Rarr 2.1.20
The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It’s design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Zarr is experiencing increasing adoption in a number of scientific fields, where multi-dimensional data are prevalent. In particular as a back-end to the The Open Microscopy Environment’s OME-NGFF format for storing bioimaging data in the cloud.
Rarr is intended to be a simple interface to reading and writing individual Zarr arrays. It is developed in R and C with no reliance on external libraries or APIs for interfacing with the Zarr arrays. Additional compression libraries (e.g. blosc) are bundled with Rarr to provide support for datasets compressed using these tools.
If you know about Zarr arrays already, you’ll probably be aware they can be stored in hierarchical groups, where additional meta data can explain the relationship between the arrays. Currently, Rarr is not designed to be aware of these hierarchical Zarr array collections. However, the component arrays can be read individually by providing the path to them directly.
Currently, there are also limitations on the Zarr datatypes that can be accessed using Rarr. For now most numeric types can be read into R, although in some instances e.g. 64-bit integers there is potential for loss of information.
The are some example Zarr arrays included with the package. These were created using the Zarr Python implementation and are primarily intended for testing the functionality of Rarr. You can use the code below to list the complete set on your system, however it’s a long list so we don’t show the output here.
list.dirs(
system.file("extdata", "zarr_examples", package = "Rarr"),
recursive = TRUE
) |>
grep(pattern = "zarr$", value = TRUE)If you want to quickly get started reading an existing Zarr array with the package, this section should have the essentials covered. First, we need to install Rarr1 you only need to do the installation step once with the commands below.
## we need BiocManager to perform the installation
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
## install Rarr
BiocManager::install("Rarr")Once Rarr is installed, we have to load it into our R session:
library(Rarr)Rarr can be used to read files either on local disk or on remote S3 storage systems. First lets take a look at reading from a local file.
To demonstrate reading a local file, we’ll pick the example file containing 32-bit integers arranged in the “column first” ordering.
zarr_example <- system.file(
"extdata",
"zarr_examples",
"column-first",
"int32.zarr",
package = "Rarr"
)We can get an summary of the array properties, such as its shape and datatype, or group properties,
using the function zarr_overview()2 This is essentially reading and formatting
the array metadata that accompanies any Zarr array, or the consolidated metadata if present in the case of a Zarr group..
zarr_overview(zarr_example)## Type: Array
## Path: /tmp/RtmpOh3tND/Rinst3a1bb1a32d017/Rarr/extdata/zarr_examples/column-first/int32.zarr
## Shape: 30 x 20 x 10
## Chunk Shape: 10 x 10 x 5
## No. of Chunks: 12 (3 x 2 x 2)
## Data Type: int32
## Endianness: little
## Compressor: blosc
## Attributes: noYou can use this to check that the location is a valid Zarr array, and that the
shape and datatype of the array content are what you are expecting. For
example, we can see in the output above that the data type (int32) corresponds
to what we expect.
The summary information retrieved above is required, as to read the array with
Rarr you need to know the shape and size of the array (unless you want to
read the entire array). From the previous output we can see our example array
has three dimensions of size 30 x 20 x 10. We can select the subset we want to
extract using a list. The list must have the same length as the number of
dimensions in our array, with each element of the list corresponding to the
indices you want to extract in that dimension.
index <- list(1:4, 1:2, 1)We then extract the subset using read_zarr_array():
read_zarr_array(zarr_example, index = index)## , , 1
##
## [,1] [,2]
## [1,] 1 2
## [2,] 1 0
## [3,] 1 0
## [4,] 1 0Read the dedicated “Working with remote Zarr arrays in R” vignette for more information on reading Zarr arrays from S3 storage.
Up until now we’ve only covered reading existing Zarr array into R. However, Rarr can also be used to write R data to disk following the Zarr specification. To explore this, lets create an example array we want to save as a Zarr. In this case it’s going to be a three dimensional array and store the values 1 to 600.
x <- array(1:600, dim = c(10, 10, 6))path_to_new_zarr <- file.path(tempdir(), "new.zarr")
write_zarr_array(
x = x,
zarr_array_path = path_to_new_zarr,
chunk_dim = c(10, 5, 1)
)We can check that the contents of the Zarr array is what we’re expecting. Since
the contents of the whole array will be too large to display here, we use the
index argument to extract rows 6 to 10, from the 10th column and 1st slice.
