11 Oct

Useful R libraries

Here is a list of useful R libraries for GIS analysis.

Data handling

Provides bindings to Frank Warmerdam’s Geospatial Data Abstraction Library (GDAL).

library(rgdal)

Reading, writing, manipulating, analyzing and modeling of gridded spatial data.
Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles.

library(maptools)

Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.

library(raster)

Classification

Among different statistical function, the useful k-means clustering function is available in this package.

library(stats)

Classification and regression based on a forest of trees using random inputs.

library(RandomForest)

Figure

An implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics.

library(ggplot2)

The rasterVis package complements raster library providing a set of methods for enhanced visualization and interaction.

library(rasterVis)

list

library(SDMTools)
library(rgl)
library(foreign)
library(nortest)
library(Hmisc)
library(plyr)
library(rgeos)
library(adegenet)
library(cluster)
library(spdep)
library(foreign)

To install all the package at once:

list.of.packages <- c("rgdal", "maptools", "raster", "stats", "RandomForest", "ggplot2", "rasterVis", "SDMTools", "rgl", "foreign", "nortest", "Hmisc", "plyr", "rgeos", "adegenet", "cluster", "spdep", "foreign")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

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