Category Archives: R computing environment

Import GRASS function console output as data.frame in R

Posted on January 23, 2013 by

In R you can use system calls or the spgrass6 package to run GRASS GIS functions. To do this, you need to run R from within GRASS GIS. This is as simple as starting GRASS GIS and subsequently starting R … Continue reading →

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Multivariate Environmental Similarity Surfaces (MESS) index in GRASS GIS

Posted on November 22, 2012 by

The Multivariate Environmental Similarity Surfaces (MESS) is an index that represents how similar a point in space is to a reference set of points, with respect to a set of predictor variables (Elith et al 2010). The function was first … Continue reading →

Posted in Data handling, GIS, GRASS GIS, R computing environment | Tagged , , , , | 2 Comments

Reading GRASS GIS vector attribute tables into R

Posted on November 9, 2012 by

Linking GRASS GIS and R will give you a very powerful set of geo-spatial analytical tools. The spgrass6 offers a very convenient interface between GRASS GIS and R. You can read more about this package in Bivand, R. 2007. Using … Continue reading →

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A faster way to calculate MESS in R – or – a tribute to Stack overflow

Posted on October 20, 2012 by

Jean-Pierre Rossi introduced a function to calculate the “Multivariate Environmental Similarity Surfaces” in R. Since then, the function has become part of the dismo package, which is a package maintained by Robert J. Hijmans and which offers a whole lot of … Continue reading →

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Integrating Maxent, R and GRASS GIS

Posted on October 13, 2012 by

The maximum-entropy (Maxent) methods is one of the most widely used approaches for species habitat modelling. It has its own dedicated software, the Maxent software (written in java and therefore cross-platform). The software is easy to use and includes fairly … Continue reading →

Posted in GIS, GIS software, GRASS GIS, R computing environment, research tools, statistics | Tagged , , , , , , , , | 4 Comments

A short note on the use of predict with the dismo or raster package

Posted on August 29, 2012 by

R has some great packages for species distribution modelling. One of these packages is the dismo package. Models objects created with one of the various distribution models available in dismo can be used to make prediction for any combination of … Continue reading →

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From attribute table to QGIS style file – step 2

Posted on July 2, 2012 by

This post is a follow up on an earlier post where I described how to use a column with RGB values in an (attribute) table to create mapping symbols for QGIS. The next step is to create the qml legend … Continue reading →

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Cross tables in R, some ways to do it faster

Posted on June 21, 2012 by

In R to create a contingency table of the counts of the combination of two variables, I would normally resort to table(). But how fast is it? A question that becomes more relevant when working on large tables and when … Continue reading →

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Creating polar diagrams with confidence intervals in R

Posted on April 24, 2012 by

The function r.polar in GRASS GIS allows you to create simple polar diagrams of e.g., the slope aspect or wind directions. These polar diagrams make it easy to spot directional bias, for example in the distribution of the slope aspect … Continue reading →

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two-minutes video tutorials for R

Posted on April 13, 2012 by

I just came across this blog with (by the time of writing) sixty two-minutes video tutorials on how to do things in R. Especially nice for those who like to learn by watching and listening. But do pay attention while … Continue reading →

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