SCSUG 2018 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	'V' for ... Variable Information Functions to the Rescue	Richann J. Watson, Karl Miller	There are times when we need to use the attributes of a variable within a data set. Normally, this can be done with a simple `CONTENTS` procedure. The information can be viewed prior to programming and then hardcoded within the program or it can be saved to a data set that can be joined back to the main data set. If the attributes are hardcoded then what happens if the data set changes structure, then the program would need to be updated accordingly. If the information from `PROC CONTENTS` is saved and then joined with the main data set, then this would need to be done for all data sets that need to be processed. This is where knowing your 'V' functions can come in handy. The 'V' functions can be used to return the label, format, length, name, type and/or value of a variable or a string within the `DATA STEP`. These functions can come quite in handy when you need to create summary statistics and if you need to perform an algorithm on a variable with a specific naming convention	proc means, macro variable, proc step, numeric variable, character variable, proc sql, baseline characteristics, label statement, sample, proc contents	source	16
2	A Hands-on Introduction to SAS DATA Step Hash Programming Techniques	Kirk Paul Lafler	SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a `DATA STEP` programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required, and simple applications of it use. Essential programming techniques will be illustrated to sort data and search memory-resident data using a simple key to find a single value	hash table, proc transpose, lookup, proc step, sas log, composite key, dataset, proc sort, sas base	source	12
3	A Visual Step-by-step Approach to Converting an RTF File to an Excel File	Kirk Paul Lafler	Rich Text Format (RTF) files incorporate basic typographical styling and word processing features in a standardized document that many programs and applications are able to read. In today's high-tech arena sometimes the contents of an RTF file needs to be viewed as, and even converted to, an `EXCEL` file. You would think that since both RTF and `EXCEL` are Microsoft standards that this would be a simple process to achieve, but you may be surprised to find out that is not the case. Learn about several "free" web-based and online applications as well as traditional SAS-based programming techniques that can be used to convert an RTF file to an `EXCEL` file	excel, txt	source	5
4	Advanced Analytics Architecture at General Motors	Joe Aarons		data science, machine learning, conjoint analysis, data governance	source	27
5	Animate Your Data!	Richann J. Watson, Kriss Harris	When reporting your safety data, do you ever feel sorry for the person who has to read all the laboratory listings and summaries? Or have you ever wondered if there is a better way to visualize safety data? Let's use animation to help the reviewer and to reveal patterns in your safety data, or in any data! This hands-on workshop demonstrates how you can use animation in SAS 9.4 to report your safety data, using techniques such as visualizing a patient's laboratory results, vital sign results, and electrocardiogram results and seeing how those safety results change over time. In addition, you will learn how to animate adverse events over time, and how to show the relationships between adverse events and laboratory results using animation. You will also learn how to use the `EXPAND` procedure to ensure that your animations are smooth. Animating your data will bring your data to life and help improve lives!	series plot, change from baseline, box plot, proc step, adverse events, by group processing, ods graphics, output delivery system, sample	source	18
6	Are you in danger of stroke? An insight into the leading causes.	Anjali Bansal	Stroke aka cerebrovascular accident is one of the leading cause of death and a significant root of disability. According to World Health Organization, 15 million people suffer stroke worldwide each year. Of these, 5 million die and another 5 million are permanently disabled. Currently it is the fifth leading cause of death in U.S. and the most important cause of disability. The question is- What leads to STROKE? A stroke is usually the result of lack of blood supply to the brain either from interruption of flow or reduction, thereby, depriving the brain tissue of oxygen and necessary nutrients. Brain cells begin to die within minutes of the event, and can lead to permanent disability. Hypertension plays a significant role in the occurrence of stroke. Hypertension weakens arterial walls in the brain that can lead to a rupture resulting in hemorrhagic stroke. This paper gives a detailed insight about the stroke occurrence in United States	odds ratio, regression analysis, sas enterprise guide, data mining, binary variable	source	17
