Introduction to R programming

Quantitative Data Analysis Working with R

Working with R What is R A computer language, with orientation toward statistical applications Advantages Completely free, just download from Internet Many add-on packages for specialized uses Open source

Getting Started: Installing R Have Internet connection Go to http://cran.r-project/ R for Windows screen, click “base” Find, click on download R Click Run, OK, or Next for all screens End up with R icon on desktop

At http://cran.r-project.org/ Haga clic para modificar el estilo de texto del patrón Segundo nivel ● Tercer nivel ● Cuarto nivel ● Quinto nivel

Downloading Base R Click on Windows Then in next screen, click on “base” Then screens for Run, OK, or Next And finally “Finish” will put R icon on desktop

Rgui and R Consolen ending with R prompt (>) Haga clic para modificar el estilo de texto del patrón Segundo nivel ● Tercer nivel ● Cuarto nivel ● Quinto nivel

The R prompt (>) > This is the “R prompt.” It says R is ready to take your command. Enter these after the prompt, observe output >2+3 >2^3+(5) >6/2+(8+5) >2 ^ 3 + (5)

Installing Packages and Libraries install.packages("akima") install.packages("chron") install.packages("lme4") install.packages("mcmc") install.packages("odesolve") install.packages("spdep") install.packages("spatstat") install.packages("tree") install.packages("lattice")

Installing Packages and Libraries

Installing Packages and Libraries R.version installed.packages() update.packages() setRepositories()

Help help(mean) ?mean help will not find a function in a package unless you install it and load it with library help.search(“aspline”) will find functions in packages installed but not loaded apropos("lm")

Help For help on whole package: help(package=akima) objects(grep("akima",search())) library(“akima”) my.packages <- search() aki <- grep("akima",my.packages) my.objects <- objects(aki)

Help example(mean) demo() demo(package = packages(all.available = TRUE)) demo(graphics) vignette(all=TRUE) V <- vignette("sp") print(V) edit(V)

Maintenance ls() / objects() search() class(a) rm(a,b,c) rm(list=ls())

Maintenance getwd() setwd() source("myprogram.R ") save(list = ls(all=TRUE), file= "all.Rdata") load("all.Rdata") save.image() savehistory()

To cite use of R To cite the use of R for statistical work, R documentation recommends the following: R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. Get the latest citation by typing citation ( ) at the prompt.

Email Support Lists http://r-project.org under "mailing lists" r-help is the most general one Before posting, read: http://www.R-project.org/postingguide.html Send the smallest possible example of your problem (generated data is handy) sessionInfo() will list your computer & R details to cut/paste to your question

Quantitative Data Analysis Programming with R

Basic concepts Code Commands Programs Objects Types Functions Operators

assignment a <- 1 assign("b", 2)

Mathematical operators + - */ ^ arithmetic > >= < <= == != relational ! & logical $ list indexing (the ‘element name’ operator) : create a sequence ~ model formulae

Logical operators ! logical NOT & logical AND | logical OR < less than <= less than or equal to > greater than >= greater than or equal to == logical equals (double =) != not equal && AND with IF || OR with IF xor(x,y) exclusive OR isTRUE(x) an abbreviation of identical(TRUE,x) all(x) any(x)

Mathematical functions log(x) log to base e of x exp(x) antilog of x ex log(x,n) log to base n of x log10(x) log to base 10 of x sqrt(x) square root of x factorial(x) x! choose(n,x) binomial coefficients n!/(x! n−x!) gamma(x) x, for real x x−1!, for integer x lgamma(x) natural log of x

Mathematical functions floor(x) greatest integer <x ceiling(x) smallest integer >x trunc(x) round(x, digits=0) round the value of x to an integer abs(x) the absolute value of x, ignoring the minus sign if there is one signif(x, digits=6) give x to 6 digits in scientific notation

Trigonometrical functions cos(x) cosine of x in radians sin(x) sine of x in radians tan(x) tangent of x in radians acos(x), asin(x), atan(x) inverse trigonometric transformations of real or complex numbers acosh(x), asinh(x), atanh(x) inverse hyperbolic trigonometric transformations of real or complex numbers

Infinity and Things that Are Not a Number Inf (is.finite,is.infinite) 3/0 2 / Inf exp(-Inf) (0:3)^Inf NaN (is.nan) 0/0

Vectors a <- c(1,2,3,4,5) a <- 1:5 a <- scan() a <- seq(1,10,2) b <- 1:4 a <- seq(1,10,along=b) x <- runif(10) which(a == 2)

Plotting functions x<-seq(-10,10,0.1) y<-x^3 plot(x,y,type=‘l’)

Vector functions max(x) maximum value in x min(x) minimum value in x sum(x) total of all the values in x sort(x) a sorted version of x rank(x) vector of the ranks of the values in x order(x) an integer vector containing the permutation to sort x into ascending order range(x) vector of minx and maxx

More functions cumsum(x) vector containing the sum of all of the elements up to that point cumprod(x) vector containing the product of all of the elements up to that point cummax(x) vector of non-decreasing numbers which are the cumulative maxima of the values in x up to that point cummin(x) vector of non-increasing numbers which are the cumulative minima of the values in x up to that point pmax(x,y,z) vector, of length equal to the longest of x y or z, containing the maximum of x y or z for the ith position in eachpmin(x,y,z) vector, of length equal to the longest of x y or z, containing the minimum of x y or z for the ith position in each rowSums(x) row totals of dataframe or matrix x colSums(x) column totals of dataframe or matrix x

functions Geometric mean (p.49) geometric<-function (x) exp(mean(log(x))) Harmonic mean (p.51) harmonic<-function (x) 1/mean(1/x)

