walkerke

library(tidycensus)
library(tidyverse)

states <- c(state.abb, "DC")

us_age_data <- map_df(states, ~{
  get_pums(
    variables = c("PUMA", "AGEP"),
    state = .x,
    survey = "acs5",

walkerke

library(idbr)
library(tidyverse)
library(gganimate)

france_data <- get_idb(
  country = "France",
  year = 1990:2021,
  age = 0:100,
  sex = c("male", "female")
)

walkerke

library(tidycensus)
library(mapdeck)
library(sf)
options(tigris_use_cache = TRUE)

tract_median_age <- get_acs(
  geography = "tract",
  variables = "B01002_001",
  state = c(state.abb, "DC", "PR"),
  geometry = TRUE

walkerke

library(tigris)
library(tidyverse)
options(tigris_class = "sf")

# Get a vector of county codes
ia_counties <- counties("IA", cb = TRUE, resolution = "20m") %>%
  pull(COUNTYFP)

# Iterate through the county codes and combine the result
ia_roads <- map(ia_counties, ~{

walkerke

library(tidycensus)
library(tidyverse)
library(sf)

racevars <- c(White = "P005003", 
              Black = "P005004", 
              Asian = "P005006", 
              Hispanic = "P004003")

race <- get_decennial(geography = "tract", variables = racevars, 

walkerke

library(tidycensus)
library(tidyverse)
library(gganimate)

williams <- get_estimates(geography = "county",
                          product = "characteristics",
                          breakdown = c("AGEGROUP", "SEX"),
                          breakdown_labels = TRUE,
                          state = "ND",
                          county = "Williams",

walkerke

library(tigris)
library(tidycensus)
library(tidyverse)
library(sf)

ma_income <- get_acs(geography = "county subdivision", 
                     variables = "B19013_001", 
                     state = "MA")

ma_subs <- county_subdivisions(state = "MA", cb = TRUE, class = "sf") %>%

walkerke

library(tidycensus)
library(mapdeck)
library(tidyverse)

token <- "your mapbox token"

hv <- get_acs(geography = "tract", 
              variables = "B25077_001", 
              state = "CA", 
              geometry = TRUE) %>%

walkerke

library(tidycensus)
library(tigris)
library(tidyverse)
library(sf)
library(extrafont)
options(tigris_use_cache = TRUE, tigris_class = "sf")

########################################################################
########################################################################
# Chart 1

walkerke

library(rnaturalearth)
library(sf)
library(plotly)
library(crosstalk)
library(viridis)

ng <- ne_states(country = "Nigeria", returnclass = "sf") %>%
  select(Name = name) 

# Source: https://dhsprogram.com/pubs/pdf/FR293/FR293.pdf