7	Automated Validation of Complex Clinical Trials Made Easy	Richann J. Watson	Validation of analysis datasets and statistical outputs (tables, listings, and figures) for clinical trials is frequently performed by double programming. Part of the validation process involves comparing the results of the two programming efforts. `COMPARE` procedure output must be carefully reviewed for various problems, some of which can be fairly subtle. In addition, the program logs must be scanned for various errors, warnings, notes, and other information that might render the results suspect. All o f this must be performed repeatedly each time the data is refreshed or a specification is changed. In this paper, we describe a complete, end-to-end, automated approach to the entire process that can improve both efficiency and effectiveness	proc compare, proc step, dataset, macro variable, data cleaning, sas output, sample, where statement, independent qc, proc report, macro parameter, macro call, macro definition	source	35
8	Backward Elimination in Firth's penalized partial likelihood Approach: Firth's Logistic and Cox Models	Chao Zhang	The observation of monotone likelihood or separation may occur in the fitting process of a Cox or a logistic model and is indicated by at least one parameter estimate diverging to ±∞. This observation is typically seen in small sample sizes with rare events. Firth's correction is an approach to reduce the bias of maximum likelihood estimates in the setting of either logistic or cox regression models. While such a correction may be implemented using current SAS procedures (version 9.4), there is no option for performing variable selection within the context of a multivariable analysis. One type of variable selection in particular, backward elimination, has an advantage over other methods since it is possible for a set of variables to have considerable predictive capability even though a subset of them does not. To address this limitation, we developed two SAS macros in which to apply Firth's correction with backwards elimination in the setting of either logistic or Cox regressions	likelihood function, backward selection, proc phreg, regression analysis, logistic distribution, logistic regression, sample	source	7
9	City of Austin Community Perception and Satisfaction Analysis	Eric Hart	Austin, Texas is often quoted as one of the best cities to live in within the United States. It is important that the city focuses on key issues to continue to be progressive. The City of Austin performs an annual community survey to assess the residents' satisfaction level with the city. The objective of this paper is to identify which areas are causing the most dissatisfaction for the City of Austin as a place to live for residents by using SAS Enterprise Miner. The dataset is comprised of over 6,300 responses and 55 variables from 2015 to 2017. The residents of Austin that were selected to take this survey were chosen randomly. The survey covers a broad spectrum of categories such as perception of the community, personal safety, infrastructure, and environmental services. By determining the services that are causing the most dissatisfaction for the city's residents, the City of Austin can be proactive in taking measures to rectify and improve the identified areas of discontent	output delivery system, data mining, proc hpsplit, response variable	source	11
10	Collaborations in SAS Programming; or Playing Nicely with Others	Kristi Busico Metzger	SAS programmers rarely work in isolation, but rather as part of a larger team that includes other SAS programmers such as data managers and data analysts, as well as non-programmers like project coordinators. Some members of the team -- including the SAS programmers -- may work in different locations. Given these complex collaborations, it is increasingly important to adopt approaches to work effectively and easily in teams. In this presentation, we discuss strategies and methods for working with colleagues in varied roles. We first address file organization -- putting documents in places easily found by team members -- including the importance of numbering programs that are executed sequentially. While documentation is often a neglected activity, we next review the importance of documenting both within SAS and in other forms for the non-SAS users of your team. We also discuss strategies for sharing formats and writing friendly SAS coding for seamless work with other SAS programmers	sas programmer, excel, regression analysis, proc step	source	9
11	Conditional Processing in the SAS Software by Example	Charu Shankar	Conditional processing is at the heart & core of computer programming. The SAS software supports conditionally selecting result values from rows in a table (or view) in the form of `DATA STEP` subsetting IF, IF-Then-Else, Select-When-Otherwise, and the `IFN`/`IFC` statements, the powerful `PROC SQL` Case Expression, and `PROC FORMAT`. Learn about best practices while crafting your conditional statements and much more	proc sql, if-then-else, character variable, missing value, proc step, subsetting if	source	9
12	Dating for SAS Programmers	Joshua M. Horstman	Every SAS programmer needs to know how to get a date... no, not that kind of date. This paper will cover the fundamentals of working with SAS date values, time values, and date/time values. Topics will include constructing date and time values from their individual pieces, extracting their constituent elements, and converting between various types of dates. We'll also explore the extensive library of built-in SAS functions, formats, and informats for working with dates and times using in-depth examples. Finally, you'll learn how to answer that age-old question... when is Easter next year?	date variable, sysdate9, datetime, time, macro variable, date and time functions, datepart, automatic macro variable, user-defined format, dhms, sas programmer, options statement, macro function	source	13
