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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 is not yet available from CRAN, but it can be installed with the devtools, remotes, or pak packages.

devtools::install_github("NIEHS/amadeus")

remotes::install_github("NIEHS/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:

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.