Tools for download and manage surface wind and sea currents data from the Global Forecasting System < https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs> and to compute connectivity between locations.
rWind contain tools for downloading, editing and transforming wind data from Global Forecast System (GFS). It also allows to use wind data to compute the minimum cost path taking into account wind speed and direction to perform connectivity analysis. For more information about data source, please check:
To install the latest released version of rWind on CRAN use
To install the latest development version
For more information and examples, please check my blog
rWind is licensed under the GPL (>=3).
rWind v1.0.4 (Release date: 2018-11-09)
o fixed bug in wind.dl_2 (wind.fit_int)
o function cost.FMGS (Felicísimo et al. 2008) is now defined outside of
o new vignette
o wind.dl_2 can be used with a time series.
o wind.fit is deleted and integrated in wind.dl.
o wind2raster is adapted to work with lists.
o flow.dispersion is adapted to work with lists.
o wind.dl returns now a "rWind data.frame" object.
o wind.fit returns now a "rWind data.frame" object.
o wind2raster returns by default a "RasterStack" object with two raster layers: wind.direction and wind.speed.
o flow.dispersion takes now as an input a RasterStack produced by wind2raster with wind.direction and wind.speed layers.
o Including "raster,stack" as an Import function.
o Including codecov badge into readme file.
o Including a tests folder with some code tests.
o Removing at the moment see currents functions.
o Including roxygen code into wind_functions.R to create package documentation automatically.
o Removing "shape" package as an Import.
o wind.dl function have change the headers of each column. Now, they are just in one row.
o wind.fit have improve his performance due to some vectorization in the data (it is much faster now). It also has been adapted to the new input provided by wind.dl function (just one row as header).
o wind.mean have improve his performance (it is much faster now).
o flow.dispersion have improve his performance, using matrix rather than raster objects to perform the maths (it is much faster now). Now you can also obtain as output either, a graph, a transitionLayer or a Sparse Matrix (see documentation).
o Two new datasets has been added to improve the example code: - "wind_data" is a downloaded data with wind.dl - "wind_series" is a downloaded series of wind data with wind.dl