processes, and to SAS Visual Analytics (including whether the user was internal or external, whether the user had access to secure data, and how much time was involved in making the changes); the process and challenges of implementing the changes; and the lessons learned. This presentation has a universal audience but is specifically intended for anyone who has gone through or plans to go through a SAS upgrade, has inherited spaghetti code and used it to do their job, or has used SAS/IntrNet, SAS Visual Analytics, SAS Stored Processes, or a PHP interface to initiate SAS programs.

The Statistical Graphics (SG) procedures and the Graph Template Language (GTL) are capable of generating powerful individual data displays. What if one wanted to visualize how a distribution changes with different parameters, or to view multiple aspects of a three-dimensional plot, just as two examples? By using macros to generate a graph for each frame, combined with the ODS PRINTER destination, it is possible to create GIF files to create effective animated data displays. This paper outlines the syntax and strategy necessary to generate these displays and provides a handful of examples. Intermediate knowledge of PROC SGPLOT, PROC TEMPLATE, and the SAS^® macro language is assumed.

For SAS^® users, PROC TABULATE and PROC REPORT (and its compute blocks) are probably among the most common procedures for calculating and displaying data. It is, however, pretty difficult to calculate and display changes from one column to another using data from other rows with just these two procedures. Compute blocks in PROC REPORT can calculate additional columns, but it would be challenging to pick up values from other rows as inputs. This presentation shows how PROC TABULATE can work with the lag(n) function to calculate rates of change from one period of time to another. This offers the flexibility of feeding into calculations the data retrieved from other rows of the report. PROC REPORT is then used to produce the desired output. The same approach can also be used in a variety of scenarios to produce customized reports.

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Have you questioned the Read throughput or Write throughput for your Windows system drive? What about the input/output (I/O) throughput of a non-system drive? One solution is use SASIOTEST.EXE to measure the Read or Write throughput for any drive connected to your system. Since SAS^® 9.2, SASIOTEST.EXE has been included with each release of SAS for Windows. This paper explains the options supported by SASIOTEST and the various ways to use SASIOTEST. It also describes how the I/O relates to SAS I/O on Windows.

Our daily work in SAS^® involves manipulation of many independent data sets, and a lot of time can be saved if independent data sets can be manipulated simultaneously. This paper presents our interface RunParallel, which opens multiple SAS sessions and controls which SAS procedures and DATA steps to run on which sessions by parsing comments such as /*EP.SINGLE*/ and /*EP.END*/. The user can easily parallelize any code by simply wrapping procedure steps and DATA steps in such comments and executing in RunParallel. The original structure of the SAS code is preserved so that it can be developed and run in serial regardless of the RunParallel comments. When applied in SAS programs with many external data sources and heavy computations, RunParallel can give major performance boosts. Among our examples we include a simulation that demonstrates how to run DATA steps in parallel, where the performance gain greatly outweighs the single minute it takes to add RunParallel comments to the code. In a world full a big data, a lot of time can be saved by running in parallel in a comprehensive way.

Marriage laws have changed in 38 states, including the District of Columbia, permitting same-sex couples to marry since 2004. However, since 1996, 13 states have banned same-sex marriages, some of which are under legal challenge. A U.S. animated map, made using the SAS^® GMAP procedure in SAS^® 9.4, shows the changes in the acceptance, rejection, or undecided legal status of this issue, year-by-year. In this study, we also present other SAS data visualization techniques to show how concentrations (percentages) of married same-sex couples and their relationships have evolved over time, thereby instigating changes in marriage laws. Using a SAS GRAPH display, we attempt to show how both the total number of same-sex-couple households and the percent that are married have changed on a state-by-state basis since 2005. These changes are also compared to the year in which marriage laws permitting same-sex marriages were enacted, and are followed over time since that year. It is possible to examine trends on an annual basis from 2005 to 2013 using the American Community Survey one-year estimates. The SAS procedures and SAS code used to create the data visualization and data manipulation techniques are provided.

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This paper introduces an extremely fast and simple implementation of the survey cell collapsing process. Prior implementations had used either several SQL queries or numerous DATA step arrays, with multiple data reads. This new approach uses a single hash object with a maximum of two data reads. The hash object provides an efficient and convenient mechanism for quick data storage and retrieval (sub-second total run time).

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The DOCUMENT procedure is a little known procedure that can save you vast amounts of time and effort when managing the output of your SAS^® programming efforts. This procedure is deeply associated with the mechanism by which SAS controls output in the Output Delivery System (ODS). Have you ever wished you didn't have to modify and rerun the report-generating program every time there was some tweak in the desired report? PROC DOCUMENT enables you to store one version of the report as an ODS Document Object and then call it out in many different output forms, such as PDF, HTML, listing, RTF, and so on, without rerunning the code. Have you ever wished you could extract those pages of the output that apply to certain BY variables such as State, StudentName, or CarModel? With PROC DOCUMENT, you have where capabilities to extract these. Do you want to customize the table of contents that assorted SAS procedures produce when you make frames for the table of contents with HTML, or use the facilities available for PDF? PROC DOCUMENT enables you to get to the inner workings of ODS and manipulate them. This paper addresses PROC DOCUMENT from the viewpoint of end results, rather than provide a complete technical review of how to do the task at hand. The emphasis is on the benefits of using the procedure, not on detailed mechanics.

