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This homework is due on April 19, 2021 at 11:00pm. Please submit as a pdf file on Canvas.

In this homework, we will work with two datasets, `US_counties` and `US_census`. The dataset `US_counties` contains the geometry of each county in the US and thus can be used for drawing maps. The dataset `US_census` contains numerous pieces of information about US counties obtained from the US census. Both datasets have a column `FIPS` which can be used to uniquely identify each county in each dataset.

``````# data preparation
rename(FIPS = GEOID)

"https://wilkelab.org/SDS375/datasets/US_census.csv",
col_types = cols(FIPS = "c")
)``````

Problem 1: (6 pts) Make a choropleth map of the percent home-ownership (column `home_ownership` in `US_census`) for all counties in the US. Choose an appropriate color scale and design for this plot. You may notice that there is one county in Alaska for which home-ownership data is not available. Write data analysis code to identify this county.

Hints:

1. Use `theme_void()` as your theme

2. You will have to join `US_counties` and `US_census`. Join them by the `FIPS` column.

3. To make nice percent labels, you can use `label = scales::label_percent(scale = 1)` in your color scale function.

4. To find rows with missing data, you may want to use the function `is.na()`.

Grade breakdown: 2pt for the plot, 2pt for the plot design, and 2pt for identifying the county in Alaska for which home ownership data is not available.

``````# code to make the plot
US_counties %>%
left_join(US_census, by = "FIPS") %>%
ggplot() +
geom_sf(aes(fill = home_ownership), size = 0.1) +
scale_fill_viridis_c(
name = "home-ownership",
option = "B",
label = scales::label_percent(scale = 1)
) +
theme_void()``````