Output Delivery System (ODS) graphics, produced by SAS® procedures, are the backbone of the Graph Template Language (GTL). Procedures such as the Statistical Graphics (SG) procedures dynamically generate GTL templates based on the plot requests made through the procedure syntax. For this paper, these templates will be referenced as procedure-driven templates. GTL generates graphs using a template definition that provides extensive control over output formats and appearance. Would you like to learn how to build your own template and make customized graphs and how to create that one highly desired, unique graph that at first glance seems impossible? Then it’s a Great Time to Learn GTL! This paper guides you through the GTL fundamentals while walking you through creating a graph that at first glance appears too complex but is truly simple once you understand how to build your own template.
We are inundated with sea of data and we need to be able to make a point using a powerful story that leaves a lasting impact on viewers. In this presentation, I will present how to combine HTML5 and SAS to making Data into Something People Can See and get a clear meaning of the data. Users will learn how to prepare SAS data for dynamic visualization. Since we are leveraging modern browsers for front-end of this data visualizations, users do not need any servers or admin rights to produce and deliver these dynamic cool data visualizations. Customers of the data visualization will need only a readily available web browser to consume the data visualization. I will show how to use SAS to prepare a JSON data file and create a powerful data visualization using the HTML 5 canvas. Also I will show how to combine these powerful charts with a HTML based presentation library such as Reveal.JS. By the end of the course, users will know how to move beyond the static power point slide or excel spreadsheets and visually communicate your data story to the world without any boundary or software tools.
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. Axes labels and value labels should be used appropriately, but sparingly. The default colors in SAS can be distracting; better use of color can enhance the readability of graphs. Graph colors may be chosen based on a company brand, journal requirements, or other objective, including accessibility for color-blind persons. Color may also be used to highlight a particular data point. These simple steps can improve your SAS graphs in order to better communicate your story.
Adding layers of organization to reports is a beneficial communication tool, particularly for lengthy reports or for client-programmer relationships where in person meetings are not frequent. In my 2018 WUSS talk and paper, I reviewed options for adding Table of Contents and Hyperlinks to one’s RTF reports. It also introduced the concept of using RTF code within SAS to make use of Microsoft Word features.
One reason users may prefer to use PDF destination over RTF output is the automatic left hand outline of PROCedures, which one can click through. Fortunately for those that want to or be required to have their reports to be in Microsoft Word, the heading feature allows one to create a similar content pane. This pane can be clicked through and tracks where one is in the document as one moves through. This talk will show users how to add multiple layers of headings to one’s RTF reports. Users should be familiar with ODS RTF destination to attend this talk and the content is for users of SAS 9.4 or higher.
Scott Leslie and Donavin Drummond
Health-care professionals who successfully turn data assets into data insights can realize benefits such as reduced costs, healthier patients and higher satisfaction rates. Some insights are discovered by comparing your data to national benchmarks while others are found dissecting your data using a different viewpoint or approach. Using SAS® Visual Analytics we demonstrate effective techniques for displaying health plan quality performance metrics and assisting users in making inferences about their data. Visualizing data enables decision makers and data analysts to uncover new associations, engage users, and communicate their messages successfully.
In clinical research, survival analysis plays a major role, and the choice of visual tool is typically the survival plot. This paper will cover ways to enhance survival plots in SAS®. The primary focus will be on the Kaplan-Meier product-limit survival curve for a right-censored survival model. We will discuss the modification of the PROC LIFETEST graph template to customize Kaplan-Meier plots following a well-known approach by Warren Kuhfeld and Ying So. Another approach utilizes a combination of ODS OUTPUT statements for PROC LIFETEST or PROC PHREG, followed by DATA steps to create a dataset that can be graphed via PROC SGPLOT.
SAS© provides some powerful, flexible tools for creating tabular reports, like PROC REPORT and PROC TABULATE. 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 control and that’s where the data step Report Writing Interface can help.
The Report Writing Interface (RWI) is just a fancy way of saying you’re using the ODSOUT object in a data step. Method calls on this object allow you to 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 your data step created output can have fonts, sizes, colors, backgrounds and borders to make your report look just like you want. And, great news: you can use the RWI with the ODS EXCEL destination!
This presentation will quickly cover some of the basics of using the ODSOUT object and then walk through some of the techniques to create output in Excel from a data step using the RWI with ODS EXCEL destination.