13	Deciphering cryptocurrencies: Sentiments and prices	Priyansh Dadar	Cryptocurrencies like Bitcoin have recently gained a lot of attention from media as well as the market. The feature that made them stand out from standard currency was the fact that it was fluctuating with high variation. This fluctuation is not institutionalized but depends on how people perceive them. The impending question always has been centered around whether to treat them as a currency or a commodity. Their prices have been soaring the skies for the past year but took a nosedive this February. Analyz ing the sentiments that people have for cryptocurrency might help us understand the underlying factors that affect its popularity and price. Social media like Twitter and other websites are a host to a lot of data to analyze these sentiments. These sources also provide the change in rates of cryptocurrencies throughout the period under consideration. Data from these sources will be used through web scrapping and APIs to procure the data and proceed further with the analysis	correlation, response variable, predictor variable	source	6
14	Detecting Fake News	Gaurav Jhanwar		proc freq, proc hpforest	source	15
15	Excel at Cutting Costs: Combining SAS and Microsoft Excel to Reduce Headcount	Keith Fredlund	Many organizations employ skilled statistical programmers to run analyses and create reports using SAS software. But the ability to program in SAS is an advanced and uncommon skill requiring special training to acquire. In comparison to the percentage of the workforce skilled in SAS programming, the percentage of people able to use Microsoft `EXCEL` is quite large. Accordingly, it can be difficult for organizations to find individuals with skills in SAS programming and companies pay a salary premium for employees with this capability. When conducting analyses or creating reports that have repeated elements across iterations, it can be cost-effective for a company to employ an individual without SAS programming skills to conduct analyses using a Microsoft `EXCEL` file as an interface to SAS	excel, macro variable, sas programmer, sas programming, batch processing, data cleaning	source	9
16	Finding Code with Key Words in UNIX Folders	James Willis	When code is stored in UNIX, the folder structure is similar to a Windows structure; however, UNIX does not have the "Search" feature that Windows has. A programmer has to know where the code is located or use the UNIX "grep command" to search through files in a folder. Using the "Find_a_variable" utility, words in UNIX files can be found, listed and sent to a txt file. SAS code can then read the .txt file, extract pertinent information, write the extract to a spreadsheet and email the spreadsheet. This utility will solve a question that was asked about "grep" commands on the SAS COMMUNITIES website. The utility also defines a macro that uses the SAS "x" statements to issue UNIX commands that change the directory, delete text files if they exist, insert "grep" values inside the files, and send the results to a text file. SAS code will then scan the text file for keywords, separate the file parts into variables, and create a SAS dataset	txt, sas enterprise guide	source	4
17	How to configure Python and SASPY	Michael McCarthy	SASPy is an interface between SAS and Python that can generate SAS code from Python3 scripts. The SASPy module enables Python objects and syntax to access SAS data. Thus, it's possible to use Python to perform machine learning techniques on existing SAS data sets using packages like Scikit-learn, SciPy, and NumPy. This paper presents a step-by- step tutorial showing the configuration of the connection from BASE SAS to an Anaconda Distribution of Python3 running on Windows 10	machine learning, dataset, sas base, proc hpsplit, mae	source	7
18	Introduction to Data-driven Programming Using SAS	Kirk Paul Lafler	Data-driven programming, or data oriented programming (DOP), is a specific programming paradigm where the data, and/or data structures, control the flow of a program and not the program logic. Often, data-driven programming approaches are applied in organizations with structured and unstructured data for filtering, aggregating, transforming and calling other programs. This paper and presentation explores several data-driven programming techniques that are available to SAS users	dictionary tables, proc step, proc sql, excel, proc contents, call execute, sas library, sas log, ods excel, output delivery system, macro facility, macro variable, dataset, select, character variable, proc format, proc print, options statement, user-defined format, proc freq	source	22
19	Is Your Visualization The Best or A Mess?	De'Ambra Unuigbey, Steven Collins	Ever wonder if your visualizations are painting a clear picture or conveying the correct message? Often in a quest to tell a story the meaning gets lost in the details. Charts are poorly designed, too busy, or are just simply the wrong chart. The design can lead to false conclusions as opposed to valid depictions of what's really going on. This paper will provide key insights on how to successfully tell a story with visualizations while avoiding common pitfalls, such as designing for yourself instead of the audience or putting too much data into one chart. We will provide good design practices including using the correct charts and graphs to display data that conveys a clear story without the ambiguities of poor color choices, sizing, titling and many other errors that frequently occur. Overall we will show how to take visualizations that are a mess and make them the best leaving the audience well informed	waterfall chart, line chart, scatter plot, excel	source	13
