With BARIS you can interact with 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.
- The package has been archived from CRAN. I'm in the process of writing a new lightweight wrapper with very few dependencies. Indeed, BARIS provided functions to read the data directly into R however I think, the user should have the flexibility to decide how the data can be read.
You can install the development version from GitHub with:
devtools::install_github("feddelegrand7/BARIS")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: 9 x 13
#> id title organization page views frequency temporal_cov_st~
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 6246ac~ Temps de ~ https://static.~ https://~ 47 irregular 2022-03-07
#> 2 623dd1~ Déclarati~ https://static.~ https://~ 30 unknown <NA>
#> 3 541014~ Prix des ~ https://static.~ https://~ 16053 unknown <NA>
#> 4 61f3ba~ Transitio~ https://static.~ https://~ 11 punctual <NA>
#> 5 620bc1~ Liste des~ https://static.~ https://~ 1 punctual 2023-12-31
#> 6 6151bd~ Feuilles ~ https://static.~ https://~ 1481 annual <NA>
#> 7 5ff5e6~ Baromètre~ https://static.~ https://~ 3445 punctual <NA>
#> 8 60190d~ Synthèse ~ https://static.~ https://~ 42012 daily <NA>
#> 9 61a73d~ Index Ega~ https://static.~ https://~ 132 daily <NA>
#> # ... 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.
The BARIS_search() function allows you to search for a specific data
set. Suppose we’re curious about the city of Marseille.
Marseille_data <- BARIS_search(query = "Marseille", page_number = 1, page_size = 20)
Marseille_data[, c("id", "title")]
#> # A tibble: 20 x 2
#> id title
#> <chr> <chr>
#> 1 5cebfa839ce2e76116c3af0d Marseille - Élus
#> 2 5cebfa8506e3e77ffdb31ef8 Marseille - Cimetières
#> 3 5cebfa869ce2e76116c3af11 Marseille - Délibérations
#> 4 5cebfa869ce2e764aac3af2b Marseille - Subventions
#> 5 5cebfa8706e3e77c78b31f0a Marseille - Crèches
#> 6 5f654cd6e39b90e17b8021e3 Marseille - Photographies
#> 7 604ff6a2b0a782adba71fa22 Marseille - Biodiversité
#> 8 604ff6ddbc5a324a0c7ce5a7 Marseille - Eclairage
#> 9 53698f14a3a729239d203643 Arrondissements de Marseille
#> 10 5878ee29a3a7291485cac7b2 Arrondissements de Marseille
#> 11 5878ee75a3a7291484cac81f Quartiers de Marseille
#> 12 5cebfa8206e3e77ffdb31ef4 Marseille - Élections municipales
#> 13 5cebfa829ce2e76116c3af0c Marseille - Élections européennes
#> 14 5cebfa8306e3e77c78b31f05 Marseille - Écoles élémentaires
#> 15 5cebfa8306e3e77ffdb31ef5 Marseille - Monuments historiques
#> 16 5cebfa8306e3e77ffdb31ef6 Marseille - Compte administratif
#> 17 5cebfa839ce2e76116c3af0e Marseille - Wifi public
#> 18 5cebfa839ce2e764aac3af27 Marseille - Marchés forains
#> 19 5cebfa8406e3e77ffdb31ef7 Marseille - Équipements sportifs
#> 20 5cebfa849ce2e764aac3af28 Marseille - Écoles maternellesSuppose 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() 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.
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.
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_protection_~ denomination adresse code_postal
#> <chr> <chr> <chr> <chr> <int>
#> 1 PA00081336 Classement : liste ~ Ancienne église de~ "/" 13002
#> 2 PA00081340 Classement: 13/09/1~ Eglise Saint-Laure~ "Esplana~ 13002
#> 3 PA00081331 Classement: 29/01/1~ Chapelle et Hospic~ "2, Rue ~ 13002
#> 4 PA00081344 Classement: 16/06/1~ Fort Saint-Jean "" 13002
#> 5 PA00081325 Inscription : 23/11~ Les deux bâtiments~ "Quai du~ 13002
#> 6 PA00081334 Inscription : 07/07~ Clocher des Accoul~ "Montée ~ 13002
#> 7 PA00081348 Classement: 12/01/1~ Hôtel Daviel (anci~ "Place D~ 13002
#> 8 PA00081363 Classement: 02/05/1~ Maison dite de l’É~ "27, Gra~ 13002
#> 9 PA00081349 Inscription : 14/10~ Hôtel-Dieu- Façade~ "6, Plac~ 13002
#> 10 PA00081354 Classement: 30/04/1~ Hôtel de Ville "Quai du~ 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 comes with an integrated Addin that generates a Shiny widget
allowing the user to interact with the package in an interactive manner.
You can trigger the addin from the Addins menu in RStudio or you can
run:
BARIS:::BARIS_ui()
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}, }
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.