IRanges (version 2.6.1)

IRanges-class: IRanges and NormalIRanges objects


The IRanges class is a simple implementation of the Ranges container where 2 integer vectors of the same length are used to store the start and width values. See the Ranges virtual class for a formal definition of Ranges objects and for their methods (all of them should work for IRanges objects).

Some subclasses of the IRanges class are: NormalIRanges, Views, etc...

A NormalIRanges object is just an IRanges object that is guaranteed to be "normal". See the Normality section in the man page for Ranges objects for the definition and properties of "normal" Ranges objects.



See ?`IRanges-constructor`.


as(from, "IRanges"): Creates an IRanges instance from a Ranges object, logical vector, or integer vector. When from is a logical vector, the resulting IRanges object contains the indices for the runs of TRUE values. When from is an integer vector, the elements are either singletons or "increase by 1" sequences.
as(from, "NormalIRanges"): Creates a NormalIRanges instance from a logical or integer vector. When from is an integer vector, the elements must be strictly increasing.


c(x, ..., ignore.mcols=FALSE) Combining IRanges objects is straightforward when they do not have any metadata columns. If only one of the IRanges object has metadata columns, then the corresponding metadata columns are attached to the other IRanges object and set to NA. When multiple IRanges object have their own metadata columns, the user must ensure that each such linkS4class{DataFrame} have identical layouts to each other (same columns defined), in order for the combination to be successful, otherwise an error will be thrown. The user can call c(x, ..., ignore.mcols=TRUE) in order to combine IRanges objects with differing sets of metadata columns, which will result in the combined object having NO metadata columns.

Methods for NormalIRanges objects

max(x): The maximum value in the finite set of integers represented by x.
min(x): The minimum value in the finite set of integers represented by x.

See Also


IRanges-constructor, IRanges-utils,

intra-range-methods for intra range transformations,

inter-range-methods for inter range transformations,



Run this code
showClass("IRanges")  # shows (some of) the known subclasses

## ---------------------------------------------------------------------
## ---------------------------------------------------------------------
## All the methods defined for Ranges objects work on IRanges objects.
## See ?Ranges for some examples.
## Also see ?`IRanges-utils` and ?`setops-methods` for additional
## operations on IRanges objects.
## Combining IRanges objects
ir1 <- IRanges(c(1, 10, 20), width=5)
mcols(ir1) <- DataFrame(score=runif(3))
ir2 <- IRanges(c(101, 110, 120), width=10)
mcols(ir2) <- DataFrame(score=runif(3))
ir3 <- IRanges(c(1001, 1010, 1020), width=20)
mcols(ir3) <- DataFrame(value=runif(3))
some.iranges <- c(ir1, ir2)
## all.iranges <- c(ir1, ir2, ir3) ## This will raise an error
all.iranges <- c(ir1, ir2, ir3, ignore.mcols=TRUE)

## ---------------------------------------------------------------------
## ---------------------------------------------------------------------
## Using an IRanges object for storing a big set of ranges is more
## efficient than using a standard R data frame:
N <- 2000000L  # nb of ranges
W <- 180L      # width of each range
start <- 1L
end <- 50000000L
range_starts <- sort(sample(end-W+1L, N))
range_widths <-, N)
## Instantiation is faster
system.time(x <- IRanges(start=range_starts, width=range_widths))
system.time(y <- data.frame(start=range_starts, width=range_widths))
## Subsetting is faster
system.time(x16 <- x[c(TRUE,, 15))])
system.time(y16 <- y[c(TRUE,, 15)), ])
## Internal representation is more compact

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