Speed up matrix median calculation in R

I decided to write this short post after wasting some hours to optimize my R code to make median calculation possible for some million of values. Hope it can help someone else. The problem is easy to explain and to solve. Median is a very expensive operation to compute as it implies ordination of values, therefore sometimes may considerably slow down the code running time.
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My example makes use of a list of matrices, like this:

pippo <- list(matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5),matrix(runif(100000),ncol=5))

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Landscape configuration and composition analysis with GRASS GIS 7

Landscape configuration and composition metrics summarize the structure of the landscape in a determined area and time. These metrics  are closely related to the occurring ecological patterns (e.g. species distribution) and processes (e.g. biogeochemical cycle). As a consequence they are often used as predictors in ecological modelling. Thereby, a researcher who wants to test whether or not the landscape structure has an influence on the studied ecological properties (i.e., species richness, ecosystem functioning, conservation status, energy flow, animal movements, species dispersion, etc), need a tool to derive them from a Land Use or Land Cover (LULC) map.
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Studying spatio-temporal changes in phytoplankton by means of remote sensing

The ocean is teeming with microscopic organisms called phytoplankton. Phytoplankton comprises two main groups: photosynthetic cyanobacteria and the single-celled algae that drift in the sunlit top layers of oceans. They provide food, directly or indirectly for virtually every other marine creature. They emit much of the oxygen that permeates our atmosphere and their fossilized remains, buried and compressed by geological forces, are transformed into oil. In addition, they play a huge role in the cycling of carbon dioxide from the atmosphere to the biosphere and back, cycling that helps to control Earth’s climate [1].air jordan 1

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Processing ERA-Interim dataset in ncdf using GDAL and GRASS GIS

Processing big data efficiently has become a necessity of the hour in ecological and climate change research. Now there are enormous number of public data available online, which demands high level of processing capabilities too.  In this post I will show you how we can process ESA-Interim data which is global atmospheric reanalysis from 1979 to present developed by ECMWF.

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Forest patches change detection using GRASS and R

As reported here, GRASS 7RC1 has been recently released. In this post I will show how to interface GRASS GIS 7 and R to study the shift in broad-leaved forest land use patches between 1990 and 2006 in Italy. The final result is shown in the figure below. You can see how there has not been so much forest patch movement during the last 20 years in Italy. This is probably due to the quite stable forest coverage in this country (from 28.1 to 30.0 % coverage of land area according to The World Bank data).
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GRASS GIS 7.0 RC1 is released

The first release candidate of feature rich GRASS 7.0 is released on January 14, 2015. The new candidate release is an output of 6 years of development after the last release of GRASS 6.4.0. One of the main feature in GRASS 7.0 is its new python user interface replacing the old tcl/tk based GUI.
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Changes and improvements on the latest version can be read here: GRASS 7.0 RC1
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