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snotelr

R toolbox to facilitate easy SNOTEL data exploration and downloads through a convenient shiny based GUI.
https://github.com/bluegreen-labs/snotelr

Category: Cryosphere
Sub Category: Snow and Permafrost

Keywords

climate-data data-retrieval precipitation-data r-package rstats snotel snow

Keywords from Contributors

phenology-modelling

Last synced: about 14 hours ago
JSON representation

Repository metadata

a snow data network (SNOTEL) R package

README.md

snotelr

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DOI

snotelr is an R toolbox to facilitate easy SNOTEL data exploration and downloads through a convenient R shiny based GUI. In addition it provides a routine to extract basic snow phenology metrics.

How to cite this package in your article

You can cite this package like this "we obtained data from SNOTEL using the snotelr R package (Hufkens 2022)". Here is the full bibliographic reference to include in your reference list:

Hufkens, K. (2022). snotelr: a toolbox to facilitate easy SNOTEL data exploration and downloads in R. Zenodo. https://doi.org/10.5281/zenodo.7012728.

Installation

stable release

To install the current stable release use a CRAN repository:

install.packages("snotelr")
library("snotelr")

The use of the GUI requires the installation of additional packages, which are side loaded.

install.packages(c("DT","shinydashboard", "plotly", "leaflet"))

development release

To install the development releases of the package run the following
commands:

if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/snotelr")
library("snotelr")

Vignettes are not rendered by default, if you want to include additional
documentation please use:

if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/snotelr", build_vignettes = TRUE)
library("snotelr")

Use

Most people will prefer the GUI to explore data on the fly. To envoke the GUI use the following command:

library(snotelr)
snotel_explorer()

This will start a shiny application with an R backend in your default browser. The first window will display all site locations, and allows for subsetting of the data based upon state or a bounding box. The bounding box can be selected by clicking top-left and bottom-right.

map

The plot data tab allows for interactive viewing of the soil water equivalent (SWE) data together with a covariate (temperature, precipitation). The SWE time series will also mark snow phenology statistics, mainly the day of:

  • first snow melt
  • a continuous snow free season (last snow melt)
  • first snow accumulation (first snow deposited)
  • continuous snow accumulation (permanent snow cover)
  • seasonal maximum SWE (and its amount)

All values are provided as relative to January first of the year mentioned (spring), and absolute dates.

time_series

To access the full list of SNOTEL sites and associated meta-data use the snotel_info() function.

# returns the site info as snotel_metadata.txt in the current working directory
snotel_info(path = ".") 

# export to data frame
meta-data <- snotel_info(path = NULL) 

# show some lines of the data frame
head(meta-data)

To query data for e.g. site 924 as shown in the image above use:

snotel_download(site_id = 924)

For in depth analysis the statistics in the GUI can be retrieved using the snotel_phenology() function

# with df a SNOTEL file or data frame in your R workspace
snotel_phenology(df)

Data (units)

By default all data is converted to metric. Depths of snow and snow water equivalent are reported in mm, although it must be noted that when stations measure snow depth (in metric) they round values to the nearest inch or cm. You can use the metric parameter to download the raw data. Note that batch downloads might fail in this instance as column names might not match due to the dual use of metric and imperial units across stations. This is for niche and debugging use mostly.

References

Hufkens, K. (2022). snotelr: a toolbox to facilitate easy SNOTEL data exploration and downloads in R. Zenodo. https://doi.org/10.5281/zenodo.7012728.

Acknowledgements

This project was in part supported by the National Science Foundation’s Macro-system Biology Program (award EF-1065029) and the Marie Skłodowska-Curie Action (H2020 grant 797668). Logo design elements are taken from the FontAwesome library according to these terms, where the globe element was inverted and intersected.


Owner metadata


GitHub Events

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Committers metadata

Last synced: 5 days ago

Total Commits: 198
Total Committers: 5
Avg Commits per committer: 39.6
Development Distribution Score (DDS): 0.066

Commits in past year: 20
Committers in past year: 2
Avg Commits per committer in past year: 10.0
Development Distribution Score (DDS) in past year: 0.05

Name Email Commits
khufkens k****s@g****m 185
Koen Hufkens k****s@P****l 10
David Hill d****h@o****u 1
Dominik Schneider d****h 1
Nicholas Potter p****t 1

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Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 30
Total pull requests: 11
Average time to close issues: 6 months
Average time to close pull requests: about 4 hours
Total issue authors: 10
Total pull request authors: 4
Average comments per issue: 1.73
Average comments per pull request: 0.36
Merged pull request: 11
Bot issues: 0
Bot pull requests: 0

Past year issues: 10
Past year pull requests: 5
Past year average time to close issues: 9 days
Past year average time to close pull requests: about 6 hours
Past year issue authors: 3
Past year pull request authors: 2
Past year average comments per issue: 2.8
Past year average comments per pull request: 0.4
Past year merged pull request: 5
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/bluegreen-labs/snotelr

Top Issue Authors

  • khufkens (18)
  • realmiketalbot (2)
  • jeffmarti (2)
  • potterzot (2)
  • JepsonNomad (1)
  • dschneiderch (1)
  • tungttnguyen (1)
  • dfosterhill (1)
  • Roctober92 (1)
  • MNewcomer (1)

Top Pull Request Authors

  • khufkens (7)
  • dfosterhill (2)
  • dschneiderch (1)
  • potterzot (1)

Top Issue Labels

  • enhancement (3)

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Package metadata

cran.r-project.org: snotelr

Calculate and Visualize 'SNOTEL' Snow Data and Seasonality

  • Homepage: https://github.com/bluegreen-labs/snotelr
  • Documentation: http://cran.r-project.org/web/packages/snotelr/snotelr.pdf
  • Licenses: AGPL-3
  • Latest release: 1.5.2 (published 5 months ago)
  • Last Synced: 2025-04-25T12:01:01.241Z (1 day ago)
  • Versions: 8
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 626 Last month
  • Rankings:
    • Forks count: 8.76%
    • Stargazers count: 15.144%
    • Average: 20.009%
    • Downloads: 23.989%
    • Dependent repos count: 24.3%
    • Dependent packages count: 27.852%
  • Maintainers (1)

Dependencies

DESCRIPTION cran
  • R >= 3.6 depends
  • httr * imports
  • magrittr * imports
  • memoise * imports
  • rvest * imports
  • shiny * imports
  • stats * imports
  • utils * imports
  • DT * suggests
  • covr * suggests
  • knitr * suggests
  • leaflet * suggests
  • plotly * suggests
  • rmarkdown * suggests
  • shinydashboard * suggests
  • testthat * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/deploy_docs.yaml actions
  • JamesIves/github-pages-deploy-action v4.4.1 composite
  • actions/checkout v3 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/test-coverage.yaml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc v1 composite
  • r-lib/actions/setup-r v1 composite

Score: 10.942349836798464