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Introduction to R Tara Jensen National Center for Atmospheric Research Boulder, Colorado USA

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Presentation on theme: "Introduction to R Tara Jensen National Center for Atmospheric Research Boulder, Colorado USA"— Presentation transcript:

1 Introduction to R Tara Jensen National Center for Atmospheric Research Boulder, Colorado USA

2 R Exercises  Find sample data and R scripts at:  ftp://ftp.ncmrwf.gov.in/pub/outgoing/rag hu/6WVMW/Tutorial/Day1/R-tutorial ftp://ftp.ncmrwf.gov.in/pub/outgoing/rag hu/6WVMW/Tutorial/Day1/R-tutorial  Download to directory on your computer  Start R  Open intro2R.2014wmo.R

3 What is R?  A statistical programming and graphics language  In part, developed from the S Programming Language from Bell Labs (John Chambers)  Created to:  Allow rapid development of methods for use in different types of data.  Require small amounts of system resources

4 Why R?  R ~ the dominant language in the statistical research community.  R is Open Source and free.  Runs on most operating systems  Nearly 2,400 packages contributed.  Packages and applications in nearly every field of science, business and economics.  See R Notes, R Journal and Journal of Statistical Software.  More than 100 books with accompanying code  Very large, active user base.  Many default parameters are chosen, but users retain complete control.

5 Why not R?  NCL, IDL, Matlab, SAS, … are all viable alternatives to R. If you are a part of an active community of researchers using another language, do likewise.  R may be limited by memory. For verification of large gridded datasets – consider using Model Evaluation Tools (MET)  R is does not produce a compiled executable so may not be desirable to some operational centers

6 The R Community  Developers  R Core Group (20 members), only 2 have left since 1997  Major update in April/October (freeze dates, beta versions, bug tracking,...)  Mailing lists  Help list ~ 150 messages/day, archived, searchable.   5 International Conferences, 2 US, 1 China

7 Everything about R is at Source code Binary compilations (Windows, Mac OS, Linux Documentation ( Main documents, plus numerous contributed. Some in foreign languages.) Newsletter (replaced by R Journal.) Mailing list (Several search engines) Packages on every topic imaginable Wiki with examples Reference list of books using R. ( more than 100) Task Manager

8 Use R with scripts  In Linux - Emacs Speaks Statistics  Provides syntax-based  Object name completion  Key stroke short cuts  Command history  Alt-x R to invoke R with Xemacs.  In Windows, use editor  Added GUI features  R sends a line or highlighted section into R.  Install package with GUIs  Save graphics by point and click.  Mac OS  Similar to Windows with advantages of system calls.

9 R Coding principles  Make verification code transparent and easy to read  Comment and document liberally  Archive your code  Share your code  Label and save your data  Share your data

10 Packages in R  Contributed by people world wide.  Allow scientists or statisticians to push their ideas.  Apply and extend R capabilities to meet the needs of specific communities.  Accompany many statistical textbooks  Accompany applied articles (Adrian Raftery, Doug Nychka, Tilman Gneiting, Barbara Casati, Matt Briggs)

11 R Packages  Mirror must be selected  Packages -> Set CRAN mirror  chooseCRANmirror()  Packages must be installed to call  Packages -> Install Package(s)  install.packages(c("package 1","package 2","package 3", etc.))  Packages must be loaded (aka called into use)  Packages -> Load Package(s)  library(“package1”)  library(“package2”) etc…  Base packages are installed by default  To see what packages are installed  Packages -> Load Package(s)  installed.packages(.Library, priority="package 1")  To see what packages are installed  remove.packages(package1,package2, lib=file.path("path to library" ) Windows or Mac Linux

12 A sample of useful packages  verification  fields (spatial stats)  radiosondes  extRemes  BMA(Bayesian Model Averaging)  BMAensemble  circular  Rsqlite  SpatialVx  Rgis, spatstat (GIS)  ncdf ( support for netcdf files )  rgdal (support for grib1 files)  rNOMADS (support for grib2 files archived by NCEP)  Rcolorbrewer  randomForests

13 Very useful functions in R  q( ) – allows you to exit R – you will then be asked if you would like to save your workspace  ls( ) – shows you the objects in your workspace  rm( ) – allows you to remove an object  system( ) – allows you to call system command from R  help(package or function) – brings up help page  ?(package or function) – brings up a help page  read.fwf – read fixed width format data  read.table – read text file with delimiters

14 More useful functions  aggregate - applies a function to groups of data subset by categories.  apply - incredibly efficient in avoiding loops. Applies functions across dimensions of arrays.  %in% - returns logical showing which elements in A are in B. (e.g A%in%B)  table – create contingency table counts.  boot – apply bootstrap function correctly  par – control everything in a graph  pairs – the most under utilized plot – plots a matrix of 4 columns in a 4x4 plot layout  xyplot (in the lattice package) slightly advance graphic techniques

15 R Exercises  Find sample data and R scripts at:  ftp://ftp.ncmrwf.gov.in/pub/outgoing/raghu/6 WVMW/Tutorial/Day1/R-tutorial  Download to directory on your computer  Start R  Click on on your desktop  type R at command line  Open intro2R.2014wmo.R  Select File -> Open Script -> select intro2R.2014wmo.R  Open in another window using your favorite editory Windows or Mac Linux


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