20	Let SAS Do Your DIRty Work	Richann J. Watson	Making sure that you have saved all the necessary information to replicate a deliverable can be a cumbersome task. You want to make sure that all the raw data sets and all the derived data sets, whether they are Study Data Tabulation Model (SDTM) data sets or Analysis Data Model (ADaM) data sets, are saved. You prefer that the date/time stamps are preserved. Not only do you need the data sets, you also need to keep a copy of all programs that were used to produce the deliverable, as well as the corresponding logs from when the programs were executed. Any other information that was needed to produce the necessary outputs also needs to be saved. You must do all of this for each deliverable, and it can be easy to overlook a step or some key information. Most people do this process manually. It can be a time-consuming process, so why not let SAS do the work for you?	macro variable, dataset, cdisc	source	8
21	Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model	Mei Najim	In the Property & Casualty Insurance industry, advanced analytics has increasingly penetrated into each of the core business operations - marketing, underwriting, risk management, actuarial pricing, and actuarial reserving, etc. Advanced analytics has been extensively used in claim areas to predict the claims' future in order to have early intervention to improve claim outcomes and claim processing efficiency. This is another important way to grow revenue and increase profit in the insurance industry. This paper will first introduce several current common claim models in predictive analytics. Then, a claim analytics standard modeling process will be introduced. Finally, a Worker's Compensation litigation propensity model will be introduced as a case study	data mining, data cleaning, feature selection, proc hpsplit, response variable, regression analysis, logistic regression, model validation, sas enterprise guide, insurance, incurred loss, neural network, proc genmod, logistic distribution, sas base, insurance claims, model testing, univariate analysis, validation set, significance level, casualty insurance, model training, predictive analytics, stepwise selection, feature engineering, data transformation, gradient boosting, claim severity, insurer, training set, correlation, imputation	source	17
22	Making Your SAS Results, Reports, Tables, Charts and Spreadsheets More Meaningful with Color	Kirk Paul Lafler	Color can help make SAS results, reports, tables, charts and spreadsheets more professional and meaningful. Instead of producing boring and ineffective results, users can enhance the appearance of their output to highlight and draw attention to important data elements and issues, including headings, subheadings, footers, minimum and maximum values, ranges, outliers, special conditions, and other elements. Color can be added to text, foreground, background, row, column, cell, summary, and total with amazing color and traffic light ing scenarios. Topics include exploring an assortment of examples to illustrate the various ways results, documents, reports, tables, charts and spreadsheets can be enhanced with color, effectively add color to PDF, RTF, HTML, and `EXCEL` spreadsheet results using `PROC PRINT`, `PROC FREQ`, `PROC REPORT`, `PROC TABULATE`, and `PROC SGPLOT` and Output Delivery System (`ODS`) with style	bar chart, proc sgplot, proc step, histogram, proc sgpanel, excel, scatter plot, vertical bar chart, box plot, bubble plot, proc freq, series plot, proc report, data science, class variable, correlation, outlier	source	20
23	Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets	Joshua M. Horstman	Although merging is one of the most frequently performed operations when manipulating SAS datasets, there are many problems which can occur, some of which can be rather subtle. This paper examines several common issues, provides examples to illustrate what can go wrong and why, and discusses best practices to avoid unintended consequences when merging	merge statement, by group processing, in=, out=, set statement, dataset, if-then-else, subsetting if, sas log, pdv, proc sql, length statement	source	16
24	Mining ECAD and WCAD Public Record Data - ETL Guide to ECAD and WCAD	Michael McCarthy	A multitude of Public Record Data (PRD) is readily available for SAS programmers interested in data mining government data. This paper presents a step-by-step tutorial on how to request Public Tax Record data, import the data into SAS, and then perform ETL and data mining with BASE SAS	sas base, data mining	source	9
