First, we load the main packages we will use in this vignette. This vignette was written under the rWind version 1.1.7

```
# use install.packages() if some is not installed
# and you can install the latest development version using the command
# devtools::install_github("jabiologo/rWind")
library(rWind)
library(raster)
library(gdistance)
```

In this simple example, we introduce the most basic functionality of rWind, to get the shortest paths between two points across Strait of Gibraltar. Notice that, as wind connectivity is anisotropic (direction dependent), shortest path from A to B usually does not match with shortest path from B to A.

First, we download wind data of a selected date (e.g. 2015 February 12th).

`<- wind.dl(2015, 2, 12, 12, -7, -4, 34.5, 37.5) w `

Next we transform this `data.frame`

into two raster layers, with values of wind direction and wind speed.

`<- wind2raster(w) wind_layer `

Then, we will use `flow.dispersion`

function to obtain a `transitionLayer`

object with conductance values, which will be used later to obtain the shortest paths.

`<-flow.dispersion(wind_layer) Conductance`

Now, we will use `shortestPath`

function from `gdistance`

package (van Etten 2018) to compute shortest path from our `Conductance`

object between the two selected points.

```
<- shortestPath(Conductance,
AtoBc(-5.5, 37), c(-5.5, 35), output="SpatialLines")
<- shortestPath(Conductance,
BtoAc(-5.5, 35), c(-5.5, 37), output="SpatialLines")
```

Finally, we plot the map and we will add the shortest paths as lines and some other features.

We need some additionally packages to be installed. This can be done using the command `install.packages(c("fields", "shape", "rworldmap"))`

.

```
library(fields)
library(shape)
library(rworldmap)
image.plot(wind_layer$wind.speed, main="least cost paths by wind direction and speed",
col=terrain.colors(10), xlab="Longitude", ylab="Lattitude", zlim=c(0,7))
lines(getMap(resolution = "low"), lwd=4)
points(-5.5, 37, pch=19, cex=3.4, col="red")
points(-5.5, 35, pch=19, cex=3.4, col="blue")
lines(AtoB, col="red", lwd=4, lty=2)
lines(BtoA, col="blue", lwd=4, lty=2)
<- arrowDir(w)
alpha Arrowhead(w$lon, w$lat, angle=alpha, arr.length = 0.4, arr.type="curved")
text(-5.75, 37.25,labels="Spain", cex= 2.5, col="red", font=2)
text(-5.25, 34.75,labels="Morocco", cex= 2.5, col="blue", font=2)
legend("toprigh", legend = c("From Spain to Morocco", "From Morocco to Spain"),
lwd=4 ,lty = 1, col=c("red","blue"), cex=0.9, bg="white")
```

Douglas Nychka, Reinhard Furrer, John Paige, and Stephan Sain. 2017. “Fields: Tools for Spatial Data.” Boulder, CO, USA: University Corporation for Atmospheric Research. https://doi.org/10.5065/D6W957CT.

Hijmans, Robert J. 2017. *Raster: Geographic Data Analysis and Modeling*. https://CRAN.R-project.org/package=raster.

Soetaert, Karline. 2018. *Shape: Functions for Plotting Graphical Shapes, Colors*. https://CRAN.R-project.org/package=shape.

South, Andy. 2011. “Rworldmap: A New r Package for Mapping Global Data.” *The R Journal* 3 (1): 35–43. https://journal.r-project.org/archive/2011-1/RJournal_2011-1_South.pdf.

van Etten, Jacob. 2018. *Gdistance: Distances and Routes on Geographical Grids*. https://CRAN.R-project.org/package=gdistance.