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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data Rarr
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* checking for file ‘Rarr/DESCRIPTION’ ... OK
* preparing ‘Rarr’:
* checking DESCRIPTION meta-information ... OK
* cleaning src
* running ‘cleanup’
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘Rarr.Rmd’ using rmarkdown

## Installation and setup

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 **Rarr**^[you only need to do the installation step once] with the
commands below.


``` r
## 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:


``` r
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.

## Reading a from a local Zarr array
To demonstrate reading a local file, we'll pick the example file containing
32-bit integers arranged in the "column first" ordering.


``` r
zarr_example <- system.file(
  "extdata", "zarr_examples", "column-first", "int32.zarr",
  package = "Rarr"
)
```

### Exploring the data
We can get an summary of the array properties, such as its shape and datatype,
using the function `zarr_overview()`^[This is essentially reading and formatting
the array metadata that accompanies any Zarr array.].


``` r
zarr_overview(zarr_example)
```

```
## Type: Array
## Path: /private/var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T/RtmpevXGjC/Rinstabf777010efb/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
```
You 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.

### Reading the Zarr array
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.


``` r
index <- list(1:4, 1:2, 1)
```

We then extract the subset using `read_zarr_array()`:


``` r
read_zarr_array(zarr_example, index = index)
```

```
## , , 1
## 
##      [,1] [,2]
## [1,]    1    2
## [2,]    1    0
## [3,]    1    0
## [4,]    1    0
```

## Reading from S3 storage {#read-s3}

Reading files in S3 storage works in a very similar fashion to local disk.  This
time the path needs to be a URL to the Zarr array.
We can again use `zarr_overview()` to quickly retrieve the array metadata.


``` r
s3_address <- "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0076A/10501752.zarr/0"
zarr_overview(s3_address)
```
















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Error: processing vignette 'Rarr.Rmd' failed with diagnostics:
Failed to perform HTTP request.
Caused by error in `curl::curl_fetch_memory()`:
! SSL connect error [uk1s3.embassy.ebi.ac.uk]:
TLS connect error: error:00000000:lib(0):func(0):reason(0)
--- failed re-building ‘Rarr.Rmd’

SUMMARY: processing the following file failed:
  ‘Rarr.Rmd’

Error: Vignette re-building failed.
Execution halted