Mark Edmondson


R library for interacting with the Google Cloud Storage JSON API (api docs).


Google Cloud Storage charges you for storage (prices here).

You can use your own Google Project with a credit card added to create buckets, where the charges will apply. This can be done in the Google API Console

Setting environment variables

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 bucket, and auto-authentication upon attaching the library. For example:

Sys.setenv("GCS_CLIENT_ID" = "mykey",
           "GCS_CLIENT_SECRET" = "mysecretkey",
           "GCS_WEB_CLIENT_ID" = "my-shiny-key",
           "GCS_WEB_CLIENT_SECRET" = "my-shiny-secret-key",
           "GCS_DEFAULT_BUCKET" = "my-default-bucket",
           "GCS_AUTH_FILE" = "/fullpath/to/service-auth.json")

These can alternatively be set on the command line or via an or .Renviron file (


Authentication can be carried out each session via gcs_auth. The first time you run this you will be sent to a Google login prompt in your browser to allow the googleCloudStorageR project access (or the Google project you configure).

Once authenticated a file named .httr-oauth is saved to your working directory. On subsequent authentication this file will hold your authentication details, and you won’t need to go via the browser. Deleting this file, or setting new_user=TRUE will start the authentication flow again.

## first time this will send you to the browser to authenticate

## to authenticate with a fresh user, delete .httr-oauth or run with new_user=TRUE
gcs_auth(new_user = TRUE) functions...etc...

Each new R session will need to run gcs_auth() to authenticate future API calls.


Alternatively, you can specify the location of a service account JSON file taken from your Google Project, or the location of a previously created .httr-oauth token in a system environment:

    Sys.setenv("GCS_AUTH_FILE" = "/fullpath/to/auth.json")

This file will then used for authentication via gcs_auth() when you load the library:

## GCS_AUTH_FILE set so auto-authentication

## no need for gcs_auth()


Setting a default Bucket

To avoid specifying the bucket in the functions below, you can set the name of your default bucket via environmental variables or via the function gcs_global_bucket(). See the Setting environment variables section below for more details.

## set bucket via environment
Sys.setenv("GCS_DEFAULT_BUCKET" = "my-default-bucket")


## optional, if you haven't set environment argument GCS_AUTH_FILE
## gcs_auth()

## check what the default bucket is
[1] "my-default-bucket"

## you can also set a default bucket after loading the library for that session
[1] "my-default-bucket-2"

Downloading objects from Google Cloud storage

Once you have a Google project and created a bucket with an object in it, you can download it as below:


## optional, if you haven't set environment argument GCS_AUTH_FILE
## gcs_auth()

## get your project name from the API console
proj <- "your-project"

## get bucket info
buckets <- gcs_list_buckets(proj)
bucket <- "your-bucket"
bucket_info <- gcs_get_bucket(bucket)

==Google Cloud Storage Bucket==
Bucket:          your-bucket 
Project Number:  1123123123 
Location:        EU 
Class:           STANDARD 
Created:         2016-04-28 11:39:06 
Updated:         2016-04-28 11:39:06 
Meta-generation: 1 
eTag:            Cxx=

## get object info in the default bucket
objects <- gcs_list_objects()

## save directly to an R object (warning, don't run out of RAM if its a big object)
## the download type is guessed into an appropriate R object
parsed_download <- gcs_get_object(objects$name[[1]])

## if you want to do your own parsing, set parseObject to FALSE
## use httr::content() to parse afterwards
raw_download <- gcs_get_object(objects$name[[1]], 
                               parseObject = FALSE)

## save directly to a file in your working directory
## parseObject has no effect, it is a httr::content(req, "raw") download
gcs_get_object(objects$name[[1]], saveToDisk = "csv_downloaded.csv")

Uploading objects < 5MB

Objects can be uploaded via files saved to disk, or passed in directly if they are data frames or list type R objects. By default, data frames will be converted to CSV via write.csv(), lists to JSON via jsonlite::toJSON.

