Month: June 2016

One-Stop Tool for Viewing Subsurface and Well Trajectories in 3D

If you are a petroleum engineer and you have Spotfire installed on your computer, you’re further ahead than you realize. Sound strange? Here’s the hint:

Spotfire can be your tool for:

  • visualizing and analyzing 3D subsurface map and
  • diagramming well trajectory

And you can do all that in one environment.

Now, I’m guessing you thought that those analyses could only be done with some high-end expensive software from say Schlumberger, Halliburton, IHS, or Baker Hughes; not anymore.

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Two Data Sources: Exchanging One for Another

Sometimes data doesn’t come in the format we wish it would. I found myself in such a situation when I was working on a data set for a template that explores Olympic history.

Specifically, I was working with data on murdered Olympians. I had the name of the athlete, their gender, their country, their sport, and the notes on their death. Frustratingly though, the country came in the form of the ioc country code. I knew that meant it would be a pain to create a relation from this data to all the other data I had brought in for my Olympic analysis. And besides, ioc codes are not intuitive to read.

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Even Me: How to Modify Your Excel Data to Easily Slide into Spotfire

Yup. That was my last time. Popping a stack of Fortran cards into a reader and watching my do-loop generate infinite amounts of paper, chunking madly out of a central machine while everyone hovered and watched and sighed because their cards had yet to run. Yup, that’s my last recollection of computer technology. Humiliation. I was a chemistry major and we had to do one computer class. I did it and was done.

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Tutorial: How to Have Selectable Countries or Regions on a Map Chart

Map chart in Spotfire is one of the coolest native tools available. The ability to have spatial data makes your analysis not only look comprehensive but also gives users another angle to observe the data from. That being said, map charts can be finicky. Having point data is one thing, but what if you want to be able to highlight regions of a map or highlight whole countries? I found myself up against this exact issue when I was working on a recent Spotfire template that looks at Olympic historical data.

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Parsing Text: Numerical Data from Historical Accounts

In a recent article on Data Shop Talk, I introduced an interesting set of analyses on Olympic data. One of the analyses focused on Olympians who had died or gone missing due to war. The data from Sports-Reference.com came in a csv file format with the following information: Athlete, Gender, Country, Sport, and Notes. This was a great start to my analysis but there was something critical missing in order to properly illustrate the timeline of deaths: the date of death.

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The Olympics: Stories You Never Knew

The Rio 2016 Summer Olympics have been getting a lot of bad press recently, but these aren’t the first Olympics to be drama filled. Consider the 1956 Olympics in Melbourne, Australia when Cold War tensions led to the withdrawal of multiple countries and the defection of many Olympic athletes (especially from Hungary). Or how about the 1916 Olympics that were originally planned to happen in Berlin, but never did because of the outbreak of World War I? 

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Comparing Average with Values in Same Plot in Spotfire

A common task for an analyst is to plot averaged values in the same chart against quantities of compared variables in order to show the deviation. For instance, a visual representation of salaries of a certain job function in 3 US cities in the last year, can include the US national average to inform viewers of the departure of each city salary from the national average.

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Data Science Design Pattern: Train & Predict

Spotfire is a great tool that lets you run asynchronous R code right next to your data and visualizations. This makes for what I like to call the Data Science Trifecta. There’s lots of applications out there that provide the Data Science Trifecta – data, visualizations, and computation – and I prefer Spotfire’s relational data model, snappy visualizations, and embedded R engine. So let’s talk about reusing predictive models in this Trifecta. If you’re eager to try it out, you can grab the template off of Exchange.ai.

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