# R: advanced statistical package

**About R**

R is a general-purpose, command-line based, environment for working with data. R is based on a functionalapproach to working with vectors and matrices of data. R is a convenient environment for processing, analyzing, and plotting data.

**Soils-Related R Packages**

The 'aqp' (Algorithms for Quantitative Pedology) package was developed to facilitate numerical extensions to classical studies of soil geography, genesis and classification. [CRAN] [R-Forge]

**R in the News**

- NY Times article (2009-01-07)
- Follow-up Blog posting to NY Times article (2009-01-07)
- R in the blog-o-sphere (c/o google)

**Getting Started**

- Paul Geissler's excellent R tutorial
- Dave Robert's Excellent Labs on Ecological Analysis
- Excellent Tutorials by David Rossitier
- Excellent tutorial an nearly every aspect of R (c/o Rob Kabacoff)
- Introduction to R by Vincent Zoonekynd
- R Cookbook
- Data Manipulation Reference
- R time series tutorial
- R Concepts and Data Types presentation by Deepayan Sarkar
- Interpreting Output From
`lm()` - The R Wiki
- An Introduction to R
- Import / Export Manual
- R Reference Cards
- KickStart
- Hints on plotting data in R
- Regression and ANOVA
- Appendices to Fox Book on Regression
- JGR a Java-based GUI for R [Mac|Windows|Linux]
- A Handbook of Statistical Analyses Using R(Brian S. Everitt and Torsten Hothorn)

**Searching for Information**

**R with Geographic Data**

- Geographically weighted regression
- Geostats with geoR
- DCluster, splancs, spdep and spatstat packages available on CRAN
- GRASS6 - R Interface
- Interesting thread on the use of kriging variance

**Misc. Articles**

- Some Reasons NOT to use Excel for Stats
- When Summary Stats Aren't Enough
- Why "dynamite plots" are a bad idea
- Many examples of plotting data in R
- Notes on R
- Scientific Graphing applications
- Ideas on how to make more informative axis in a plot
- Tips on R graphics
- Color space visualization

**Pages in Section**

- Access Data Stored in a Postgresql Database
- Additive Time Series Decomposition in R: Soil Moisture and Temperature Data
- Aggregating SSURGO Data in R
- Cluster Analysis 1: finding groups in a randomly generated 2-dimensional dataset
- Color Functions
- Comparison of Slope and Intercept Terms for Multi-Level Model
- Comparison of Slope and Intercept Terms for Multi-Level Model II: Using Contrasts
- Creating a Custom Panel Function (R - Lattice Graphics)
- Customized Scatterplot Ideas
- Estimating Missing Data with aregImpute() {R}
- Exploration of Multivariate Data
- Interactive 3D plots with the rgl package
- Making Soil Property vs. Depth Plots
- Numerical Integration/Differentiation in R: FTIR Spectra
- Plotting XRD (X-Ray Diffraction) Data
- Using lm() and predict() to apply a standard curve to Analytical Data
- Working with Spatial Data
- Comparison of PSA Results: Pipette vs. Laser Granulometer

## Software

- General Purpose Programming with Scripting Languages
- LaTeX Tips and Tricks
- PostGIS: Spatially enabled Relational Database Sytem
- PROJ: forward and reverse geographic projections
- GDAL and OGR: geodata conversion and re-projection tools
- R: advanced statistical package
- Access Data Stored in a Postgresql Database
- Additive Time Series Decomposition in R: Soil Moisture and Temperature Data
- Aggregating SSURGO Data in R
- Cluster Analysis 1: finding groups in a randomly generated 2-dimensional dataset
- Color Functions
- Comparison of Slope and Intercept Terms for Multi-Level Model
- Comparison of Slope and Intercept Terms for Multi-Level Model II: Using Contrasts
- Creating a Custom Panel Function (R - Lattice Graphics)
- Customized Scatterplot Ideas
- Estimating Missing Data with aregImpute() {R}
- Exploration of Multivariate Data
- Interactive 3D plots with the rgl package
- Making Soil Property vs. Depth Plots
- Numerical Integration/Differentiation in R: FTIR Spectra
- Plotting XRD (X-Ray Diffraction) Data
- Using lm() and predict() to apply a standard curve to Analytical Data
- Working with Spatial Data
- Comparison of PSA Results: Pipette vs. Laser Granulometer

- GRASS GIS: raster, vector, and imagery analysis
- Generic Mapping Tools: high quality map production