25	More Time on Thinking, Less Time on Coding - An efficient approach in SAS EG	Charles Ling	In general, SAS programmers follow and work with statisticians and the clinical team on a daily basis in a fast pace environment before a clinical study is complete. Therefore most of the programmers barely sit down and think about some important issues/questions: are the mock-ups make sense, do we interpret our primary/secondary endpoints correctly based on Protocol and SAP? Do the lay-out interpret the important questions that the study is expected. Do we produce too much unnecessary output or not enough on the key concepts? Do we fully understand the emails/meeting notes from the clinical team or do they fully understand our questions when we work in a fast-pace environment? When facing overwhelming tasks to finish, how can we reduce programmers' work by identifying some efficient way of coding on SAS EG (SAS EG is the only platform for discussion in this paper. Many key concepts presented in this paper are through demonstrations, rather than texts here)	sas enterprise guide, excel	source	5
26	NBA Draft Analytics: Boom or Bust?	Isaiah Hartman		proc hpsplit, regression analysis, response variable, data merging, key performance indicator, data cleaning	source	17
27	Oh, There's No Place Like SAS ODS Graphics for the Holidays!	Ted Conway	Already a SAS `ODS` Graphics user at work, the author used the (free!) SAS University Edition software he'd recently downloaded and installed on his home laptop to knock out a connect -the-dots Tom Turkey with `PROC SGPLOT` to commemorate Thanksgiving 2015. And so began an ongoing series of "Fun with SAS `ODS` Graphics" posts on the SAS Support Communities and Twitter that celebrated major holidays and other events. While creating these admittedly frivolous charts from the comfort of his easy chair, the author learned some useful techniques for creating serious data vizzes, which will be shared in this session and the accompanying paper	proc step, bubble plot	source	25
28	Order, Order! Four Ways to Reorder Your Variables, Ranked by Elegance and Efficiency	Louise S. Hadden	SAS practitioners are frequently required to present variables in an output data file in a particular order, or standards may require variables in a production data file to be in a particular order. This paper and presentation offer several methods for reordering variables in a data file, encompassing both `DATA STEP` and procedural methods. Relative efficiency and elegance of the solutions will be discussed	dataset, proc sql, proc step, length statement, proc contents, array, attrib statement, sample	source	20
29	Patient-Level Longitudinal Analysis Plots Showing Adverse Event Timelines and Dose Titration Levels	John R. Gerlach	The analyses in a clinical trial often include subject-specific longitudinal plots that depict, for example, a lab measurement taken at various times during a study period. Because the study involves dose titration levels, the plots might include vertical lines indicating dose levels, which can increase and decrease during the study period. Perhaps the analysis includes another component: the timelines of adverse events, if any. Both the vertical reference lines and horizontal time lines are superimposed on the longitudinal plot. Obviously, the task becomes more intricate since each plot is subject-specific, having different measurements, dose titration levels, and adverse events. This paper explains how to produce this intricate graphical analysis	adverse events, study day, dose, laboratory data, proc sgplot, proc step, proc sql, macro variable, ordinal variable, proc format, where statement, proc gplot	source	15
30	Predicting the Federal Reserve?s Next Federal Funds Rate Decisions	Nhan Nguyen	Federal funds rate has a chain impact on major interest rates and performance of the US economy. Predicting the next rate moves by the Federal Reserve, either an accommodative decision (reduce or hold the target federal funds rate) or a restrictive one (increase the target federal funds rate), would be of great significance to various groups of stakeholders, including equity and fixed income investors who wish to manage their investment portfolios effectively. In this project, R and SAS Enterprise Miner are deployed to build models to predict the rate decisions, using past decisions and economic indicators as the main variables. A sentiment measurement, text clusters, and text topics supplement these variables. This unique combination augments modeling and improves accuracy in a contractionary policy but fails to outperform a conventional model with only quantitative variables in an expansionary policy	interest rate, proc hpsplit, data mining, numeric variable, regression analysis, data cleaning, likelihood function	source	13
31	Predicting the success of a startup company	Vrushank Shah	More than 50% of startup companies fail in the initial four years. Further, three out of every four venture-backed firms fail. The algorithm proposed in this paper will help to predict the success of a startup company based on financial and managerial variables. This prediction will help investors to get an idea whether the investing in a startup will be successful or not? Apart from implementing model consisting of all the factors mentioned below and predicting the success of a startup company, various other models will be created representing various milestones achieved by the company. This paper will help the startup companies to know which factors are essential for getting an investment. The algorithm will be based on more than 15,000 companies' data collected from crunchbase.com	regression analysis, survival analysis	source	10
