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 analysis 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 datasets 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.
You can install the BARIS package from CRAN with:
You can also install the development version from GitHub with:
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 5eaa~ "Ind~ Ministère d~ http~ 15 unknown <NA>
#> 2 5eb2~ "Sit~ Ministère d~ http~ 0 hourly <NA>
#> 3 5e7d~ "Don~ Santé publi~ http~ 9 unknown <NA>
#> 4 5e74~ "Don~ Santé publi~ http~ 43 unknown <NA>
#> 5 5e7e~ "Don~ Santé publi~ http~ 108 daily <NA>
#> 6 5ea7~ "Niv~ Santé publi~ http~ 3 daily <NA>
#> 7 5ec3~ "Mes~ Unions de R~ http~ 0 unknown <NA>
#> 8 5e9d~ "Don~ Etalab http~ 1 daily 2020-12-31
#> 9 5ebc~ "Don~ Etalab http~ 0 unknown <NA>
#> 10 5ac4~ "Ann~ Caisse nati~ http~ 1 monthly 2019-05-01
#> 11 5ebc~ "Don~ Etalab http~ 0 weekly <NA>
#> 12 5e9d~ "SIN~ ADEME http~ 0 unknown <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 condensated 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.
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 5369~ Déco~ OpenStreetM~ http~ 28 annual 2013-11-1~ 2020-01-02T1~
#> 3 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 4 5e5a~ Cave~ <NA> http~ 0 irregular 2020-02-2~ 2020-03-01T1~
#> 5 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-2~ 2019-11-15T0~
#> 6 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2015-07-1~ 2019-05-09T0~
#> 7 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 8 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2017-07-2~ 2019-05-09T0~
#> 9 5dd7~ Quar~ Datactivist http~ 0 unknown 2019-11-2~ 2019-11-22T1~
#> 10 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2013-10-2~ 2019-05-09T0~
#> 11 5e87~ Mars~ Ville de Ma~ http~ 0 unknown 2020-04-0~ 2020-04-03T0~
#> 12 5878~ Quar~ NosDonnées.~ http~ 0 unknown 2016-03-3~ 2017-07-10T0~
#> 13 5878~ Arro~ NosDonnées.~ http~ 0 unknown 2014-03-0~ 2017-07-10T0~
#> 14 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2018-02-0~ 2019-05-09T0~
#> 15 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2009-06-0~ 2019-05-27T0~
#> 16 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 17 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-12-30T0~
#> 18 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-12-0~ 2019-05-09T0~
#> 19 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2014-05-0~ 2019-05-09T0~
#> 20 5ceb~ Mars~ Ville de Ma~ http~ 0 unknown 2012-11-2~ 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 53699233a3a729239d203~ Découpage administratif communal français issu d'Open~
#> 3 5cebfa8506e3e77ffdb31~ Marseille - Cimetières
#> 4 5e5a7bc2634f413b2369e~ Caves à bière
#> 5 5cebfa869ce2e76116c3a~ Marseille - Délibérations
#> 6 5cebfa8706e3e77c78b31~ Marseille - Crèches
#> 7 5cebfa839ce2e76116c3a~ Marseille - Élus
#> 8 5cebfa869ce2e764aac3a~ Marseille - Subventions
#> 9 5dd7a9a78b4c41277a7fb~ Quartiers de Marseille
#> 10 5cebfa8306e3e77ffdb31~ Marseille - Monuments historiques
#> 11 5e87cef997cf8d9b8cd10~ Marseille - COVID19 - crèches ouvertes
#> 12 5878ee75a3a7291484cac~ Quartiers de Marseille
#> 13 5878ee29a3a7291485cac~ Arrondissements de Marseille
#> 14 5cebfa889ce2e76116c3a~ Marseille - Tournages de films et séries
#> 15 5cebfa829ce2e76116c3a~ Marseille - Élections européennes
#> 16 5cebfa839ce2e764aac3a~ Marseille - Marchés forains
#> 17 5cebfa859ce2e76116c3a~ Marseille - Budget primitif
#> 18 5cebfa8306e3e77c78b31~ Marseille - Bases nautiques et plages
#> 19 5cebfa8706e3e77ffdb31~ Marseille - Élections législatives
#> 20 5cebfa879ce2e76116c3a~ Marseille - Localisation des stations de taxisSuppose 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_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 comes with an integrated Addin that generates a Shiny widget allowing the user to interact with the package in an interactive manner.
If you use the BARIS package for your work, research or teaching, I’d appreciate if you could cite it as follows:
citation("BARIS")
#>
#> To cite package 'BARIS' in publications use:
#>
#> Mohamed El Fodil Ihaddaden (NA). BARIS: Access and Import Data from
#> the French Open Data Portal. R package version 1.1.0.
#> https://github.com/feddelegrand7/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},
#> note = {R package version 1.1.0},
#> url = {https://github.com/feddelegrand7/BARIS},
#> }
#>
#> ATTENTION: This citation information has been auto-generated from the
#> package DESCRIPTION file and may need manual editing, see
#> 'help("citation")'.Finally, I appreciate any feedback, feel free to reach out at moh_fodil or open an issue on Github.