amadeus

amadeus is a mechanism for data, environments, and user setup for common environmental and climate health datasets in R. amadeus has been developed to improve access to and utility with large scale, publicly available environmental data in R.

Installation

amadeus can be installed from CRAN, or with pak.

install.packages("amadeus")

pak::pak("NIEHS/amadeus")

Contribution

To add or edit functionality for new data sources or datasets, open a Pull request into the main branch with a detailed description of the proposed changes. Pull requests must pass all status checks, and then will be approved or rejected by amadeus's authors.

Utilize Issues to notify the authors of bugs, questions, or recommendations. Identify each issue with the appropriate label to help ensure a timely response.

Download

download_data accesses and downloads raw geospatial data from a variety of open source data repositories. The function is a wrapper that calls source-specific download functions, each of which account for the source's unique combination of URL, file naming conventions, and data types. Download functions cover the following sources:

Data Source	File Type	Data Genre
Climatology Lab TerraClimate	netCDF	Meteorology
Climatology Lab GridMet	netCDF	Climate Water
Köppen-Geiger Climate Classification	GeoTIFF	Climate Classification
MRLC1 Consortium National Land Cover Database (NLCD)	GeoTIFF	Land Use
NASA2 Moderate Resolution Imaging Spectroradiometer (MODIS)	HDF	Atmosphere Meteorology Land Use Satellite
NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)	netCDF	Atmosphere Meteorology
NASA SEDAC3 UN WPP-Adjusted Population Density	GeoTIFF netCDF	Population
NASA SEDAC Global Roads Open Access Data Set	Shapefile Geodatabase	Roadways
NASA Goddard Earth Observing System Composition Forcasting (GEOS-CF)	netCDF	Atmosphere Meteorology
NOAA Hazard Mapping System Fire and Smoke Product	Shapefile KML	Wildfire Smoke
NOAA NCEP4 North American Regional Reanalysis (NARR)	netCDF	Atmosphere Meteorology
OpenGeoHub Foundation OpenLandMap	GeoTIFF	Climate Elevation Soil Land Use Satellite
Parameter Elevation Regression on Independent Slopes Model (PRISM)	BIL ASCII	Climate
US EPA5 Air Data Pre-Generated Data Files	CSV	Air Pollution
US EPA Ecoregions	Shapefile	Climate Regions
US EPA National Emissions Inventory (NEI)	CSV	Emissions
US EPA Toxic Release Inventory (TRI) Program	CSV	Chemicals Pollution
USGS6 Global Multi-resolution Terrain Elevation Data (GMTED2010)	ESRI ASCII Grid	Elevation
USGS National Hydrography Dataset (NHD)	Geopackage Geodatabase	Hydrography

See the "download_data" vignette for a detailed description of source-specific download functions.

Example use of download_data using NOAA NCEP North American Regional Reanalysis's (NARR) "weasd" (Daily Accumulated Snow at Surface) variable.

directory <- "/  EXAMPLE  /  FILE  /  PATH  /"
download_data(
  dataset_name = "narr",
  year = c(2022, 2022),
  variable = "weasd",
  directory_to_save = directory,
  acknowledgement = TRUE,
  download = TRUE
)

Downloading requested files...
Requested files have been downloaded.

list.files(paste0(directory, "weasd"))

[1] "weasd.2022.nc"

Process

process_covariates imports and cleans raw geospatial data (downloaded with download_data), and returns a single SpatRaster or SpatVector into the user's R environment. process_covariates "cleans" the data by defining interpretable layer names, ensuring a coordinate reference system is present, and managing `timedata (if applicable).

To avoid errors when using process_covariates, do not edit the raw downloaded data files. Passing user-generated or edited data into process_covariates may result in errors as the underlying functions are adapted to each sources' raw data file type.

Example use of process_covariates using the downloaded "weasd" data.

weasd <- process_covariates(
  covariate = "narr",
  date = c("2022-01-01", "2022-01-05"),
  variable = "weasd",
  path = paste0(directory, "weasd"),
  extent = NULL
)

Cleaning weasd data for January, 2022...
Detected monolevel data...
Returning daily weasd data from 2022-01-01 to 2022-01-05.

weasd

class       : SpatRaster
dimensions  : 277, 349, 5  (nrow, ncol, nlyr)
resolution  : 32462.99, 32463  (x, y)
extent      : -16231.49, 11313351, -16231.5, 8976020  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +lat_0=50 +lon_0=-107 +lat_1=50 +lat_2=50 +x_0=5632642.22547 +y_0=4612545.65137 +datum=WGS84 +units=m +no_defs
source      : weasd.2022.nc:weasd
varname     : weasd (Daily Accumulated Snow at Surface)
names       : weasd_20220101, weasd_20220102, weasd_20220103, weasd_20220104, weasd_20220105
unit        :         kg/m^2,         kg/m^2,         kg/m^2,         kg/m^2,         kg/m^2
time        : 2022-01-01 to 2022-01-05 UTC

Calculate Covariates

calc_covariates stems from the beethoven project's need for various types of data extracted at precise locations. calc_covariates, therefore, extracts data from the "cleaned" SpatRaster or SpatVector object at user defined locations. Users can choose to buffer the locations. The function returns a data.frame or SpatVector with data extracted at all locations for each layer or row in the SpatRaster or SpatVector object, respectively.

Example of calc_covariates using processed "weasd" data.

locs <- data.frame(id = "001", lon = -78.8277, lat = 35.95013)
weasd_covar <- calc_covariates(
  covariate = "narr",
  from = weasd_process,
  locs = locs,
  locs_id = "id",
  radius = 0,
  geom = FALSE
)

Detected `data.frame` extraction locations...
Calculating weasd covariates for 2022-01-01...
Calculating weasd covariates for 2022-01-02...
Calculating weasd covariates for 2022-01-03...
Calculating weasd covariates for 2022-01-04...
Calculating weasd covariates for 2022-01-05...
Returning extracted covariates.

weasd_covar

    id       time     weasd_0
1 0001 2022-01-01 0.000000000
2 0001 2022-01-02 0.000000000
3 0001 2022-01-03 0.000000000
4 0001 2022-01-04 0.000000000
5 0001 2022-01-05 0.001953125

Additional Resources

The following R packages can also be used to access climate and weather data in R, but each differs from amadeus in the data sources covered or type of functionality provided.

Package	Source
dataRetrieval	USGS Hydrological Data and EPA Water Quality Data
daymetr	Daymet
ecmwfr	ECMWF Reanalysis v5 (ERA5)
RClimChange7	NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)
rNOMADS	NOAA Operational Model Archive and Distribution System
sen2r8	Sentinel-2

Footnotes

1. Multi-Resolution Land Characteristics ↩

2. National Aeronautics and Space Administration ↩

3. Socioeconomic Data and Applications Center ↩

4. National Centers for Environmental Prediction ↩

5. United States Environmental Protection Agency ↩

6. United States Geological Survey ↩

7. Last updated more than two years ago. ↩

8. Archived; no longer maintained. ↩