| Title: | RADIS - Spatial Weather and Environmental datas Extraction Package |
|---|---|
| Description: | The RADIS package aims to facilitate the extraction of environmental data from various cartographic or spatial sources or APIs. It allows for the retrieval of this information using specific coordinates or spatial grids. This package also formats the extracted data in a way that makes it easily exploitable for further analysis. |
| Authors: | David Dorchies [aut, cre] (ORCID: <https://orcid.org/0000-0002-6595-7984>), Florian Ricquier [aut] (ORCID: <https://orcid.org/0009-0009-3737-1386>), Kevin Bosirany Orlando [aut] (ORCID: <https://orcid.org/0009-0009-2784-3108>), Claire Richert [aut] (ORCID: <https://orcid.org/0000-0001-7456-6689>), Pierre Rouault [ctb] (ORCID: <https://orcid.org/0009-0002-9473-7232>) |
| Maintainer: | David Dorchies <david.dorchies@inrae.fr> |
| License: | AGPL (>= 3) |
| Version: | 0.3.0 |
| Built: | 2026-09-26 09:53:09 UTC |
| Source: | https://forge.inrae.fr/umr-g-eau/radis |
Convert list provided by the APIs into a tibble
convert_list_to_tibble(l)convert_list_to_tibble(l)
l |
This function is used internally by all the retrieving data functions for converting data after the call to query_api.
A tibble::tibble with one row by record and one column by field.
Downloads a file from a Dataverse dataset using its DOI and caches it
locally. If the file already exists in the cache directory and
cache is not "force", the cached file is used. Otherwise, the file is
downloaded from the Dataverse server.
get_data_from_dataverse( doi, server, key = "", filename, cache_dir = radis_cache_dir("dataverse"), cache = c("yes", "no", "force"), progress = FALSE, force_download = NULL )get_data_from_dataverse( doi, server, key = "", filename, cache_dir = radis_cache_dir("dataverse"), cache = c("yes", "no", "force"), progress = FALSE, force_download = NULL )
doi |
character The DOI of the Dataverse dataset. |
server |
character The base URL of the Dataverse server. |
key |
character The API token from dataverse website. See here for more information about token usage on dataverse platforms. It is required for some datasets (e.g. Info&Sols). By default, it is an empty string. |
filename |
character The name of the file to download from the dataset. |
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
progress |
logical If |
force_download |
Deprecated, use |
character The local file path to the downloaded (or cached) file.
Generic and method-specific functions to retrieve daily DRIAS data from the DRIAS/Explore2 climate database. The data are extracted from the RADIS API hosted at https://api.g-eau.fr.
get_drias_daily(x = NULL, ...) ## S3 method for class ''NULL'' get_drias_daily(x, LAMBX__greater, ...) ## S3 method for class 'numeric' get_drias_daily( x, LAMBX__less, LAMBY__greater, LAMBY__less, LAMBX__greater = x, GCM, RCM, BC, RCP, ETP = NULL, DATE__greater = NULL, DATE__less = NULL, fields = NULL, base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), cache_dir = radis_cache_dir("drias_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ..., force_download = NULL ) ## S3 method for class 'bbox' get_drias_daily(x, ...) ## S3 method for class 'sf' get_drias_daily( x, overlay_mode = "extent", cache_dir = radis_cache_dir("drias_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... )get_drias_daily(x = NULL, ...) ## S3 method for class ''NULL'' get_drias_daily(x, LAMBX__greater, ...) ## S3 method for class 'numeric' get_drias_daily( x, LAMBX__less, LAMBY__greater, LAMBY__less, LAMBX__greater = x, GCM, RCM, BC, RCP, ETP = NULL, DATE__greater = NULL, DATE__less = NULL, fields = NULL, base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), cache_dir = radis_cache_dir("drias_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ..., force_download = NULL ) ## S3 method for class 'bbox' get_drias_daily(x, ...) ## S3 method for class 'sf' get_drias_daily( x, overlay_mode = "extent", cache_dir = radis_cache_dir("drias_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... )
x |
Input object. Can be |
... |
Additional arguments passed to methods, query_api, and spatial_combination. |
LAMBX__greater |
integer Minimum X coordinate in meters (EPSG:27572). |
LAMBX__less |
integer Maximum X coordinate in meters (EPSG:27572). |
LAMBY__greater |
integer Minimum Y coordinate in meters (EPSG:27572). |
LAMBY__less |
integer Maximum Y coordinate in meters (EPSG:27572). |
GCM |
character Global Climate Model (GCM) |
RCM |
character Regional Climate Model (RCM) |
BC |
character Bias Correction method ("ADAMONT" or "CDFt") |
RCP |
character Representative Concentration Pathway (RCP) scenario ("rcp26", "rcp45", or "rcp85") |
ETP |
character Method used to compute potential evapotranspiration ("FAO" or "Hg0175"). Required when requesting "evspsblpot" field. |
DATE__greater |
character or Date Minimum date (YYYY-MM-DD). If
|
DATE__less |
character or Date Maximum date (YYYY-MM-DD). If
|
fields |
character vector Variables to extract, See below, the description of the returned values for available variables |
base_url |
Base URL for the API (default: "https://api.g-eau.fr"). |
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
force_download |
Deprecated, use |
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
This function retrieves DRIAS/Explore2 climate projection data for France via the RADIS API (https://api.g-eau.fr/__docs__).
Marson, Paola; Corre, Lola; Soubeyroux, Jean-Michel; Sauquet, Éric, 2024, "Rapport de synthèse sur les projections climatiques régionalisées", doi:10.57745/PUR7ML, Recherche Data Gouv, V1
More information about DRIAS climate projections is available at: https://www.drias-climat.fr
Before using this function, you need to determine which combinations of
GCM, RCM, BC, RCP, and ETP parameters are available. Use the
get_drias_scenario() function to query available combinations
The returned data frame shows valid combinations of parameters that can be used with this function.
