Introduction to

Damaris ZurellDynamic MacroecologySwiss Federal Research Institute WSLdamaris.zurell@wsl.ch

http://www.r-project.org/

R is a tool …

Data manipulation

y ~ x Data modelling

Data visualisation

R is a tool …

Data manipulation

y ~ x Data modelling

Data visualisation

• Integrating different data sources

• Aggregating data, disintegrate, transform ...

• Statistical modelling• Numeric simulations

• Visualising models• Make your own graphics

R is an environment

The R environment: „more than an incremental accretion of very specific and inflexible tools“

„fully planned and coherent system“

R history

• First, there was S – developed in 1976 byJohn Chambers in Bell Laboratories at AT&T, as programming language for statistics, stochastic simulation, and graphical display

• 1988, commercial implementation in S-PLUS (Insightful Corp.)

• 1992, Ross Ihaka and Robert Gentleman start free implementation R under the GNU General Public License, mainly for teaching purposes

R history

• 1997, founding of R Development Core Team (abbrev: R Core Team) with today 20 persons from science and economy

• 1998, founding of Comprehensive R Archive Network (CRAN) – today >4000 additional packages

• 2000, first version completely compatible with S : R-1.0.0

R pros R cons• Open Source, on many operating

systems• „at the pulse of science“ – new

methods by scientists/developers implemented in R and available as packages

• Publication ready graphics• Excellent for simulations,

programming, computer intensive analyses, automating

• Best option for statistical computing

• Active user community: help by R Core Team, R-Help mailing list, fast bug-fixing

R pros R cons• Open Source, on many operating

systems• „at the pulse of science“ – new

methods by scientists/developers implemented in R and available as packages

• Publication ready graphics• Excellent for simulations,

programming, computer intensive analyses, automating

• Best option for statistical computing

• Active user community: help by R Core Team, R-Help mailing list, fast bug-fixing

• no fancy graphical user interface, bulky – steep learning curve for newbies, high beginner‘s frustration

• Easy to make mistakes• Computation of big data sets is

limited by RAM• „Many ways lead to Rome“

R is …

• An interpreted programming language – Commands are executed immediately

• Data types: empty values, numerical, logical, character• Data structures/object types: scalar, vector, matrix, array,data

frame, list• During one session, all objects are stored in your workspace• built-in and self-defined functions

R is plain

R is plain

Command line language: This is the prompt:>All commands follow after the prompt

R is a great calculator• Simple algebra

> 2+24

• Assign your results to a variable> X <- 2+2 # assignment operator „<-“> x^216

• Vector based calculations> mass<- c(10,13,6) # 3 Massen> acceleration <- c(2.2,1.7,3.1)> (force <- mass * acceleration )22.0 22.1 18.6

R is a great calculator• Simple statistics

> (x <- sample (1:20,10))4 15 12 14 18 3 9 20 19 16> mean(x)13> sd(x)5.981453

• Set operationsunion intersect setdiff

• Advanced statisticspbinom(40,100,0.5) # coin toss: is the coin unbiased?0.02844397(pshare <- pbirthday(18,366,coincident=2))0.3461382

R is a numeric simulator

• Built-in functions for common probability distributions

• e.g. simulate 10 000 pseudo-random numbers from 100 coin tosses– How often do you get heads?

> heads<-rbinom(10000,100,0.5)> hist(heads)

R Probability distributions

functions: d (density) probability density functionp (probability) cummulative distribution functionQ calculate quantilesR draw random numbers

Examples:Normal dnorm pnorm qnorm rnorm

Binomial dbinom pbinom …

Poisson dpois ..

R Probability distributions? distributions

Function Distribution

_beta() Beta

_binom() Binomial

_cauchy() Cauchy

_chisqu() χ2

_exp() Exponential

_f() F

_gamma() Gamma

_geom() Geometric

_hyper() Hypergeometric

_logis() Logistic

_lnorm() Lognormal

_multinom() Multinomial

_nbinom() Negative binomial

_norm() Normal

_pois() Poisson

_signrank() Wilcox signed rank statistic (One sample case)

_t() T

_unif() Uniform

_weibull() Weibull

_wilcox() Wilcox signed rank statistic (Two sample case)

R accepts all kinds of data sources

• Files (text, binary, data sets from other statistic programs)> Example <- read.csv(“example.csv",header=T)> example2 <- read.table(“example2.txt",header=T)

Cclipboard > cohesion<-read.table(file="clipboard",sep="\t",header=T)

• Database > library(RODBC)

> mdbConnect<-odbcConnectAccess("GPDDdist")> sqlTables(mdbConnect)

• Web > con <- url('http:/anywebsite.com/test.txt')> example3 <- read.table(con, header=T)

• R Objects (binary)> load(“example.RData")

R writes to all kinds of data sources

• to files> write.csv(example,“example.csv")> write.table(example,“example2.txt",row.names=F)

• to the clipboard> write.table(CORMAT,file="clipboard",sep="\t",col.names=NA)

• to data bases> channel <- odbcConnect("test")> sqlSave(channel, USArrests, rownames = "state", addPK=TRUE)> close(channel)

to R Objects> save(example3,“example.RData")

R visualising

• Many graphic functions are generic – they respond „intelligently“ to different object types> plot(iris)> plot(Petal.Length,Petal.Width, pch=as.numeric(Species))

R visualising

• Many graphic functions are generic – they respond „intelligently“ to different object types> boxplot(iris)> boxplot(Petal.Length~Species,data=iris,ylab="Petal.Length")

R visualising

• R Graph Gallery: http://gallery.r-enthusiasts.com/thumbs.php

R statistical modelling

• Linear model> fm <- lm(y ~ x, data=dummy)> summary(fm)Call:lm(formula = y ~ x, data = dummy)

Residuals: Min 1Q Median 3Q Max -4.3400 -1.7353 -0.2107 1.4644 4.8445

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.9150 1.2155 1.575 0.133 x 0.8581 0.1015 8.457 1.1e-07 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.617 on 18 degrees of freedomMultiple R-squared: 0.7989, Adjusted R-squared: 0.7877 F-statistic: 71.52 on 1 and 18 DF, p-value: 1.102e-07

R statistical modelling

And much more ...

Dormann & Kühn (2009): Angewandte Statistik für die biologischen Wissenschaften.

R geostatistical analyses

• variograms, Kriging etc.

www.mathworks.de

Hengl 2009

R as programming language> hi.there <- function() {+ cat("Hello World!\n")+ }> hi.there()Hello World!

R as programming language> hi.there <- function() {+ cat("Hello World!\n")+ }> hi.there()Hello World!

• Built your own function to keep your code tidy

• Built „new“ functions (and write packages)

• Dynamic models …

R extensions

• Integrate other source codes

• Batch processing

• Call from terminal

R GUIs

http://www.rcommander.com/

http://rstudio.org/

R Literature

• http://www.r-project.org/– Manuals– „Contributed

Documentation“