Intro

R

Vectors

• everything is a vector: integer(), character(), numeric(), logical(), raw(), complex()
• sometimes called 'atomic'

x = rnorm(1000)
y = x + rnorm(sd=.5, 1000)

• 'API' (Application Programming Interface) – how can you work with a vector?
  • [ - single bracket subset; 'endomorphism'
  • length()
  • c()
  • [<- – subset-assign
  • (names())

functions: argument names;

• can be optional arguments
• named (sd; can be partial, e.g., s=) – matched before unnamed

• positional – unnamed are matched by position

  rnorms = lapply(0:3, function(mean) {
   rnorm(1000, mean)
  })
  rnorms = lapply(0:3, rnorm, n=1000, mean=0)
  

  • matrix()

• atomic vectors with 'dim' and 'class' attributes

• 'API' – two- (n-) dimensional [, [<-

  m = matrix(1:6, 2)
  dput(m)
  

  ## structure(1:6, .Dim = 2:3)
  

  factor() – decorated integer() vector

list()

• recurssive data structre
• heterogeneous elements
• 'API'
  • 'inherits' (very loose sense) from vector
  • [[, $ – extract element of list
  • [[<-, $<- – assign new element
  • (unlist())
  • (assign NULL)

data.frame()

• list of vectors, all vectors the same length
• 'class' attribute
• inherits 'list' API, and also 'matrix' API

closures

acctFactory = function() {
  balance <- 0
  list(deposit=function(amt) {
    balance <<- balance + amt
  }, currBalance=function() {
    balance
  })
}

S3 classes and methods

x = rnorm(1000)
y = x + rnorm(sd=.5, 1000)
df = data.frame(X=x, Y=y)

Use of data.frame():

• groups vectors in a useful way
• e.g., avoiding bookkeeping errors when subsetting
• ensures confromance with data.frame() 'contract'
• motivates data structures more elaborate than vector

fit = lm(Y ~ X, df)
plot(Y ~ X, df)
abline(fit, lwd=4, col="red")

anova(fit)

## Analysis of Variance Table
## 
## Response: Y
##            Df  Sum Sq Mean Sq F value    Pr(>F)    
## X           1 1018.73 1018.73  4098.4 < 2.2e-16 ***
## Residuals 998  248.07    0.25                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

• fit is an S3 object (instance, class)
  • list() with a class attribute
  • structure is visible, but irrelevant to the user
  • class() to discover the class(!)
• anova is a generic, with a method appropriate for the class of fit
• discovery: methods(“anova”), methods(class=“lm”)
• help: ?plot (for the generic), ?plot.lm (for the method)

Bioconductor

S4

suppressPackageStartupMessages({
    library(IRanges)
})
start <- as.integer(runif(1000, 1, 1e4))
width <- as.integer(runif(length(start), 50, 100))
ir <- IRanges(start, width=width)
coverage(ir)

## integer-Rle of length 10092 with 1723 runs
##   Lengths: 34  5  4  8  3  1  8  5  6 16 ...  3  1  7  7 14  1  4 25 15 17
##   Values :  1  2  3  4  5  6  5  6  7  8 ... 10  9  8  7  6  5  4  3  2  1

• S4 is more formal than S3

  • Specify class structure
  • Complicated inheritance
  • Multiple dispatch possible

• discovery

  • class(ir); could look at (but why bother?) structure using getClass(class(ir))
  • Especiallly useful for inheritance
  • showMethods("coverage"), showMethods(class=class(ir), where=search())

• help

  • ?coverage – help on the generic
  • ?IRanges – Constructor; recent convention: also documents class & important methods
  • selectMethod("coverage", signature=class(ir)) to figure out method dispatch, and to see the function definition
  • method?"coverage,Ranges" (tab completion!)
  • class?IRanges (tab completion!)

Essential classes

Sequences

• DNAString, DNAStringSet

Ranges

• GRanges, GRangesList

Integrated containers

• SummarizedExperiment

Working with large data

Brief review of lecture material

Efficient R code

• R programing sins and corrections, of primary importance is correctness
• Important to ask how algorithm scales with problem size; many naive approaches scale quadratically (bad!).
• Complier (compiler::cmpfun()) surprisingly effective at improving f1() – better than sapply().
• Explored vapply(). Faster and safer than sapply(), so should be a best practice
• Large gains available from writing effective R code; makes appeal to C++ / parallel evaluation less compelling

GenomicFiles and BiocParallel

• Extended development of reduceByYield() to iterate through files
• Easy to parallelize across files via bplapply().
• Oops, Rstudio swallows bplapply() output. :(

Annotation

Brief review of lecture material

General importance of select() interface, including to on-line resources such as biomaRt

AnnotationHub

• Very easy to wrangle web-based genome annotation files, e.g., UCSC chain files

  • Simplified discovery, download, and local management
  • Easy use in Bioconductor work flows

• Role of AnnotationHub in deploying more complicated and heavily curated resources, like the GRASP2 data base of GWAS variants

  • dplyr makes working with data bases fun.