GCM: Global Climate Model - varies by scenario (e.g., "CNRM-CERFACS-CNRM-CM5", "ICHEC-EC-EARTH", "MOHC-HadGEM2-ES")
RCM: Regional Climate Model - varies by GCM (e.g., "CNRM-ALADIN63", "SMHI-RCA4", "CLMcom-CCLM4-8-17")
BC: Bias Correction method - "ADAMONT" or "CDFt" (note: CDFt has limited variable availability)
RCP: Representative Concentration Pathway - "rcp26", "rcp45", or "rcp85" for future projections, or use historical data
ETP: Evapotranspiration method - "FAO" or "Hg0175" (required only when requesting "evspsblpot" variable)
Coordinates must be provided in Lambert II extended projection (EPSG:27572):
LAMBX: X coordinate (Easting) in meters
LAMBY: Y coordinate (Northing) in meters
If you have coordinates in a different CRS, use sf::st_transform() to
convert to EPSG:27572 before querying, or pass an sf object directly
(conversion is handled automatically)
Here are the available fields:
"evspsblpot": Potential evapotranspiration (mm/d)
"huss": Specific humidity at 2m (kg/kg)
"prsn": Snowfall (mm/d)
"prtot": Total precipitation (mm/d)
"rlds": Downward longwave radiation (W/m²)
"rsds": Downward shortwave radiation (W/m²)
"sfcWind": Surface wind speed (m/s)
"tas": Air temperature at 2m (°C)
"tasmax": Maximum air temperature at 2m (°C)
"tasmin": Minimum air temperature at 2m (°C)
A [stars][stars::st_as_stars][stars::st_as_stars()] object containing the DRIAS daily
weather data. If overlay_mode is specified (sf method), the output will
be masked by the
input spatial object.
get_drias_scenario() to query available scenario combinations
# First, check available scenarios scenarios <- get_drias_scenario() scenarios <- scenarios[sample(nrow(scenarios), 1), ] scenarios # Then retrieve climate data for a specific scenario climate <- get_drias_daily( LAMBX__greater = 275000, LAMBX__less = 304000, LAMBY__greater = 1810000, LAMBY__less = 1840000, DATE__greater = "2000-01-01", DATE__less = "2000-01-31", GCM = scenarios$GCM, RCM = scenarios$RCM, BC = scenarios$BC, RCP = scenarios$RCP, fields = scenarios$variable, ETP = scenarios$ETP ) # Using an sf object (coordinates automatically transformed) sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) climate_sf <- get_drias_daily( sf, DATE__greater = "2000-01-01", DATE__less = "2000-01-31", GCM = scenarios$GCM, RCM = scenarios$RCM, BC = scenarios$BC, RCP = scenarios$RCP, fields = scenarios$variable, ETP = scenarios$ETP )# First, check available scenarios scenarios <- get_drias_scenario() scenarios <- scenarios[sample(nrow(scenarios), 1), ] scenarios # Then retrieve climate data for a specific scenario climate <- get_drias_daily( LAMBX__greater = 275000, LAMBX__less = 304000, LAMBY__greater = 1810000, LAMBY__less = 1840000, DATE__greater = "2000-01-01", DATE__less = "2000-01-31", GCM = scenarios$GCM, RCM = scenarios$RCM, BC = scenarios$BC, RCP = scenarios$RCP, fields = scenarios$variable, ETP = scenarios$ETP ) # Using an sf object (coordinates automatically transformed) sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) climate_sf <- get_drias_daily( sf, DATE__greater = "2000-01-01", DATE__less = "2000-01-31", GCM = scenarios$GCM, RCM = scenarios$RCM, BC = scenarios$BC, RCP = scenarios$RCP, fields = scenarios$variable, ETP = scenarios$ETP )
Retrieve the list of available DRIAS climate scenario combinations from the
RADIS API. This function is useful for discovering which combinations of
Global Climate Models (GCM), Regional Climate Models (RCM), Bias Correction
methods (BC), scenarios (RCP), variables, and evapotranspiration methods
(ETP) are available before calling get_drias_daily().
get_drias_scenario( GCM = "", RCM = "", BC = "", RCP = "", variable = "", ETP = "", base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr") )get_drias_scenario( GCM = "", RCM = "", BC = "", RCP = "", variable = "", ETP = "", base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr") )
GCM |
character Global Climate Model name. If empty (default), all available GCMs are returned. |
RCM |
character Regional Climate Model name. If empty (default), all available RCMs are returned. |
BC |
character Bias Correction method ("ADAMONT" or "CDFt"). If empty (default), all available methods are returned. |
RCP |
character RCP name ("rcp26", "rcp45", or "rcp85"). If empty (default), all available RCPs are returned. |
variable |
character Climate variable name. If empty (default), all available variables are returned. See Details for available variables. |
ETP |
character Evapotranspiration calculation method ("FAO" or "Hg0175"). If empty (default), all available methods are returned. |
base_url |
character Base URL of the RADIS API (default: "https://api.g-eau.fr"). |
This function queries the /drias_scenario endpoint of the RADIS API at
https://api.g-eau.fr/__docs__ to retrieve available DRIAS/Explore2 climate
scenario combinations.
Marson, Paola; Corre, Lola; Soubeyroux, Jean-Michel; Sauquet, Éric, 2024, "Rapport de synthèse sur les projections climatiques régionalisées", doi:10.57745/PUR7ML, Recherche Data Gouv, V1
More information: https://www.drias-climat.fr
The following climate variables are available:
"evspsblpot": Potential evapotranspiration (mm/d)
"huss": Specific humidity at 2m (kg/kg)
"prsn": Snowfall (mm/d)
"prtot": Total precipitation (mm/d)
"rlds": Downward longwave radiation (W/m²)
"rsds": Downward shortwave radiation (W/m²)
"sfcWind": Surface wind speed (m/s)
"tas": Air temperature at 2m (°C)
"tasmax": Maximum air temperature at 2m (°C)
"tasmin": Minimum air temperature at 2m (°C)
Call without arguments to see all available combinations
Filter by specific parameters to narrow down options
Use the results to select valid parameter combinations for
get_drias_daily()
A data.frame containing available scenario combinations with
columns: GCM, RCM, BC, RCP, variable, and ETP.
get_drias_daily() to retrieve actual climate data
# Get all available scenarios all_scenarios <- get_drias_scenario() # Get only ADAMONT bias-corrected scenarios adamont_scenarios <- get_drias_scenario(BC = "ADAMONT") # Get scenarios for a specific GCM and RCP specific_scenarios <- get_drias_scenario( GCM = "CNRM-CERFACS-CNRM-CM5", RCP = "rcp45" ) # Get available precipitation scenarios precip_scenarios <- get_drias_scenario(variable = "prtot")# Get all available scenarios all_scenarios <- get_drias_scenario() # Get only ADAMONT bias-corrected scenarios adamont_scenarios <- get_drias_scenario(BC = "ADAMONT") # Get scenarios for a specific GCM and RCP specific_scenarios <- get_drias_scenario( GCM = "CNRM-CERFACS-CNRM-CM5", RCP = "rcp45" ) # Get available precipitation scenarios precip_scenarios <- get_drias_scenario(variable = "prtot")
Generic and method-specific functions to retrieve daily FYRE Climate data via the G-EAU API, based on spatial and temporal filters.
This dataset has a spatial resolution equivalent to the SAFRAN reanalysis (See get_sim2_daily) and provides an ensemble of 25 members from 1871 to 2012 for temperature and precipitation.
Supports input as sf
objects, bounding boxes, or explicit coordinate and date ranges.
get_fyre_climate(x = NULL, ...) ## S3 method for class 'sf' get_fyre_climate( x, api_format = "ncdf", overlay_mode = "extent", cache_dir = radis_cache_dir("fyre_climate"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... ) ## S3 method for class 'bbox' get_fyre_climate(x, ...) ## S3 method for class ''NULL'' get_fyre_climate(x = NULL, LAMBX__greater, ...) ## S3 method for class 'numeric' get_fyre_climate( x, LAMBX__less, LAMBY__greater, LAMBY__less, DATE__greater = NULL, DATE__less = NULL, member, fields = NULL, base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), api_format = "ncdf", overlay_mode = "extent", LAMBX__greater = x, cache_dir = radis_cache_dir("fyre_climate"), cache = Sys.getenv("RADIS_CACHE", "yes"), ..., force_download = NULL )get_fyre_climate(x = NULL, ...) ## S3 method for class 'sf' get_fyre_climate( x, api_format = "ncdf", overlay_mode = "extent", cache_dir = radis_cache_dir("fyre_climate"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... ) ## S3 method for class 'bbox' get_fyre_climate(x, ...) ## S3 method for class ''NULL'' get_fyre_climate(x = NULL, LAMBX__greater, ...) ## S3 method for class 'numeric' get_fyre_climate( x, LAMBX__less, LAMBY__greater, LAMBY__less, DATE__greater = NULL, DATE__less = NULL, member, fields = NULL, base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), api_format = "ncdf", overlay_mode = "extent", LAMBX__greater = x, cache_dir = radis_cache_dir("fyre_climate"), cache = Sys.getenv("RADIS_CACHE", "yes"), ..., force_download = NULL )
x |
Input object. Can be |
... |
Additional arguments passed to methods, query_api, and spatial_combination. |
api_format |
Data format either |
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
LAMBX__greater |
integer Minimum X coordinate in meters (EPSG:27572). |
LAMBX__less |
integer Maximum X coordinate in meters (EPSG:27572). |
LAMBY__greater |
integer Minimum Y coordinate in meters (EPSG:27572). |
LAMBY__less |
integer Maximum Y coordinate in meters (EPSG:27572). |
DATE__greater |
character or Date Minimum date (YYYY-MM-DD). If
|
DATE__less |
character or Date Maximum date (YYYY-MM-DD). If
|
member |
integer Ensemble member number (1 to 25) |
fields |
character vector Variables to extract, See below, the description of the returned values for available variables |
base_url |
Base URL for the API (default: "https://api.g-eau.fr"). |
force_download |
Deprecated, use |
Excerpt from the notice available on https://doi.org/10.5281/zenodo.4005573: FYRE Climate (French hydrometeorological REanalysis Climate dataset) is a 25-member ensemble of 142-year high-resolution reanalysis of precipitation and temperature over France, from 1 January 1871 to 29 December 2012. FYRE Climate results from the assimilation of historical daily station observations of temperature and precipitation into the SCOPE Climate reconstructions (Caillouet et al., 2019) through a Kalman filter ensemble approach (Devers et al., 2020). FYRE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded bivariate time series. Data are available at a daily time step on a 8 km grid over France. Values cover grid cells located only within metropolitan France national borders (including Corsica). The FYRE Climate dataset is fully described by Devers et al. (2021).
References to cite:
Devers, A., Vidal, J.-P., Lauvernet, C., & Vannier, O. (2020). FYRE Climate: Precipitation [Data set]. In Climate of the Past (v1.0.0, Vol. 17, Numéro 5, p. 1857‑1879). Zenodo. https://doi.org/10.5281/zenodo.4005573
Devers, A., Vidal, J.-P., Lauvernet, C., & Vannier, O. (2020). FYRE Climate: Temperature [Data set]. In Climate of the Past (v1.0.0, Vol. 17, Numéro 5, p. 1857‑1879). Zenodo. https://doi.org/10.5281/zenodo.4006472
get_fyre_climate(): Generic function to dispatch methods based on the class
of x.
get_fyre_climate.sf(): Accepts an sf object, transforms it to EPSG:27572,
extracts its bounding box, and dispatches to the bbox method.
get_fyre_climate.bbox(): Accepts a bounding box and dispatches to the
default method with extracted coordinates.
get_fyre_climate.default(): Accepts explicit coordinate and date range
arguments, constructs an API query, and returns the result.
