Assignment 6

Author

Connor Perfetto

Go to the shared posit.cloud workspace for this class and open the assign06 project. Open the assign06.qmd file and complete the exercises.

This is a very open-ended assignment. There are three musts:

You must use the tidycensus package to get either decennial or ACS data from the US Census Bureau.
You must get data for two different variables and they can’t be population or median home values.
You must show all the code you used to get the data and create the table or chart.

You can then either create a cool table or chart comparing the two variables. They can be from any region and for any geography…it doesn’t necessarily need to be Maine.

Note: you will receive deductions for not using tidyverse syntax in this assignment. That includes the use of filter, mutate, and the up-to-date pipe operator |>.

The Grading Rubric is available at the end of this document.

We’ll preload the following potentially useful packages

library(tidyverse)
library(tidycensus)
library(gapminder)
library(gt)
library(gtExtras)
library(scales)

This is your work area. Add as many code cells as you need.

census_api_key("ef4c6f65b724d1a826956c4c4459608d9aac0992", 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] "ef4c6f65b724d1a826956c4c4459608d9aac0992"

update.packages("tidycensus")
decennial_vars_2010 <- load_variables(2010, "sf1", cache = TRUE)
head(decennial_vars_2010)

# A tibble: 6 × 3
  name    label                                concept        
  <chr>   <chr>                                <chr>          
1 H001001 Total                                HOUSING UNITS  
2 H002001 Total                                URBAN AND RURAL
3 H002002 Total!!Urban                         URBAN AND RURAL
4 H002003 Total!!Urban!!Inside urbanized areas URBAN AND RURAL
5 H002004 Total!!Urban!!Inside urban clusters  URBAN AND RURAL
6 H002005 Total!!Rural                         URBAN AND RURAL

housing_vars_2010 <- decennial_vars_2010 %>% filter(grepl("housing", label, ignore.case = TRUE))
age_vars_2010 <- decennial_vars_2010 %>%
  filter(grepl("age", label, ignore.case = TRUE))
head(housing_vars_2010)

# A tibble: 6 × 3
  name     label                                                         concept
  <chr>    <chr>                                                         <chr>  
1 H011001  Total population in occupied housing units                    TOTAL …
2 H011002  Total population in occupied housing units!!Owned with a mor… TOTAL …
3 H011003  Total population in occupied housing units!!Owned free and c… TOTAL …
4 H011004  Total population in occupied housing units!!Renter occupied   TOTAL …
5 H011A001 Population in occupied housing units                          TOTAL …
6 H011A002 Population in occupied housing units!!Owned with a mortgage … TOTAL …

head(age_vars_2010)

# A tibble: 6 × 3
  name     label                                                         concept
  <chr>    <chr>                                                         <chr>  
1 H004002  Total!!Owned with a mortgage or a loan                        TENURE 
2 H011002  Total population in occupied housing units!!Owned with a mor… TOTAL …
3 H011A002 Population in occupied housing units!!Owned with a mortgage … TOTAL …
4 H011B002 Population in occupied housing units!!Owned with a mortgage … TOTAL …
5 H011C002 Population in occupied housing units!!Owned with a mortgage … TOTAL …
6 H011D002 Population in occupied housing units!!Owned with a mortgage … TOTAL …

census_data_2010 <- get_decennial(
  geography = "state",
  variables = c("H001001", "P013001"), 
  year = 2010,
  dataset = "sf1" 
)

Getting data from the 2010 decennial Census

Using Census Summary File 1

head(census_data_2010)

# A tibble: 6 × 4
  GEOID NAME       variable    value
  <chr> <chr>      <chr>       <dbl>
1 01    Alabama    H001001   2171853
2 02    Alaska     H001001    306967
3 04    Arizona    H001001   2844526
4 05    Arkansas   H001001   1316299
5 06    California H001001  13680081
6 22    Louisiana  H001001   1964981

census_data_2010 <- get_decennial(
  geography = "state",
  variables = c("H001001", "P013001"), 
  year = 2010,
  dataset = "sf1"
)

Getting data from the 2010 decennial Census

Using Census Summary File 1

census_data_long <- census_data_2010 %>%
  select(NAME, variable, value) %>%
  pivot_wider(names_from = variable, values_from = value) %>%
  rename(State = NAME, Total_Housing_Units = H001001, Median_Age = P013001) %>%
  pivot_longer(cols = c(Total_Housing_Units, Median_Age), names_to = "Variable",    values_to = "Value")

ggplot(census_data_long, aes(x = reorder(State, -Value), y = Value, fill = Variable)) +
  geom_bar(stat = "identity") +
  facet_wrap(~Variable, scales = "free_y") +
  labs(
    title = "Total Housing Units and Median Age by State (2010)",
    x = "State",
    y = "Value"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 8),  
    strip.text = element_text(size = 12)  
  )

Submission

To submit your assignment:

Change the author name to your name in the YAML portion at the top of this document
Render your document to html and publish it to RPubs.
Submit the link to your Rpubs document in the Brightspace comments section for this assignment.
Click on the “Add a File” button and upload your .qmd file for this assignment to Brightspace.

Grading Rubric

Item (percent overall)	100% - flawless	67% - minor issues	33% - moderate issues	0% - major issues or not attempted
Chart or table accuracy. (45%)	No errors, good labels, everything is clearly visible in the rendered document.
At least two valid variables used from US census data (can be census or ACS) (40%)
Messages and/or errors suppressed from rendered document and all code is shown. (7%)
Submitted properly to Brightspace (8%)		NA	NA	You must submit according to instructions to receive any credit for this portion.