GCSConnection 1.0.1
You need to have a service account credentials to authenticate
with Google Cloud Storage API. The credentials determines which data sources
are accessable to you. Please follow the instructions on Google
Authentication to create your credentials file and download it to
your local machine(Note that it can be done in the Cloud Console).
The package will search for the credentials file from
the environment variable GOOGLE_APPLICATION_CREDENTIALS when it is
loaded. If it fails to find the credentials, it will look for the
environment variable GCS_AUTH_FILE instead. If both environment
variables are empty, you will only be able to access public data.
If you set the environment variables after the package is loaded, you can rerun the auto
authentication by gcs_cloud_auth().
The function gcs_cloud_auth provides two ways to authenticate with Google Cloud. When you have a service account credentials, you can authenticate with Google Cloud by calling gcs_cloud_auth(credentials_file_path). The second method is to get credentials from Cloud SDK. The package is able to interact with the command-line tool gcloud. Once the account has been set in the gcloud program, the package can be authenticated by gcs_cloud_auth(gcloud = TRUE). By default, the package will use the first account in gcloud to authenticate with Google. If there are multiple accounts in gcloud, you can switch the account by gcs_cloud_auth(gcloud = TRUE, email = "your email address").
Not all the cloud buckets require a credentials to access. You can read public data without authenticating with Google. By default, if the package fails to find the credentials from the environment variables, it will use anouymous access method to query data from google cloud. Otherwise, you can switch to the anouymous mode by calling gcs_cloud_auth(json_file = NULL).
Once the credentials is set, you can check your token and authenticate type via:
gcs_get_cloud_auth()
#> Token: NULL
#> authen source: JSON fileCreating a connection to a file in a bucket is simple,
you can simply provide an URL to gcs_connection. Below is an example to create
a read connection to a public dataset on Google Cloud Platform.
file <- "gs://genomics-public-data/NA12878.chr20.sample.DeepVariant-0.7.2.vcf"
con <- gcs_connection(description = file, open = "r")
readLines(con, n = 2L)
#> [1] "##fileformat=VCFv4.2"
#> [2] "##FILTER=<ID=PASS,Description=\"All filters passed\">"
close(con)Note that you do not need a credentials for accessing the public data.
the character “r” in the parameter open is an abbreviation of read, please see ?gcs_connection for more details.
It is also possible to specify file and bucket name separately. For example:
file_name <- "NA12878.chr20.sample.DeepVariant-0.7.2.vcf"
bucket_name <- "genomics-public-data"
con <- gcs_connection(
description = file_name, open = "r", bucket = bucket_name
)
readLines(con, n = 2L)
#> [1] "##fileformat=VCFv4.2"
#> [2] "##FILTER=<ID=PASS,Description=\"All filters passed\">"
close(con)The above code can create the same connection as the previous
example. Note that you cannot provide both URI and bucket name to gcs_connection.
For the write connection, it can be made by specifying an appropriate
open mode in the gcs_connection function. Please note that due to
the limitation of the Google Cloud Storage, the write connection is
not seekable, which means you cannot use the seek function to
navigate through the file. When the write connection is created.
It always starts in the beginning of the file. If there exists a file
with the same name, the old file will be deleted. After the write connection is closed, the file will become immutable and no further change on the file
can be made.
Besides manually creating a connection, the package provides a simple and convinent S4 class to manage files. You can list all files in a bucket/folder by
## These are equivalent
## x <- gcs_dir("gs://genomics-public-data/clinvar/")
## x <- gcs_dir("genomics-public-data/clinvar/")
x <- gcs_dir("gs://genomics-public-data/clinvar/")
x
#> 4 items in the folder `genomics-public-data/clinvar/`:
#> --------------------
#> Name Size
#> 1 README.txt 227B
#> 2 disease_names.txt 2.5MB
#> 3 variant_summary.txt 89.3MB
#> 4 variant_summary.txt.gz 8.3MB
#> --------------------
#> Total Size : 99.9MBNote that for listing files in a bucket, the trailing slash does not make any differences. However, if you want to list files in a folder inside a bucket, it is recommended to explicitly add a / at the end. The trailing slash is not mandatory, but if no trailing slash presents, the package need one extra communication with the cloud to determine whether the path is a file or a folder, which can double your time cost.
