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rsdmx: Tools for reading SDMX data and metadata documents in R


rsdmx is a package to parse/read SDMX data and metadata in R. It provides: * a set of classes and methods to read data and metadata documents exchanged through the Statistical Data and Metadata Exchange (SDMX) framework. The package currently focuses on the SDMX XML standard format (SDMX-ML). * an interface to SDMX web-services for a list of well-known data providers, such as EUROSTAT, OECD, and others Learn more.


We thank in advance people that use rsdmx for citing it in their work / publication(s). For this, please use the citation provided at this link DOI

Collating scattered SDMX data sources

In spite they are some R package initiatives relying on rsdmx that aim to provide a wrapper for a single data source (e.g. OECD, EUROSTAT), it is strongly recommended to rely directly on rsdmx. Indeed, one main objective of rsdmx is to promote and facilitate collating scattered data from a growing number of SDMX data providers, whatever the organization.

It is already possible to query well-known datasources, using the embedded helpers. Pull requests are welcome to support additional data providers by default in rsdmx.

SDMX standards compliance

SDMX_Compliance_Badge_1.0 SDMX_Compliance_Badge_2.0 SDMX_Compliance_Badge_2.1


At now, the package allows to read: * Datasets (GenericData, CompactData, StructureSpecificData, StructureSpecificTimeSeriesData, CrossSectionalData, UtilityData and MessageGroup SDMX-ML types) * Concepts (Concept, ConceptScheme and Concepts SDMX-ML types) * Codelists (Code, Codelist and Codelists SDMX-ML types) * DataStructures / KeyFamilies - with all subtypes * Data Structure Definitions (DSDs) - with all subtypes


rsdmx is looking for sponsors. You have been using rsdmx and you wish to support its development? Please help us to make the package growing!


Copyright (C) 2014 Emmanuel Blondel




rsdmx is available on the Comprehensive R Archive Network (CRAN). See the R CRAN check results at:

Please note that following a new submission to CRAN, or eventually a modification of CRAN policies, the package might be temporarily archived, and removed from CRAN. In case you notice that the package is not back in few time, please contact me.

on R-Universe

rsdmx is available on the R-Universe public cloud server. The package version corresponds to the ongoing revision (master branch in Github). See


rsdmx offers a low-level set of tools to read data and metadata in SDMX format. Its strategy is to make it very easy for the user. For this, a unique function named readSDMX has to be used, whatever it is a data or metadata document, or if it is local or remote datasource.

It is important to highlight that one of the major benefits of rsdmx is to focus first on the SDMX format specifications (acting as format abstraction library). This allows rsdmx reading SDMX data from remote datasources, or from local SDMX files. For accessing remote datasources, it also means that rsdmx does not bound to SDMX service specifications, and can read a wider ranger of datasources.

Install rsdmx

rsdmx can be installed from CRAN {r, echo = FALSE} install.packages("rsdmx")

or from its development repository hosted in Github (using the devtools package):

{r, echo = FALSE} devtools::install_github("opensdmx/rsdmx")

Load rsdmx

To load rsdmx in R, do the following:

{r, echo = FALSE} library(rsdmx)

readSDMX & helper functions

readSDMX as low-level function

The readSDMX function is then first designed at low-level so it can take as parameters a url (isURL=TRUE by default) or a file. So wherever is located the SDMX document, readSDMX will allow you to read it, as follows:

```{r, echo = FALSE}

#read a remote file sdmx <- readSDMX(file = “someUrl”)

#read a local file sdmx <- readSDMX(file = “somelocalfile”, isURL = FALSE)

In addition, in order to facilitate querying datasources, ``readSDMX`` also providers helpers to query well-known remote datasources. This allows not to specify the entire URL, but rather specify a simple provider ID, and the different parameters to build a SDMX query (e.g. for a dataset query: operation, key, filter, startPeriod and endPeriod). 

This is made possible as a list of SDMX service providers is embedded within ``rsdmx``, and such list provides all the information required for ``readSDMX`` to build the SDMX request (url) before accessing the datasource.

#### get list of SDMX service providers

The list of known SDMX service providers can be queried as follows:

```{r, echo = FALSE}

providers <- getSDMXServiceProviders()

create/add a SDMX service provider

It also also possible to create and add a new SDMX service providers in this list (so readSDMX can be aware of it). A provider can be created with the SDMXServiceProvider, and is made of various parameters: * agencyId (provider identifier) * name * scale (international or national) * country ISO 3-alpha code (if national) * builder

The request builder can be created with SDMXRequestBuilder which takes various arguments: * regUrl: URL of the service registry endpoint * repoUrl: URL of the service repository endpoint (Note that we use 2 different arguments for registry and repository endpoints, since some providers use different URLs, but in most cases those are identical) * formatter list of functions to format the request params (one function per type of resource, e.g. “dataflow”, “datastructure”, “data”) * handler list of functions which will allow to build the web request *compliant logical parameter (either the request builder is compliant with some web-service specifications)

rsdmx yet provides common builders, that can be customized if needed, by overriding either the formatter or the handler functions: * SDMXREST20RequestBuilder: connector for SDMX REST 2.0 web-services * SDMXREST21RequestBuilder: connector for SDMX REST 2.1 web-services * SDMXDotStatRequestBuilder: connector for SDMX .Stat (“DotStat”) web-services implementations

Let’s see it with an example:

First create a request builder for our provider:

```{r, echo = FALSE}

myBuilder <- SDMXRequestBuilder( regUrl = “”, repoUrl = “”, formatter = list( dataflow = function(obj){ #format each dataflow id with some prefix obj@resourceId <- paste0(“df_”,obj@resourceId) return(obj) }, datastructure = function(obj){ #do nothing return(obj) }, data = function(obj){ #format each dataset id with some prefix obj@flowRef <- paste0(“data_”,obj@flowRef) return(obj) } ), handler = list( dataflow = function(obj){ req <- sprintf(“%s/dataflow”,obj@regUrl) return(req) }, datastructure = function(obj){ req <- sprintf(“%s/datastructure”,obj@regUrl) return(req) }, data = function(obj){ req <- sprintf(“%s/data”,obj@regUrl) return(req) } ), compliant = FALSE )

As you can see, we built a custom ``SDMXRequestBuilder`` that will be able to 
create SDMX web-requests for the different resources of a SDMX web-service.

We can create a provider with the above request builder, and add it to the list 
of known SDMX service providers:

```{r, echo = FALSE}

#create the provider
provider <- SDMXServiceProvider(
agencyId = "MYORG",
name = "My Organization",
builder = myBuilder

#add it to the list

#check provider has been added

find a SDMX service provider

A another helper allows you to interrogate rsdmx if a specific provider is known, given an id:

{r, echo = FALSE} oecd <- findSDMXServiceProvider("OECD")

readSDMX as helper function

Now you know how to add a SDMX provider, you can consider using readSDMX without having to specifying a entire URL, but just by specifying the agencyId of the provider, and the different query parameters to reach your SDMX document:

{r, echo = FALSE} sdmx <- readSDMX(providerId = "MYORG", providerKey = NULL resource = "data", flowRef="MYSERIE", key = "all", key.mode = "SDMX", start = 2000, end = 2015)

For embedded service providers that require a user authentication/subscription key or token, it is possible to specify it in readSDMX with the providerKey argument. If provided, and that the embedded provider requires a specific key parameter, the latter will be appended to the SDMX web-request. For example, it’s the case for the new UNESCO SDMX API.

The following sections will show you how to query SDMX documents, by using readSDMX in different ways: either for local or remote files, using readSDMX as low-level or with the helpers (embedded service providers).

Read dataset documents

This section will introduce you on how to read SDMX dataset documents.

Read remote datasets

The following code snipet shows you how to read a dataset from a remote data source, taking as example the OECD StatExtracts portal:

{r, echo = FALSE} myUrl <- "" dataset <- readSDMX(myUrl) stats <-

You can try it out with other datasources, such as: * EUROSTAT portal: [ * European Central Bank (ECB):

The online rsdmx documentation also provides a list of data providers, either from international or national institutions.