When api_format = "csv", a tibble::tibble with the following columns
depending on fields parameter:
x: Easting coordinate (meters) of the cell centroid in the Lambert
II étendu projection (EPSG:27572)
y: Northing coordinate (meters) of the cell centroid in the Lambert
II étendu projection (EPSG:27572)
date: Date (YYYYMMDD)
ptot: Total precipitation (daily cumulative 06-06 UTC) (mm and
tenths)
tas: Temperature (daily average) (°C and hundredths).
When api_format = "ncdf", a stars object with
dimensions x, y, and time, and variables ptot and tas depending
on fields parameter.
# Extracting data from FYRE Climate using an sf object sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) get_fyre_climate(sf, member = 1, api_format = "csv") # Comparing precipitation time series from the 25 members at Paris during winter 1909-1910 climate_members <- lapply(1:25, function(m) { get_fyre_climate( LAMBX__greater = 602510, LAMBX__less = 602510, LAMBY__greater = 2427300, LAMBY__less = 2427300, DATE__greater = "1909-10-01", DATE__less = "1910-01-31", member = m, fields = "ptot", api_format = "csv" ) %>% dplyr::mutate(member = m) }) %>% dplyr::bind_rows() library(ggplot2) ggplot(climate_members, aes(x = as.Date(date), y = ptot, color = as.factor(member))) + geom_point() + labs( title = "FYRE Climate Precipitation Time Series at Paris (1909-1910)", x = "Date", y = "Precipitation (mm)", color = "Member" ) + theme_minimal()# Extracting data from FYRE Climate using an sf object sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) get_fyre_climate(sf, member = 1, api_format = "csv") # Comparing precipitation time series from the 25 members at Paris during winter 1909-1910 climate_members <- lapply(1:25, function(m) { get_fyre_climate( LAMBX__greater = 602510, LAMBX__less = 602510, LAMBY__greater = 2427300, LAMBY__less = 2427300, DATE__greater = "1909-10-01", DATE__less = "1910-01-31", member = m, fields = "ptot", api_format = "csv" ) %>% dplyr::mutate(member = m) }) %>% dplyr::bind_rows() library(ggplot2) ggplot(climate_members, aes(x = as.Date(date), y = ptot, color = as.factor(member))) + geom_point() + labs( title = "FYRE Climate Precipitation Time Series at Paris (1909-1910)", x = "Date", y = "Precipitation (mm)", color = "Member" ) + theme_minimal()
This function retrieves crop data from the Registre Parcellaire Graphique (RPG) via IGN Web Feature Service and / or from the completed RPG via the G-EAU API. Completed RPG data are produced by INRAE US-ODR. The completed RPG databases contain agricultural plots that are not in the RPG. They are produced combining several IGN databases (cadastre, BD Parcellaire, BD TOPO) and OSO maps produced by CESBIO/Théia.
get_rpg_data(sf, year, id, crs = sf::st_crs(sf), source = c("IGN", "ODR")) get_rpg_data_from_ign(sf, year, crs) get_rpg_data_from_odr(sf, year, crs)get_rpg_data(sf, year, id, crs = sf::st_crs(sf), source = c("IGN", "ODR")) get_rpg_data_from_ign(sf, year, crs) get_rpg_data_from_odr(sf, year, crs)
sf |
An |
year |
integer specifying the year for which crop data is required. Must be between 2010 and 2022 if the source is only "IGN", between 2018 and 2023 if the source is only "ODR" and between 2018 and 2022 if the sources are both "IGN" and "ODR". |
id |
character specifying the column name in |
crs |
Coordinate Reference System of the returned object |
source |
character vector sources of the RPG data (can be "IGN" and/or "ODR"). |
An sf object containing crop data joined with the input spatial
object. The returned object includes the following columns:
id_parcel: The parcel identifier (ODR parcels are prefixed with "0DR").
surf_parc: The declared surface area of the parcel.
code_cultu: The crop code.
source: The source of the data ("IGN" or "ODR").
geometry: The geometry of the parcel.
library(sf) # Example spatial object sf <- read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) # Retrieve crop data for the year 2020 crop_data <- get_rpg_data(sf, year = 2020, id = "id")library(sf) # Example spatial object sf <- read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) # Retrieve crop data for the year 2020 crop_data <- get_rpg_data(sf, year = 2020, id = "id")
Generic and method-specific functions to retrieve daily SIM2 data
(obtained with the Safran - Isba model) via the
G-EAU API based on spatial and temporal filters.
These data are produced by Météo-France and located at
https://www.data.gouv.fr/fr/datasets/6569b27598256cc583c917a7/.
They are updated daily, have a spatial resolution of about 8km, and
are available from 1958.
