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== Looking at installed packages ==
== Looking at installed packages ==
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You can see the list of installed R libraries by the library() call
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You can see the list of installed R libraries by the library() call in R
<source lang="r">
<source lang="r">
library();
library();
</source>
</source>
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For example, currently on FarmShare these libraries are installed
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We have a lot of packages already installed, you can ask us to install more, or just install them quickly in your homedir.
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<source lang="sh">
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$ R
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R version 2.15.2 (2012-10-26) -- "Trick or Treat"
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Copyright (C) 2012 The R Foundation for Statistical Computing
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ISBN 3-900051-07-0
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Platform: x86_64-pc-linux-gnu (64-bit)
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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[Previously saved workspace restored]
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> library()
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Packages in library '/usr/lib/R/site-library':
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AMORE                  A MORE flexible neural network package
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Biobase                Biobase: Base functions for Bioconductor
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DBI                    R Database Interface
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GenABEL                genome-wide SNP association analysis
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HilbertVis              Hilbert curve visualization
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Hmisc                  Harrell Miscellaneous
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MCMCpack                Markov chain Monte Carlo (MCMC) Package
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MNP                    R Package for Fitting the Multinomial Probit
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                        Model
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MatchIt                MatchIt
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RColorBrewer            ColorBrewer palettes
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RGtk2                  R bindings for Gtk 2.8.0 and above
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RMySQL                  R interface to the MySQL database
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RODBC                  ODBC Database Access
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RQuantLib              R interface to the QuantLib library
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RSQLite                SQLite interface for R
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Rcmdr                  R Commander
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Rcpp                    Seamless R and C++ Integration
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Rglpk                  R/GNU Linear Programming Kit Interface
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Rmpi                    Interface (Wrapper) to MPI (Message-Passing
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                        Interface)
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Rserve                  Binary R server
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TeachingDemos          Demonstrations for teaching and learning
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VGAM                    Vector Generalized Linear and Additive Models
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XML                    Tools for parsing and generating XML within R
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                        and S-Plus.
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Zelig                  Everyones Statistical Software
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abind                  Combine multi-dimensional arrays
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bayesm                  Bayesian Inference for
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                        Marketing/Micro-econometrics
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bio3d                  Biological Structure Analysis
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bitops                  Functions for Bitwise operations
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caTools                Tools: moving window statistics, GIF, Base64,
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                        ROC AUC, etc.
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cairoDevice            Cairo-based cross-platform antialiased graphics
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                        device driver.
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car                    Companion to Applied Regression
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chron                  Chronological objects which can handle dates
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                        and times
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coda                    Output analysis and diagnostics for MCMC
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colorspace              Color Space Manipulation
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combinat                combinatorics utilities
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cummeRbund              Analysis, exploration, manipulation, and
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                        visualization of Cufflinks high-throughput
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                        sequencing data.
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date                    Functions for handling dates
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digest                  Create cryptographic hash digests of R objects
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eco                    R Package for Ecological Inference in 2x2
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                        Tables
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edgeR                  Empirical analysis of digital gene expression
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                        data in R
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effects                Effect Displays for Linear, Generalized Linear,
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                        Multinomial-Logit, Proportional-Odds Logit
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                        Models and Mixed-Effects Models
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fAssets                Rmetrics - Assets Selection and Modelling
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fBasics                Rmetrics - Markets and Basic Statistics
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fCopulae                Rmetrics - Dependence Structures with Copulas
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fExtremes              Rmetrics - Extreme Financial Market Data
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fGarch                  Rmetrics - Autoregressive Conditional
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                        Heteroskedastic Modelling
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fMultivar              Multivariate Market Analysis
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fOptions                Basics of Option Valuation
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fPortfolio              Rmetrics - Portfolio Selection and Optimization
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                        - ebook available at www.rmetrics.org
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fTrading                Technical Trading Analysis
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g.data                  Delayed-Data Packages
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gdata                  Various R programming tools for data
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                        manipulation
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genetics                Population Genetics
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ggplot2                An implementation of the Grammar of Graphics
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gmodels                Various R programming tools for model fitting
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gplots                  Various R programming tools for plotting data
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gregmisc                Gregs Miscellaneous Functions
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gtools                  Various R programming tools
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haplo.stats            Statistical Analysis of Haplotypes with Traits
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                        and Covariates when Linkage Phase is Ambiguous
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happy                  Quantitative Trait Locus genetic analysis in
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                        Heterogeneous Stocks
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hdf5                    HDF5
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its                    Irregular Time Series
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latticeExtra            Extra Graphical Utilities Based on Lattice
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limma                  Linear Models for Microarray Data
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lme4                    Linear mixed-effects models using S4 classes
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lmtest                  Testing Linear Regression Models
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mapdata                Extra Map Databases