Now, the service providers above mentioned are known by rsdmx which let users using readSDMX with the helper parameters. It may also be the case for a provider that you register in rsdmx.

Let’s see how it would look like for querying an OECD datasource:

{r, message = FALSE} sdmx <- readSDMX(providerId = "OECD", resource = "data", flowRef = "MIG", key = list("TOT", NULL, NULL), start = 2010, end = 2011) df <- head(df)

It is also possible to query a dataset together with its “definition”, handled in a separate SDMX-ML document named DataStructureDefinition (DSD). It is particularly useful when you want to enrich your dataset with all labels. For this, you need the DSD which contains all reference data.

To do so, you only need to append dsd = TRUE (default value is FALSE), to the previous request, and specify labels = TRUE when calling, as follows:

{r, message = FALSE} sdmx <- readSDMX(providerId = "OECD", resource = "data", flowRef = "MIG", key = list("TOT", NULL, NULL), start = 2010, end = 2011, dsd = TRUE) df <-, labels = TRUE) head(df)

Note that in case you are reading SDMX-ML documents with the native approach (with URLs), instead of the embedded providers, it is also possible to associate a DSD to a dataset by using the function setDSD. Let’s try how it works:

```{r, message = FALSE} #data without DSD <- readSDMX(providerId = “OECD”, resource = “data”, flowRef = “MIG”, key = list(“TOT”, NULL, NULL), start = 2010, end = 2011)

#DSD sdmx.dsd <- readSDMX(providerId = “OECD”, resource = “datastructure”, resourceId = “MIG”)

#associate data and dsd <- setDSD(, sdmx.dsd)

#### Read _local_ datasets

This example shows you how to use ``rsdmx`` with _local_ SDMX files, previously downloaded from [EUROSTAT](

```{r, echo = FALSE}
#bulk download from Eurostat
tf <- tempfile(tmpdir = tdir <- tempdir()) #temp file and folder
download.file("", tf)
sdmx_files <- unzip(tf, exdir = tdir)

sdmx <- readSDMX(sdmx_files[2], isURL = FALSE)
stats <-

By default, readSDMX considers the data source is remote. To read a local file, add isURL = FALSE.

Read metadata documents

This section will introduce you on how to read SDMX metadata complete data structure definitions (DSD)

#### Data Structures

This example illustrates how to read the complete list of data structures (or key families) from the [OECD StatExtracts portal](

```{r, echo = FALSE}
dsUrl <- ""
ds <- readSDMX(dsUrl)
dsdf <-

Data Structure Definition (DSD)

This example illustrates how to read a complete DSD using a OECD StatExtracts portal data source.

{r, echo = FALSE} dsdUrl <- "" dsd <- readSDMX(dsdUrl)

rsdmx is implemented in object-oriented way with S4 classes and methods. The properties of S4 objects are named slots and can be accessed with the slot method. The following code snippet allows to extract the list of codelists contained in the DSD document, and read one codelist as data.frame.

{r, echo = FALSE} #get codelists from DSD cls <- slot(dsd, "codelists") codelists <- sapply(slot(cls, "codelists"), function(x) slot(x, "id")) #get list of codelists codelist <-, "codelists"), codelistId = "CL_TABLE1_FLOWS") #get a codelist

In a similar way, the concepts of the dataset can be extracted from the DSD and read as data.frame.

{r, echo = FALSE} #get concepts from DSD concepts <-, "concepts"))

Save & Reload SDMX R objects

It is possible to save SDMX R objects as RData file (.RData, .rda, .rds), to then be able to reload them into the R session. It could be of added value for users that want to keep their SDMX objects in R data files, but also for fast loading of large SDMX objects (e.g. DSD objects) for use in statistical analyses and R-based web-applications.

To save a SDMX R object to RData file:

{r, echo = FALSE} saveSDMX(sdmx, "tmp.RData")

To reload a SDMX R object from RData file:

{r, echo = FALSE} sdmx <- readSDMX("tmp.RData", isRData = TRUE)