[R] Life Cycle Assessment with R.

Mike Marchywka marchywka at hotmail.com
Tue Jul 26 12:42:49 CEST 2011

Date: Mon, 25 Jul 2011 19:03:08 +0100
From: jbustosmelo at yahoo.es
To: r-help at r-project.org
Subject: [R] Life Cycle Assessment with R.

Hello everyone,
There's something really important about climate change and how many institutions around the globe are looking for softwares solutions in order to achieve they (and everyone) needs to improve life conditions in all the planet.
Currently, they're many comercial softwares working with this important topic named as: "Life Cycle Assesment", monitoring carbon emition,  but as many of you may know, commercial softwares controlling or managing our planet could be another big mistake.
To sum up briefly, it could be a good idea creating a R package doing Life Cycle Assessment (if has not being created) in order to gain a better understanding and making these important decisions about global warming and how can we as humanity control how Carbon Footprint is measured by commercial or not commercial propouses.
Who knows if there's people working in Life Cycle Assesment (carbon emition) with R? or If there's someone interested in doing a package about it, please let me know!
I explain this here, because of  the R philosophy.Best wishes!José BustosChilean Biostatistician

[ my text below]
Well, generally R packages are more general purpose tools than specific applications such as this- although there may be an iphone save the world app LOL.  
I have no idea but usually the issue here is getting the required 
data in an open form. Many govt agencies think excel is "open" and that you would not want
to do an analysis that wasn't supported in such popular software. This comes up with financial data all the time, I even asked for account information
in csv and I got a reply, "thank you for asking about exporting to excel" and a detailed explanation that was completely irrelevant. Modelling of complicated
systems is often well complicated and predictions about the future involve assumptions that are often made to suit the needs of the immediate
analyst. On topics which involve money or emotion, getting unbiased analysis is impossible and getting data can be very difficult.  This list is
not designed for advocacy  or even discussion of analysis results but ability to get data in form usable by R may be or more general interest
to those seeking help on R.


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