In fact, there is no hierarchical structure in a bucket, all files are in the same level. By using the slash delimiter, the cloud makes the files appear as though they are stored in folders. Though the cloud does not have any restriction on the use of / in the file name, a good practice is to not use / at the end of a file name for it can cause unnecessary confusion.
Once obtaining a list of files, you can changes your current directory or view the detail of a file through $ or [[ operator
## equivalent: x$README.txt
myfile <- x[["README.txt"]]
myfile
#> File: clinvar/README.txt
#> Bucket: genomics-public-data
#> Size: 227B
#> Type: text/plain
#> URI: gs://genomics-public-data/clinvar/README.txt
#> Last modified: 2018-10-16T15:11:31.610ZYou can also use .. to go to the parent folder
## equivalent: x$`..`
x[[".."]]
#> 18 items in the bucket `genomics-public-data`:
#> --------------------
#> Name Size
#> 1 NA12878.chr20.sample.DeepVariant-0.7.2.vcf 7.2KB
#> 2 NA12878.chr20.sample.bam 55.8KB
#> 3 README 379B
#> 4 1000-genomes-phase-3/ *
#> 5 1000-genomes/ *
#> 6 clinvar/ *
#> 7 cwl-examples/ *
#> 8 ftp-trace.ncbi.nih.gov/ *
#> 9 gatk-examples/ *
#> 10 gce-images/ *
#> 11 linkage-disequilibrium/ *
#> 12 platinum-genomes/ *
#> 13 precision-fda/ *
#> 14 references/ *
#> 15 resources/ *
#> 16 simons-genome-diversity-project/ *
#> 17 test-data/ *
#> 18 ucsc/ *
#> --------------------
#> Total Size : 63.4KB
myfile[[".."]]
#> 4 items in the folder `genomics-public-data/clinvar/`:
#> --------------------
#> Name Size
#> 1 README.txt 227B
#> 2 disease_names.txt 2.5MB
#> 3 variant_summary.txt 89.3MB
#> 4 variant_summary.txt.gz 8.3MB
#> --------------------
#> Total Size : 99.9MBThe connection can be made by
con <- myfile$get_connection(open = "r")
con
#> A connection with
#> description "gs://genomics-public-data/clinvar/README.txt"
#> class "google cloud storage"
#> mode "r"
#> text "text"
#> opened "opened"
#> can read "yes"
#> can write "no"
close(con)You can also query file info, copy, delete files through via the file manager
## Get file name
myfile$file_name
#> [1] "README.txt"
## copy file
## For the destination, you can specify a path to the file,
## or a path to a folder.
destination <-tempdir()
myfile$copy_to(destination)
file.exists(file.path(destination,myfile$file_name))
#> [1] TRUE
## Delete file, the function is excutable
## only when you have the right to delete the file.
## Use `quiet = TRUE` to suppress the confirmation before deleting the file.
# x$README.txt$delete(quiet = FALSE)Note that you cannot delete a folder since beforementioned file hierarchy issue. Once all files in a folder has been deleted, the folder will be removed.
For reducing the number of network requests and speeding up the performance
of the connection, there is a buffer associated with each connection. The default
size is 1Mb for a buffer. You can set or get the buffer size via gcs_read_buff,
gcs_write_buff, gcs_get_read_buff and gcs_get_write_buff functions:
gcs_get_read_buff()
#> [1] 1048576
gcs_get_write_buff()
#> [1] 1048576Please note the minimum buffer size for a write connection is 256 Kb. Creating a connection with small buffer size may impact the performance.