Does anyone has an idea on how to plot a kernel density map based in the occurrence of events using ggplot2
and sf
?
For example, considering the meuse
dataset from the sp
package (let's pretend that each point is an event occurrence)
library(sf)
library(ggplot2)
# data
data(meuse, package = "sp")
# as_sf
meuse_sf <- st_as_sf(meuse, coords = c("x", "y"), crs = 28992)
# example
ggplot(data = meuse_sf) +
geom_sf(alpha = .3) +
theme_bw()
I would like to create a bi-dimensional kernel density using geom_sf.
P.s.: It would be easy using stat_density_2d
, however I'm working with spatial data and it has a polygon border.
Please let me know if this approach works. I was able to create a density surface by extracting the coordinates from the geometry column in the sf object. The purrr function map_dbl returns a numeric vector by applying some function to each element of a list. In this case, each point feature's geometry is represented by a numeric vector of length 2, so we take the first element from each geometry for our vector of x coordinates, and then we take the second element for our vector of y coordinates.
library(sf)
library(ggplot2)
data(meuse, package = "sp")
meuse_sf <- st_as_sf(meuse, coords = c("x", "y"), crs = 28992)
ggplot(data = meuse_sf) +
geom_sf() +
theme_bw() +
stat_density_2d(mapping = ggplot2::aes(x = purrr::map_dbl(geometry, ~.[1]),
y = purrr::map_dbl(geometry, ~.[2]),
fill = stat(density)),
geom = 'tile',
contour = FALSE,
alpha = 0.5)
> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.0.0 sf_0.7-0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.19 rstudioapi_0.8 bindr_0.1.1 magrittr_1.5 MASS_7.3-50 units_0.6-1
[7] tidyselect_0.2.4 munsell_0.5.0 colorspace_1.3-2 R6_2.2.2 rlang_0.2.2 plyr_1.8.4
[13] dplyr_0.7.6 tools_3.5.1 grid_3.5.1 gtable_0.2.0 e1071_1.7-0 DBI_1.0.0.9000
[19] withr_2.1.2 class_7.3-14 digest_0.6.17 yaml_2.2.0 lazyeval_0.2.1 assertthat_0.2.0
[25] tibble_1.4.2 crayon_1.3.4 bindrcpp_0.2.2 spData_0.2.9.4 purrr_0.2.5 glue_1.3.0
[31] labeling_0.3 compiler_3.5.1 pillar_1.3.0 scales_1.0.0 classInt_0.2-3 pkgconfig_2.0.2
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