Supports input as sf
objects, bounding boxes, or explicit coordinate and date ranges.
get_sim2_daily(x = NULL, ...) ## S3 method for class 'sf' get_sim2_daily( x, api_format = "ncdf", overlay_mode = "extent", cache_dir = radis_cache_dir("sim2_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... ) ## S3 method for class 'bbox' get_sim2_daily(x, ...) ## S3 method for class ''NULL'' get_sim2_daily(x = NULL, LAMBX__greater, ...) ## S3 method for class 'numeric' get_sim2_daily( x, LAMBX__less, LAMBY__greater, LAMBY__less, DATE__greater = NULL, DATE__less = NULL, fields = NULL, base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), api_format = "ncdf", overlay_mode = "extent", LAMBX__greater = x, cache_dir = radis_cache_dir("sim2_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ..., force_download = NULL )get_sim2_daily(x = NULL, ...) ## S3 method for class 'sf' get_sim2_daily( x, api_format = "ncdf", overlay_mode = "extent", cache_dir = radis_cache_dir("sim2_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... ) ## S3 method for class 'bbox' get_sim2_daily(x, ...) ## S3 method for class ''NULL'' get_sim2_daily(x = NULL, LAMBX__greater, ...) ## S3 method for class 'numeric' get_sim2_daily( x, LAMBX__less, LAMBY__greater, LAMBY__less, DATE__greater = NULL, DATE__less = NULL, fields = NULL, base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), api_format = "ncdf", overlay_mode = "extent", LAMBX__greater = x, cache_dir = radis_cache_dir("sim2_daily"), cache = Sys.getenv("RADIS_CACHE", "yes"), ..., force_download = NULL )
x |
Input object. Can be |
... |
Additional arguments passed to methods, query_api, and spatial_combination. |
api_format |
Data format either |
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
LAMBX__greater |
integer Minimum X coordinate in meters (EPSG:27572). |
LAMBX__less |
integer Maximum X coordinate in meters (EPSG:27572). |
LAMBY__greater |
integer Minimum Y coordinate in meters (EPSG:27572). |
LAMBY__less |
integer Maximum Y coordinate in meters (EPSG:27572). |
DATE__greater |
character or Date Minimum date (YYYY-MM-DD). If
|
DATE__less |
character or Date Maximum date (YYYY-MM-DD). If
|
fields |
character vector Variables to extract, See below, the description of the returned values for available variables |
base_url |
Base URL for the API (default: "https://api.g-eau.fr"). |
force_download |
Deprecated, use |
get_sim2_daily(): Generic function to dispatch methods based on the class
of x.
get_sim2_daily.sf(): Accepts an sf object, transforms it to EPSG:27572,
extracts its bounding box, and dispatches to the bbox method.
get_sim2_daily.bbox(): Accepts a bounding box and dispatches to the
default method with extracted coordinates.
get_sim2_daily.default(): Accepts explicit coordinate and date range
arguments, constructs an API query, and returns the result as a tibble.
When api_format = "csv", a tibble with possibly the following columns
depending on fields parameter:
LAMBX: Easting coordinate of cell centroid (meters) in the Lambert II étendu projection (EPSG:27572)
LAMBY: Northing coordinate of cell centroid (meters) in the Lambert II étendu projection (EPSG:27572)
DATE: Date (YYYYMMDD)
PRENEI_Q: Solid precipitation (daily cumulative 06-06 UTC) (mm and
tenths)
PRELIQ_Q: Liquid precipitation (daily cumulative 06-06 UTC) (mm and
tenths)
PE_Q: Effective rain (daily cumulative) (mm and tenths)
T_Q: Temperature (daily average) (°C and tenths)
TINF_H_Q: Minimum temperature of the 24 hourly temperatures (°C and
tenths)
TSUP_H_Q: Maximum temperature of the 24 hourly temperatures (°C and
tenths)
FF_Q: Wind (daily average) (m/s and tenths)
DLI_Q: Atmospheric radiation (daily cumulative) (J/cm2)
SSI_Q: Visible radiation (daily cumulative) (J/cm2)
EVAP_Q: Actual evapotranspiration (daily cumulative 06-06 UTC) (mm
and tenths)
ETP_Q: Potential evapotranspiration (Penman-Monteith formula) (mm and
tenths)
ETP_H0175: Potential evapotranspiration (Hargreaves formula) (mm and tenths)
Q_Q: Specific humidity (daily average) (g/kg)
HU_Q: Relative humidity (daily average) (%)
SWI_Q: Soil moisture index (daily average 06-06 UTC) (%)
DRAINC_Q: Drainage (daily cumulative 06-06 UTC) (mm and tenths)
RUNC_Q: Runoff (daily cumulative 06-06 UTC) (mm and tenths)
WG_RACINE_Q: Liquid water content in the root layer at 06 UTC (m3/m3)
WGI_RACINE_Q: Frozen water content in the root layer at 06 UTC
(m3/m3)
RESR_NEIGE_Q: Snow water equivalent (daily average 06-06 UTC) (mm and
tenths)
RESR_NEIGE6_Q: Snow water equivalent at 06 UTC (mm and tenths)
HTEURNEIGE_Q: Snow depth (daily average 06-06 UTC) (m)
HTEURNEIGE6_Q: Snow depth at 06 UTC (m)
HTEURNEIGEX_Q: Maximum snow depth during the day (m)
SNOW_FRAC_Q: Fraction of the grid cell covered by snow (daily average
06-06 UTC) (%)
ECOULEMENT_Q: Flow at the base of the snowpack (mm and tenths)
When api_format = "ncdf", a stars object containing
the same variables is returned.
