# Tidy and geospatial kernel density quiver estimate Tidy and geospatial versions of a kernel density quiver estimate for 2-dimensional data. ```r tidy_kquiver(data, thin=5, transf=1/4, neg.grad=FALSE) st_kquiver(x, thin=5, transf=1/4, neg.grad=FALSE, scale=1) ``` ## Arguments - `data`: tidy kernel density gradient estimate (output from `tidy_kdde(deriv_order=1)`) - `x`: geospatial kernel density gradient estimate (output from `st_kdde(deriv_order=1)`) - `thin`: number to thin out estimation grid. Default is 5. - `transf`: power index in transformation. Default is 1/4. - `neg.grad`: flag to compute arrows in negative gradient direction. Default is FALSE. - `scale`: scale factor to normalise length of arrows. Default is 1. ## Returns The output from `tidy_kquiver` has the same structure as the input kernel density gradient estimate, with the added columns `u,v` for the density gradient value in the $x$-, $y$-axis. This structure is compatible with the `ggquiver::geom_quiver` layer function for quiver plots. Since `ggquiver::geom_quiver` is not compatible with `geom_sf` layers, the output from `st_kquiver` has added columns `lon`, `lat`, `lon_end`, `lat_end`, `len` which are required in `geom_segment`. ## Details A kernel quiver estimate is a modification of the standard kernel density gradient estimate in `*_kdde` where the density derivatives are not given in the separate groups as indexed in `deriv_group`, but as extra columns `u` (for `deriv_group=(1,0)`) and `v` (for `deriv_group=(0,1)`). The bandwidth matrix of smoothing parameters is computed as in `ks::kdde(deriv_order=1)`. ## Examples ```r ## tidy kernel quiver estimate library(ggplot2) data(crabs, package="MASS") crabs2 <- dplyr::select(crabs, FL, CW) t1 <- tidy_kde(crabs2) t2 <- tidy_kdde(crabs2, deriv_order=1) t3 <- tidy_kquiver(t2, thin=5) gt <- ggplot(t1, aes(x=FL, y=CW)) gt + geom_contour_filled_ks(alpha=0.5) + ggquiver::geom_quiver(data=t3, aes(u=u, v=v), colour=1) ## geospatial kernel `quiver' estimate data(wa) data(grevilleasf) hakeoides <- dplyr::filter(grevilleasf, species=="hakeoides") s1 <- st_kde(hakeoides) s2 <- st_kdde(hakeoides, deriv_order=1) s3 <- st_kquiver(s2, thin=5) ## base R plot xlim <- c(1.2e5, 1.1e6); ylim <- c(6.1e6, 7.2e6) plot(wa, xlim=xlim, ylim=ylim) plot(s1, add=TRUE, alpha=0.5, border="grey50") plot(s3$tidy_ks$ks[[1]], thin=5, add=TRUE, display="quiver") ## geom_sf plot gs <- ggplot(s1) + geom_sf(data=wa, fill=NA) + ggthemes::theme_map() gs + geom_sf(data=st_get_contour(s1), aes(fill=contperc), alpha=0.5) + ggquiver::geom_quiver(data=s3$sf, aes(x=lon, y=lat, u=u, v=v), colour=1) + coord_sf(xlim=xlim, ylim=ylim) ```

tidyst_kquiver function