Opal Projects API

Yannick Marcon


Opal stores data and meta-data in projects that are accessible through web services. See the Variables and Data documentation page that explains the data model of Opal. The Opal R package exposes projects related functions:

Note that these functions do not create a R session on the server side: it is only accessing the content of the Opal server (permission checks apply).


Setup the connection with Opal:

o <- opal.login("administrator", "password", url = "https://opal-demo.obiba.org")

Project Operations

List the projects:


List the datasources (there is one per project) to get the details about how the data are persisted and what are the tables:


List the tables in a datasource, with their count of variables and entities:

opal.tables(o, "CNSIM", counts = TRUE)

Table Operations

The table object can be retrieved as follow:

opal.table(o, "CNSIM", "CNSIM1", counts = TRUE)

The existence of a table can be checked:

opal.table_exists(o, "CNSIM", "CNSIM1")

And more specifically, it is easy to verify whether a table is a view or not:

opal.table_exists(o, "CNSIM", "CNSIM1", view = TRUE)

A table can be created, either as a raw table or a view:

# drop table if it exists
opal.table_delete(o, "CNSIM", "CNSIM123")
# then create a view, no variables
opal.table_create(o, "CNSIM", "CNSIM123", tables = c("CNSIM.CNSIM1", "CNSIM.CNSIM2", "CNSIM.CNSIM3"))
opal.table(o, "CNSIM", "CNSIM123", counts = TRUE)

Table Dictionary Operations

List the variables of a table and get the details of the variable annotations (one column per variable attribute with namespace). This list also includes the category properties:

opal.variables(o, "CNSIM", "CNSIM1")

It is also possible to get the full data dictionary of a table, as separate data frames of variables and categories. This is the recommended format for working with a data dictionary:

dico <- opal.table_dictionary_get(o, "CNSIM", "CNSIM1")

Here we modify the data dictionary by appending a derivation script to each of the variables:

dico$variables$script <- paste0("$('", dico$variables$name, "')")

Then we apply this derived variables dictionary to the view we have previously created and verify the counts of columns (variables) and rows (entities) in this table:

opal.table_dictionary_update(o, "CNSIM", "CNSIM123", variables = dico$variables, categories = dico$categories)
opal.table(o, "CNSIM", "CNSIM123", counts = TRUE)

Table Values Operations

Get the values in a table for a specific Participant entity:

opal.valueset(o, "CNSIM", "CNSIM123", identifier = "1454")

Get all the values of a table in our local R session as a data.frame (tibble) object:

cnsim1 <- opal.table_get(o, "CNSIM", "CNSIM1")
cnsim2 <- opal.table_get(o, "CNSIM", "CNSIM2")
cnsim3 <- opal.table_get(o, "CNSIM", "CNSIM3")

Then do some alterations on this data.frame and save it back as a raw table:

cnsim123 <- rbind(cnsim1, cnsim2, cnsim3)
cnsim123$DIS_AMI <- NULL
cnsim123$DIS_CVA <- NULL
cnsim123$DIS_DIAB <- NULL
opal.table_save(o, cnsim123, "CNSIM", "CNSIM", overwrite = TRUE, force = TRUE)
opal.table(o, "CNSIM", "CNSIM", counts = TRUE)

Verify that this raw table resulting from the merge of the other tables as same values for a given Participant:

opal.valueset(o, "CNSIM", "CNSIM", identifier = "1454")

It is possible to truncate a table, i.e. delete ALL the values of a table (which must not be a view):

opal.table_truncate(o, "CNSIM", "CNSIM")
opal.table(o, "CNSIM", "CNSIM", counts = TRUE)

Taxonomy Operations

Variable attributes can be described by taxonomies. List the taxonomies:


List the vocabularies of a taxonomy:

opal.vocabularies(o, taxonomy = "Mlstr_area")

List the terms of a vocabulary:

opal.terms(o, taxonomy = "Mlstr_area", vocabulary = "Lifestyle_behaviours")


Good practice is to free server resources by sending a logout request: