rirods

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The rirods package is an R client for iRODS.

Installation

You can install the development version of rirods like so:

install.packages("rirods")

Or, the development version from GitHub, like so:

# install.packages("devtools")
devtools::install_github("irods/irods_client_library_rirods")

Prerequisites

This package connects to the iRODS C++ REST API - https://github.com/irods/irods_client_rest_cpp.

Launch a local demonstration iRODS service (including the REST API):

# load
library(rirods)
# setup a mock iRODS server (https://github.com/irods/irods_demo)
use_irods_demo("alice", "passWORD")

This will result in the demonstration REST API running at http://localhost/irods-rest/0.9.3 (or later version).

These Docker containers are designed to easily stand up a DEMONSTRATION of the iRODS server. It is intended for education and exploration. (See also vignette("demo").)

DO NOT USE IN PRODUCTION

Example Usage

To connect to the REST API endpoint of your choice, load rirods, connect with create_irods(), and authenticate with your iRODS credentials:

# connect
create_irods("http://localhost/irods-rest/0.9.3", "/tempZone/home")

Authentication

In this example Alice is a user of iRODS and she can authenticate herself with iauth(). This prompts a dialog where you can enter your username and password without hardcoding this information in your scripts.

# login as alice with password "passWORD"
iauth() # or iauth("alice", "passWORD")

Save R objects

Suppose Alice would like to upload an R object from her current R session to an iRODS collection. For this, use the isaveRDS() command:

# some data
foo <- data.frame(x = c(1, 8, 9), y = c("x", "y", "z"))

# check where we are in the iRODS namespace
ipwd()
#> [1] "/tempZone/home/alice"

# store data in iRODS
isaveRDS(foo, "foo.rds")

Metadata

To truly appreciate the strength of iRODS, we can add some metadata that describes the data object “foo”:

# add some metadata
imeta(
  "foo.rds", 
  "data_object", 
  operations = 
    data.frame(operation = "add", attribute = "foo", value = "bar", units = "baz")
)

# check if file is stored with associated metadata
ils(metadata = TRUE)
#> 
#> ========
#> metadata
#> ========
#> /tempZone/home/alice/foo.rds :
#>  attribute value units
#>        foo   bar   baz
#> 
#> 
#> ==========
#> iRODS Zone
#> ==========
#>                  logical_path        type
#>  /tempZone/home/alice/foo.rds data_object

For more on using metadata, check out vignette("metadata").

Read R objects

If Alice wanted to copy the foo R object from an iRODS collection to her current R session, she would use ireadRDS():

# retrieve in native R format
ireadRDS("foo.rds")
#>   x y
#> 1 1 x
#> 2 8 y
#> 3 9 z

Other file formats

Possibly Alice does not want a native R object to be stored on iRODS but a file type that can be accessed by other programs. For this, use the iput() command:

library(readr)

# creates a csv file of foo
write_csv(foo, "foo.csv")

# send file
iput("foo.csv", "foo.csv")

# check whether it is stored
ils()
#> 
#> ==========
#> iRODS Zone
#> ==========
#>                  logical_path        type
#>  /tempZone/home/alice/foo.csv data_object
#>  /tempZone/home/alice/foo.rds data_object

Later on somebody else might want to download this file again and store it locally:

# retrieve it again later
iget("foo.csv", "foo.csv")
read_csv("foo.csv")
#> Rows: 3 Columns: 2
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): y
#> dbl (1): x
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 3 × 2
#>       x y    
#>   <dbl> <chr>
#> 1     1 x    
#> 2     8 y    
#> 3     9 z

Query

By adding metadata you and others can more easily discover data in future projects. Objects can be searched with General Queries and iquery():

# look for objects in the home collection with a wildcard `%`
iquery("SELECT COLL_NAME, DATA_NAME WHERE COLL_NAME LIKE '/tempZone/home/%'")
#>              COLL_NAME DATA_NAME
#> 1 /tempZone/home/alice   foo.csv
#> 2 /tempZone/home/alice   foo.rds
# or for data objects with a name that starts with "foo"
iquery("SELECT COLL_NAME, DATA_NAME WHERE DATA_NAME LIKE 'foo%'")
#>              COLL_NAME DATA_NAME
#> 1 /tempZone/home/alice   foo.csv
#> 2 /tempZone/home/alice   foo.rds

For more on querying, check out vignette("metadata").

Cleanup

Finally, we can clean up Alice’s home collection:

# delete object
irm("foo.rds", force = TRUE)
irm("foo.csv", force = TRUE)

# check if objects are removed
ils()
#> This collection does not contain any objects or collections.
# close the server
stop_irods_demo()
# optionally remove the Docker images
# irods:::remove_docker_images()