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vecrep

vecrep provides rep_altrep(), an ALTREP alternative to base::rep() that works with most vector types. Rather than duplicating data immediately, it stores a compact reference to the original vector and only expands it if a write forces materialisation. This makes it well-suited to vectors with many repetitions, especially if the reference vector is a regular sequence represented with ALTREP. ALTREP sequences can be combined with ALTREP replicates to create repeating regular sub-sequences.

Several common operations are accelerated by working directly on the reference vector rather than the full expanded result:

  • sum(): computed on the reference vector and scaled by the number of replications.
  • min() / max(): dispatched to the reference vector without scanning replicated values.
  • is.na() / anyNA(): NA checks are performed on the reference vector and the result tiled, avoiding a full scan of repeated elements.
  • sort(): if the reference vector is already sorted, and the vector is only replicated by element (i.e. each > 1 but times == 1), then the result is known to be sorted.

Installation

You can install the released version of vecrep from CRAN with:

Or install the development version from GitHub:

# install.packages("pak")
pak::pak("mitchelloharawild/vecrep")

Usage 

library(vecrep)

x <- as.numeric(1:5)

# Create a repeated vector — no extra allocation
y <- rep_altrep(x, times = 4)

length(y)   # 20
#> [1] 20
y[1:10]     # reads directly from x
#>  [1] 1 2 3 4 5 1 2 3 4 5
sum(y)      # aggregates stay lazy too
#> [1] 60

Read operations ([, sum(), mean(), anyNA()) work directly on the parent vector without expanding it. The full vector is only materialised on the first write, and copy-on-write ensures the parent is never modified.

parent <- as.numeric(1:5)
y <- rep_altrep(parent, 3)

y[1] <- 999   # triggers expansion

parent        # unchanged
#> [1] 1 2 3 4 5
y[1:6]
#> [1] 999   2   3   4   5   1

The each argument repeats each element in turn before moving to the next, matching the behaviour of base::rep(..., each = n):

x <- as.numeric(1:3)

# Each element repeated 3 times: 1 1 1 2 2 2 3 3 3
rep_altrep(x, each = 3)
#> [1] 1 1 1 2 2 2 3 3 3

# times and each can be combined — each is applied first, then times repeats the result
rep_altrep(x, times = 2, each = 3)
#>  [1] 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3

Supported types

rep_altrep() supports most vector types:

# integer
rep_altrep(1L:3L, 3L)
#> [1] 1 2 3 1 2 3 1 2 3

# logical
rep_altrep(c(TRUE, FALSE, NA), 2L)
#> [1]  TRUE FALSE    NA  TRUE FALSE    NA

# complex
rep_altrep(c(1+1i, 2+2i), 4L)
#> [1] 1+1i 2+2i 1+1i 2+2i 1+1i 2+2i 1+1i 2+2i

# raw
rep_altrep(as.raw(c(0x01, 0x02, 0x03)), 2L)
#> [1] 01 02 03 01 02 03

# character
rep_altrep(c("foo", "bar", "baz"), 3L)
#> [1] "foo" "bar" "baz" "foo" "bar" "baz" "foo" "bar" "baz"

# list
rep_altrep(list(1L, "a", TRUE), 2L)
#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] "a"
#> 
#> [[3]]
#> [1] TRUE
#> 
#> [[4]]
#> [1] 1
#> 
#> [[5]]
#> [1] "a"
#> 
#> [[6]]
#> [1] TRUE

Classed vectors such as factor, Date, and POSIXct are handled transparently. The class and relevant attributes (e.g. levels for factors) are preserved on the ALTREP object without forcing materialisation, so S3 dispatch works as expected:

# factor: levels preserved without expansion
f <- rep_altrep(factor(c("cat", "dog", "cat")), 3L)
class(f)
#> [1] "factor"
levels(f)
#> [1] "cat" "dog"
table(f)
#> f
#> cat dog 
#>   6   3

# Date
d <- rep_altrep(as.Date("2024-01-01") + 0:2, 2L)
class(d)
#> [1] "Date"
d
#> [1] "2024-01-01" "2024-01-02" "2024-01-03" "2024-01-01" "2024-01-02"
#> [6] "2024-01-03"

# POSIXct
p <- rep_altrep(as.POSIXct("2024-01-01") + 0:2, 2L)
class(p)
#> [1] "POSIXct" "POSIXt"

Replication of named vectors also ALTREP replicates the names themselves:

x <- c(a = 1.0, b = 2.0, c = 3.0)
y <- rep_altrep(x, 3L)
names(y)
#> [1] "a" "b" "c" "a" "b" "c" "a" "b" "c"

Caveats

  • Serialisation with saveRDS() expands the vector (it is correct but no longer compact).
  • sort() materialises the vector if it is not already sorted, the ALTREP API does not provide any method for implementing a replicate-aware sorting algorithm.

Acknowledgements

The initial codebase was adapted from Gabriel Becker’s vectorwindow example, presented in his Bioconductor Developers Forum talk (YouTube).

Substantial proportions of this package were developed in tandem with Claude Sonnet 4.6 (Anthropic). All code has been reviewed and guided by humans.