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• Instructions on how to get started using R
  
  1. The first few steps using R: Where do I start?
     
  2. A very helpful R tutorial
  3. A reference card you will find helpful when computing with R: R reference card
     
  4. A few more simple commands that show you how to create graphs in R, how to read a data set from a file and how to do simple manipulations (you will need to create a directory in your computer where you should have your code and the data set you are working with: in this case, Soil magnesium from below. Plotting and reading in data
     A first example of a spatial data set: MG.txt
  5. A very nice introductory tour of the package we'll use the most the first part of the semester, geoR, can be found at: Intro to geoR
     
  6. Link to SPDEP and a list of functions and data sets
     available here
  7. A quick guide to SPLANCS
     
  8. A useful overview of SPATSTAT package

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• Programs and Data sets:
  
  1. Swiss rain data set (and a detailed description) of the data) and R code to construct basic empirical variograms
  2. Jura data set
  3. R code for fitting theoretical models to sample variograms and a simulated data set (Emp.txt) used as illustration.
  4. R code for Ordinary Kriging and cross-validation
  5. R code for Removing trend and detecting anisotropy and the wheat data set

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  6. R code for exploratory analysis of lattice data. Two data sets: drumlins data(observations are counts) and NC yield data(observations could be assumed to follow a normal distribution)
  7. R code for exploratory analysis of binary data on a lattice. The data set used for illustration contains records of presence and absence of two plant species at a given location for 96 permanent sampling plots: plants data
  8. Using Moran's I to detect trend in the moisture/elevation washita data set (HW#3): Moran_Washita.r
  9. R functions to fit various spatial models to binary data
     

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  10. R function for exploratory point pattern analysis used for the nests data set
  11. Swamp trees data useful for the discussion of the paper by Philip Dixon on Ripley's K function