aws.s3 is a simple client package for the Amazon Web Services (AWS) Simple Storage Service (S3) REST API. While other packages currently connect R to S3, they do so incompletely (mapping only some of the API endpoints to R) and most implementations rely on the AWS command-line tools, which users may not have installed on their system.

To use the package, you will need an AWS account and enter your credentials into R. Your keypair can be generated on the IAM Management Console under the heading Access Keys. Note that you only have access to your secret key once. After it is generated, you need to save it in a secure location. New keypairs can be generated at any time if yours has been lost, stolen, or forgotten.

By default, all cloudyr packages look for the access key ID and secret access key in environment variables. You can also use this to specify a default region or a temporary "session token". For example:

Sys.setenv("AWS_ACCESS_KEY_ID" = "mykey",
           "AWS_SECRET_ACCESS_KEY" = "mysecretkey",
           "AWS_DEFAULT_REGION" = "us-east-1",
           "AWS_SESSION_TOKEN" = "mytoken")

These can alternatively be set on the command line prior to starting R or via an Renviron.site or .Renviron file, which are used to set environment variables in R during startup (see ? Startup).

If you work with multiple AWS accounts, another option that is consistent with other Amazon SDKs is to create a centralized ~/.aws/credentials file, containing credentials for multiple accounts. You can then use credentials from this file on-the-fly by simply doing:

# use your 'default' account credentials
aws.signature::use_credentials()

# use an alternative credentials profile
aws.signature::use_credentials(profile = "bob")

Temporary session tokens are stored in environment variable AWS_SESSION_TOKEN (and will be stored there by the use_credentials() function). The aws.iam package provides an R interface to IAM roles and the generation of temporary session tokens via the security token service (STS).

The package can be used to examine publicly accessible S3 buckets and publicly accessible S3 objects without registering an AWS account. If credentials have been generated in the AWS console and made available in R, you can find your available buckets using:

library("aws.s3")
bucketlist()

If your credentials are incorrect, this function will return an error. Otherwise, it will return a list of information about the buckets you have access to.

To get a listing of all objects in a public bucket, simply call

get_bucket(bucket = '1000genomes')

Amazon maintains a listing of Public Data Sets on S3.

To get a listing for all objects in a private bucket, pass your AWS key and secret in as parameters. (As described above, all functions in aws.s3 will look for your keys as environment variables by default, greatly simplifying the process of making a s3 request.)

# specify keys in-line
get_bucket(
  bucket = 'my_bucket',
  key = YOUR_AWS_ACCESS_KEY,
  secret = YOUR_AWS_SECRET_ACCESS_KEY
)

# specify keys as environment variables
Sys.setenv("AWS_ACCESS_KEY_ID" = "mykey",
           "AWS_SECRET_ACCESS_KEY" = "mysecretkey")
get_bucket("my_bucket")

S3 can be a bit picky about region specifications. bucketlist() will return buckets from all regions, but all other functions require specifying a region. A default of "us-east-1" is relied upon if none is specified explicitly and the correct region can't be detected automatically. (Note: using an incorrect region is one of the most common - and hardest to figure out - errors when working with S3.)

There are eight main functions that will be useful for working with objects in S3:

1. s3read_using() provides a generic interface for reading from S3 objects using a user-defined function
2. s3write_using() provides a generic interface for writing to S3 objects using a user-defined function
3. get_object() returns a raw vector representation of an S3 object. This might then be parsed in a number of ways, such as rawToChar(), xml2::read_xml(), jsonlite::fromJSON(), and so forth depending on the file format of the object
4. save_object() saves an S3 object to a specified local file
5. put_object() stores a local file into an S3 bucket
6. s3save() saves one or more in-memory R objects to an .Rdata file in S3 (analogously to save()). s3saveRDS() is an analogue for saveRDS()
7. s3load() loads one or more objects into memory from an .Rdata file stored in S3 (analogously to load()). s3readRDS() is an analogue for saveRDS()
8. s3source() sources an R script directly from S3

They behave as you would probably expect:

# save an in-memory R object into S3
s3save(mtcars, bucket = "my_bucket", object = "mtcars.Rdata")

# `load()` R objects from the file
s3load("mtcars.Rdata", bucket = "my_bucket")

# get file as raw vector
get_object("mtcars.Rdata", bucket = "my_bucket")
# alternative 'S3 URI' syntax:
get_object("s3://my_bucket/mtcars.Rdata")

# save file locally
save_object("mtcars.Rdata", file = "mtcars.Rdata", bucket = "my_bucket")

# put local file into S3
put_object(file = "mtcars.Rdata", object = "mtcars2.Rdata", bucket = "my_bucket")

This package is not yet on CRAN. To install the latest development version you can install from the cloudyr drat repository:

# latest stable version
install.packages("aws.s3", repos = c("cloudyr" = "http://cloudyr.github.io/drat"))

# on windows you may need:
install.packages("aws.s3", repos = c("cloudyr" = "http://cloudyr.github.io/drat"), INSTALL_opts = "--no-multiarch")

Or, to pull a potentially unstable version directly from GitHub:

if (!require("ghit")) {
    install.packages("ghit")
}
ghit::install_github("cloudyr/aws.s3")