Make grid map from spatial data - r

Question

I have spatial coordinates in a data frame where each row (Longitude, Latitude) corresponds to the occurrence of an event I am following. I tried to map these data but instead of using points, I want to create a grid with cells of a resolution of 5 nautical miles (~ 0.083333) and count the number of occurrences of the event is each cell and plot it.
This is the code I came to write with the help of some resources. But it doesn't look the way I expected it to be. Can you figure out what's I'm doing wrong? I attached the raw positions and the resulting map I get.
Here is the link to the data.
re_pi = read.csv(file = "~/Desktop/Events.csv")
gridx <- seq(from=-19,to=-10,by=0.083333)
gridy <- seq(from=20,to=29,by=0.083333)
xcell <- unlist(lapply(re_pi$LON,function(x) min(which(gridx>x))))
ycell <- unlist(lapply(re_pi$LAT,function(y) min(which(gridy>y))))
re_pi$cell <- (length(gridx) - 1) * ycell + xcell
rr = re_pi %>%
group_by(cell)%>%
summarise(Lat = mean(LAT),Lon = mean(LON),Freq = length(cell))
my_theme <- theme_bw() + theme(panel.ontop=TRUE, panel.background=element_blank())
my_cols <- scale_color_distiller(palette='Spectral')
my_fill <- scale_fill_distiller(palette='Spectral')
ggplot(rr, aes(y=Lat, x=Lon, fill=Effort)) + geom_tile(width=1.2, height=1.2) +
borders('world', xlim=range(rr$Lon), ylim=range(rr$Lat), colour='black') + my_theme + my_fill +
coord_quickmap(xlim=range(rr$Lon), ylim=range(rr$Lat))

Answer 1

Nice dataset, assume these are fishing vessel VMS data. Here may be one way to achieve your objective, heavily reliant on the tidyverse and by-passing raster and shapes.
library(tidyverse)
library(mapdata) # higher resolution maps
# poor man's gridding function
grade <- function (x, dx) {
if (dx > 1)
warning("Not tested for grids larger than one")
brks <- seq(floor(min(x)), ceiling(max(x)), dx)
ints <- findInterval(x, brks, all.inside = TRUE)
x <- (brks[ints] + brks[ints + 1])/2
return(x)
}
d <-
read_csv("https://raw.githubusercontent.com/abenmhamed/data/main/Events.csv") %>%
janitor::clean_names() %>%
# make a grid 0.01 x 0.01 longitude / latitude
mutate(lon = grade(lon, 0.01),
lat = grade(lat, 0.01)) %>%
group_by(lon, lat) %>%
count() %>%
# not much happening south of 21 and north of 26
filter(between(lat, 21, 26.25))
d %>%
ggplot() +
theme_bw() +
geom_tile(aes(lon, lat, fill = n)) +
scale_fill_viridis_c(option = "B", direction = -1) +
# only data within the data-bounds
borders(database = "worldHires",
xlim = range(d$lon), ylim = range(d$lat),
fill = "grey") +
labs(x = NULL, y = NULL, fill = "Effort") +
# limit plot
coord_quickmap(xlim = range(d$lon), ylim = range(d$lat)) +
# legends within plot
theme(legend.position = c(0.77, 0.26))

Answer 2

Here is my attempt using the sf package. First I imported your data and converted it to an sf object. Then, I created another sf object which includes the grids. I used the raster package and the sf package in order to create the grids. Once I had the two sf object, I counted how many data points exist in each grid and added the results as a new column in foo. Finally, I drew a graphic.
library(tidyverse)
library(sf)
library(raster)
library(viridis)
# Import the data and convert it to an sf object
mydata <- read_csv("https://raw.githubusercontent.com/abenmhamed/data/main/Events.csv") %>%
st_as_sf(coords = c("LON", "LAT"),
crs = 4326, agr = "constant")
# Create an sf object for the grid
gridx <- seq(from = -19,to = -10, by = 0.083333)
gridy <- seq(from = 20,to = 29, by = 0.083333)
foo <- raster(xmn = -19, xmx = -10,
ymn = 20, ymx = 29,
nrows = length(gridx),
ncols = length(gridy)) %>%
rasterToPolygons() %>%
st_as_sf(crs = 4326) %>%
mutate(group = 1:(length(gridx)*length(gridy))) %>%
st_cast("MULTIPOLYGON")
# Now count how many data points exist in each grid
mutate(foo,
count = lengths(st_intersects(x = foo, y = mydata))) -> foo
# Draw a graphic
ggplot() +
geom_sf(data = foo, aes(fill = count)) +
scale_fill_viridis(option = "D") -> g