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mapproj                Map Projections
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maps                    Draw Geographical Maps
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misc3d                  Miscellaneous 3D Plots
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mnormt                  The multivariate normal and t distributions
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msm                    Multi-state Markov and hidden Markov models in
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                        continuous time
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multcomp                Simultaneous Inference in General Parametric
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                        Models
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multicore              Parallel processing of R code on machines with
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                        multiple cores or CPUs
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mvtnorm                Multivariate Normal and t Distributions
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plyr                    Tools for splitting, applying and combining
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                        data
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proto                  Prototype object-based programming
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psy                    Various procedures used in psychometry
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pvclust                Hierarchical Clustering with P-Values via
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                        Multiscale Bootstrap Resampling
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qtl                    Tools for analyzing QTL experiments
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quadprog                Functions to solve Quadratic Programming
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                        Problems.
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qvalue                  Q-value estimation for false discovery rate
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                        control
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randomForest            Breiman and Cutlers random forests for
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                        classification and regression
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relimp                  Relative Contribution of Effects in a
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                        Regression Model
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reshape                Flexibly reshape data.
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reshape2                Flexibly reshape data: a reboot of the reshape
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                        package.
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rggobi                  Interface between R and GGobi
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rgl                    3D visualization device system (OpenGL)
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rkward                  Provides functions related to the RKWard GUI
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rkwardtests            RKWard Plugin Test Suite Framework
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rms                    Regression Modeling Strategies
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robustbase              Basic Robust Statistics
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rotRPackage            Statistical functions needed by the OpenTURNS
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                        project, see www.openturns.org
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rsprng                  R interface to SPRNG (Scalable Parallel Random
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                        Number Generators)
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sandwich                Robust Covariance Matrix Estimators
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slam                    Sparse Lightweight Arrays and Matrices
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sm                      Smoothing methods for nonparametric regression
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                        and density estimation
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sn                      The skew-normal and skew-t distributions
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snow                    Simple Network of Workstations
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sp                      classes and methods for spatial data
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stabledist              Stable Distribution Functions
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stringr                Make it easier to work with strings.
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strucchange            Testing, Monitoring, and Dating Structural
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                        Changes
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timeDate                Rmetrics - Chronological and Calendar Objects
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timeSeries              Rmetrics - Financial Time Series Objects
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tkrplot                TK Rplot
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tseries                Time series analysis and computational finance
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timeSeries              Rmetrics - Financial Time Series Objects
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tkrplot                TK Rplot
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tseries                Time series analysis and computational finance
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vcd                    Visualizing Categorical Data
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zoo                    S3 Infrastructure for Regular and Irregular
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                        Time Series (Zs ordered observations)
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Packages in library '/usr/lib/R/library':
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KernSmooth              Functions for kernel smoothing for Wand & Jones
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                        (1995)
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MASS                    Support Functions and Datasets for Venables and
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                        Ripleys MASS
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Matrix                  Sparse and Dense Matrix Classes and Methods
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base                    The R Base Package
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boot                    Bootstrap Functions (originally by Angelo Canty
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                        for S)
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class                  Functions for Classification
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cluster                Cluster Analysis Extended Rousseeuw et al.
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codetools              Code Analysis Tools for R
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compiler                The R Compiler Package
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datasets                The R Datasets Package
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foreign                Read Data Stored by Minitab, S, SAS, SPSS,
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                        Stata, Systat, dBase, ...
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grDevices              The R Graphics Devices and Support for Colours
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                        and Fonts
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graphics                The R Graphics Package
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grid                    The Grid Graphics Package
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lattice                Lattice Graphics
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methods                Formal Methods and Classes
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mgcv                    Mixed GAM Computation Vehicle with GCV/AIC/REML
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                        smoothness estimation
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nlme                    Linear and Nonlinear Mixed Effects Models
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nnet                    Feed-forward Neural Networks and Multinomial
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                        Log-Linear Models
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parallel                Support for Parallel computation in R
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rpart                  Recursive Partitioning
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spatial                Functions for Kriging and Point Pattern
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                        Analysis
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splines                Regression Spline Functions and Classes
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stats                  The R Stats Package
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stats4                  Statistical Functions using S4 Classes
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survival                Survival Analysis
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tcltk                  Tcl/Tk Interface
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tools                  Tools for Package Development
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utils                  The R Utils Package
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</source>
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== Which R are you using? ==
== Which R are you using? ==

