South America in Points
Author

Ana Luisa Bodevan

Day 1: Points

Challenge Classic: Map with point data (e.g., individual locations, points of interest, clusters). Focus on effective symbolization and density visualization.

Loading libraries

Code 
library(pacman)
pacman::p_load(tidyverse, sf, dplyr, rnaturalearth, showtext, ragg, glue)
showtext_auto()

Loading and preparing data

Code 
# get south america polygon
sa <- ne_countries(continent = "South America", returnclass = "sf") |>
  st_union() |>  # single polygon
  st_transform(3857)  # project to meters for equal grid spacing

# create a regular grid of points 
grid_spacing <- 60000 # in meters; smaller = denser grid
grid <- st_make_grid(sa,
                     what = "centers",
                     cellsize = grid_spacing) |> 
  st_as_sf() |>
  st_intersection(sa)  # keep only points inside polygon

Colors

Code 
# color pallete 
set.seed(123)
golden_palette <- c("#F9C74F", "#F8961E", "#F3722C", "#F9844A")
grid$color <- sample(golden_palette, nrow(grid), replace = TRUE)
grid$y <- st_coordinates(grid)[, 2]

Plot

Code 
ggplot() +
  geom_sf(data = grid, aes(color = y), size = 0.8, show.legend = FALSE) +

  scale_color_gradientn(colors = golden_palette) +

  coord_sf(expand = FALSE, clip = "off") +
  
  theme_void() +
  theme(
    plot.background = element_rect(fill = "white", color = NA),
    plot.margin = margin(t = 0,  # Top margin
                             r = 1,  # Right margin
                             b = 0,  # Bottom margin
                             l = 1, # Left margin
                             unit = "cm"))

Final plot

Last few tweaks made on Canva