Exercises Finding the value in a vector that is closest to a specified value closest<-function(xv,sv){ xv[which(abs(xv-sv)==min(abs(xv-sv)))] } Calculate a trimmed mean of x which ignores both the smallest and largest values trimmed.mean <- function (x) { mean(x[-c(which(x==min(x)),which(x==max(x)))]) }

Sets union(x,y) intersect(x,y) setdiff(x,y) setequal(x,y), is.element(el,set)

Matrices X<-matrix(c(1,0,0,0,1,0,0,0,1),nrow=3) dim(X) is.matrix(X) vector<-c(1,2,3,4,4,3,2,1) V<-matrix(vector,byrow=T,nrow=2) dim(vector) <- c(2,4)

Matrices X<-rbind(X,apply(X,2,mean)) X<-cbind(X,apply(X,1,var))

sweep matdata<-read.table("datasweepdata.txt") cols<-apply(matdata,2,mean) sweep(matdata,2,cols)

lists person <- list() person$name <- "Alberto” person$age <- 37 person$nationality <- "Spain“ class(persona) [1] "list" > persona $name [1] "Alberto" $age [1] 37 $nationality [1] "Spain" names(persona) [1] “name" “age" "nationality"

Strings phrase<-"the quick brown fox jumps over the lazy dog" letras <- table(strsplit(phrase,split=character(0))) numwords<-1+table(strsplit(phrase,split=character(0)))[1] words <- unlist(strsplit(phrase,split=" ")) words[grep("o",words)] "fox" %in% unlist(strsplit(phrase,split=" ")) unlist(strsplit(phrase,,split=" ")) %in% c("fox","dog")

Strings nchar(words) paste(words[1],words[2]) toupper(words)

Regular expressions grep("^t", words) words[grep("^t", words)] words[grep("s$", words)] gsub("o","O",words) regexp()

Dataframes lista <- data.frame() lista[1,1] = "Alberto" lista[1,2] = 37 lista[2,1] = "Ana" lista[2,2] = 23 names(lista) <- c("Ana", "Edad")

Missing values NA (is.na) x<-c(1:8,NA) mean(x) mean(x,na.rm=T) which(is.na(x)) as.vector(na.omit(x)) x[!is.na(x)]

Dates and Times in R date() date<- as.POSIXlt(Sys.time()) unlist(unclass(date)) difftime() excel.dates <- c("27/02/2004", "27/02/2005", "14/01/2003“,"28/06/2005", "01/01/1999") strptime(excel.dates,format="%d/%m/%Y")

if if (y > 0) print(1) else print (-1) z <- ifelse (y < 0, -1, 1)

Loops and Repeats for (i in 1:10) print(i^2) t = 1 while(t<=10) { print(i^2) i <- i + 1 } t = 1 repeat { if (i > 10)break print(i^2) i <- i + 1 }

Exercise Compute the Fibonacci series 1, 1, 2, 3, 5, 8 fibonacci<-function(n) { a<-1 b<-0 while(n>0) {swap<-a a<-a+b b<-swap n<-n-1 } b }

Avoid loops x<-runif(10000000) system.time(max(x)) pc<-proc.time() cmax<-x[1] for (i in 2:length(x)) { if(x[i]>cmax) cmax<-x[i] } proc.time()-pc

switch central<-function(y, measure) { switch(measure, Mean = mean(y), Geometric = exp(mean(log(y))), Harmonic = 1/mean(1/y), Median = median(y), stop("Measure not included")) }

Quantitative Data Analysis Working with datasets

Help for Datasets To list built-in datasets: data() data(package = .packages(all.available = TRUE)) data(swiss) For help on a dataset: help(swiss) “Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.”

The attach Command To access individual variables, do this: > attach(swiss) Now try: > mean(Fertility) > detach(swiss)

Using R Functions: Simple Stuff rownames(swiss) colnames(swiss) • summary(swiss) Applying functions mean(swiss$Fertility) sd(swiss$Fertility) apply(swiss,2,max)

Factors class(Detergent) nlevels(Detergent) levels(Detergent) as.factor()

Working with your dataset fix(swiss) hist(Agriculture) plot(Catholic,Fertility)

Working with your own datasets write.table(swiss, "swiss.txt") swiss2 <- read.table("swiss.txt") data<- read.table(file.choose(),header=T) readLines()

Reading data from files read.table(file) reads a file in table format and creates a data frame from it; the default separator sep="" is any whitespace; use header=TRUE to read the first line as a header of column names; use as.is=TRUE to prevent character vectors from being converted to factors; use comment.char="" to prevent "#" from being interpreted as a comment; use skip=n to skip n lines before reading data; see the help for options on row naming, NA treatment, and others read.csv("filename", header=TRUE) id. but with defaults set for reading comma-delimited files read.delim("filename", header=TRUE) id. but with defaults set for reading tab-delimited files read.fwf(file,widths) read a table of f ixed width f ormatted data into a ’data.frame’; widths is an integer vector, giving the widths of the fixed-width fields

Example data<- read.table(".datadaphnia.txt",header=T) names(data) attach(data) table(Detergent) tapply(Growth.rate,Detergent,mean) aggregate(Growth.rate,list(Detergent), mean) tapply(Growth.rate,list(Water,Daphnia),media n) with(data,boxplot(Growth.rate ~ Detergent))

Introduction to R programming

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