SolveBio for R

This version of SolveBio for R is compatible with Vault-based datasets only (released on July 28th, 2017).

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This package contains the SolveBio R language bindings. SolveBio makes it easy to access genomic reference data.

Features of this package include:

Please see the SolveBio documentation for more information about the platform.


Installing this package requires an installed R environment.



# By default it will look for a key in the $SOLVEBIO_API_KEY environment variable.

# You may also supply an API key in your code
login(api_key="<Your API key>")
# RStudio users can put the following line in ~/.Rprofile
# Sys.setenv(SOLVEBIO_API_KEY="<Your API key>")

# Retrieve a list of all datasets
datasets <- Dataset.all()

# Retrieve a specific dataset (metadata)
ClinVar <- Dataset.get_by_full_path("solvebio:public:/ClinVar/3.7.4-2017-01-30/Variants-GRCh37")

# Query a dataset with filters as JSON:
filters <- '[["gene_symbol", "BRCA1"]]'
# or, filters as R code:
filters <- list(list('gene_symbol', 'BRCA1'), list('clinical_significance',

# Execute the queries
# NOTE: paginate=TRUE may issue multiple requests, depending on the dataset and filters
results <- Dataset.query(id = ClinVar$id, filters = filters, limit = 1000, paginate = TRUE)

# Access the results (flattened by default)


To use SolveBio in your Shiny app, refer to the docs on Developing Applications with R Shiny and SolveBio.

This package provides a Shiny server wrapper called solvebio::protectedServer() which requires users to authenticate with SolveBio and authorize the app before proceeding. In addition, you may enable token cookie storage by installing ShinyJS and adding JS code (solvebio::protectedServerJS()) to your Shiny UI.

An example app is available in the solvebio-shiny-example GitHub repository.


To install the development version of this package from GitHub, you will need the devtools package.

install.packages(c("devtools", "httr", "jsonlite"))
devtools::install_github("solvebio/solvebio-r", ref="master")

To run the test suite:

make test

Migrating to Version 2

Version 2 of the R client removes support for the Depository and DepositoryVersion methods, and adds support for the Vault and Object methods.

A vault is similar to a filesystem in that it provides a folder-based hierarchy in which additional folders, files, and SolveBio Datasets can be stored. The folders, files, and SolveBio Datasets in a vault are collectively referred to as “objects” and can be accessed using the Vault, Object, and Dataset methods.

Vaults have an advanced permission model that provides for three different levels of access: read, write, and admin. Permissions are settable through the SolveBio UI. For detailed information on the permission model, please visit this link:

As part of the migration onto Version 2, SolveBio has automatically applied the permissions set on existing Depositories to new Vaults which we have created to replace them.

It is likely that any scripts you have written which utilize the R client will need to be modified to be compatible with Version 2. Below is an exhaustive list of all the things that have changed in the user-facing methods of the client. If you encounter any issues migrating your code, please submit a support ticket and we would be happy to assist you.

Naming Conventions

It is useful to know the different names for the various entities (or combined entities) that are available via the Client. The naming conventions are as follows:

(1) - Account Domain
(2) - Vault Name
(3) - Vault Full Path
(4) - Object Path
(5) - Object Filename
(6) - Object Full Path

Changes in Version 2

  1. Dataset creation changes
Old: Dataset.get_or_create_by_full_name(<full_name>)
New: Dataset.get_or_create_by_full_path(account_domain:vault_name:/parent/path/dataset_name)

For example, if you belong to the “acme” domain, then to create a dataset named named “EGFR_analysis” in the “/July-2017” folder of the “Research” vault, make the following call:


You can optionally leave off the account domain in front, but note that this will not work if your object path includes a colon:

  1. Dataset retrieval changes

A dataset’s “full_path” is a triplet consisting of account domain, vault name, and the dataset’s path in the vault (see above). Retrieval of a dataset by its full path can be performed in a single call:


In order to get the full path of an existing dataset, search for datasets within a vault.

# Get all of the Clinvar datasets that are version 3 and above
public <- Vault.get_by_full_path('solvebio:public')
Vault.datasets(public$id, query='/ClinVar/3')
  1. Removal of Depository and DepositoryVersion classes.

Depository has been replaced by the Vault class.

DepositoryVersion was functionality is now provided by the Object class. Objects are files, folders, or SolveBio Datasets that exist inside a vault. As part of your account’s migration onto Version 2 of SolveBio, we have automatically moved datasets located in Depository “X” and DepositoryVersion “Y” to a Vault named “X” and a folder named “Y”.

  1. Removal of DatasetCommit approval. The auto_approve, is_approved and approved_by attributes have been removed. The /approve endpoint has also been removed. All commits will be approved automatically.

Vault Browsing

List all the vaults you currently have access to.


Your Personal Vault

Each user has a personal vault that is accessible to that user only. Other users cannot list the contents of this vault, cannot access the objects contained in it, and cannot modify it in any way. To provide access to objects stored in your personal vault, you must copy the objects into a different vault.

Your personal dataset can be retrieved using the following method:



Browsing the contents of a vault can be easily performed using the following shortcuts.

First, retrieve a vault:

vault = Vault.get_personal_vault()
vault = Vault.get_by_full_path('solvebio:public')
vault = Vault.get_by_full_path('your_account_domain:vault_name')
vault = Vault.get_by_full_path('vault_name')  # Searches inside your account domain

Then, you may call the appropriate method:

Vault.objects(vault$id)  # Includes files, folders, and datasets

Vault.files(vault$id, filename='hello.txt')   # Can pass filters to all of these methods

Search for files, folders, and datasets in a vault using the search method:$id, query='hello')$id, 'hello', object_type='folder')$id, 'hello', object_type='file')$id, 'hello', object_type='dataset')

To get or create a new Vault, use the following method:


File Uploads

vault <- Vault.get_personal_vault()
object <- Object.upload_file('./analysis.tsv', vault$id, '/')

Re-uploading the same file to the same path auto-increments the filename on the server. This is required because no two objects can have the same full path.

You can optionally specify a new filename for the uploaded file:

vault <- Vault.get_personal_vault()
object <- Object.upload_file('./analysis.tsv', vault$id, '/', 'analysis_v2.tsv')

To delete an object, you need its ID. This action cannot be undone.


Dataset Imports

The functionality of Dataset Imports remains the same, except that you can now pass an object’s ID (after uploading it into a Vault):

vault <- Vault.get_personal_vault()
# Upload a file into your personal vault
object <- Object.upload_file('./analysis.tsv', vault$id, '/')

# Create (or get) a dataset
dataset_full_path = paste(vault$name, '/My New Dataset', sep=":")
dataset <- Dataset.get_or_create_by_full_path(dataset_full_path)

# Create the import
DatasetImport.create(dataset_id = dataset$id, commit_mode = 'append', object_id = object$id)

Packaging and Releasing

  1. Bump the version using the bumpversion command (pip install bumpversion).

  2. Update the with changes.

  3. Update the DESCRIPTION file with the latest date.

  4. Regenerate roxygen2 and build/check the tarball:

    make clean make make check

  5. Submit to CRAN.