BARIS 
With BARIS you can exploit the French Official Open Data Portal API directly from R. The package offers several capabilities, from listing the available data sets to extracting the needed resources. Nevertheless, there are many features offered by the API (e.g. uploading a data set, removing a resource … among others) that are not covered within the BARIS package which instead focus on the data extraction aspect of the API. The good news is that the user doesn’t need an API key or any credential to run the available functions provided by BARIS. Finally, in order to fully apprehend the package, a distinction has to be made. The data.gouv API provides several data sets which contain one or many data frames. The unique identifier (ID) of a data set has this form : 53699934a3a729239d2051a1 while the ID of an individual data frame or resource has this form: 59ea7bba-f38a-4d75-b85f-2d1955050e53.
Installation
You can install the BARIS package from CRAN with:
install.packages("BARIS")You can also install the development version from GitHub with:
devtools::install_github("feddelegrand7/BARIS")BARIS_home()
Using the function BARIS_home() you can list the displayed datasets
within the home page of the data.gouv
website. The function doesn’t take any
argument. It will return a data frame with many useful information about
the data set.
library(BARIS)
BARIS_home()
#> # A tibble: 12 x 13
#> id title organization page views frequency temporal_cov_st~
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 5eeb~ Répe~ France comp~ http~ 0 weekly <NA>
#> 2 5efa~ Muni~ Ministère d~ http~ 0 punctual <NA>
#> 3 5eda~ Elec~ Ministère d~ http~ 0 unknown <NA>
#> 4 5ee9~ Indi~ Ministère d~ http~ 1 weekly <NA>
#> 5 5e7e~ Donn~ Santé publi~ http~ 119 daily <NA>
#> 6 5eb2~ Site~ Ministère d~ http~ 1 hourly <NA>
#> 7 5ecf~ Base~ ADEME http~ 0 unknown <NA>
#> 8 5ee2~ Diag~ ADEME http~ 0 unknown <NA>
#> 9 5e9d~ SINO~ ADEME http~ 0 unknown <NA>
#> 10 5ee4~ Aide~ Unions de R~ http~ 0 unknown <NA>
#> 11 5ec3~ Mesu~ Unions de R~ http~ 0 unknown <NA>
#> 12 5e9d~ Donn~ Etalab http~ 1 daily 2020-12-31
#> # ... with 6 more variables: temporal_cov_end <chr>, created_at <chr>,
#> # last_modified <chr>, last_update <chr>, archived <chr>, deleted <chr>The data is quite condensed so you should use the View() or
DT::datatable() functions.
BARIS_search()
The BARIS_search() function allows you to search for a specific data
set. Suppose we’re curious about the city of Marseille.
BARIS_search(query = "Marseille", n_pages = 20)
#> # A tibble: 20 x 11
#> id title organization page views frequency created_at last_modified
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 5369~ Traf~ Ministère d~ http~ 0 annual 2013-07-0~ 2016-03-04T0~
#> 2 5f03~ Coll~ Ville de Ma~ http~ 0 unknown 2017-03-2~ 2019-05-09T0~
#> 3 5369~ Déco~ OpenStreetM~ http~ 28 annual 2013-11-1~ 2020-01-02T1~
#> 4 5e5a~ Cave~ <NA> http~ 0 irregular 2020-02-2~ 2020-03-01T1~
#> 5 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 6 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2015-07-1~ 2019-05-09T0~
#> 7 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-2~ 2019-11-15T0~
#> 8 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 9 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2017-07-2~ 2019-05-09T0~
#> 10 5dd7~ Quar~ Datactivist http~ 0 unknown 2019-11-2~ 2019-11-22T1~
#> 11 5878~ Arro~ NosDonnées.~ http~ 0 unknown 2014-03-0~ 2017-07-10T0~
#> 12 5878~ Quar~ NosDonnées.~ http~ 0 unknown 2016-03-3~ 2017-07-10T0~
#> 13 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2013-10-2~ 2020-06-29T0~
#> 14 5e87~ Mars~ Ville de Ma~ http~ 0 unknown 2020-04-0~ 2020-04-03T0~
#> 15 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2011-06-1~ 2019-05-09T0~
#> 16 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2018-08-22T0~
#> 17 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 18 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-11-2~ 2019-05-09T0~
#> 19 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2011-06-1~ 2019-05-09T0~
#> 20 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2014-05-0~ 2019-05-09T0~
#> # ... with 3 more variables: last_update <chr>, archived <chr>, deleted <chr>The n_page argument is used to specify the number of pages to search for according to the pagination system of the API, by default its value is equal to 20. Now, in order to choose a dataset, let’s have a look at two columns, the ID and the title of each dataset.