Revision as of 12:45, 10 July 2014

Contents

Looking at installed packages

You can see the list of installed R libraries by the library() call in R 

library();

We have a lot of packages already installed, you can ask us to install more, or just install them quickly in your homedir.

Which R are you using?

Try run 

 which R

Try run 

 R --version

Installing CRAN Packages

Most CRAN packages can be installed per-user by running install.packages() in an interactive session: 

install.packages("package_name", dependencies = TRUE)

R initially attempts to install to /usr/local/lib/R, but will prompt for the creation of a library subdirectory in ~/R (if necessary) and fall back to installation there when the initial attempt fails. If your package requires dependencies available from the standard Ubuntu repositories you can submit a HelpSU ticket requesting installation. We can install from the Debian/Ubuntu package repositories or into the shared FarmShare fs.

You can, of course, install R libraries into any arbitrary path and just add that path to your R env. That will probably break the next time R is upgraded to a new version, since your packages are built with the older version.

NOTE: when you install a package in corn, it will be available to you in Barley.

R Sample Job

Here's an example R file that generates a large array, fills it with some random numbers, then sleeps for 5mins. This happens to use up almost exactly 8GB of RAM.

Save this as 8GB.R: 

x <- array(1:1073741824, dim=c(1024,1024,1024)) 
x <- gaussian()
Sys.sleep(300)

Here's an example SGE submit script that runs that R file. 

#!/bin/bash

# use the current directory
#$ -cwd
#$ -S /bin/bash

# mail this address
#$ -M chekh@stanford.edu
# send mail on begin, end, suspend
#$ -m bes

R --vanilla --no-save < 8GB.R

You can submit it with just 

 qsub r_test.script

Here are the output files that I get, one from stderr, one from stdout 

$ cat r_test.script.o497 
R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.


> x <- array(1:1073741824, dim=c(1024,1024,1024)) 
> x <- gaussian()
> Sys.sleep(300)
>


In the mail that you get about the ending of the job, the maxvmem number is actually incorrect, it is a known bug in this version of SGE. The R script on this page actually uses 8GB of vmem.

Another R Sample Job

R script, let's call it R-rjags.R 

print("Hello World")
library(rjags)
#this just loaded some settings from that library
print("Finished")

Job script, let's call it R-jags.submit.script 

#!/bin/bash

# use the current directory
#$ -cwd
#$ -S /bin/bash

# mail this address
#$ -M chekh@stanford.edu
# send mail on begin, end, suspend
#$ -m bes

R --vanilla --no-save < R-jags.R

Submit it to the test queue with a small memory requirement: 

 qsub -l mem_free=200M -l testq=1 R-jags.submit.script


Looking at the output files, it errored out because R can't find the package rjags. You have two alternatives:

  • include the R library from /mnt/glusterfs/software
  • use modules to specify the full R install from /mnt/glusterfs/software

The first way, you would add this line to your R script: 

 .libPaths(c("/mnt/glusterfs/software/free/R-2.15.0/lib/R/library", "/usr/lib/R/library"))

The second way, your script will look like this: 

$ cat R-jags.submit.script
#!/bin/bash

# use the current directory
#$ -cwd
#$ -S /bin/bash

# mail this address
#$ -M chekh@stanford.edu
# send mail on begin, end, suspend
#$ -m bes

eval `tclsh /mnt/glusterfs/software/free/modules/tcl/modulecmd.tcl sh autoinit`
module load R-2.15.0
R --vanilla --no-save < R-jags.R

Links

Some other departments have some other more detailed examples:

building our local R

Here's how I usually do it.

2014-07-10

R 3.1.1 released today, I compiled it as chekh on corn40 (Ubuntu 13.10) 

R is now configured for x86_64-unknown-linux-gnu

  Source directory:          .
  Installation directory:    /usr/local

  C compiler:                gcc -std=gnu99  -g -O2
  Fortran 77 compiler:       gfortran  -g -O2

  C++ compiler:              g++  -g -O2
  C++ 11 compiler:           g++  -std=c++11 -g -O2
  Fortran 90/95 compiler:    gfortran -g -O2
  Obj-C compiler:	     gcc -g -O2 -fobjc-exceptions

  Interfaces supported:      X11, tcltk
  External libraries:        readline, ICU, lzma
  Additional capabilities:   PNG, JPEG, TIFF, NLS, cairo
  Options enabled:           shared R library, shared BLAS, R profiling

  Recommended packages:      yes
  • make
  • write /farmshare/software/mf/saucy/r/3.1.1.lua

lapack issues

If you see messages like: 

  unable to load shared object '/usr/lib/R/modules//lapack.so':

most likely you're mixing R versions and libraries.

Double check that you are not setting R library path to point to directories with older libraries.

This test script should run fine if you have everything set correctly 

$ cat lapack.r 
data(iris)
zz = lm(Sepal.Length ~., data = iris) 
summary(zz)

$ R --no-save < lapack.r
Retrieved from "https://web.stanford.edu/group/farmshare/cgi-bin/wiki/index.php/R"
Toolbox
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