1 R
1.1 Language and environment for statistical computing and graphics
- Full-featured programming language
- Interactive and interpretted – convenient and forgiving
- Coherent, extensive documentation
- Statistical, e.g.
factor(),NA - Extensible – CRAN, Bioconductor, github, …
1.2 Vector, class, object
- Efficient vectorized calculations on ‘atomic’ vectors
logical,integer,numeric,complex,character,byte - Atomic vectors are building blocks for more complicated objects
matrix– atomic vector with ‘dim’ attributedata.frame– list of equal length atomic vectors- Formal classes represent complicated combinations of vectors, e.g., the return value of
lm(), below
1.3 Function, generic, method
- Functions transform inputs to outputs, perhaps with side effects, e.g.,
rnorm(1000) - Argument matching first by name, then by position
- Functions may define (some) arguments to have default values
- Generic functions dispatch to specific methods based on class of argument(s), e.g.,
print(). - Methods are functions that implement specific generics, e.g.,
print.factor; methods are invoked indirectly, via the generic. - Many but not all functions able to manipulate a particular class are methods, e.g.,
abline()used below is a plain-old-funciton.
1.4 Programming
Iteration:
lapply()args(lapply)## function (X, FUN, ...) ## NULL- Meaning: for a vector
X(typically alist()), apply a functionFUNto each vector element, returning the result as a list....are additional arguments toFUN. FUNcan be built-in, or a user-defined functionlst <- list(a=1:2, b=2:4) lapply(lst, log) # 'base' argument default; natural log## $a ## [1] 0.0000000 0.6931472 ## ## $b ## [1] 0.6931472 1.0986123 1.3862944lapply(lst, log, 10) # '10' is second argument to 'log()', i.e., log base 10## $a ## [1] 0.00000 0.30103 ## ## $b ## [1] 0.3010300 0.4771213 0.6020600sapply()– likelapply(), but simplify the result to a vector, matrix, or array, if possible.vapply()– likesapply(), but requires that the return type ofFUNis specified; this can be safer – an error when the result is of an unexpected type.mapply()(alsoMap())args(mapply)## function (FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE) ## NULL...are one or more vectors, recycled to be of the same length.FUNis a function that takes as many arguments as there are components of....mapplyreturns the result of applyingFUNto the elements of the vectors in....mapply(seq, 1:3, 4:6, SIMPLIFY=FALSE) # seq(1, 4); seq(2, 5); seq(3, 6)## [[1]] ## [1] 1 2 3 4 ## ## [[2]] ## [1] 2 3 4 5 ## ## [[3]] ## [1] 3 4 5 6apply()args(apply)## function (X, MARGIN, FUN, ...) ## NULLFor a matrix or array
X, applyFUNto eachMARGIN(dimension, e.g.,MARGIN=1means applyFUNto each row,MARGIN=2means applyFUNto each column)Traditional iteration programming constructs
repeat {},for () {}Almost always more error-prone, less efficient, and harder to understand than
lapply()!
Conditional
if (test) {
## code if TEST == TRUE
} else {
## code if TEST == FALSE
}Functions (see table below for a few favorites)
- Easy to define your own functions
fun <- function(x) {
length(unique(x))
}
## list of length 5, each containsing a sample (with replacement) of letters
lets <- replicate(5, sample(letters, 50, TRUE), simplify=FALSE)
sapply(lets, fun)## [1] 23 22 22 23 231.5 Introspection & Help
Introspection
- General properties, e.g.,
class(),str() - Class-specific properties, e.g.,
dim()
Help
?"print": help on the generic print?"print.data.frame": help on print method for objects of class data.frame.help(package="GenomeInfoDb")browseVignettes("GenomicRanges")methods("plot")methods(class="lm")
1.6 Examples
R vectors, vectorized operations, data.frame(), formulas, functions, objects, class and method discovery (introspection).
