Graphically Speaking
Data Visualization with a focus on SAS ODS Graphics![Quick and easy with MODSTYLE](https://blogs.sas.com/content/graphicallyspeaking/files/2012/06/ModStyle12.png)
ODS Graphics components like GTL and SG procedures are designed to work with Styles to create graphs that are effective in the delivery of information and aesthetically pleasing out of the box. You no longer have to tweak the colors to ensure a nice graph. The graph derives all the
![LFT Panels](https://blogs.sas.com/content/graphicallyspeaking/files/2012/06/LFT_Panel_11.png)
CTSPedia.org is a website of Knowledge Base for Clinical and Translational Research. On this site you can find sample graphs for statistical analysis of safety data for Clinical Research. Graphs included in this resource have been submitted by contributors, and include a graph for Liver Function for different tests by treatment.
![Bar chart with response sort](https://blogs.sas.com/content/graphicallyspeaking/files/2012/06/Medal-463x336.png)
A graph in a recent article in Fortune magazine caught my eye. The graph shows the cost of hosting the Summer Olympics over the past eight events. Here is what I termed the "Medal" graph. Now, practitioners of the art of Effective Graphics would likely find some shortcomings in the graph. Clearly
![Broken Y-Axis with SAS 9.2](https://blogs.sas.com/content/graphicallyspeaking/files/2012/06/High_Low1-300x336.png)
In the previous post on Broken Y-Axis, I reviewed different ways to display data as a Bar Chart, where the response values for some categories are many orders of magnitude larger than the other values. These tall bars force the display of other values to be squeezed down thus making it harder to compare
![Broken Y-Axis](https://blogs.sas.com/content/graphicallyspeaking/files/2012/05/Data11.png)
Often we want to display data as a bar chart where a few observations have large values compared to the rest. Comparison between the smaller values becomes hard as the small bars are squeezed by the tall bars. Here is an example data, and a bar chart showing the data. The large values
![Bivariate response graph](https://blogs.sas.com/content/graphicallyspeaking/files/2012/05/Diagram_A7.png)
Recently, I came across an interesting graph showing Euro contries bank exposuro to GIIPS countries, as percent of GNP. Here is the graph: I thought I would see how far I can get in making a similar graph using SAS. I made up some data with response values for a Product x