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In our wrapr pipe RJournal article we used piping into ggplot2 layers/geoms/items as an example.

You want to invest in artificial intelligence (AI) and machine learning to take advantage of the wealth of available data at your fingertips. You know you’re going to need help. But rapid change, vendor churn, hype, and jargon make it hard to choose an AI vendor.

At: Temple University Location: Philadelphia, PAWeb: www.temple.eduPosition: Faculty Positions (Assistant/Associate/Full Professor)

The third release of the binb package just arrived on CRAN, and it comes with a new (and very crispy) theme: Monash. With that we are also thrilled to welcome Rob Hyndman as a co-author.

My prior post on animated flow in the Delaware River contained the last work I performed on my previous lap top just before it gave up the ghost.  When a colleague asked me to share it, I was distressed to realize that the script was lost.  I decided to recreate the script from scratch and to fix one the limitations of the older version by making it easily transportable to other river systems.  Specifically, all the USGS gage numbers, drainage areas, and river miles are contained in a .csv file.  To apply the new script to the different river, I just need to populate the .csv with local information.  I’m glad I did that, since there were several high flow events at other rivers that I wanted to inspect using this script.

What was the largest dataset you analyzed / data mined?

“If you will believe in your heart and confess with your lips, surely you will be saved one day” – The Mountain Goats paraphrasing Romans 10:9