Ruths.ai has developed an easy-to-use well logging tool that enables the digital diagramming of log curves and tracks within Spotfire:
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.
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.
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?
I’m trying to visualize the spread of the Zika virus in Latin America. I’ve got two data sets:
- PAHO_EPICURVES.CSV, a time series outlining the spread of zik through Latin America
- TM_WORLD_BORDERS.SHP, a shape file with all the countries of the world
Basically this is an “unboxing” of the new KPI chart, so I’ll share some thoughts on the new tool.
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.
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.
Spotfire has everything you need to build your own rules engine and master your data issues. For requirements we created this so that it would work on the web, IronPython was just fine for this task. Basically the workflow is that the data comes in on two tables. We want to inspect an InputTable to see whether it fails the rules contained in a RulesTable.
The deliverability of a system is its ability to deliver gas as a function of pressure. Ruths.ai Well Deliverability tool is developed to assist oilfield operators in determining the flow rates of gas-drive wells using inflow performance relationship (IPR) and tubing performance relationship (TPR) of reservoir, wellbore and production data.