Lynn
Steven Li
2023-02-26
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.2## Warning: package 'ggplot2' was built under R version 4.2.2## Warning: package 'tidyr' was built under R version 4.2.2## Warning: package 'readr' was built under R version 4.2.2## Warning: package 'purrr' was built under R version 4.2.2## Warning: package 'dplyr' was built under R version 4.2.2## Warning: package 'stringr' was built under R version 4.2.2## Warning: package 'forcats' was built under R version 4.2.2## Warning: package 'lubridate' was built under R version 4.2.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(tidycensus)## Warning: package 'tidycensus' was built under R version 4.2.2library(sf)## Warning: package 'sf' was built under R version 4.2.2## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUElibrary(tigris)## Warning: package 'tigris' was built under R version 4.2.2## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.library(ggthemes)
library(viridis)## Loading required package: viridisLite## Warning: package 'viridisLite' was built under R version 4.2.2v21 <- load_variables(2021, "acs5", cache = TRUE)
places <- places(state="MA") %>% filter(NAME == "Lynn") %>%
st_transform(2249)## Retrieving data for the year 2021##
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|======================================================================| 100%lynn_tracts <- tracts(state = "MA",county = "Essex") %>%
st_transform(2249) %>%
st_intersection(places) %>%
erase_water()## Retrieving data for the year 2021##
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|======================================================================| 100%## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries## Fetching area water data for your dataset's location...
## Erasing water area...
## If this is slow, try a larger area threshold value.vars <- c(
population="B01001_001"
)
df_state <- get_acs(geography = "state",
year = 2021,
variables = vars,
output = "wide")## Getting data from the 2017-2021 5-year ACSdf_county <- get_acs(geography = "county",
state="MA",
year = 2021,
variables = vars,
output = "wide") %>%
filter(GEOID=="25009")## Getting data from the 2017-2021 5-year ACSdf_place <- get_acs(geography = "place",
state="MA",
year = 2021,
variables = vars,
output = "wide") %>%
filter(GEOID=="2537490")## Getting data from the 2017-2021 5-year ACS##Employment
demographics <- c(
median_hh_income="B19013_001",
median_age="B01002_001",
average_hh_size="B25010_001",
average_hh_size_owner="B25010_002",
average_hh_size_renter="B25010_003",
education="B06009_001",
education_less_than_hs="B06009_002",
education_hs="B06009_003",
education_some_college="B06009_004",
education_bac="B06009_005",
education_grad="B06009_006"
)
demo_df <- get_acs(geography = "tract",
state="MA",
year = 2021,
variables = demographics,
output = "wide") ## Getting data from the 2017-2021 5-year ACSdemo_tract <- lynn_tracts %>% left_join(demo_df,by="GEOID")
demo_tract %>%
ggplot(aes(fill=median_hh_incomeE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
demo_tract %>%
ggplot(aes(fill=median_ageE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
demo_tract %>%
ggplot(aes(fill=average_hh_sizeE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
demo_tract %>%
ggplot(aes(fill=average_hh_size_ownerE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
demo_tract %>%
ggplot(aes(fill=average_hh_size_renterE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
demo_tract %>%
ggplot(aes(fill=(education_bacE+education_gradE)/educationE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()+
theme(legend.position="bottom")
##transportation
transport <- c(
transport="B08006_001",
travel_time_work="B08013_001",
workers="B08012_001",
transport_drove_alone="B08006_003",
transport_carpool="B08006_004",
transport_transit="B08006_008",
transport_bike="B08006_014",
transport_ped="B08006_015",
transport_wfh="B08006_016"
)
transport_df <- get_acs(geography = "tract",
state="MA",
county = "Essex",
year = 2021,
variables = transport,
output = "wide",
geometry = FALSE)## Getting data from the 2017-2021 5-year ACStransport_tract <- lynn_tracts %>% left_join(transport_df,by="GEOID")
transport_tract %>%
ggplot(aes(fill=travel_time_workE/workersE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map() +
theme(legend.background=element_blank())
transport_tract %>%
ggplot(aes(fill=transport_drove_aloneE/transportE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
transport_tract %>%
ggplot(aes(fill=transport_carpoolE/transportE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
transport_tract %>%
ggplot(aes(fill=transport_transitE/transportE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
transport_tract %>%
ggplot(aes(fill=transport_bikeE/transportE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
transport_tract %>%
ggplot(aes(fill=transport_pedE/transportE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
transport_tract %>%
ggplot(aes(fill=transport_wfhE/transportE))+
geom_sf(color = "white") +
scale_fill_viridis()+
theme_map()
##housing
## Getting data from the 2017-2021 5-year ACS
