library(tidyverse)
library(tidycensus)
library(sf)
library(scales)
library(viridis)
library(plotly)
library(RColorBrewer)Borough boundaries are represented with a shapefile that was downloaded from NYC Open Data.
Census tract boundaries and data are from the 2026-18 5-year American Community Survey, accessed with the tidyverse R package. Census data includes:
The proportion of people with West Indian ancestry for each centure tract was calculated and the cendud tract with no residents were removed.
# load all acs variables
acs201620 <- load_variables(2020, "acs5", cache = T)
## Import table of PEOPLE REPORTING ANCESTRY: B04006
raw_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") ## 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.## import borough shapefiles from NYC Open Data
boros <- st_read("~/Desktop/Parsons/Fall 2024/Urban Ecologies Methods 1/part2/data/raw/geo/Borough Boundaries.geojson")
## import Neighborhood Tabulation Areas for NYC
nabes <- st_read("~/Desktop/Parsons/Fall 2024/Urban Ecologies Methods 1/part2/data/raw/geo/nynta2020_24c/nynta2020.shp")
west_indian <- raw_ancestry |>
mutate(pct_west_indian = west_indianE/ancestry_popE)
west_indian <- raw_ancestry |>
mutate(pct_west_indian = west_indianE/ancestry_popE,
pct_west_indian = ifelse(is.nan(pct_west_indian), NA, pct_west_indian)) # if it is not west indian then call it na if not call it west indian
na_tracts <- west_indian |>
filter(is.na(pct_west_indian)) # NaN is Not a Number## Map
west_indian_map <- ggplot() +
geom_sf(data = west_indian |>
filter(!is.na(pct_west_indian)), #need to remove nas for ggploty to work
mapping = aes(fill = pct_west_indian,
text = paste0(NAME, ":",
"<br>Percent West Indian ancestry: ", #br next line
scales::percent(pct_west_indian, accuracy=1))), #scales when you are just using the package
color = "transparent") +
theme_void() +
scale_fill_distiller(breaks=c(0, .2, .4, .6, .8, 1),
direction = 1,
na.value = "#fafafa", #light grey or "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) #line width## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: textggplotly(west_indian_map, tooltip = "text")west_indian_2263 <- st_transform(west_indian, 2263) #west indian into the right projection
#remove unnecessary fields in the neighborhood shapefile
nabes_selected <- nabes |>
select(BoroCode, BoroName, NTA2020, NTAName)
#Perform the spatial join
west_indian_nabes <- west_indian_2263 |>
st_join(nabes_selected,
left = TRUE, #left join
join = st_intersects, #df
largest = TRUE) ## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries#Create a map of Crown Heights North, code
ch_west_indian_map <- ggplot() +
geom_sf(data = west_indian_nabes |>
filter(!is.na(pct_west_indian)) |>
filter(NTAName == "Crown Heights (North)"),
mapping = aes(fill = pct_west_indian,
text = paste0(NAME, ":",
"<br>Percent West Indian ancestry: ",
scales::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 = "transparent",
name="Percent West Indian Ancestry (%)",
labels=percent_format(accuracy = 1L)) +
labs(
title = "Crown Heights, West Indian Ancestry by Census Tract",
caption = "Source: American Community Survey, 2018-22")## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text geom_sf(data = nabes |>
filter(NTAName == "Crown Heights (North)"),
color = "black", fill = "transparent", lwd = 0.5)## [[1]]
## mapping:
## geom_sf: na.rm = FALSE
## stat_sf: na.rm = FALSE
## position_identity
##
## [[2]]
## <ggproto object: Class CoordSf, CoordCartesian, Coord, gg>
## aspect: function
## backtransform_range: function
## clip: on
## crs: NULL
## datum: crs
## default: TRUE
## default_crs: NULL
## determine_crs: function
## distance: function
## expand: TRUE
## fixup_graticule_labels: function
## get_default_crs: function
## is_free: function
## is_linear: function
## label_axes: list
## label_graticule:
## labels: function
## limits: list
## lims_method: cross
## modify_scales: function
## ndiscr: 100
## params: list
## range: function
## record_bbox: function
## render_axis_h: function
## render_axis_v: function
## render_bg: function
## render_fg: function
## setup_data: function
## setup_layout: function
## setup_panel_guides: function
## setup_panel_params: function
## setup_params: function
## train_panel_guides: function
## transform: function
## super: <ggproto object: Class CoordSf, CoordCartesian, Coord, gg>ggplotly(ch_west_indian_map, tooltip = "text")