tmap: Thematic Maps

library(stars)
library(sf)
library(data.table)
library(pryr)
library(profvis)


source("sandbox/test_data.R")
World$gdp_est_mln = World$gdp_cap_est * World$pop_est / 1e6
World$well_being2 = round(World$well_being * rnorm(nrow(World), mean = 1, sd = .2), 1)
set.seed = 1234
World$r1 = round(runif(nrow(World), min = 0, max = 255))
World$g1 = round(runif(nrow(World), min = 0, max = 255))
World$b1 = round(runif(nrow(World), min = 0, max = 255))
World$r2 = round(pmin(pmax(World$r1 + rnorm(nrow(World), mean = 0, sd = 50), 0), 255))
World$g2 = round(pmin(pmax(World$g1 + rnorm(nrow(World), mean = 0, sd = 50), 0), 255))
World$b2 = round(pmin(pmax(World$b1 + rnorm(nrow(World), mean = 0, sd = 50), 0), 255))

World$alpha_class = factor(floor(seq(1, 5, length.out = nrow(World) + 1)[1:nrow(World)]), labels = LETTERS[1:4])
World$pop_class = cut(World$pop_est, breaks = c(0, 10, 100, 1000, Inf) * 1e6, labels = c("Small", "Medium", "Large", "Extra Large"))					   

World$income_grp_int = as.integer(World$income_grp)
World$HPI2 = World$HPI / 2
World$HPI3 = round(World$HPI)

World$HPI_class = cut(World$HPI, breaks = seq(10, 50, by = 10))
World$well_being_class = cut(World$well_being, breaks = seq(2, 8, by = 2))
World$footprint_class = cut(World$footprint, breaks = seq(0, 16, by = 4))


metro$alpha_class = factor(floor(seq(1, 5, length.out = nrow(metro) + 1)[1:nrow(metro)]), labels = LETTERS[1:4])