Using tidycensus and leaflet to map Median Age
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.1.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.8
## ✔ tidyr 0.8.2 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(tidycensus)
library(leaflet)
library(stringr)
library(sf)## Linking to GEOS 3.5.1, GDAL 2.1.3, PROJ 4.9.2#To prevent display of data loading in knitted document...
options(tigris_use_cache = TRUE)See Kyle Walker’s article about the basic usage of his tidycensus package. The article includes instructions for obtaining and installing the census_api_key. It also includes information about finding parameter values to use in the get_acs() and get_decennial() functions.
# https://walkerke.github.io/tidycensus/articles/basic-usage.html
# census_api_key(overwrite = TRUE,"f4caf892e49de4ac853951c6ddfb4c8a2572c88e", install = TRUE)
# readRenviron("~/.Renviron")
# List of values for the 'variables = ' parameters
# v15 <- load_variables(2016, "acs5", cache = TRUE)
# View(v15)
wa_age <- get_acs(geography = "county",
variables = "B01002_001",
state = "WA",
geometry = TRUE) ## Getting data from the 2012-2016 5-year ACSwa_age## Simple feature collection with 39 features and 5 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7631 ymin: 45.54354 xmax: -116.916 ymax: 49.00249
## epsg (SRID): 4269
## proj4string: +proj=longlat +datum=NAD83 +no_defs
## First 10 features:
## GEOID NAME variable estimate moe
## 1 53001 Adams County, Washington B01002_001 28.6 0.3
## 2 53003 Asotin County, Washington B01002_001 44.6 0.2
## 3 53005 Benton County, Washington B01002_001 35.5 0.2
## 4 53007 Chelan County, Washington B01002_001 39.8 0.8
## 5 53009 Clallam County, Washington B01002_001 50.3 0.3
## 6 53011 Clark County, Washington B01002_001 37.8 0.2
## 7 53013 Columbia County, Washington B01002_001 49.9 1.1
## 8 53015 Cowlitz County, Washington B01002_001 41.7 0.4
## 9 53017 Douglas County, Washington B01002_001 37.1 0.7
## 10 53019 Ferry County, Washington B01002_001 48.2 0.7
## geometry
## 1 MULTIPOLYGON (((-119.3694 4...
## 2 MULTIPOLYGON (((-117.48 46....
## 3 MULTIPOLYGON (((-119.8751 4...
## 4 MULTIPOLYGON (((-121.1789 4...
## 5 MULTIPOLYGON (((-124.6022 4...
## 6 MULTIPOLYGON (((-122.796 45...
## 7 MULTIPOLYGON (((-118.2415 4...
## 8 MULTIPOLYGON (((-123.2183 4...
## 9 MULTIPOLYGON (((-120.3178 4...
## 10 MULTIPOLYGON (((-118.8696 4...summary(wa_age)## GEOID NAME variable estimate
## Length:39 Length:39 Length:39 Min. :24.20
## Class :character Class :character Class :character 1st Qu.:37.15
## Mode :character Mode :character Mode :character Median :41.70
## Mean :41.71
## 3rd Qu.:47.05
## Max. :56.20
## moe geometry
## Min. :0.1000 MULTIPOLYGON :39
## 1st Qu.:0.2000 epsg:4269 : 0
## Median :0.3000 +proj=long...: 0
## Mean :0.5231
## 3rd Qu.:0.5500
## Max. :4.9000See Julia Silge’s blog post about using leaflet with tinycensus.
# https://juliasilge.com/blog/using-tidycensus/
# Careful: colorNumeric vs. colorQuantile
# https://leaflet-extras.github.io/leaflet-providers/preview/
# ? colorNumeric
# 'palette = ' options: "viridis", "magma", "inferno", or "plasma".
wa_pal <- colorNumeric(palette = "plasma", domain = wa_age$estimate)
wa_age %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "CartoDB.Positron") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ wa_pal(estimate)) %>%
addLegend("bottomright",
pal = wa_pal,
values = ~ estimate,
title = "WA Median Age",
opacity = 1)fl_age <- get_acs(geography = "county",
variables = "B01002_001",
state = "FL",
geometry = TRUE) ## Getting data from the 2012-2016 5-year ACS#fl_agefl_pal <- colorNumeric(palette = "plasma", domain = fl_age$estimate)
fl_age %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "CartoDB.Positron") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ fl_pal(estimate)) %>%
addLegend("bottomright",
pal = fl_pal,
values = ~ estimate,
title = "FL Median Age",
opacity = 1)us_age <- get_acs(geography = "state",
variables = "B01002_001",
geometry = TRUE) ## Getting data from the 2012-2016 5-year ACS# us_ageus_pal <- colorNumeric(palette = "viridis", domain = us_age$estimate)
us_age %>%
st_transform(crs = "+init=epsg:4326") %>%
leaflet(width = "100%") %>%
addProviderTiles(provider = "CartoDB.Positron") %>%
addPolygons(popup = ~ str_extract(NAME, "^([^,]*)"),
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.7,
color = ~ us_pal(estimate)) %>%
addLegend("bottomright",
pal = us_pal,
values = ~ estimate,
title = "US Median Age",
opacity = 1)