Introduction into R. A Short Overview. Thomas Girke. December 8, Introduction into R Slide 1/21

Transcription

1 Introduction into R A Short Overview Thomas Girke December 8, 212 Introduction into R Slide 1/21

2 Introduction Look and Feel of the R Environment R Library Depositories Installation Getting Around Basic Syntax Data Types and Subsetting Basic Calculations Reading and Writing External Data Some Great R Functions Graphics Utilities Online Tutorial Introduction into R Slide 2/21

3 Outline Introduction Look and Feel of the R Environment R Library Depositories Installation Getting Around Basic Syntax Data Types and Subsetting Basic Calculations Reading and Writing External Data Some Great R Functions Graphics Utilities Online Tutorial Introduction into R Introduction Slide 3/21

4 What You ll Get? R Gui: OS X Command-line R: Linux/OS X R Gui: Windows Introduction into R Introduction Look and Feel of the R Environment Slide 4/21

5 RStudio: Alternative Working Environment for R New integrated development environment (IDE) for R that works well for beginners and developers. Introduction into R Introduction Look and Feel of the R Environment Slide 5/21

6 Why Using R Complete statistical package and programming language Efficient functions and data structures for data analysis Powerful graphics Access to fast growing number of analysis packages Most widely used language in bioinformatics Is standard for data mining and biostatistical analysis Technical advantages: free, open-source, available for all OSs Books & Documentation simpler - Using R for Introductory Statistics (Gentleman et al., 25) Bioinformatics and Computational Biology Solutions Using R and Bioconductor (John Verzani, 24) UCR Manual (Thomas Girke) Introduction into R Introduction Look and Feel of the R Environment Slide 6/21

7 Package Depositories CRAN (>3 packages) general data analysis BioConductor (>5 packages) bioscience data analysis Omegahat (>3 packages) programming interfaces Introduction into R Introduction R Library Depositories Slide 7/21

8 Installation Install R binary for your operating system from: Installation of CRAN Packages > install.packages(c("pkg1", "pkg2")) > install.packages("pkg.zip", repos=null) Installation of BioConductor Packages > source(" > bioclite() > bioclite(c("pkg1", "pkg2")) Introduction into R Introduction Installation Slide 8/21

9 Startup/Closing Behavior Starting R The R GUI versions under Windows and Mac OS X can be opened by double-clicking their icons. Alternatively, one can start it by typing R in a terminal (default under Linux). Startup/Closing Behavior The R environment is controlled by hidden files in the startup directory:.rdata,.rhistory and.rprofile (optional). ## Closing R > q() Save workspace image? [y/n/c]: Note When responding with y, then the entire R workspace will be written to the.rdata file which can become very large. Often it is sufficient to just save an analysis protocol in an R source file. This way one can quickly regenerate all data sets and objects. Introduction into R Introduction Getting Around Slide 9/21

10 Getting Around ## Create an object with the assignment operator <- (or = ) > object <-... ## List objects in current R session > ls() ## Return content of current working directory > dir() ## Return path of current working directory > getwd() ## Change current working directory > setwd("/home/user") Introduction into R Introduction Getting Around Slide 1/21

11 Basic R Syntax ## General R command syntax > object <- function(arguments) > object <- object[arguments] ## Execute an R script > source("my script.r") ## Execute an R script from command-line $ R CMD BATCH my script.r $ R --slave < my script.r ## Finding help >?function ## Load a library > library("my library") ## Summary of all functions within a library > library(help="my library") ## Load library manual (PDF file) > vignette() Introduction into R Introduction Basic Syntax Slide 11/21

12 Data Types ## Numeric data: 1, 2, 3 > x <- c(1, 2, 3); x; is.numeric(x); as.character(x) ## Character data: "a", "b", "c" > x <- c("1", "2", "3"); x; is.character(x); as.numeric(x) ## Complex data: 1, b, 3 > c(1, "b", 3) ## Logical data: TRUE, FALSE, TRUE > x <- 1:1 < 5; x >!x ## Return indices for the TRUEs in logical vector > which(x) Introduction into R Introduction Data Types and Subsetting Slide 12/21