If you want to use other functions for transforming R objects, for example setting row.names = FALSE or using write.csv2, pass the function through object_function

## upload a file - type will be guessed from file extension or supply type  
write.csv(mtcars, file = filename)

## upload an R data.frame directly - will be converted to csv via write.csv

## upload an R list - will be converted to json via jsonlite::toJSON
gcs_upload(list(a = 1, b = 3, c = list(d = 2, e = 5)))

## upload an R data.frame directly, with a custom function
## function should have arguments 'input' and 'output'
## safest to supply type too
f <- function(input, output) write.csv(input, row.names = FALSE, file = output)

           object_function = f,
           type = "text/csv")

Upload metadata

You can pass metadata with an object via the function gcs_metadata_object().

the name you pass to the metadata object will override the name if it is also set elsewhere.

meta <- gcs_metadata_object("mtcars.csv",
                             metadata = list(custom1 = 2,
                                             custom_key = 'dfsdfsdfsfs))
gcs_upload(mtcars, object_metadata = meta)

Resumable uploads for files > 5MB up to 5TB

If the file/object is under 5MB, simple uploads are used.

For files > 5MB, resumable uploads are used. This allows you to upload up to 5TB.

If you get an interrupted connection when uploading, gcs_upload will retry 3 times, if it fails it will return a Retry object, that you can try again later from where the upload stopped. Call this via gcs_retry_upload

## write a big object to a file
big_file <- "big_filename.csv"
write.csv(big_object, file = big_file)

## attempt upload
upload_try <- gcs_upload(big_file)

## if successful, upload_try is an object metadata object
==Google Cloud Storage Object==
Name:            "big_filename.csv" 
Size:            8.5 Gb 
Media URL 
Bucket:          your-bucket 
ID:              your-bucket/"test.pdf"/xxxx
MD5 Hash:        rshao1nxxxxxY68JZQ== 
Class:           STANDARD 
Created:         2016-08-12 17:33:05 
Updated:         2016-08-12 17:33:05 
Generation:      1471023185977000 
Meta Generation: 1 
eTag:            CKi90xxxxxEAE= 
crc32c:          j4i1sQ== 

## if unsuccessful after 3 retries, upload_try is a Retry object
==Google Cloud Storage Upload Retry Object==
File Location:     big_filename.csv
Retry Upload URL:  http://xxxx
Created:           2016-08-12 17:33:05 
Type:              csv
File Size:        8.5 Gb
Upload Byte:      4343
Upload remaining: 8.1 Gb

## you can retry to upload the remaining data using gcs_retry_upload()
try2 <- gcs_retry_upload(upload_try)

Updating user access to objects

You can change who can access objects via gcs_update_acl to one of READER or OWNER, on a user, group, domain, project or public for all users or authenticated users.

By default you are “OWNER” of all the objects and buckets you upload and create.

## update access of object to READER for all public
gcs_update_object_acl("your-object.csv", entity_type = "allUsers")

## update access of object for user to OWNER
               entity = "", 
               role = "OWNER")

## update access of object for googlegroup users to READER
                      entity = "", 
                      entity_type = "group")

## update access of object for all users to OWNER on your Google Apps domain
                      entity = "", 
                      entity_type = "domain", 
                      role = OWNER)

Deleting an object

Delete an object by passing its name (and bucket if not default)

## returns TRUE is successful, a 404 error if not found

Viewing current access level to objects

Use gcs_get_object_acl() to see what the current access is for an entity + entity_type.

## default entity_type is user
acl <- gcs_get_object_acl("your-object.csv", 
                         entity = "")
[1] "OWNER"

## for allUsers and allAuthenticated users, you don't need to supply entity
acl <- gcs_get_object_acl("your-object.csv", 
                          entity_type = "allUsers")
[1] "READER"

R Session helpers

Versions of save.image(), save() and load() are implemented called gcs_save_image(), gcs_save() and gcs_load(). These functions save and load the global R session to the cloud.

## save the current R session including all objects

### wipe environment
rm(list = ls())

## load up environment again

Save specific objects:

cc <- 3
d <- "test1"
gcs_save("cc","d", file = "gcs_save_test.RData")

## remove the objects saved in cloud from local environment

## load them back in from GCS
gcs_load(file = "gcs_save_test.RData")
cc == 3
[1] TRUE
d == "test1"
[1] TRUE

You can also upload .R code files and source them directly using gcs_source:

## make a R source file and upload it
cat("x <- 'hello world!'\nx", file = "example.R")
gcs_upload("example.R", name = "example.R")

## source the file to run its code

## the code from the upload file has run
[1] "hello world!"

Uploading via a Shiny app

The library is also compatible with Shiny authentication flows, so you can create Shiny apps that lets users log in and upload their own data.

