library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidycensus)
library(sf)## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUElibrary(scales)##
## Attaching package: 'scales'
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## col_factorlibrary(RColorBrewer)
library(plotly)## Warning: package 'plotly' was built under R version 4.3.2##
## Attaching package: 'plotly'
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## layout#### import borough shapefiles from NYC Open Data
boros <- st_read("D:/Parson Schoool Of Design/Design Method/Lesson/Week1/Part2/data/processed/geo/Borough Boundaries.geojson")## Reading layer `Borough Boundaries' from data source
## `D:\Parson Schoool Of Design\Design Method\Lesson\Week1\Part2\data\processed\geo\Borough Boundaries.geojson'
## using driver `GeoJSON'
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS: WGS 84raw_ancestry <- get_acs(geography = "tract",
variables = c(ancestry_pop = "B04006_001",
west_indian = "B04006_094"),
state='NY',
county = 'Kings',
geometry = T,
year = 2020,
output = "wide") ## Getting data from the 2016-2020 5-year ACS## Warning: • You have not set a Census API key. Users without a key are limited to 500
## queries per day and may experience performance limitations.
## ℹ For best results, get a Census API key at
## http://api.census.gov/data/key_signup.html and then supply the key to the
## `census_api_key()` function to use it throughout your tidycensus session.
## This warning is displayed once per session.## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 100%west_indian <- raw_ancestry |>
mutate(pct_west_indian = west_indianE/ancestry_popE) |>
filter(ancestry_popE > 0)
west_indian_map <- ggplot() +
geom_sf(data = west_indian,
mapping = aes(fill = pct_west_indian,
text = paste0(NAME, ":",
"<br>Percent West Indian ancestry: ",
percent(pct_west_indian, accuracy=1))),
color = "transparent") +
theme_void() +
scale_fill_distiller(breaks=c(0, .2, .4, .6, .8, 1),
direction = 1,
na.value = "#fafafa",
# na.value = "transparent",
name="Percent West Indian Ancestry (%)",
labels=percent_format(accuracy = 1L)) +
labs(
title = "Brooklyn, West Indian Ancestry by Census Tract",
caption = "Source: American Community Survey, 2016-20"
) +
geom_sf(data = boros |> filter(boro_name == "Brooklyn"),
color = "black", fill = NA, lwd = .5)## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text
ggplotly(west_indian_map, tooltip = "text")