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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 can be installed from CRAN, or with pak.

install.packages("amadeus")

pak::pak("NIEHS/amadeus")

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 = 2022,
  variable = "weasd",
  directory_to_save = directory,
  acknowledgement = TRUE,
  download = TRUE,
  hash = TRUE
)

Downloading requested files...
Requested files have been downloaded.
[1] "5655d4281b76f4d4d5bee234c2938f720cfec879"

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

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

weasd_process

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

calculate_covariates stems from the beethoven project’s need for various types of data extracted at precise locations. calculate_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, sf, or SpatVector with data extracted at all locations for each layer or row in the SpatRaster or SpatVector object, respectively.

Example of calculate_covariates using processed “weasd” data.

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

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

Simple feature collection with 5 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 8184606 ymin: 3523283 xmax: 8184606 ymax: 3523283
Projected CRS: unnamed
   id       time     weasd_0                geometry
1 001 2022-01-01 0.000000000 POINT (8184606 3523283)
2 001 2022-01-02 0.000000000 POINT (8184606 3523283)
3 001 2022-01-03 0.000000000 POINT (8184606 3523283)
4 001 2022-01-04 0.000000000 POINT (8184606 3523283)
5 001 2022-01-05 0.001953125 POINT (8184606 3523283)

Climate and Health Outcomes Research Data Systems

The amadeus package has been developed as part of the National Institute of Environmental Health Science’s (NIEHS) Climate and Health Outcomes Research Data Systems (CHORDS) program. CHORDS aims to “build and strengthen data infrastructure for patient-centered outcomes research on climate change and health” by providing curated data, analysis tools, and educational resources. Visit the CHORDS catalog at https://niehs.github.io/chords_landing/index.html.

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.

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.

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.
Health stats visible at Monitor.