Basic R Part 1 BTI Plant Bioinformatics Course

Transcription

1 Basic R Part 1 BTI Plant Bioinformatics Course Spring 2013 Sol Genomics Network Boyce Thompson Institute for Plant Research by Jeremy D. Edwards

2 What is R? Statistical programming language Derived from S Developed at Bell Labs (also developed C programming language and UNIX)

3 Why use R? R is Free Software : Think 'free' as in 'free speech' not as in 'free beer' (it's actually both) Free to install anywhere Scripting Reproducibility Contributions from the scientific community Publication-quality plots

4 How to run R Type R Linux terminal: use via SSH, use pipes, etc. One line at a time or run scripts GUI RStudio R Commander

5 How to quit R Type quit() or q() Saving your workspace > q() Save workspace image? [y/n/c]:

6 How to get help in R help() for general help help(topic) or?topic where topic is a specific topic, e.g., help(q) help.search( topic ) or??topic for help finding pages on a vague topic, e.g.,??quit help.start() launch HTML help page library(help="package") Get help for a specific package, e,g., library(help= base ) for help on base package

7 Comment lines in R R interprets anything following the # character to the end of the line as a comment Works the same way in the Linux command line Useful to document code Useful to temporarily remove lines from a script (i.e., comment out) > #comment line example > help(q) #get help

8 R basic operations: computation > [1] 2 Try: 2 * 2 4 ^ 2 abs (-1) Result log (10) sqrt (4) pi

9 R basic operations: assignment operator assignment operator > x < > x [1] 2 Using <- is considered better style than using = In function calls, using = will declare the variable only within the scope of the function, and <- will declare it for the user workspace.

10 Vectors Create a vector combine > x <- c(2,4,6,8) [1] Create a vector of consecutive integers > y <- 1:4 [1]

11 Character vectors Create a vector of character strings > colors <- c("red","green","blue") > colors [1] "red" "green" "blue"

12 > x [1] Logical vectors >#elements in x less than 5 > z <- x<5 > z [1] TRUE TRUE FALSE FALSE >#numerical to logical > z <- as.logical(c(1,0,0,1)) > z [1] TRUE FALSE FALSE TRUE

13 > x [1] Vector operations > x + 1 #scalar addition [1] > x * 2 #scalar multiplication [1] > sum(x) #sum of elements [1] 20

14 Vectors: component extraction > x [1] > x[3] #third element of x [1] 6 > x[2:4] #second to fourth element of x [1] > x[-2] #all except the second element [1] > x[x>5] #elements of x greater than 5 [1] 6 8 > z #a logical vector [1] TRUE FALSE FALSE TRUE > x[z] #get elements with logical vector [1] 2 8

15 Vectors: naming components > colors [1] "red" "green" "blue" > wavelength <- c(650,510,475) > names(wavelength) <- colors #assign names > wavelength red green blue > wavelength[ green ] #component by name green 510

16 Arrays An array can be considered as a multiply subscripted collection of data entries The data vector contains the values in the array The dimension vector gives the dimensions of the array data vector dimension vector > x <- array( 1:20, c(4,5) ) > x [,1] [,2] [,3] [,4] [,5] [1,] [2,] [3,] [4,]

17 Arrays: component extraction > xyz <- array(c(1:27), dim=c(3, 3, 3)) > xyz[2,2,2] # a single numeric element [1] 14 > xyz[2,2, ] # a vector (1 dimension array) [1] > xyz[2,, ] # a 2 dimension array [,1] [,2] [,3] [1,] [2,] [3,]

18 Matrices A matrix is a two dimensional array > xy <- matrix(c(1:9), ncol=3, nrow=3) > xy [,1] [,2] [,3] [1,] [2,] [3,] 3 6 9

19 Matrices: index names Matrix indexes can be replaced with names > xy <- matrix(c(1:9), ncol=3, nrow=3, dimnames=list(c("a","b","c"), c("x", "y", "z"))) > xy x y z A B C 3 6 9

20 Creating matrices > xy <- matrix(c(1:9), ncol=3, nrow=3, dimnames=list(c("a","b","c"), c("x", "y", "z"))) > xy x y z A B C > x <- c(1,2,3); y <- c(4,5,6) > col_xy <- cbind(x, y) > row_xy <- rbind(x, y) > col_xy x y [1,] 1 4 [2,] 2 5 [3,] 3 6 > row_xy [,1] [,2] [,3] x y 4 5 6

21 Lists A list is an ordered collection of objects > c <- c("m82", "Alisa Craig", "Microtom") > y <- c(2006, 2008) > l <- c("ca", "FL") > s <- array(c(2, 1, 3, 4, 6, 2, 5, 7, 5, 6, 3, 2, 2), dim=c(3, 2, 2)) > phenom <- list(cultivars=c, years=y, localizations=l, size=s) > list

22 Lists: objects by name > phenom$cultivars [1] "M82" "Alisa Craig" "Microtom"

23 Data frames Data frames are a list with class "data.frame" with the following features: The components must be vectors (numeric, character, or logical), factors, numeric matrices, lists, or other data frames. Matrices, lists, and data frames provide as many variables to the new data frame as they have columns, elements, or variables, respectively. Numeric vectors, logicals and factors are included as is, and character vectors are coerced to be factors, whose levels are the unique values appearing in the vector. Vector structures appearing as variables of the data frame must all have the same length, and matrix structures must all have the same row size.

24 Data frames: example > accessions <- c("alisa Craig", "Black Cherry", "Comete", "Gnom") > fruit_size <- matrix(c(7, 8, 5, 7, 6, 8, 9, 8), ncol=2, nrow=4, byrow=true, dimnames=list(accessions, c(2006,2007))) > sugar_content <- matrix(c(2.1, 3.2, 3, 2.1, 4.1, 2.3, 2.8, 3.1), ncol=1, nrow=4, byrow=true, dimnames=list(accessions, c(2008))) > phenome <- data.frame(fruit_size, sugar_content) > phenome X2006 X2007 X2008 Alisa Craig Black Cherry Comete Gnom

25 Data frames: accessing data > phenome data.frame(fruit_size, sugar_content) # As a matrix: > phenome[1,] # for a row > phenome[,1] # for a column > phenome[1,1] # for a single data # Based in the column names > phenome$x2007 # To divide/join the data.frame in its columns use attach/detach function > attach(phenome) > X2007 >summary(phenome) #stats about this dataframe

26 Data frames: import and export data read.table()/write.table() > phenome read.table( example.data ); read.table() arguments: header=false/true, sep=, quote= \ '

27 Functions > do_some_stuff <-{ls; a<-100; a;} [1] 100

28 Packages Set of functions and data that can be downloaded and installed from R repository CRAN. Example: 'ade4', is a package of analysis of ecological Data. Important Commands: > install.packages( ade4 ) > library( ade4 ) > packagedescription( ade4 )