workshop5

Workshop 5

Load libraries and census data

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidycensus)

## Warning: package 'tidycensus' was built under R version 4.3.3

census_api_key("1ef8386987d5b937e863ac8b0ba1273ff9de5ec0")

## To install your API key for use in future sessions, run this function with `install = TRUE`.

v22 <- load_variables(2022, "acs5", cache = TRUE)

Load Florida South Asian population data:

fl <- get_acs(geography = "county", 
    variables = c(
      tot_pop = "B01003_001",
      asian_indian = "B02001_004",
      bangladeshi = "B02001_005",
      pakistani = "B02001_006",
      sri_lankan = "B02001_007"
    ),  state = "FL", geometry = TRUE, year = 2022)

## Getting data from the 2018-2022 5-year ACS

## Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

Transform the data:

fl_wider <- fl |>
  select(-moe) |>
  pivot_wider(names_from = variable, values_from = estimate) |>
  mutate(
    south_asian_total = asian_indian + bangladeshi + pakistani + sri_lankan, 
    south_asian_pct = south_asian_total / tot_pop) 

Plot the data:

fl_wider |>
  ggplot(aes(fill = south_asian_pct)) +
  geom_sf(color = NA) + 
  scale_fill_viridis_c(option = "mako")

Load Florida number of naturalized citizens data:

fl_nt_citizens <- get_acs(geography = "county", 
    variables = c(tot_pop = "B01003_001", nat_citz = 'B05005_002'),  state = "FL", geometry = TRUE, year = 2022)

## Getting data from the 2018-2022 5-year ACS

## Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

Mutate to find percentage:

fl_wider_NC <- fl_nt_citizens |>
  select(-moe) |>
  pivot_wider(names_from = variable, values_from = estimate) |>
  mutate(
    nat_pct = nat_citz / tot_pop) 

Plot the data:

fl_wider_NC |>
  ggplot(aes(fill = nat_pct)) +
  geom_sf(color = NA) + 
  scale_fill_viridis_c(option = "mako")