walkerke

library(tidycensus)
library(mapgl)

tx_income <- get_acs(
  geography = "tract",
  variables = "B19013_001",
  state = "TX",
  geometry = TRUE
)

walkerke

library(mapgl)
library(shiny)
library(geojsonsf)

ui <- fluidPage(
  tags$head(
    tags$style(HTML(
      "
      body { margin: 0; padding: 0; }
      .container-fluid { padding: 0 !important; margin: 0 !important; }

walkerke

# DuckDB Vector Tiles with mapgl Example
# This script demonstrates how to serve vector tiles from DuckDB using ST_AsMVT()
# and display them in a mapgl map using httpuv

library(mapgl)
library(duckdb) # Requires the latest DuckDB version (>= 1.4.0)
library(httpuv)
library(sf)
library(duckspatial)
library(tigris)

walkerke

library(mapgl)
library(tidycensus)

tarrant_age <- get_acs(
  geography = "tract",
  variables = "B01002_001",
  state = "TX",
  county = "Tarrant",
  geometry = TRUE
)

walkerke

library(mapgl)
library(tigris)
library(dplyr)
library(sf)

texas <- states(cb = TRUE) |> 
  filter(NAME == "Texas")

points <- st_sample(texas, 5000) |> 
  st_sfc() |> 

walkerke

library(tigris)
library(tidyverse)
library(sf)
library(mapgl)
library(bslib)
library(shiny)
options(tigris_use_cache = TRUE)

dfw_cbsa <- core_based_statistical_areas(cb = TRUE) |> filter(GEOID == "19100")

walkerke

library(shiny)
library(mapgl)
library(mapboxapi)
library(tigris)
library(bslib)

tx <- counties("TX", cb = TRUE)

ui <- page_fluid(
  mapboxglOutput("map", height = "90vh")

walkerke

library(tidyverse)
library(tidycensus)

# Get the data
pyramid_data <- get_estimates(
  geography = "state",
  product = "characteristics",
  breakdown = c("AGEGROUP", "SEX"),
  breakdown_labels = TRUE,
  vintage = 2024,

walkerke

library(mapgl) # pak::pak("walkerke/mapgl")
library(tidycensus)
library(dplyr)
library(viridisLite)

# Get viridis colors
viridis_colors <- viridis(5)

# Get median household income by county in Texas
tx_income <- get_acs(

walkerke

library(tidycensus)
library(mapgl)

# load_variables(2023, "acs1/profile") |> View()

births_per_1000 <- get_acs(
  geography = "puma",
  year = 2023,
  variables = "DP02_0040",
  survey = "acs5",