An example of that is shown below:

options(googleAuthR.scopes.selected = "")
## optional, if you want to use your own Google project
# options("googleAuthR.client_id" = "YOUR_CLIENT_ID")
# options("googleAuthR.client_secret" = "YOUR_CLIENT_SECRET")

## you need to start Shiny app on port 1221
## as thats what the default googleAuthR project expects for OAuth2 authentication

## options(shiny.port = 1221)
## print(source('shiny_test.R')$value) or push the "Run App" button in RStudio

  ui = shinyUI(
        fileInput("picture", "picture"),
        textInput("filename", label = "Name on Google Cloud Storage",value = "myObject"),
        actionButton("submit", "submit"),
  server = shinyServer(function(input, output, session){

    access_token <- shiny::callModule(googleAuth, "login")

    meta <- eventReactive(input$submit, {

      message("Uploading to Google Cloud Storage")
      # from googleCloudStorageR
                 file = input$picture$datapath,
                 # enter your bucket name here
                 bucket = "gogauth-test",  
                 type = input$picture$type,
                 name = input$filename,
                 shiny_access_token = access_token())


    output$meta_file <- renderText({


      paste("Uploaded: ", meta()$name)



Bucket administration

There are various functions to manipulate Buckets:

Object administration

You can get meta data about an object by passing meta=TRUE to gcs_get_object

gcs_get_object("your-object", "your-bucket", meta = TRUE)

Explanation of Google Project access

googleCloudStorageR has its own Google project which is used to call the Google Cloud Storage API, but does not have access to the objects or buckets in your Google Project unless you give permission for the library to access your own buckets during the OAuth2 authentication process.

No other user, including the owner of the Google Cloud Storage API project has access unless you have given them access, but you may want to change to use your own Google Project (that could or could not be the same as the one that holds your buckets).

Configuring your own Google Project

The instructions below are for when you visit the Google API console (

For local use

  1. Click ‘Create a new Client ID’, and choose “Installed Application”.
  2. Note your Client ID and secret.
  3. Add them by modifying your .Renviron file, or under the following entries:

    Sys.setenv("GCS_CLIENT_ID" = "mykey",
               "GCS_CLIENT_SECRET" = "mysecretkey")
  4. Alternatively, modify these options after googleAuthR has been loaded:

    options("googleAuthR.client_id" = "YOUR_CLIENT_ID")
    options("googleAuthR.client_secret" = "YOUR_CLIENT_SECRET")

For Shiny use

  1. Click ‘Create a new Client ID’, and choose “Web Application”.
  2. Note your Client ID and secret.
  3. Add the URL of where your Shiny app will run, with no port number. e.g.
  4. And/Or also put in localhost or with a port number for local testing. Remember the port number you use as you will need it later to launch the app e.g.
  5. Add them by modifying your .Renviron file, or under the following entries:

    Sys.setenv("GCS_WEB_CLIENT_ID" = "mykey",
               "GCS_WEB_CLIENT_SECRET" = "mysecretkey")
  6. Alternatively, in your Shiny script modify these options:

    options("googleAuthR.webapp.client_id" = "YOUR_CLIENT_ID")
    options("googleAuthR.webapp.client_secret" = "YOUR_CLIENT_SECRET")
  7. To run the app locally specifying the port number you used in step 4 e.g. shiny::runApp(port=1221) or set a shiny option to default to it: options(shiny.port = 1221) and launch via the RunApp button in RStudio.
  8. Running on your Shiny Server will work only for the URL from step 3.

Activate API

  1. Click on “APIs”
  2. Select and activate the Cloud Storage JSON API
  3. After loading the package via library(googleCloudStorage), it will look to see if "" is set in getOption("googleAuthR.scopes.selected") and set it if it is not, adding to the existing scopes.
  4. Alternativly, set the googleAuthR option for Google Cloud storage scope after the library has been loaded but before authentication.

    options(googleAuthR.scopes.selected = "")