Graphically Speaking
Data Visualization with a focus on SAS ODS Graphics
I have written several blog posts about longevity, and here is another one related to that topic. Cardiovascular disease (cvd) is one of the more common causes of death, and I was wondering how those numbers have changed over time. Are fewer people dying from cvd, or are more people
With the US census coming in 2020, I've decided to sharpen my skills at graphing census data. And today I'm working on creating a population pyramid chart to analyze the age and gender distribution. Follow along if you'd like to see how to create such a chart ... or jump
Plotting just your data often helps you gain insight into how it has changed over time. But what if you want to know why it changed? Although correlation does not always imply causation, it is often useful to graph multiple things together, that might logically be related. For example, recessions
In the previous Graphically Speaking blog for PROC SGMAP, you used PROC GPROJECT so map regions would match OpenStreetMap and Esri background images. This time, the same British Columbia shapefile is used with: PROC GREMOVE to remove unwanted boundary lines PROC GREDUCE to reduce map data PROC GPROJECT to zoom
You might have noticed I've been trying out SAS ODS Graphics lately, whereas in the past I mainly used SAS/Graph for my samples. In this blog post I step you through my latest fancy SGplot graph - hopefully you'll learn some tips & techniques, as you follow along. (I don't
Many areas of the US are experiencing record low unemployment. This is great at the national level, and also great at a personal level (for example, I now have fewer unemployed friends asking to borrow money!) But just how low is the US unemployment rate, and how does it compare