Household Income: Polk County – R for Social Good

I am working through the process working with Census Data using the tidycensus package. I have been copying and modifying the examples to get a sense for how I can use this data.

library(tidycensus)
library(tidyverse)
library(ggplot2)
library(scales)
options(tigris_use_cache = TRUE)

polk <- get_acs(
  state = "FL",
  county = "Polk",
  geography = "tract",
  variables = "B19013_001",
  geometry = TRUE,
  year = 2021
)

head(polk)

Simple feature collection with 6 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -82.01433 ymin: 27.86185 xmax: -81.82854 ymax: 28.17202
Geodetic CRS:  NAD83
        GEOID                                      NAME   variable estimate
1 12105015102 Census Tract 151.02, Polk County, Florida B19013_001    83750
2 12105010402 Census Tract 104.02, Polk County, Florida B19013_001    50721
3 12105010800    Census Tract 108, Polk County, Florida B19013_001    49265
4 12105014905 Census Tract 149.05, Polk County, Florida B19013_001    62784
5 12105011838 Census Tract 118.38, Polk County, Florida B19013_001    89300
6 12105012303 Census Tract 123.03, Polk County, Florida B19013_001    65094
    moe                       geometry
1  6800 MULTIPOLYGON (((-81.84696 2...
2  6762 MULTIPOLYGON (((-81.95061 2...
3 18923 MULTIPOLYGON (((-81.97355 2...
4  7841 MULTIPOLYGON (((-82.01411 2...
5 16974 MULTIPOLYGON (((-81.92359 2...
6 20419 MULTIPOLYGON (((-81.95816 2...

options(scipen = 999)
polk_p <- polk %>%
  ggplot(aes(fill = estimate)) + 
  geom_sf(color = NA)  +
  scale_fill_viridis_c(option = "magma", labels = dollar_format()) +
  labs(
       title = "Household Income: Estimates", 
       subtitle = "Polk County, Florida", 
       caption = "Data source: US Census Bureau population estimates & tidycensus R package") +

  theme_void()

polk_p

I needed help from ChatGPT to get the legend to dollars–here is the link to the assistance log. Please note the variable names changed.

Lets make the plot interactive.

plotly::ggplotly(polk_p)

racevars <- c(White = "P2_005N",   
              Black = "P2_006N", 
              Asian = "P2_008N", 
              Hispanic = "P2_002N")

### This is an interesting techinuqe pythonish 

polk_race <- get_decennial(
  geography = "tract",
  variables = racevars,
  state = "FL",
  county = "Polk County",
  geometry = TRUE,
  summary_var = "P2_001N", #this is the total population in the census tract. 
  year = 2020,
  sumfile = "pl"
)

Getting data from the 2020 decennial Census

Using the PL 94-171 Redistricting Data Summary File

Note: 2020 decennial Census data use differential privacy, a technique that
introduces errors into data to preserve respondent confidentiality.
ℹ Small counts should be interpreted with caution.
ℹ See https://www.census.gov/library/fact-sheets/2021/protecting-the-confidentiality-of-the-2020-census-redistricting-data.html for additional guidance.
This message is displayed once per session.

head(polk_race)

Simple feature collection with 6 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -82.0564 ymin: 27.99637 xmax: -81.97668 ymax: 28.08173
Geodetic CRS:  NAD83
# A tibble: 6 × 6
  GEOID       NAME        variable value summary_value                  geometry
  <chr>       <chr>       <chr>    <dbl>         <dbl>        <MULTIPOLYGON [°]>
1 12105012004 Census Tra… White     1686          2556 (((-82.05595 28.03673, -…
2 12105012004 Census Tra… Black      147          2556 (((-82.05595 28.03673, -…
3 12105012004 Census Tra… Asian       42          2556 (((-82.05595 28.03673, -…
4 12105012004 Census Tra… Hispanic   555          2556 (((-82.05595 28.03673, -…
5 12105012001 Census Tra… White     2825          5798 (((-82.0564 28.08038, -8…
6 12105012001 Census Tra… Black     1460          5798 (((-82.0564 28.08038, -8…

polk_race_p <- polk_race %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  facet_wrap(~variable) +
  geom_sf(color = NA) +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "% of population\n(2020 Census)",
   title = "Race Populatin Estimates", 
       subtitle = "Polk County, Florida", 
       caption = "Data source: US Census Bureau population estimates & tidycensus R package")

plotly::ggplotly(polk_race_p)

Conclusion

What may useful is to also show the Census Tract for additional exploration.

Conclusion

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