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A number of SAS^® tools can be used to report data, such as the PRINT, MEANS, TABULATE, and REPORT procedures. The REPORT procedure is a single tool that can produce many of the same results as other SAS tools. Not only can it create detailed reports like PROC PRINT can, but it can summarize and calculate data like the MEANS and TABULATE procedures do. Unfortunately, despite its power, PROC REPORT seems to be used less often than the other tools, possibly due to its seemingly complex coding. This paper uses PROC REPORT and the Output Delivery System (ODS) to export a big data set into a customized XML file that a user who is not familiar with SAS can easily read. Several options for the COLUMN, DEFINE, and COMPUTE statements are shown that enable you to present your data in a more colorful way. We show how to control the format of the selected columns and rows, make column headings more meaningful, and how to color selected cells differently to bring attention to the most important data.

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One of the truths about SAS^® is that there are, at a minimum, three approaches to achieve any intended task and each approach has its own pros and cons. Identifying and using efficient SAS programming techniques are recommended and efficient programming becomes mandatory when larger data sets are accessed. This paper describes the efficiency of virtual access and various situations to use virtual access of data sets using OPEN, FETCH and CLOSE functions with %SYSFUNC and %DO loops. There are several ways to get data set attributes like number of observations, variables, types of variables, and so on. It becomes more efficient to access larger data sets using the OPEN function with %SYSFUNC. The FETCH with OPEN function gives the ability to access the values of the variables from a SAS data set for conditional and iterative executions with %DO loops. In the following situations, virtual access of the SAS data set becomes more efficient on larger data sets. Situation 1: It is often required to split the master data set into several pieces depending upon a certain criterion for the batch submission, as it is required that data sets be independent. Situation 2: For many reports and dashboards and for the array process, it is required to keep the relevant variables together instead of maintaining the original data set order. Situation 3: For most of the statistical analysis, particularly for correlation and regression, a widely and frequently used SAS procedure requires a dynamic variable list to be passed. Creating a single macro variable list might run into an issue with the macro variable length on the larger transactional data sets. It is recommended that you prepare the list of the variables as a data set and access them using the proposed approach in many places, instead of creating several macro variables.

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Few data visualizations are as striking or as useful as heat maps, and fortunately there are many applications for them. A heat map displaying eye-tracking data is a particularly potent example: the intensity of a viewer's gaze is quantified and superimposed over the image being viewed, and the resulting data display is often stunning and informative. This paper shows how to use Base SAS^® to prepare, smooth, and transform eye-tracking data and ultimately render it on top of a corresponding image. By customizing a graphical template and using specific Graph Template Language (GTL) options, a heat map can be drawn precisely so that the user maintains pixel-level control. In this talk, and in the related paper, eye-tracking data is used primarily, but the techniques provided are easily adapted to other fields such as sports, weather, and marketing.

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Making a data delivery to a client is a complicated endeavor. There are many aspects that must be carefully considered and planned for: de-identification, public use versus restricted access, documentation, ancillary files such as programs, formats, and so on, and methods of data transfer, among others. This paper provides a blueprint for planning and executing your data delivery.

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Software quality comprises a combination of both functional and performance requirements that specify not only what software should accomplish, but also how well it should accomplish it. Recoverability--a common performance objective--represents the timeliness and efficiency with which software or a system can resume functioning following a catastrophic failure. Thus, requirements for high availability software often specify the recovery time objective (RTO), or the maximum amount of time that software might be down following an unplanned failure or a planned outage. While systems demanding high or near perfect availability require redundant hardware and network resources, and additional infrastructure, software must also facilitate rapid recovery. And, in environments in which system or hardware redundancy is infeasible, recoverability can be improved only through effective software development practices. Because even the most robust code can fail under duress or due to unavoidable or unpredictable circumstances, software reliability must incorporate recoverability principles and methods. This text introduces the TEACH mnemonic that describes guiding principles that software recovery should be timely, efficient, autonomous, constant, and harmless. Moreover, the text introduces the SPICIER mnemonic that describes discrete phases in the recovery period, each of which can benefit from and be optimized with TEACH principles. Software failure is inevitable, but negative impacts can be minimized through SAS^® development best practices.

Even if you're not a GIS mapping pro, it pays to have some geographic problem-solving techniques in your back pocket. In this paper we illustrate a general approach to finding the closest location to any given US zip code, with a specific, user-accessible example of how to do it, using only Base SAS^®. We also suggest a method for implementing the solution in a production environment, as well as demonstrate how parallel processing can be used to cut down on computing time if there are hardware constraints.

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This paper shows how to create maps from the output of the SAS/STAT^® SPP procedure using the same layer of the data. This cut can be done with PROC GINSIDE and the final plot with PROC GMAP. The result is a map that is nicer than the plot produced by PROC SPP.

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I get annoyed when macros tread all over my SAS^® environment, macro variables and data sets. So how do you write macros that play nicely and then clean up afterwards? This paper describes techniques for writing macros that do just that.

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Do you write reports that sometimes have missing categories across all class variables? Some programmers write all sorts of additional DATA step code in order to show the zeros for the missing rows or columns. Did you ever wonder whether there is an easier way to accomplish this? PROC MEANS and PROC TABULATE, in conjunction with PROC FORMAT, can handle this situation with a couple of powerful options. With PROC TABULATE, we can use the PRELOADFMT and PRINTMISS options in conjunction with a user-defined format in PROC FORMAT to accomplish this task. With PROC SUMMARY, we can use the COMPLETETYPES option to get all the rows with zeros. This paper uses examples from Census Bureau tabulations to illustrate the use of these procedures and options to preserve missing rows or columns.

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