13 Data Objects ## Vectors (1D) > myvec <- 1:1; names(myvec) <- letters[1:1] > myvec[1:5]; myvec[c(2,4,6,8)]; myvec[c("b", "d", "f")] ## Factors (1D): vectors with grouping information > factor(c("dog", "cat", "mouse", "dog", "dog", "cat")) ## Matrices (2D), Data Frames (2D) and Arrays ( 2D) > myma <- matrix(1:3, 3, 1, byrow = T) > mydf <- data.frame(col1=1:1, Col2=1:1) > mydf[1:4, ]; mydf[,c("col2", "Col1", "Col1")] ## Lists: containers for any object type > myl <- list(name="fred", wife="mary", no.children=3, child.ages=c(4,7,9)) > myl[[4]][1:2] ## Functions: piece of code > myfct <- function(arg1, arg2,...) { function body } Introduction into R Introduction Data Types and Subsetting Slide 13/21

14 General Subsetting Rules ## Subsetting by indices > myvec <- 1:26; names(myvec) <- LETTERS > myvec[1:4] ## Subsetting by same length logical vectors > mylog <- myvec > 1 > myvec[mylog] ## Subsetting by field names > myvec[c("b", "K", "M")] ## Special case > iris$species Introduction into R Introduction Data Types and Subsetting Slide 14/21

15 Basic Operators and Calculations Comparison operators: ==,! =, <, >, <=, >= ## Example: > 1==1 Logical operators: AND: &, OR:, NOT:! ## Example: > x <- 1:1; y <- 1:1 > x > y & x > 5 Calculations: ## Example: > x + y; sum(x); mean(x), sd(x); sqrt(x) > apply(iris[,1:3], 1, mean) Introduction into R Introduction Basic Calculations Slide 15/21

16 Reading and Writing External Data ## Import Data into R > read.delim("mydata.xls", sep="\t") ## Export Data from R to File > write.table(myframe, file="myfile.xls", sep="\t", quote=f) Introduction into R Introduction Reading and Writing External Data Slide 16/21

17 Some Great R Functions ## The unique() function to make vector entries unique > unique(iris$sepal.length); length(unique(iris$sepal.length)) ## The table() function counts the occurrences of entries > table(iris$species) ## The aggregate() function computes statistics of data aggregates > aggregate(iris[,1:4], by=list(iris$species), FUN=mean, na.rm=t) ## The %in% function returns the intersect between two vectors > month.name[month.name %in% c("may", "July")] ## The merge() function joins data frames based on a common key column > merge(frame1, frame2, by.x=1, by.y=1, all = TRUE) Introduction into R Introduction Some Great R Functions Slide 17/21

18 Graphics Utilities Color Key Venn Diagram A -1 1 Row Z-Score g99 g96 g72 g42 g43 g94 g51 g93 g34 g64 g27 g52 g16 g31 g35 g17 g39 g2 g47 g45 g41 g57 g48 g68 g84 g1 g2 g9 g36 g6 g63 g18 g9 g24 g33 g65 g53 g7 g32 g61 g4 g76 g49 g73 g28 g97 g98 g29 g83 g78 g92 g74 g26 g14 g55 g37 g1 g8 g8 g66 g67 g21 g82 g71 g11 g6 g56 g44 g7 g58 g3 g75 g15 g91 g23 g81 g38 g87 g85 g3 g95 g79 g54 g13 g89 g4 g25 g88 g22 g46 g5 g62 g19 g77 g12 g86 g5 g59 g69 g1 E D B C t4 t5 t3 t1 t2 Unique objects: All = 25; S1 = 18; S2 = 16; S3 = 2; S4 = 22; S5 = 18 Introduction into R Introduction Graphics Utilities Slide 18/21

19 Some Graphics Commands ## Dot plots > plot(1:1) > plot(iris[,1:4]) ## Barplot > barplot(1:1) ## Help with plots >?plot;?par Demo Graphics Utilities Introduction into R Introduction Graphics Utilities Slide 19/21

20 Outline Introduction Look and Feel of the R Environment R Library Depositories Installation Getting Around Basic Syntax Data Types and Subsetting Basic Calculations Reading and Writing External Data Some Great R Functions Graphics Utilities Online Tutorial Introduction into R Online Tutorial Slide 2/21

21 More Details and Exercises Continue in R Manual Introduction into R Online Tutorial Slide 21/21