x <- rnorm(1000) # atomic vectors
y <- x + rnorm(1000, sd=.5)
df <- data.frame(x=x, y=y) # object of class 'data.frame'
plot(y ~ x, df) # generic plot, method plot.formula
fit <- lm(y ~x, df) # object of class 'lm'
methods(class=class(fit)) # introspection## [1] add1 alias anova case.names
## [5] coerce confint cooks.distance deviance
## [9] dfbeta dfbetas drop1 dummy.coef
## [13] effects extractAIC family formula
## [17] hatvalues influence initialize kappa
## [21] labels logLik model.frame model.matrix
## [25] nobs plot predict print
## [29] proj qr residuals rstandard
## [33] rstudent show simulate slotsFromS3
## [37] summary variable.names vcov
## see '?methods' for accessing help and source codeanova(fit)## Analysis of Variance Table
##
## Response: y
## Df Sum Sq Mean Sq F value Pr(>F)
## x 1 894.03 894.03 3463.3 < 2.2e-16 ***
## Residuals 998 257.62 0.26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(y ~ x, df) # methods(plot); ?plot.formula
abline(fit, col="red", lwd=3, lty=2) # a function, not generic.method
Programming example – group 1000 SYMBOLs into GO identifiers
## example data
fl <- file.choose() ## symgo.csvsymgo <- read.csv(fl, row.names=1, stringsAsFactors=FALSE)
head(symgo)## SYMBOL GO EVIDENCE ONTOLOGY
## 1 PPIAP28 <NA> <NA> <NA>
## 2 PTLAH <NA> <NA> <NA>
## 3 HIST1H2BC GO:0000786 NAS CC
## 4 HIST1H2BC GO:0000788 IBA CC
## 5 HIST1H2BC GO:0002227 IDA BP
## 6 HIST1H2BC GO:0003677 IBA MFdim(symgo)## [1] 5041 4length(unique(symgo$SYMBOL))## [1] 1000## split-sapply
go2sym <- split(symgo$SYMBOL, symgo$GO)
len1 <- sapply(go2sym, length) # compare with lapply, vapply
## built-in functions for common actions
len2 <- lengths(go2sym)
identical(len1, len2)## [1] TRUE## smarter built-in functions, e.g., omiting NAs
len3 <- aggregate(SYMBOL ~ GO, symgo, length)
head(len3)## GO SYMBOL
## 1 GO:0000049 3
## 2 GO:0000050 2
## 3 GO:0000060 1
## 4 GO:0000077 1
## 5 GO:0000086 3
## 6 GO:0000118 1## more fun with aggregate()
head(aggregate(GO ~ SYMBOL, symgo, length))## SYMBOL GO
## 1 ABCD4 15
## 2 ABCG2 22
## 3 ACE 57
## 4 ADAMTSL2 6
## 5 ALDH1L2 11
## 6 ALOX5 19head(aggregate(SYMBOL ~ GO, symgo, c))## GO SYMBOL
## 1 GO:0000049 YARS2, YARS2, EEF1A1
## 2 GO:0000050 ASL, ASL
## 3 GO:0000060 OPRD1
## 4 GO:0000077 PEA15
## 5 GO:0000086 TUBB4A, CENPF, CLASP1
## 6 GO:0000118 CIR1## your own function -- unique, lower-case identifiers
uidfun <- function(x) {
unique(tolower(x))
}
head(aggregate(SYMBOL ~ GO , symgo, uidfun))## GO SYMBOL
## 1 GO:0000049 yars2, eef1a1
## 2 GO:0000050 asl
## 3 GO:0000060 oprd1
## 4 GO:0000077 pea15
## 5 GO:0000086 tubb4a, cenpf, clasp1
## 6 GO:0000118 cir1## as an 'anonymous' function
head(aggregate(SYMBOL ~ GO, symgo, function(x) {
unique(tolower(x))
}))## GO SYMBOL
## 1 GO:0000049 yars2, eef1a1
## 2 GO:0000050 asl
## 3 GO:0000060 oprd1
## 4 GO:0000077 pea15
## 5 GO:0000086 tubb4a, cenpf, clasp1
## 6 GO:0000118 cir1
One can also change the shape of the points for the female and male groups
or plot Male and Female in different panels using 
By default, 