walkerke

library(tidycensus)
library(tidyverse)
library(RColorBrewer)

natchg <- get_estimates(
  geography = "state",
  variables = "RNATURALCHG",
  vintage = 2024
)

walkerke

library(tidycensus)
library(tidyverse)
library(geofacet)
library(scales)

state_birth_rates <- get_estimates(
  geography = "state",
  variables = "RBIRTH",
  vintage = 2024,
  time_series = TRUE

walkerke

library(tidycensus)
library(tidyverse)
library(scales)

state_pop <- get_estimates(
  geography = "state",
  variables = "POPESTIMATE",
  vintage = 2024,
  time_series = TRUE
)

walkerke

library(tidycensus)
library(ggplot2)
library(dplyr)

state_mig <- get_estimates(
  geography = "state", 
  variables = c("RDOMESTICMIG", "RINTERNATIONALMIG"),
  vintage = 2024,
  output = "wide"
)

walkerke

library(mapgl)

mapboxgl(
  zoom = 2,
  center = c(-28, 47),
  style = mapbox_style("dark")
) |> 
  add_layer(
    id = "wind-layer", 
    type = "raster-particle",

walkerke

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",

walkerke

# 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)

walkerke

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,

walkerke

# remotes::install_github("walkerke/mapgl")
library(mapgl)
library(sf)
library(dplyr)

# Set seed for reproducibility
set.seed(42)

# Define the bounding box for Dallas (approximately)
dallas_bbox <- st_bbox(c(

walkerke

library(shiny)
library(bslib)
library(mapgl)
library(sf)

ui <- page_fluid(
  theme = bs_theme(version = 5, bootswatch = "flatly"),
  
  h2("mapgl Drawing Demo"),