Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/paws-r/paws
Paws, a package for Amazon Web Services in R
https://github.com/paws-r/paws
aws aws-sdk r
Last synced: 15 days ago
JSON representation
Paws, a package for Amazon Web Services in R
- Host: GitHub
- URL: https://github.com/paws-r/paws
- Owner: paws-r
- License: other
- Created: 2018-10-24T01:28:47.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-07-30T17:35:43.000Z (3 months ago)
- Last Synced: 2024-07-30T21:53:25.764Z (3 months ago)
- Topics: aws, aws-sdk, r
- Language: R
- Homepage: https://www.paws-r-sdk.com
- Size: 121 MB
- Stars: 312
- Watchers: 10
- Forks: 37
- Open Issues: 44
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - paws-r/paws - Paws, a package for Amazon Web Services in R (R)
README
# Paws, an AWS SDK for R [
](https://www.paws-r-sdk.com)
[](https://cran.r-project.org/package=paws)
[](https://github.com/paws-r/paws/actions?workflow=Unit%20Tests)
[](https://codecov.io/gh/paws-r/paws)
[](https://github.com/paws-r/paws/tree/main/examples)
[](https://paws-r.r-universe.dev)
## Overview
Paws is a **P**ackage for **A**mazon **W**eb **S**ervices in R. Paws provides
access to the full suite of AWS services from within R.
Visit [our home page](https://www.paws-r-sdk.com) to see online documentation.
Disclaimer: Paws is not a product of or supported by Amazon Web Services.
## Installation
Install Paws using:
``` r
install.packages("paws")
```
If you are using Linux, you will need to install the following OS packages:
* Debian/Ubuntu: `libcurl4-openssl-dev libssl-dev libxml2-dev`
* CentOS/Fedora/Red Hat: `libcurl-devel libxml2-devel openssl-devel`
Or install the development version from [r-universe](https://paws-r.r-universe.dev/ui#builds):
``` r
install.packages('paws', repos = c(pawsr = 'https://paws-r.r-universe.dev', CRAN = 'https://cloud.r-project.org'))
```
As of `paws v0.3.0` it's possible to install paws from pre-build binaries from a CRAN like repository host on AWS S3. We currently provide
pre-built binaries for Linux as Mac and Windows binaries are supported on the CRAN. The main focus of this is to provide linux with some pre-built binaries so that the install time is spead up.
To get the latest pre-built binaries you can use the following:
```r
install.packages('paws', repos = c(pawsr = 'https://paws-r-builds.s3.amazonaws.com/packages/latest/', CRAN = 'https://cloud.r-project.org'))
```
You can also get a specific version of paws by setting the version:
```r
install.packages('paws', repos = c(pawsr = 'https://paws-r-builds.s3.amazonaws.com/packages/0.3.0/', CRAN = 'https://cloud.r-project.org'))
```
## Credentials
You'll need to set up your AWS credentials and region. Paws supports setting
these per-service, or using R and OS environment variables, AWS credential
files, and IAM roles. See [docs/credentials.md](docs/credentials.md) for more
info.
In the example below, we set them with R environment variables.
Warning: Do not save your credentials in your code, which could reveal
them to others. Use one of the other methods above instead. See also
[RStudio's best practices for securing credentials](https://db.rstudio.com/best-practices/managing-credentials/#encrypt-credentials-with-keyring).
``` r
Sys.setenv(
AWS_ACCESS_KEY_ID = "abc",
AWS_SECRET_ACCESS_KEY = "123",
AWS_REGION = "us-east-1"
)
```
## Usage
To use a service, create a client. All of a service's operations
can be accessed from this object.
``` r
ec2 <- paws::ec2()
```
Launch an EC2 instance using the `run_instances` function.
``` r
resp <- ec2$run_instances(
ImageId = "ami-f973ab84",
InstanceType = "t2.micro",
KeyName = "default",
MinCount = 1,
MaxCount = 1,
TagSpecifications = list(
list(
ResourceType = "instance",
Tags = list(
list(Key = "webserver", Value = "production")
)
)
)
)
```
List all of your instances with `describe_instances`.
``` r
ec2$describe_instances()
```
Shut down the instance you started with `terminate_instances`.
``` r
ec2$terminate_instances(
InstanceIds = resp$Instances[[1]]$InstanceId
)
```
## Documentation
You can browse all available services by looking at the package documentation.
``` r
help(package = "paws")
```
You can also jump to a specific service and see all its operations.
``` r
?paws::ec2
```
RStudio's code completion will show you the available services,
their operations, and each operation's parameters.
There are also examples for [EC2](examples/ec2.R), [S3](examples/s3.R),
[SQS](examples/sqs.R), [SNS](examples/sns.R),
[DynamoDB](examples/dynamodb.R), [Lambda](examples/lambda.R),
[Batch](examples/batch.R), and [Comprehend](examples/comprehend.R).
## Related packages
* [`cognitoR`](https://github.com/chi2labs/cognitoR) provides
authentication for Shiny applications using Amazon Cognito.
* [`noctua`](https://dyfanjones.github.io/noctua/) is an interface to the
[Athena](https://aws.amazon.com/athena/) serverless interactive query
service, which allows you to query files stored in S3 using SQL or
[`dplyr`](https://dplyr.tidyverse.org/).
* [`R6sagemaker`](https://github.com/DyfanJones/sagemaker-r-sdk) is an
interface to the [SageMaker](https://aws.amazon.com/sagemaker/) machine
learning service, designed to work like the Python SageMaker SDK.
* [`redshiftTools`](https://github.com/RedOakStrategic/redshiftTools) is
a collection of tools for working with the
[Redshift](https://aws.amazon.com/redshift/) data warehouse service,
such as performing bulk uploads.
* [`stepfunctions`](https://github.com/DyfanJones/aws-step-functions-data-science-sdk-r)
is an SDK for building machine learning workflows and pipelines on AWS
using the Step Functions service.
## Examples, tutorials, and workshops
* [AWS AI Services for R Users](https://github.com/alex23lemm/AWS-AI-Services-R-Workshop)
shows how to use AWS to add deep learning capabilities like image recognition,
text translation, and text-to-speech conversion to R and Shiny applications.
* [Using Amazon Rekognition from R](https://alex23lemm.github.io/posts/2021-01-03-using-amazon-rekognition-custom-labels-from-r/)
is an end to end example of how to build and deploy a model to detect Nike
swooshes in images using the Rekognition computer vision service.
## Credits
API specifications from [AWS SDK for JavaScript](https://github.com/aws/aws-sdk-js);
design based on [AWS SDK for Go](https://github.com/aws/aws-sdk-go).
[Logo](docs/logo.png) by [Hsinyi Chen](https://linktr.ee/starfolio).
[Home page design](https://www.paws-r-sdk.com) and [cheat sheet](docs/cheat_sheet.pdf) by Mara Ursu.
Supported by the AWS Open Source promotional credits program.