Marseille_data <- BARIS_search(query = "Marseille", n_pages = 20)
Marseille_data[, c("id", "title")]
#> # A tibble: 20 x 2
#> id title
#> <chr> <chr>
#> 1 5369a248a3a729239d206~ Trafic aéroport Marseille-Provence : passagers et mou~
#> 2 5f031bed84d60df5d5d05~ Collections du Musée ZIEM
#> 3 53699233a3a729239d203~ Découpage administratif communal français issu d'Open~
#> 4 5e5a7bc2634f413b2369e~ Caves à bière
#> 5 5cebfa8506e3e77ffdb31~ Marseille - Cimetières
#> 6 5cebfa8706e3e77c78b31~ Marseille - Crèches
#> 7 5cebfa869ce2e76116c3a~ Marseille - Délibérations
#> 8 5cebfa839ce2e76116c3a~ Marseille - Élus
#> 9 5cebfa869ce2e764aac3a~ Marseille - Subventions
#> 10 5dd7a9a78b4c41277a7fb~ Quartiers de Marseille
#> 11 5878ee29a3a7291485cac~ Arrondissements de Marseille
#> 12 5878ee75a3a7291484cac~ Quartiers de Marseille
#> 13 5cebfa8306e3e77ffdb31~ Marseille - Monuments historiques
#> 14 5e87cef997cf8d9b8cd10~ Marseille - COVID19 - crèches ouvertes
#> 15 5cebfa8306e3e77c78b31~ Marseille - Écoles élémentaires
#> 16 5cebfa869ce2e764aac3a~ Marseille - Lieux culturels
#> 17 5cebfa869ce2e76116c3a~ Marseille - Équipements sociaux
#> 18 5cebfa839ce2e76116c3a~ Marseille - Wifi public
#> 19 5cebfa849ce2e764aac3a~ Marseille - Écoles maternelles
#> 20 5cebfa8706e3e77ffdb31~ Marseille - Élections législativesSuppose we’re interested in the dataset entitled Marseille - Monuments
historiques with its corresponding ID: 5cebfa8306e3e77ffdb31ef5
and we want to know more about this data. In this case, the
BARIS_explain() function can be useful.
BARIS_explain()
BARIS_explain() returns a description of a dataset. It has one
argument which is the ID of the dataset of interest.
BARIS_explain("5cebfa8306e3e77ffdb31ef5")
#> [1] "Monuments historiques situés sur le territoire de Marseille, avec adresse, numéro de base Mérimée (base de données du Ministère de la Culture recensant les monuments historiques de toute la France) et points de géolocalisation"The description is in French but even non-French speakers can use this function in conjunction with a translation tool, for the example the googleLanguageR package.
BARIS_resources()
As mentioned previously, each data set contains one or several data
frames or as the API call them resources. The BARIS_resources()
function allows you to list all the available resources within a
determined data set.
BARIS_resources("5cebfa8306e3e77ffdb31ef5") # The "Marseille - Monuments historiques" ID
#> # A tibble: 2 x 6
#> id title format published url description
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 59ea7bba-~ MARSEILLE_~ csv 2019-05-27~ https://trouve~ Monuments historiqu~
#> 2 6328f8b3-~ Plan des M~ pdf 2019-05-27~ https://trouve~ Edition Janvier 2013Many useful information related to the resource are provided: The id, the title, the format, the date of publication, the url of the resource, and a description.
BARIS_extract()
The BARIS_extract() function allows you to extract the needed resource
into your R session. You have to specify the id of the resource and its
format. 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 whatever you need.
As an example, let us extract the above listed csv file: MARSEILLE_MONUMENTS_HISTORIQUES_2018.csv:
BARIS_extract(resourceId = "59ea7bba-f38a-4d75-b85f-2d1955050e53", format = "csv")
#> # A tibble: 80 x 10
#> n_base_merimee date_de_protect~ denomination adresse code_postal
#> <chr> <chr> <chr> <chr> <int>
#> 1 PA00081336 Classement : li~ Ancienne ég~ "/" 13002
#> 2 PA00081340 Classement: 13/~ Eglise Sain~ "Espla~ 13002
#> 3 PA00081331 Classement: 29/~ Chapelle et~ "2, Ru~ 13002
#> 4 PA00081344 Classement: 16/~ Fort Saint-~ "" 13002
#> 5 PA00081325 Inscription : 2~ Les deux bâ~ "Quai ~ 13002
#> 6 PA00081334 Inscription : 0~ Clocher des~ "Monté~ 13002
#> 7 PA00081348 Classement: 12/~ Hôtel Davie~ "Place~ 13002
#> 8 PA00081363 Classement: 02/~ Maison dite~ "27, G~ 13002
#> 9 PA00081349 Inscription : 1~ Hôtel-Dieu-~ "6, Pl~ 13002
#> 10 PA00081354 Classement: 30/~ Hôtel de Vi~ "Quai ~ 13002
#> # ... with 70 more rows, and 5 more variables: proprietaire_du_monument <chr>,
#> # epoque_de_construction <chr>, date_de_construction <chr>, longitude <dbl>,
#> # latitude <dbl>BARIS Addin
BARIS comes with an integrated Addin that generates a Shiny widget allowing the user to interact with the package in an interactive manner.
Citation
If you use the BARIS package for your work, research or teaching, I’d appreciate if you could cite it as follows:
Mohamed El Fodil Ihaddaden (2020). BARIS: Access and Import Data from the French Open Data Portal. R package version 1.1.1. https://CRAN.R-project.org/package=BARIS
A BibTeX entry for LaTeX users is
@Manual{, title = {BARIS: Access and Import Data from the French Open Data Portal}, author = {Mohamed El Fodil Ihaddaden}, year = {2020}, note = {R package version 1.1.1}, url = {https://CRAN.R-project.org/package=BARIS}, }
Code of Conduct
Please note that the BARIS project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. Finally, I appreciate any feedback, feel free to reach out at moh_fodil or open an issue on Github.