walkerke

library(tidycensus)
library(tidyverse)

dc_density <- get_acs(
  geography = "tract",
  state = "DC",
  variables = "B01003_001",
  geometry = TRUE,
  keep_geo_vars = TRUE
) %>%

walkerke

library(shiny)
library(leaflet)
library(mapboxapi)
library(glue)

# If publishing to a Shiny server you'll need to be explicit about your token
# Swap in your token for your tileset
mapbox_token <- "pk.ey..."

# Define a simple UI; set the default cursor to pointer 

walkerke

library(mapboxapi)
library(tidycensus)
library(tidyverse)
library(sf)
library(ggspatial)
options(tigris_use_cache = TRUE)

# Get the data from the ACS
tarrant <- get_acs(
  geography = "tract",

walkerke

library(mapboxapi)
library(tidycensus)
library(tidyverse)
library(tmap)
library(ggspatial)
options(tigris_use_cache = TRUE)

# Get the data from the ACS
#
# Requires a Census API key: read https://walker-data.com/census-r/an-introduction-to-tidycensus.html#getting-started-with-tidycensus

walkerke

library(tidycensus)
library(tidyverse)
library(sf)
library(mapview)
library(crsuggest)

# Get data on median home value by block group in Fort Bend County
fort_bend_value <- get_acs(
  geography = "block group",
  variables = "B25077_001",

walkerke

library(tidycensus) # Make sure you have tidycensus 1.1 or higher
library(redist)
library(sf)
library(tidyverse)
library(tigris) # Make sure you have tigris 1.5 or higher
library(httr)
options(tigris_use_cache = TRUE)

set.seed(123456)

walkerke

library(tidycensus)
library(ggiraph)
library(tidyverse)
library(patchwork)

vt_income <- get_acs(
  geography = "county",
  variables = "B19013_001",
  state = "VT",
  year = 2019,

walkerke

library(tidycensus)
library(tidyverse)
library(tigris)
library(sf)
options(tigris_use_cache = TRUE)

us_states <- c(state.abb, "DC", "PR")

# To get the map to work correctly,
# empty geometries must be removed along with

walkerke

library(tidycensus)
library(tigris)
library(ggiraph)
library(tidyverse)

us_median_age <- get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2019,
  survey = "acs5",

walkerke

library(tigris)
library(sf)
# Get shapefile from UNL Drought Monitor
# https://droughtmonitor.unl.edu/data/shapefiles_m/USDM_20210427_M.zip

# Explode the shapes then shift geometry
drought <- st_read("USDM_20210427.shp") %>%
  st_cast("POLYGON") %>%
  shift_geometry()