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and Permafrost","monthly_downloads":564,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"# snotelr \u003ca href='https://bluegreen-labs.github.io/snotelr/'\u003e\u003cimg src='https://raw.githubusercontent.com/bluegreen-labs/snotelr/master/logo.png' align=\"right\" height=\"139\" /\u003e\u003c/a\u003e\n\n[![R-CMD-check](https://github.com/bluegreen-labs/snotelr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/bluegreen-labs/snotelr/actions/workflows/R-CMD-check.yaml)\n[![codecov](https://codecov.io/gh/bluegreen-labs/snotelr/branch/master/graph/badge.svg)](https://app.codecov.io/gh/bluegreen-labs/snotelr)\n[![CRAN\\_Status\\_Badge](https://www.r-pkg.org/badges/version/snotelr)](https://cran.r-project.org/package=snotelr)\n[![](https://cranlogs.r-pkg.org/badges/grand-total/snotelr)](https://cran.r-project.org/package=snotelr)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7012728.svg)](https://doi.org/10.5281/zenodo.7012728)\n\n`snotelr` is an R toolbox to facilitate easy SNOTEL data exploration and downloads through a convenient R [shiny](https://shiny.posit.co/) based GUI. In addition it provides a routine to extract basic snow phenology metrics.\n\n## How to cite this package in your article\n\nYou 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:\n\n\u003e Hufkens, K. (2022). snotelr: a toolbox to facilitate easy SNOTEL data exploration and downloads in R. Zenodo. https://doi.org/10.5281/zenodo.7012728.\n\n## Installation\n\n### stable release\n\nTo install the current stable release use a CRAN repository:\n\n```r\ninstall.packages(\"snotelr\")\nlibrary(\"snotelr\")\n```\n\nThe use of the GUI requires the installation of additional packages, which are side loaded.\n\n```r\ninstall.packages(c(\"DT\",\"shinydashboard\", \"plotly\", \"leaflet\"))\n```\n\n### development release\n\nTo install the development releases of the package run the following\ncommands:\n\n```r\nif(!require(remotes)){install.packages(\"remotes\")}\nremotes::install_github(\"bluegreen-labs/snotelr\")\nlibrary(\"snotelr\")\n```\n\nVignettes are not rendered by default, if you want to include additional\ndocumentation please use:\n\n```r\nif(!require(remotes)){install.packages(\"remotes\")}\nremotes::install_github(\"bluegreen-labs/snotelr\", build_vignettes = TRUE)\nlibrary(\"snotelr\")\n```\n\n## Use\n\nMost people will prefer the GUI to explore data on the fly. To envoke the GUI use the following command:\n\n```r\nlibrary(snotelr)\nsnotel_explorer()\n```\n\nThis 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.\n\n![map](https://github.com/bluegreen-labs/snotelr/assets/1354258/f191081c-d5e9-4827-9cee-3e25376fc97c)\n\nThe *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:\n\n- first snow melt\n- a continuous snow free season (last snow melt)\n- first snow accumulation (first snow deposited)\n- continuous snow accumulation (permanent snow cover)\n- seasonal maximum SWE (and its amount)\n\nAll values are provided as relative to January first of the year mentioned (spring), and absolute dates.\n\n![time_series](https://github.com/bluegreen-labs/snotelr/assets/1354258/c430abbc-b714-45e1-8e31-0fdecb7d3796)\n\nTo access the full list of SNOTEL sites and associated meta-data use the **snotel_info()** function.\n\n```r\n# returns the site info as snotel_metadata.txt in the current working directory\nsnotel_info(path = \".\") \n\n# export to data frame\nmeta-data \u003c- snotel_info(path = NULL) \n\n# show some lines of the data frame\nhead(meta-data)\n```\n\nTo query data for e.g. site 924 as shown in the image above use:\n\n```r\nsnotel_download(site_id = 924)\n```\n\nFor in depth analysis the statistics in the GUI can be retrieved using the **snotel_phenology()** function\n\n```r\n# with df a SNOTEL file or data frame in your R workspace\nsnotel_phenology(df)\n```\n\n## Data (units)\n\nBy 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.\n\n# References\n\nHufkens, K. (2022). snotelr: a toolbox to facilitate easy SNOTEL data exploration and downloads in R. Zenodo. https://doi.org/10.5281/zenodo.7012728.\n\n# Acknowledgements\n\nThis 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](https://fontawesome.com/license), where the globe element was inverted and intersected.\n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.7012728"],"works":{"https://doi.org/10.5281/zenodo.7012728":null},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["phenology-modelling"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/1571","html_url":"https://ost.ecosyste.ms/projects/1571"}