collapse = TRUE,
  comment = "#>"

The French official open data portal offers a huge quantity of information. They also provide a well structured API. The BARIS package allows you to exploit this API in order to get the required data from the portal.

Within the portal there is the concept of a data set which contains one or several data frames or resources. So, if I use the resource term, you need to apprehend it as the data frame inside a data set.

The package is available on CRAN, you can also install the development version from Github:


Too much talking, let's dive into a reproducible example.


The BARIS_search() function allows you to search for a specified data set. A quick tip: within your query, use plain Nouns and avoid prepositions and determinants: le, la, de, des, en, à ... and so on :


BARIS_search(query = "Monuments Historiques Marseille")

Cool we have our data set ... but wait it would be better to get some explanation about it.


The BARIS_explain() function provides a description of a data set. The function takes one argument which is the ID of the data set:

BARIS_explain(datasetId = "5cebfa8306e3e77ffdb31ef5")

Don't panic if you're not a french speaker. You can always use the great googleLanguageR.

Now, it's time to list the resources contained within this data set !!!


The BARIS_resources function displays the available resources or data frames within a data set. The function takes as argument the ID of the data set:

BARIS_resources(datasetId = "5cebfa8306e3e77ffdb31ef5")

You can see from above that the data set has two resources, a csv and a pdf. Now, we've reached the interesting part: extracting the data frame that you'll work on !


Using BARIS_extract() you can extract directly into your R session the needed data set. Currently, “only” theses formats are supported: json, csv, xls, xlsx, xml, geojson and shp, nevertheless you can always rely on the url of the resource to download it manually.

In order to use the function you'll have to specify two arguments: The ID of the resource and its format.

You can visually catch the structure difference between the ID of a data set and the ID of a resource.

data <- BARIS_extract(resourceId = "59ea7bba-f38a-4d75-b85f-2d1955050e53", format = "csv")


End of the vignette.

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BARIS documentation built on July 2, 2020, 2:26 a.m.