- Android botany climate change Data handling Data sources decision support tool forest GIS GIS software GRASS GIS history LibreOffice / OpenOffice Mobile tools Modelling online mapping Open access Operating system QGIS R computing environment research tools Software release Spatialite SQLite statistics tools Ubuntu Uncategorized vegetation
May 2013 M T W T F S S « Apr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Meta
Tag Archives: R
Import GRASS function console output as data.frame in R
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
Posted in GIS, GRASS GIS, R computing environment
Tagged data I/O, function output, GRASS GIS, R, tutorial
Leave a comment
Calculating the raster cell area of an unprojected raster layer
What if you get a raster layer with number of people per raster cell, like for example the population layer from Afripop, and you want to convert it to a population density layer? Well, obviously, you need to divide the … Continue reading
Posted in GIS, GIS software, GRASS GIS, research tools, tools
Tagged GRASS GIS, R, raster, surface area
Leave a comment
Multivariate Environmental Similarity Surfaces (MESS) index in GRASS GIS
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 add on, GRASS GIS, MESS similarity index, R, scripting
2 Comments
Reading GRASS GIS vector attribute tables into R
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
Posted in GIS, GRASS GIS, R computing environment
Tagged attribute table, data export, data import, GRASS GIS, R, spgrass6
1 Comment
A faster way to calculate MESS in R – or – a tribute to Stack overflow
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
Integrating Maxent, R and GRASS GIS
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
Better winGRASS 7 with R-integration
If you are a Window user and are using or want to use R and GRASS in combination, you will be happy to by this announcement on the GFOSS blog that there is now a better integration of WinGRASS 7 … Continue reading
A short note on the use of predict with the dismo or raster package
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
Posted in Modelling, R computing environment
Tagged bioclim, dismo, glm, R, spatial models, suitability modelling
Leave a comment
Cross tables in R, some ways to do it faster
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
Posted in Data handling, R computing environment
Tagged contingency table, cross table, data.table, multicore, parallelization, R, speed, table, tapply
2 Comments
two-minutes video tutorials for R
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