32	PROC COMPARE: What Did I Inadvertently Change?	Melissa R. Pfeiffer	SAS `PROC COMPARE` is a valuable tool to use for comparing specific variables within a single data set, all variables across two different data sets, or specific variables across two different data sets. For example, when working with new functions for the first time, you may want to verify the results of the code against those generated by code you have used previously and are familiar with. Similarly, when revising inherited code for efficiency, it can be helpful to compare a data set resulting from the new code with a previously generated data set. Information generated by `PROC COMPARE` includes the number of variables and observations in each data set, the number of variables and observations with unequal values, and displays of specific value differences, which can be extremely helpful in verifying that values have not inadvertently changed	proc compare, proc step, by group processing, variable list	source	11
33	Programming the Provider Previews: Extreme SAS Reporting	Louise S. Hadden	During each month over the past nine years, we produce over 15,500 data-driven, 3 page reports for nursing homes previewing their five-star ratings on various measures. These reports are produced entirely in SAS, including a macro toggle for test, one off or production mode and zipping all 15,500 PDF reports into a single multi-gigabyte zip file. The elements of the program include macro language, `DATA STEP` programming, `PROC SQL`, and `PROC REPORT`, utilizing `ODS PDF`. Each PDF is named so that it will seamlessly upload into specific electronic mailboxes for each nursing home facility. This paper will detail the production process, begun using SAS 9.1.3 and currently being run in	macro variable, ods pdf, proc step, proc report, output delivery system, proc sql, character variable, macro definition	source	9
34	Secret to SAS Dates	Alison Little	How do you know whether the field you are working with which is called "birth date" or "procedure date" etc. is a real SAS date? And how can you work with date fields, whether they are real or fake SAS dates? This paper will help you feel confident identifying what you are using and working with date variables	sysdate9, numeric variable, character variable, missing value, format statement, date variable	source	9
35	Simple Steps for Great Graphics: Beyond the Default Settings for More Effective Data Visualization	Kristi Busico Metzger	Graphs are an efficient way to present large quantities of data. The human brain is able to recognize patterns in pictures more easily than numbers or text. In oral presentations and written reports, researchers often use graphic displays of data to help tell the story of their research. Data visualization expert Stephanie Evergreen has cited that the default settings in statistical software packages are one of the biggest obstacles to effective data visualization. Features of better graphics, such as more white space, clean lines, and judicious use of color, are not automatic with the default settings in SAS. A few extra lines of code in your graphing programs can easily produce more effective data visualization graphics. Maximize white space by reducing clutter, such as by eliminating borders and removing default text like titles and labels. The graph axes can be streamlined by using appropriate ranges, intervals, and scales	data visualization, proc template, proc step, output delivery system	source	10
36	Standardisation of clinical laboratory data using SAS	Laxmi Samhitha Bontha	For regulatory bodies to better understand clinical data, data should be in a standard format. To achieve this the collected data must be cleaned and combined from various sources. Standardization of data improves decision making, productivity and quality. Laboratory data is one of the largest and relatively complex in clinical trial data. Laboratory data is collected both on case report forms or by using electronic data capture, obtained from different sources i.e. central laboratories or local laboratories and obtained in different formats like SAS datasets, delimited text, `EXCEL` and `CSV` files. These cause huge discrepancies in the laboratory data. Standardization of data will lead to effective analysis and generation of reports. This paper gives an insight on how SAS can be used to convert clinical data to standard format of study data tabulation model	laboratory data, clinical data	source	4
37	Text Analysis of Users? Review for Airbnb	Prashant Gour	The objective of this paper is to increase credibility of website by doing text analysis of users' reviews to understand what facilities guest wants and what are the things/events which caused inconvenience during their stay. In this paper, my goal is to analyze the overall sentiment prevailing in AirBnB reviews. In order to achieve this, I have extracted real time data from AirBnB studied the responses of the people. I have used SAS Enterprise Miner and SAS Sentimental Analy sis Studio to evaluate key questions regarding the do's and don'ts for a host such as understanding the needs and expectations of guest and perception of guest about what they like and what they didn't like during their stay. This analysis will allow Airbnb to plan further to improve their service by indicating their host about dos and don'ts	data mining, data cleaning	source	7
