walkerke

make_GeodesicBuffer <- function(XY.dg, dg.step=5, dst.m, crs){
    #######################################################################################################
    ## Function to make a circle-buffer around given points (long-lat coordinates)
    ## Is different from rgeos::gBuffer() by the fact that it allows the user to create 
    ## a geodesic buffer with a width expressed in metric units.
    ## Otherwise the user needs to project and apply an Euclidian buffer with rgeos::gBuffer(),
    ## which will introduce distortions that vary greatly with latitude and the radius of the circle buffer. 
    ## See also my question addressed here:
    ## https://gis.stackexchange.com/questions/250389/euclidean-and-geodesic-buffering-in-r
    ##

walkerke

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

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

alltracts <- map(st, function(x) {
  tracts(state = x, cb = TRUE, class = "sf")
}) %>%
  rbind_tigris()

walkerke

library(cancensus)
library(tidyverse)
library(sf)
library(tmap)
library(tmaptools)
options(cancensus.api_key = "your key goes here")

montreal <- get_census(dataset = "CA16", regions = list(CMA = "24462"), 
                       vectors = c("v_CA16_1364", "v_CA16_1367"), level = "CT", 
                       geo_format = "sf", labels = "short")

walkerke

library(tidycensus)
library(tidyverse)
library(extrafont)
library(plotly)

place00 <- get_decennial(geography = "place", state = "TX", 
                         variables = "P001001", year = 2000) %>%
  mutate(NAME = str_replace(NAME, " city.*", "")) %>%
  select(NAME, pop00 = value)

walkerke

library(tidycensus)
library(tmap)
library(tmaptools)
library(sf)
library(tigris)
library(magick)
library(tidyverse)
options(tigris_use_cache = TRUE)

ctys <- c("Dallas", "Tarrant", "Collin County", "Denton", 

walkerke

library(tidycensus)

mn <- get_acs(geography = "school district (unified)", 
              state = "MN", 
              variables = c(hhincome = "B19013_001"))
              
mn

# A tibble: 326 x 5
   GEOID   NAME                                                            variable estimate   moe

walkerke

library(tidycensus)
library(tidyverse)
library(extrafont)

city16 <- get_acs(geography = "place", 
                   variables = "B01003_001", 
                   survey = "acs1") %>%
  filter(estimate > 500000) %>%
  rename(estimate16 = estimate)

walkerke

library(tidycensus)
library(tidyverse)
library(extrafont)

info <- get_acs(geography = "metropolitan statistical area/micropolitan statistical area", 
                variables = "DP03_0039P", 
                summary_var = "B01003_001", 
                survey = "acs1") %>%
  filter(summary_est > 1500000) %>%
  mutate(NAME = str_replace(NAME, "-.*", "")) %>%

walkerke

library(tidycensus)
library(plotly)
library(ggplot2) # devtools::install_github("tidyverse/ggplot2")
library(crosstalk)
# Set your Census API key with `census_api_key()` if not already installed

tx <- get_acs(geography = "county", 
              variables = c(pctcollege = "DP02_0067P", 
                            hhincome = "DP03_0062"), 
              state = "TX", 

walkerke

library(tidycensus)
library(leaflet)
library(sf)
library(viridis)
options(tigris_use_cache = TRUE)

il1 <- get_acs(geography = "county", 
               variables = c(hhincome = "B19013_001"), 
               state = "IL", 
               geometry = TRUE) %>%