# Retrieve data in CSV format # Retrieve daily SIM2 climate data within specified bounding box climate <- get_sim2_daily( LAMBX__greater = 275000, LAMBX__less = 304000, LAMBY__greater = 1810000, LAMBY__less = 1840000, DATE__greater = "2023-01-01", DATE__less = "2023-01-31", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "csv" ) head(climate) # Using an sf object sf <- sf::read_sf( system.file( "extdata/study_area/sub_basins/sub-basins.geojson", package = "RADIS" ) ) get_sim2_daily( sf, DATE__greater = "2023-01-01", DATE__less = "2023-01-31", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "csv", ) # Retrieve data in NetCDF format and mask with an sf object nc_data <- get_sim2_daily( sf, DATE__greater = "2023-01-01", DATE__less = "2023-01-08", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "ncdf" ) print(nc_data) # Plot the grid of the temperature variable for the first date library(ggplot2) ggplot() + stars::geom_stars(data = nc_data["T_Q"]) + facet_wrap(~time, nrow = 2) + scale_fill_distiller( palette = "RdBu", direction = -1, # reverse so blue = low, red = high name = "Temp (°C)", limits = c(0, 15) ) # Retrieve data in NetCDF and aggregate spatially using overlay_mode nc_data_agg <- get_sim2_daily( sf, DATE__greater = "2023-01-01", DATE__less = "2023-01-08", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "ncdf", overlay_mode = "aggregate" ) print(nc_data_agg) # Plot the temperature variable for the first date ggplot() + stars::geom_stars(data = nc_data_agg["T_Q"]) + facet_wrap(~time, nrow = 2) + scale_fill_distiller( palette = "RdBu", direction = -1, # reverse so blue = low, red = high name = "Temp (°C)", limits = c(0, 15) )# Retrieve data in CSV format # Retrieve daily SIM2 climate data within specified bounding box climate <- get_sim2_daily( LAMBX__greater = 275000, LAMBX__less = 304000, LAMBY__greater = 1810000, LAMBY__less = 1840000, DATE__greater = "2023-01-01", DATE__less = "2023-01-31", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "csv" ) head(climate) # Using an sf object sf <- sf::read_sf( system.file( "extdata/study_area/sub_basins/sub-basins.geojson", package = "RADIS" ) ) get_sim2_daily( sf, DATE__greater = "2023-01-01", DATE__less = "2023-01-31", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "csv", ) # Retrieve data in NetCDF format and mask with an sf object nc_data <- get_sim2_daily( sf, DATE__greater = "2023-01-01", DATE__less = "2023-01-08", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "ncdf" ) print(nc_data) # Plot the grid of the temperature variable for the first date library(ggplot2) ggplot() + stars::geom_stars(data = nc_data["T_Q"]) + facet_wrap(~time, nrow = 2) + scale_fill_distiller( palette = "RdBu", direction = -1, # reverse so blue = low, red = high name = "Temp (°C)", limits = c(0, 15) ) # Retrieve data in NetCDF and aggregate spatially using overlay_mode nc_data_agg <- get_sim2_daily( sf, DATE__greater = "2023-01-01", DATE__less = "2023-01-08", fields = c("PRENEI_Q", "PRELIQ_Q", "T_Q", "ETP_Q"), api_format = "ncdf", overlay_mode = "aggregate" ) print(nc_data_agg) # Plot the temperature variable for the first date ggplot() + stars::geom_stars(data = nc_data_agg["T_Q"]) + facet_wrap(~time, nrow = 2) + scale_fill_distiller( palette = "RdBu", direction = -1, # reverse so blue = low, red = high name = "Temp (°C)", limits = c(0, 15) )
Retrieves soil available water capacity (AWC) spatial data for a given study area. The function supports different data sources, with "BDGSF" as the default.
get_soil_awc( sf, source = "BDGSF", overlay_mode = "aggregate", key = Sys.getenv("API_TOKEN_DATAVERSE"), cache_dir = radis_cache_dir("soil_awc", source), cache = c("yes", "no", "force"), ..., force_download = NULL ) get_soil_awc_from_bdgsf( sf, cache_dir, cache = c("yes", "no", "force"), ..., force_download = NULL ) get_soil_awc_from_infosols( sf, with_coarse_elements = TRUE, key, cache_dir, cache = c("yes", "no", "force"), base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), ..., force_download = NULL )get_soil_awc( sf, source = "BDGSF", overlay_mode = "aggregate", key = Sys.getenv("API_TOKEN_DATAVERSE"), cache_dir = radis_cache_dir("soil_awc", source), cache = c("yes", "no", "force"), ..., force_download = NULL ) get_soil_awc_from_bdgsf( sf, cache_dir, cache = c("yes", "no", "force"), ..., force_download = NULL ) get_soil_awc_from_infosols( sf, with_coarse_elements = TRUE, key, cache_dir, cache = c("yes", "no", "force"), base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), ..., force_download = NULL )
sf |
An |
source |
character The source of the soil AWC data. Can be either "BDGSF" (default, described here) or "Info&Sols" (described here). |
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
key |
character The API token from dataverse website. See here for more information about token usage on dataverse platforms. It is required for some datasets (e.g. Info&Sols). By default, it is an empty string. |
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
... |
Further arguments passed to spatial_combination and query_api |
force_download |
Deprecated, use |
with_coarse_elements |
logical Only for "Info&Sols" source. Whether to
include coarse elements in the soil AWC data. If |
base_url |
Base URL for the API (default: "https://api.g-eau.fr", or the
value of the |
By default, the API token is retrieved from the environment variable
API_TOKEN_DATAVERSE. So you need to set this variable with your
Dataverse API token before running the function or pass it directly
as an argument.
BDGSF datasets don't require an API token for access, so we don't need to
pass the key argument to the download function.
A spatial object containing soil AWC data for the study area. The format
depends on the data source and overlay_mode parameter. For BDGSF source, an
sf object. For Info&Sols source, a SpatRaster
object processed by spatial_combination.
# Using the example study area sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) # BDGSF source awc_bdgsf <- get_soil_awc(sf = sf, source = "BDGSF") awc_bdgsf plot(awc_bdgsf[, c("soil_awc_max", "soil_awc_min")]) # Info & Sols source (90 m raster) awc_infosols <- get_soil_awc(sf = sf, source = "Info&Sols") awc_infosols plot(awc_infosols[, 1:2])# Using the example study area sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) # BDGSF source awc_bdgsf <- get_soil_awc(sf = sf, source = "BDGSF") awc_bdgsf plot(awc_bdgsf[, c("soil_awc_max", "soil_awc_min")]) # Info & Sols source (90 m raster) awc_infosols <- get_soil_awc(sf = sf, source = "Info&Sols") awc_infosols plot(awc_infosols[, 1:2])
Retrieves soil depth data for a given study area. The function supports multiple data sources, with "Base de Données Géographique des Sols de France" (BDGSF) as the default source. The function will:
Download and cache soil depth data if not already available locally.
