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library(mapgl)
pmtiles <- "https://r2-public.protomaps.com/protomaps-sample-datasets/cb_2018_us_zcta510_500k.pmtiles"
maplibre(center = c(-97, 35), zoom= 3) |>
set_projection("globe") |>
add_vector_source("pmtiles_source",
url = paste0("pmtiles://", pmtiles)
) |>
add_fill_layer(
library(tidyverse)
library(tidycensus)
library(showtext)
font_add_google("Montserrat")
showtext_auto()
pres_results <- read_csv("https://raw.githubusercontent.com/tonmcg/US_County_Level_Election_Results_08-24/refs/heads/master/2024_US_County_Level_Presidential_Results.csv") %>%
county_mig <- get_estimates(
library(tidycensus)
library(tidyverse)
library(RColorBrewer)
natchg <- get_estimates(
geography = "state",
variables = "RNATURALCHG",
vintage = 2024
)
library(tidycensus)
library(tidyverse)
library(geofacet)
library(scales)
state_birth_rates <- get_estimates(
geography = "state",
variables = "RBIRTH",
vintage = 2024,
time_series = TRUE
library(tidycensus)
library(tidyverse)
library(scales)
state_pop <- get_estimates(
geography = "state",
variables = "POPESTIMATE",
vintage = 2024,
time_series = TRUE
)
library(tidycensus)
library(ggplot2)
library(dplyr)
state_mig <- get_estimates(
geography = "state",
variables = c("RDOMESTICMIG", "RINTERNATIONALMIG"),
vintage = 2024,
output = "wide"
)
library(mapgl)
mapboxgl(
zoom = 2,
center = c(-28, 47),
style = mapbox_style("dark")
) |>
add_layer(
id = "wind-layer",
type = "raster-particle",
library(mapgl)
library(tidycensus)
library(tigris)
options(tigris_use_cache = TRUE)
manhattan_income <- get_acs(
geography = "tract",
variables = "B19013_001",
state = "NY",
county = "New York",
# You'll need the dev version of mapgl
# remotes::install_github("walkerke/mapgl")
library(shiny)
library(bslib)
library(mapgl)
library(tigris)
library(sf)
library(dplyr)
library(sortable)
library(tidycensus)
library(tigris)
library(tidyverse)
options(tigris_use_cache = TRUE)
# Grab the data
us_income <- get_acs(
geography = "puma",
variables = "B19013_001",
year = 2023,