Tag: ODS Graphics

Data Visualization
Sanjay Matange 0
PharmaSUG 2012 update

PharmaSUG 2012 conference drew to a close today, concluding two and a half days packed with papers, presentations, posters, hands-on demos and super demos by SAS staff.  While the weather outside was a bit chilly from time to time, the conference what hopping with many user papers on how to

Data Visualization
Sanjay Matange 2
Let them eat pie

ODS Graphics system was initially motivated by the need for high quality graphs for SAS Base, STAT, and other analytical procedures.  Use of SG Procedures, ODS Graphics Designer and GTL by users too has initially focused on analytical graphs.  But just like wheels on carryon bags that started for the specific needs of flight

Data Visualization
Sanjay Matange 1
High resolution graphs

Creating a graph that looks nice, with readable, high resolution fonts is important and should be easy to do.  With SG procedures and GTL, this is easy to do with a simple option, but not the default. Creating a high resolution (image) for a graph consumes higher system resources.  When working on a graph,

Data Visualization
Sanjay Matange 4
Beer, diapers and heat map

The parable of beer and diapers is often related when teaching data mining techniques.  Whether fact or fiction, a Heat Map is useful to view the claimed associations.  A co-worker recently enquired about possible ways to display associations or dependency between variables.  One option is to show the dependency as a node

Data Visualization
Pratik Phadke 11
Calendar Heatmaps in GTL

Calendar Heatmaps are an interesting alternative view of time-series data. The measured value is displayed as color mapped cells in a calendar. Calendar Heatmaps can be easily created with SAS 9.3 using just the HEATMAPPARM, SERIESPLOT and BLOCKPLOT statements in GTL and some simple data manipulation. The example below shows

Data Visualization
Sanjay Matange 5
Adding a spark to your data

When viewing time series data, often we only want to see the trend in the data over time and we are not so concerned about the actual data values.  With multiple time series plots, forecasting software can find clusters to help us view series with similar trends. Recently I saw a graph showing the trend of unemployment