Call spatial_combination to process the data for the study area according
to the parameter overlay_mode.
get_soil_depth( sf, source = "BDGSF", overlay_mode = "aggregate", cache_dir = radis_cache_dir("soil_depth/BDGSF"), cache = c("yes", "no", "force"), ..., force_download = NULL ) get_soil_depth_from_bdgsf( sf, cache_dir, cache = c("yes", "no", "force"), ..., force_download = NULL )get_soil_depth( sf, source = "BDGSF", overlay_mode = "aggregate", cache_dir = radis_cache_dir("soil_depth/BDGSF"), cache = c("yes", "no", "force"), ..., force_download = NULL ) get_soil_depth_from_bdgsf( sf, cache_dir, cache = c("yes", "no", "force"), ..., force_download = NULL )
sf |
An |
source |
character The data source to use for soil depth. Currently
supports |
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
... |
Further arguments passed to spatial_combination and query_api |
force_download |
Deprecated, use |
A spatial object containing soil depth information for the study
area, including columns soil_depth_max, soil_depth_min, soil_depth,
and area_m2. The format of the returned object depends on the processing
performed by spatial_combination.
# Using the default study area sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) plot(get_soil_depth(sf = sf, cache = "yes"))# Using the default study area sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) plot(get_soil_depth(sf = sf, cache = "yes"))
These functions retrieve soil texture information for a given spatial
feature (sf) object from a specified data source.
get_soil_texture( sf, source = "infosols", overlay_mode = "aggregate", ..., cache_dir = radis_cache_dir("soil_textures", source), cache = c("yes", "no", "force"), force_download = NULL ) get_soil_texture_from_radisapi( sf, source, cache_dir = radis_cache_dir("soil_textures", source), cache = c("yes", "no", "force"), base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), ..., force_download = NULL ) get_soil_texture_from_wcs( sf, vars = c("sand", "silt", "clay"), depths = c("0-5cm", "5-15cm", "15-30cm", "30-60cm", "60-100cm", "100-200cm"), stat = "mean", cache_dir = radis_cache_dir("soil_textures/soilgrids"), cache = c("yes", "no", "force"), ..., force_download = NULL )get_soil_texture( sf, source = "infosols", overlay_mode = "aggregate", ..., cache_dir = radis_cache_dir("soil_textures", source), cache = c("yes", "no", "force"), force_download = NULL ) get_soil_texture_from_radisapi( sf, source, cache_dir = radis_cache_dir("soil_textures", source), cache = c("yes", "no", "force"), base_url = Sys.getenv("RADIS_API_BASE_URL", "https://api.g-eau.fr"), ..., force_download = NULL ) get_soil_texture_from_wcs( sf, vars = c("sand", "silt", "clay"), depths = c("0-5cm", "5-15cm", "15-30cm", "30-60cm", "60-100cm", "100-200cm"), stat = "mean", cache_dir = radis_cache_dir("soil_textures/soilgrids"), cache = c("yes", "no", "force"), ..., force_download = NULL )
sf |
An |
source |
A character string specifying the data source to use. Available options are:
|
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
... |
Additional arguments passed to the underlying soil texture retrieval functions, and to spatial_combination. |
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
force_download |
Deprecated, use |
base_url |
A character scalar with the base URL of the Info&Sols API.
Default is "https://api.g-eau.fr", or the value of the
|
vars |
Character vector of variables to retrieve (e.g. c("sand","silt", "clay")). |
depths |
Character vector of depth intervals (e.g. c("0-5cm", "5-15cm",...)). |
stat |
Character; statistic to retrieve (default "mean", options include "Q0.5","Q0.05","Q0.95"). |
get_soil_texture() acts as a wrapper that not only calls the appropriate
source-specific retrieval function (depending on the source argument)
— such as "HWSD", "Info&Sols", or "Soilgrids" — but also performs a
spatial combination of the retrieved soil data with the input geometries,
according to the method specified by the overlay_mode parameter.
Disclaimer: The server providing the Regridded Harmonized World Soil Database (HWSD) is not reliable as of the date of this release (2025-11-05). Use at your own risk.
An object containing soil texture information corresponding to the
input sf features.
A SpatRaster object containing the requested
soil texture layer clipped to the extent of sf.
A SpatRaster stack containing requested
variables.
library(sf) # Using an sf object sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) # Retrieve soil texture from infos&sols sf_texture <- get_soil_texture(sf, source = "infosols") # Retrieve soil texture from HWSD sf_texture_hwsd <- get_soil_texture(sf, source = "hwsd") # Retrieve soil texture from SoilGrids sf_texture_soilgrids <- get_soil_texture(sf, source = "soilgrids")library(sf) # Using an sf object sf <- sf::read_sf( system.file("extdata/study_area/test.shp", package = "RADIS") ) # Retrieve soil texture from infos&sols sf_texture <- get_soil_texture(sf, source = "infosols") # Retrieve soil texture from HWSD sf_texture_hwsd <- get_soil_texture(sf, source = "hwsd") # Retrieve soil texture from SoilGrids sf_texture_soilgrids <- get_soil_texture(sf, source = "soilgrids")
Get the spatial format of a geospatial object
get_spatial_format(s)get_spatial_format(s)
s |
A stars object or any object convertible to stars. |
A character string indicating the spatial format: "raster" or "geometry".
Query API
query_api( url, ..., output_format = "file", file_ext = NULL, cache_dir = radis_cache_dir(basename(url)), cache = Sys.getenv("RADIS_CACHE", "yes"), user_agent = getOption("RADIS.user_agent", "RADIS R package"), force_download = NULL )query_api( url, ..., output_format = "file", file_ext = NULL, cache_dir = radis_cache_dir(basename(url)), cache = Sys.getenv("RADIS_CACHE", "yes"), user_agent = getOption("RADIS.user_agent", "RADIS R package"), force_download = NULL )
url |
API base URL. |
... |
Query parameters in format |
output_format |
API data format, either:
|
file_ext |
File extension for the saved file if |
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
user_agent |
User agent to use for HTTP requests. Defaults
to "RADIS R package". Can be defined globally with the option |
force_download |
Deprecated, use |
json and tibble_json handle API with paginated response through the
links field in the API response. The function will automatically
follow the next link to retrieve all pages of data.