That should be the values 96, 97, 98, 99, 100, but retaining the 3-dimensional
array structure of the original array. The second line below uses identical()
to confirm that reading the whole Zarr returns something equivalent to our
original input x.
read_zarr_array(zarr_array_path = path_to_new_zarr, index = list(6:10, 10, 1))## , , 1
##
## [,1]
## [1,] 96
## [2,] 97
## [3,] 98
## [4,] 99
## [5,] 100identical(read_zarr_array(zarr_array_path = path_to_new_zarr), x)## [1] TRUEBy default the zarr_overview() function prints a summary of the array to
screen, so you can get a quick idea of the array properties. However, there are
times when it might be useful to compute on that metadata, in which case
printing to screen isn’t very helpful. If his is the case the function also has
the argument as_data_frame which toggles whether to print the output to
screen, as seen before above, or to return a data.frame containing the array
details.
zarr_overview(zarr_example, as_data_frame = TRUE)## path
## 1 /tmp/RtmpOh3tND/Rinst3a1bb1a32d017/Rarr/extdata/zarr_examples/column-first/int32.zarr
## data_type endianness compressor dim chunk_dim nchunks attributes
## 1 int32 little blosc 30, 20, 10 10, 10, 5 3, 2, 2 FALSEOne of the key features of the Zarr specification is that the arrays are
chunked, allowing rapid access to the required data without needed to read or
write everything else. If you want to modify a subset of a Zarr array, it is
very inefficient to write all chunks to disk, which is what write_zarr_array()
does. Instead, Rarr provides two functions for reducing the amount of
writing required if the circumstances allow: create_empty_zarr_array() and
update_zarr_array().
Despite the name, you can actually think of create_empty_zarr_array() as
creating an array where all the values are the same. The Zarr specification
allows for “uninitialized” chunks, which are not actually present on disk. In
this case, any reading application assumes the entirety of the chunk is filled
with a single value, which is found in the array metadata. This allows for very
efficient creation of the new array, since only a small metadata file is
actually written. However it is necessary to provide some additional details,
such as the shape of the array, since there’s no R array to infer these from.
Let’s look at an example:
path <- tempfile()
create_empty_zarr_array(
zarr_array_path = path,
dim = c(50, 20),
chunk_dim = c(5, 10),
data_type = "integer",
fill_value = 7L
)First we have to provide a location for the array to be created using the
zarr_array_path argument. Then we provide the dimensions of the new array,
and the shape of the chunks it should be split into. These two arguments must
be compatible with one another i.e. have the same number of dimensions and no
value in chunk_dim should exceed the corresponding value in dim. The
data_type argument defines what type of values will be stored in the array.
Finally we use the fill_value argument to provide our default value for the
uninitialized chunks.
The next few lines check what’s actually been created on our file system.
First, we use list.files() to confirm that that only file that’s been created
is the zarr.json metadata; there are no chunk files. Then we use table() to
check the contents of the array, and confirm that when it’s read the resulting
array in R is full of 7s, our fill value.
list.files(path, all.files = TRUE, no.. = TRUE)## [1] "zarr.json"table(read_zarr_array(path))##
## 7
## 1000Lets assume we want to update the first row of our array to contain the sequence
of integers from 1 to 20. In the code below we create an example vector
containing the new data. We then use update_zarr_array(), passing the
location of the Zarr and the new values to be inserted. Finally, we provide the
index argument which defines which elements in the Zarr array should be
updated. It’s important that the shape and number of values in x corresponds
to the total count of points in the Zarr array we want to update e.g. in this
case we’re updating a single row of 20 values.
x <- 1:20
update_zarr_array(
zarr_array_path = path,
x = x,
index = list(1, 1:20)
)As before, we can take a look at what’s happened on disk and confirm the values are present in the array if we read it into R.
list.files(path, all.files = TRUE, no.. = TRUE)## [1] "c" "zarr.json"read_zarr_array(path, index = list(1:2, 1:5))## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 7 7 7 7 7table(read_zarr_array(path))##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 1 1 1 1 1 1 981 1 1 1 1 1 1 1 1 1 1 1 1 1Here list.files() confirms that there’s now two chunk files that have been
created. When we first created the Zarr we specified that the chunks should be
10 columns wide, so by modifying 20 columns we’d expect at least two chunks to
be realized on disk. We use read_zarr_array() to confirm visually that the
first row contains our sequence of values, whilst the second row is still all 7.
We use table() to confirm that the total contents is as expected.
## R version 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Rarr_2.1.20 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] crayon_1.5.3 cli_3.6.6 knitr_1.51
## [4] rlang_1.2.0 xfun_0.59 otel_0.2.0
## [7] jsonlite_2.0.0 glue_1.8.1 grumpy_0.1.1
## [10] htmltools_0.5.9 sass_0.4.10 rappdirs_0.3.4
## [13] rmarkdown_2.31 evaluate_1.0.5 jquerylib_0.1.4
## [16] fastmap_1.2.0 yaml_2.3.12 lifecycle_1.0.5
## [19] httr2_1.2.3 bookdown_0.47 BiocManager_1.30.27
## [22] compiler_4.6.0 Rcpp_1.1.1-1.1 R.oo_1.27.1
## [25] R.utils_2.13.0 digest_0.6.39 R6_2.6.1
## [28] curl_7.1.0 paws.common_0.8.10 paws.storage_0.10.0
## [31] magrittr_2.0.5 bslib_0.11.0 R.methodsS3_1.8.2
## [34] tools_4.6.0 cachem_1.1.0