walkerke

library(tidyverse)
library(animation)
library(readxl)
library(extrafont)

# Download file from: https://esa.un.org/unpd/wup/cd-rom/WUP2014_XLS_CD_FILES/WUP2014-F11a-30_Largest_Cities.xls

df <- read_excel('WUP2014-F11a-30_Largest_Cities.xls', skip = 17, 
                 col_names = c("index", "year", "order", "ccode", 
                               "country", "city", "name", "note", "lat", 

walkerke

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

rent <- get_acs(geography = "tract", variables = "DP04_0134", 
                state = c("WA", "OR", "CA"), geometry = TRUE)

walkerke

library(tidycensus)
library(tidyverse)
library(tigris)
library(sf)
library(ggbeeswarm)
library(viridis)
library(extrafont)
options(tigris_use_cache = TRUE)

df <- get_acs(geography = "tract", state = "TX",  

walkerke

# devtools::install_github("walkerke/tidycensus")
library(tidycensus)
library(tidyverse)
library(geofacet)
library(stringr)
library(extrafont)

# 2010 Census

age <- get_decennial(geography = "county", state = "AZ", table = "P012", 

walkerke

# devtools::install_github("walkerke/tidycensus")
library(tidycensus)
library(tidyverse)
library(geofacet)
library(stringr)
library(extrafont)

# 2010 Census

age <- get_decennial(geography = "county", state = "AK", table = "P012", 

walkerke

library(tidycensus)
library(tidyverse)
library(geofacet)
library(stringr)
library(extrafont)

age <- get_acs(geography = "county", state = "AL", table = "B01001", 
               summary_var = "B01001_001") %>%
  mutate(variable = str_replace(variable, "B01001_0", "")) %>%
  filter(!variable %in% c("01", "02", "26"))

walkerke

walkerke

library(tidycensus)
library(tidyverse)

# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html)
# census_api_key("YOUR KEY HERE", install = TRUE)

get_acs(geography = "metropolitan statistical area/micropolitan statistical area", 
        variables = "DP03_0021PE", 
        summary_var = "B01003_001", 
        survey = "acs1", 

walkerke

Here is the process I used to create the 2016 presidential election by county map with zero-population blocks removed.

1. I obtained US county boundaries and Census block boundaries by state from NHGIS, http://www.nhgis.org, along with Table P1 (Total Population) from the 2010 US Census. Your NHGIS IDs would be different.
2. I ran the following R code to walk through every block boundary dataset, identify the no-population blocks, then write out the result as a shapefile:

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

walkerke