Workshop 4

library(tidycensus)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

census_api_key("a613904e39830d89c16a19999e1cfaead6422fd1", install = TRUE, overwrite = TRUE)

## Your original .Renviron will be backed up and stored in your R HOME directory if needed.

## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY"). 
## To use now, restart R or run `readRenviron("~/.Renviron")`

## [1] "a613904e39830d89c16a19999e1cfaead6422fd1"

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

## # A tibble: 28,152 × 4
##    name        label                                    concept        geography
##    <chr>       <chr>                                    <chr>          <chr>    
##  1 B01001A_001 Estimate!!Total:                         Sex by Age (W… tract    
##  2 B01001A_002 Estimate!!Total:!!Male:                  Sex by Age (W… tract    
##  3 B01001A_003 Estimate!!Total:!!Male:!!Under 5 years   Sex by Age (W… tract    
##  4 B01001A_004 Estimate!!Total:!!Male:!!5 to 9 years    Sex by Age (W… tract    
##  5 B01001A_005 Estimate!!Total:!!Male:!!10 to 14 years  Sex by Age (W… tract    
##  6 B01001A_006 Estimate!!Total:!!Male:!!15 to 17 years  Sex by Age (W… tract    
##  7 B01001A_007 Estimate!!Total:!!Male:!!18 and 19 years Sex by Age (W… tract    
##  8 B01001A_008 Estimate!!Total:!!Male:!!20 to 24 years  Sex by Age (W… tract    
##  9 B01001A_009 Estimate!!Total:!!Male:!!25 to 29 years  Sex by Age (W… tract    
## 10 B01001A_010 Estimate!!Total:!!Male:!!30 to 34 years  Sex by Age (W… tract    
## # ℹ 28,142 more rows

write.csv(v22,"C:/Users/khori/OneDrive/POS4931/v22.csv")

f1 <- get_acs(geography = "county",
              variables = c(naturalized_citzn = "B05001_005", naturalized_asia = "B05002_016"),
              state = "FL",
              geometry = T,
              year = 2022)

f1_wider <- f1 |>
  select(-moe) |>
  pivot_wider(names_from = variable, values_from = estimate) |>
  mutate(asia_naturalized_pct = naturalized_asia/naturalized_citzn)

f1_wider |>
  ggplot(aes(fill = asia_naturalized_pct)) +
  geom_sf(color = "white")

  scale_fill_viridis_c(option = "cividis")

## <ScaleContinuous>
##  Range:  
##  Limits:    0 --    1