Creating an elevation map with GPS coordinate points

library(raster)
library(dplyr)
library(ggplot2)
library(readxl)
library(janitor)

getData("ISO3")

# Get data for South Africa
southafrica <- getData("alt", country="South Africa", mask = T)

# For some reason, the South Africa map doesn't save as a RasterLayer object, so run the equivalent of this line, changing the filepath appropriately to access the _msk_alt.grd file generated
southafrica_1 = southafrica$`C:/Users/s1000334/Documents/PhD_Tetramesa/R Scripts/map_plotting/ZAF1_msk_alt.grd`

# Check that it's a RasterLayer object
class(southafrica_1)

##################################
# Using base-R
##################################

# Quick peek at the map
plot(southafrica_1, main = "SA elevation", col = grey(1:100/100))

# Import GPS points from an Excel file on GitHub
sample_gps = readxl::read_xlsx("G:/My Drive/Tetramesa Project/Source Modifiers/source_modifiers_updated_10_06_2021.xlsx", sheet = "Tetramesa_Clarke")

# Apply some filtering specific to this Excel sheet
sample_gps = filter(sample_gps, mitochondrial_COI == "TRUE" | nuclear_28S == "TRUE")

# Check data import 
head(sample_gps)

# Clean the column names
sample_gps <- sample_gps %>%
  janitor::clean_names()

# Inspect the data
head(sample_gps)

# Generate the plot
plot( southafrica_1, main = "", col = grey(1:100/100), ylab = "Latitude", xlab = "Longitude" )
# If no colour is specified, the default palette used is rev(terrain.colors(255))
# E.g of a different coloured plot:
plot( southafrica_1, main = "", col = colorRampPalette(c("black", "orange"))( 255 ), ylab = "Latitude", xlab = "Longitude" )

# Add the GPS coordinates as red points
points(sample_gps$longitude, sample_gps$latitude, bg = "red", col = "black", pch = 21, cex = 2)

# Add a scalebar to the map
scalebar(d = 200, type = "bar", below = "km", xy=c(31,-34))