/macrosheds

An R package for querying and manipulating data from the MacroSheds platform

Primary LanguageRMIT LicenseMIT

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MIT License


macrosheds

The macrosheds R package provides functions for accessing, manipulating, and analyzing the MacroSheds dataset, a growing collection of data from long term, small-watershed ecosystem studies across North America and beyond.

  1. Installation
  2. Usage
  3. Changelog
  4. Issues

Installation

install.packages("devtools")
devtools::install_github("https://github.com/MacroSHEDS/macrosheds.git")

If you'll be using our watershed delineator ms_delineate_watershed, you'll also need to run

whitebox::wbt_init()
whitebox::install_whitebox()

which initializes the WhiteboxTools geospatial backend. More info here.

Usage

library(macrosheds)

ms_root <- 'path/to/wherever'

ms_sites <- ms_load_sites() #site metadata
ms_vars <- ms_load_variables('timeseries') #variable metadata

selected_domains <- c('niwot', 'hjandrews')
ms_download_core_data(ms_root, domains = selected_domains)

ms_download_ws_attr(ms_root, dataset = 'time series')

macrosheds_data <- ms_load_product(ms_root, prodname = 'stream_chemistry')
macrosheds_data <- ms_load_product(ms_root, prodname = 'discharge')
macrosheds_data <- ms_load_product(ms_root, prodname = 'stream_load_annual_scaled')
macrosheds_data <- ms_load_product(ms_root, prodname = 'ws_attr_timeseries:all')

#etc

For more examples, see the Vignettes.

Changelog

v2 (2024-09-30):

  • Now able to retrieve any available version of the MacroSheds dataset. Dataset v2 is being published concurrently.
    • If you already had v1 files stored locally in macrosheds_root, these will be moved to macrosheds_root/v1 the first time you use ms_download_core_data, ms_download_ws_attr, ms_load_product, or ms_load_spatial_product.
  • MacroSheds won't include sub-daily data any time soon, so the datetime column is now date.
  • We've done away with variable prefixes and added a Boolean grab_sample column to core time-series data. Watershed attribute sources and categories are in the variable metadata, which can be joined to any table.
  • We still report standard uncertainty via the val_err column, but none of the package functions attempt to carry it. It is there to be propagated on your own terms, if you so choose.
  • See the changelog for the MacroSheds dataset here.

Issues

[https://github.com/MacroSHEDS/macrosheds/issues]