38	Text Analytics and Linguistics	Rohit Banerjee	Text Analytics is the process of examining large collections of written resources to generate new information; transforming unstructured text into structured data helps us find meaningful insights from the text. It is a subgroup of Nat ural Language Processing (NLP). S tatistical methods, rule- based modeling, and machine learning technique s are applied in text analytics allowing for the extraction of topics, keywords, semantics , and sentiments from the raw text in an effort to categorize terms	macro definition, sas output	source	7
39	The Opioid Crisis and Predicting Drug-Seeking Behavior	Alexander Lobman	Both private and public health institutions have been trying to find ways to combat the growing opioid crisis. One suggested strategy has been to identify drug-seeking behavior because those individuals should be more likely to overdose. Drug-seeking behaviors and its relationship to opioid overdoses was examined using a dataset generated by the Centers for Health Innovations, which has access to Cerner's Electronic Healthcare Records. The dataset contained over 500,000 patients who received an Oxycodone prescription from 2012 to 2016. The study found that for specific regions in the US, drug-seeking behaviors did increase the chance of overdose. However, the results do not suggest that implementing policies intended to reduce drug-seeking behavior will have a significant effect on the number of opioid overdoses		source	12
40	Tips for the lazy typist (and other strategies for smart coding)	Melissa R. Pfeiffer	Time and experience are your best tools for developing your SAS coding prowess; experience includes both working on a variety of tasks and working with different programmers. Through our combined 40 years working with SAS, we have worked on a wide assortment of projects with different data management needs and collaborators. Through these experiences we have learned to employ techniques that reduce the likelihood of introducing errors into our coding, help identify errors when they do appear, and generally make our programming lives easier. These tips include strategies for dealing with a growing number of user-defined formats, employing keyboard macros and `%INCLUDE` statements for commonly used code, establishing standardized naming conventions, using arrays and do loops to cut down on coding, and cleaning up our "WORK" space. We all become better programmers when we share tips and strategies; our hope is that the suggestions we share expand your programming repertoire	user-defined format, excel, do over, do loop, retain, character variable, where statement, array	source	9
41	Understanding general trends in Permanent Visa Applications and Predicting Visa Decisions using SAS Enterprise Miner	Arun Teja Baireddlapalli Krishna Reddy			source	12
42	Using a Picture Format to Create Visit Windows	Richann J. Watson	Creating visit windows is sometimes required for analysis of data. We need to make sure that we get the visit/day in the proper window so that the data can be analyzed properly. However, defining these visit windows can be quite cumbersome especially if they have to be defined in numerous programs. This task can be made easier by applying a picture format, which can save a lot of time and coding. A format is easier to maintain than a bunch of individual programs. If a change to the algorithm is require d, the format can be updated instead of updating all of the individual programs containing the visit definition code	if-then-else, proc format	source	5
43	Using SAS DDE, SAS Macro and Excel VBA Macros to Create Automated Graphs for Multiple Excel Reports	Farah Salahuddin		excel, xls, macro definition	source	36
44	Visual Storytelling - The Art of Communicating Information with Graphics	Kirk Paul Lafler	Telling a story with facts and data alone can be boring, while stories told visually engage. It's been said that humans tend to process visual elements many times faster than reading words. The data analysis process involves the gathering and collection, cleansing, transforming, modeling and storytelling of data from various sources. The objective is to discover, evaluate, understand and derive useful information from the data to support decision-making. Unfortunately, data analysts sometimes omit a very crucial step - the development of a visual narrative about the data analysis process and outcome. This omission not only fails to bring context, insight and interpretation of the data analysis results in a clear and precise way, it neglects to bring meaning, relevance and interest to the "key" points of the data analysis results	bar chart, scatter plot, proc sgpanel, histogram, proc sgplot, box plot, vertical bar chart, proc freq, bubble plot, data science, sas base, class variable, correlation	source	16
45	Wow! You Did That Map With SAS?! Round II	Louise S. Hadden	This paper explores the creation of complex maps with SAS software. This presentation incorporates explores the wide range of possibilities provided by `SAS/GRAPH` and polygon plots in the SG procedures, as well as replays, overlays in both `SAS/GRAPH` and SG procedures, and annotations including Zip Code level processing. The more recent GfK maps now provided by SAS, that underlie newer SAS products such as Visual Analytics as well as traditional SAS products, will be discussed. The pre-production `SGMAP` procedure released with Version 9.4 Maintenance release 5 will be discussed in context	proc step, proc gmap, proc sgplot, sample, sas base, dataset, macro variable, proc sql	source	30

Back to Conf go 2019 go 2017

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.