Other formats (csv, raw, string) do not handle paginated response and
will only return the first page of data.
If output_format = "file", the path of the cached file recorded
from API output; otherwise, an object depending on the requested format.
Get radis cache directory
radis_cache_dir( ..., base_dir = file.path(Sys.getenv("RADIS_CACHE_DIR", rappdirs::user_cache_dir("radis")), "data-raw"), create_dir = TRUE ) radis_cache_clean(cache_root = radis_cache_dir(""), erase = FALSE) clean_radis_cache(cache_dir = radis_cache_dir(""))radis_cache_dir( ..., base_dir = file.path(Sys.getenv("RADIS_CACHE_DIR", rappdirs::user_cache_dir("radis")), "data-raw"), create_dir = TRUE ) radis_cache_clean(cache_root = radis_cache_dir(""), erase = FALSE) clean_radis_cache(cache_dir = radis_cache_dir(""))
... |
path inside the radis cache directory |
base_dir |
Base directory for the radis cache. By default, it uses the
|
create_dir |
Logical indicating whether to create the directory if it
does not exist. Default is |
cache_root |
Path to the radis cache directory. |
erase |
Logical. If |
cache_dir |
Path to the radis cache directory to be cleaned. |
The option radis.cache.max.size accepts a human-readable size string
(e.g., "1GB", "500MB") or a numeric value in bytes. Accepted suffixes
are KB, MB, GB, TB (case-insensitive). Default is "1GB".
Path to the radis cache directory or a subdirectory inside it
Invisible NULL. Side-effect: files may be deleted from the cache.
radis_cache_dir(): Returns the path to a radis cache directory,
creating the directory if it does not already exist. On every call, automatic
clean-up of the cache is triggered: files are removed (oldest first by access
time) until the total cache size is at or below the configured maximum. Set
radis.cache.max.size to NULL, a negative number, or NA to disable
automatic cleaning.
radis_cache_clean(): Performs automatic clean-up of the cache. This is
called internally by radis_cache_dir() on every call. Users can also call
it directly to trigger clean-up or to erase the entire cache.
clean_radis_cache(): Cleans the entire radis cache directory by deleting
all files. This function is deprecated; use radis_cache_clean(erase = TRUE)
instead (deprecated).
## Not run: # Call cache dir (triggers auto-clean on base_dir) radis_cache_dir("") # Disable auto-clean options("radis.cache.max.size" = NULL) # Set custom max size options("radis.cache.max.size" = "500MB") ## End(Not run)## Not run: # Call cache dir (triggers auto-clean on base_dir) radis_cache_dir("") # Disable auto-clean options("radis.cache.max.size" = NULL) # Set custom max size options("radis.cache.max.size" = "500MB") ## End(Not run)
Combine a spatial dataset with mask polygons
spatial_combination( data, mask, overlay_mode, output_format = "default", crs = sf::st_crs(mask), cache_dir = radis_cache_dir("spatial_combination"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... )spatial_combination( data, mask, overlay_mode, output_format = "default", crs = sf::st_crs(mask), cache_dir = radis_cache_dir("spatial_combination"), cache = Sys.getenv("RADIS_CACHE", "yes"), ... )
data |
Spatial dataset to combine with mask polygons |
mask |
An |
overlay_mode |
Character. Specifies how the spatial mask is applied to the input dataset. Must be one of:
|
output_format |
character defining the object format or returned value (See details) |
crs |
Coordinate Reference System of the returned object |
cache_dir |
character The directory where the file should be cached. By default, it uses a subdirectory in the radis cache directory (See radis_cache_dir). |
cache |
"yes" use the cache, "no" ignore the cache, "force" force re-download of the file even if it exists in the cache. |
... |
Query parameters in format |
output_format should be one of:
"default": returns a sf, SpatRaster or a stars object depending if
the output is respectively a polygon layer, a raster, or a time series.
Other possible values are "SpatVector", "SpatRaster", "stars", "sf",
or "raster"
An object of the type defined by output_format (see Details) containing
the data transformed according to overlay_mode.
If overlay_mode is "intersecting", the return value is a list containing one item
by mask polygon with each item containing the rows of data intersecting the
concerned mask polygon.
Displays an sf object on an interactive map using the mapview package, with optional overlay of a second spatial layer.
view_data_map(sf, rpg_sf = NULL, cols_to_show, basemaps = "OpenStreetMap")view_data_map(sf, rpg_sf = NULL, cols_to_show, basemaps = "OpenStreetMap")
sf |
An |
rpg_sf |
Optional. An |
cols_to_show |
Character vector of column names in |
basemaps |
Character vector specifying basemap(s) to use. Default is |
Only the columns specified in cols_to_show (plus the geometry column) are retained for display.
If rpg_sf is provided, it is added as a separate overlay group ("RPG") with a distinct style.
Layer controls allow toggling the visibility of the main and overlay layers.
A mapview object displaying the selected spatial data and optional overlay, with interactive layer controls.
## Not run: view_data_map(my_sf, rpg_sf = my_rpg, cols_to_show = c("name", "value")) view_data_map(my_sf, cols_to_show = c("id", "type"), basemaps = c("CartoDB.Positron")) ## End(Not run)## Not run: view_data_map(my_sf, rpg_sf = my_rpg, cols_to_show = c("name", "value")) view_data_map(my_sf, cols_to_show = c("id", "type"), basemaps = c("CartoDB.Positron")) ## End(Not run)