Assignment 3

Author

Brian Dibra

Go to the shared posit.cloud workspace for this class and open the lab03_assign03 project. Open the assign03.qmd file and complete the exercises.

We will be using two files complied from the American Community Survey five year estimates for 2022 (which is built over a sample of surveys sent out over the five year period from 2017-2022). Both files contain data from towns in Cumberland County, Maine. The first file loaded is cumberland_places.csv and contains the following columns:

town - town name
county - Cumberland for all records
state - ME for all records
pop - an estimate of the population
top5 - median income for the top 5% of earners in the town
median_income - median income for all adults in the town
median_age - median age for the town

The second file, cumberland_edu.csv contains:

town, county, state - same values as the first file
n_pop_over_25 - the number of people over 25 in the town
n_masters_above - the number of people over 25 that have a Master’s degree or higher in the town.

We’ll start by loading the tidyverse family of packages, ggrepel for our graph, and gt (optional) for making pretty tables, and read in the data into two tibbles (cumberland_places and cumberland_edu). We’ll be using the message: false option to suppress the output message from loading tidyverse and gt

```{r}
#| message: false
library(tidyverse)
library(ggrepel)
library(gt)
cumberland_places <- read_csv("https://jsuleiman.com/datasets/cumberland_places.csv")
cumberland_edu <- read_csv("https://jsuleiman.com/datasets/cumberland_edu.csv")
```

1 Exercises

There are five exercises in this assignment. The Grading Rubric is available at the end of this document. You will need to create your own code chunks for this assignment. Remember, you can do this with Insert -> Executable Cell -> R while in Visual mode.

1.1 Exercise 1

Create a tibble called maine_data that joins the two tibbles. Think about what matching columns you will be using in your join by clause. Review the joined data and state explicitly in the narrative whether you see any NA values and why you think they might exist.

maine_data <- cumberland_places |> 
  inner_join(cumberland_edu)

Joining with `by = join_by(town, county, state)`

glimpse(maine_data)

Rows: 28
Columns: 9
$ town            <chr> "Baldwin", "Bridgton", "Brunswick", "Cape Elizabeth", …
$ county          <chr> "Cumberland", "Cumberland", "Cumberland", "Cumberland"…
$ state           <chr> "ME", "ME", "ME", "ME", "ME", "ME", "ME", "ME", "ME", …
$ pop             <dbl> 1180, 5471, 21691, 9519, 3657, 597, 8443, 12504, 8700,…
$ top5            <dbl> 212856, 315914, 418666, 833724, 392392, 668189, 998035…
$ median_income   <dbl> 68625, 78546, 71236, 144250, 60708, 58571, 144167, 144…
$ median_age      <dbl> 50.3, 47.6, 41.3, 48.6, 51.1, 55.6, 40.6, 47.7, 47.5, …
$ n_pop_over_25   <dbl> 927, 4170, 14910, 6809, 3006, 430, 5782, 8800, 6516, 2…
$ n_masters_above <dbl> 88, 564, 3628, 2803, 465, 125, 2222, 2954, 1904, 4, 13…

In the tenth value of the top5 group, there is an NA value. I’d assume because the top5% of earners in Frye Island isn’t provided by the data.

1.2 Exercise 2

Since the dataset only has 28 towns, you don’t need to show code to answer the questions in Exercise 2, you can simply look at the table and add the answers to your narrative. Make sure you specify the town name and the actual value that answers the question.

maine_data |>
  distinct(town) |>
  pull ()

 [1] "Baldwin"          "Bridgton"         "Brunswick"        "Cape Elizabeth"  
 [5] "Casco"            "Chebeague Island" "Cumberland"       "Falmouth"        
 [9] "Freeport"         "Frye Island"      "Gorham"           "Gray"            
[13] "Harpswell"        "Harrison"         "Long Island"      "Naples"          
[17] "New Gloucester"   "North Yarmouth"   "Portland"         "Pownal"          
[21] "Raymond"          "Scarborough"      "Sebago"           "South Portland"  
[25] "Standish"         "Westbrook"        "Windham"          "Yarmouth"

There is no question to answer in Exercise 2, from what I can see, so this is the data I pulled from the table.

1.3 Exercise 3

Add a column to maine_data called to pct_grad_degree that shows the percentage of graduate degrees for the town, which is defined as n_masters_above / n_pop_over_25

maine_data <- maine_data |>
  mutate(pct_grad_degree = n_masters_above / n_pop_over_25)
glimpse(maine_data)

Rows: 28
Columns: 10
$ town            <chr> "Baldwin", "Bridgton", "Brunswick", "Cape Elizabeth", …
$ county          <chr> "Cumberland", "Cumberland", "Cumberland", "Cumberland"…
$ state           <chr> "ME", "ME", "ME", "ME", "ME", "ME", "ME", "ME", "ME", …
$ pop             <dbl> 1180, 5471, 21691, 9519, 3657, 597, 8443, 12504, 8700,…
$ top5            <dbl> 212856, 315914, 418666, 833724, 392392, 668189, 998035…
$ median_income   <dbl> 68625, 78546, 71236, 144250, 60708, 58571, 144167, 144…
$ median_age      <dbl> 50.3, 47.6, 41.3, 48.6, 51.1, 55.6, 40.6, 47.7, 47.5, …
$ n_pop_over_25   <dbl> 927, 4170, 14910, 6809, 3006, 430, 5782, 8800, 6516, 2…
$ n_masters_above <dbl> 88, 564, 3628, 2803, 465, 125, 2222, 2954, 1904, 4, 13…
$ pct_grad_degree <dbl> 0.09492988, 0.13525180, 0.24332663, 0.41166104, 0.1546…

1.4 Exercise 4

What town has the lowest percentage of graduate degrees for people over 25?

maine_data |>
  select(town, pct_grad_degree, n_pop_over_25) |>
  filter(pct_grad_degree == min(pct_grad_degree))

# A tibble: 1 × 3
  town    pct_grad_degree n_pop_over_25
  <chr>             <dbl>         <dbl>
1 Baldwin          0.0949           927

Baldwin holds the lowest percentage of graduate degrees for people older 25.

1.5 Exercise 5

Replicate this graph. Note: use geom_label_repel() just like you would use geom_label() Discuss any patterns you see in the narrative.

library(ggrepel)
maine_data |>
  ggplot(aes(x = median_income, y = pct_grad_degree)) +
  geom_label_repel(aes(label = town), color = "blue", max.overlaps = 20) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

The graph shows a positive correlation between median income and the percentage of residents with graduate degrees in Maine towns. Higher-income towns generally have a higher percentage of residents with graduate degrees, indicating that education and income levels are closely linked.

2 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.

3 Grading Rubric

Item (percent overall)	67% - minor issues	33% - moderate issues	0% - major issues or not attempted
Narrative: typos and grammatical errors (8%)
Document formatting: correctly implemented instructions (8%)
Exercises (15% each)
Submitted properly to Brightspace (9%)	NA	NA	You must submit according to instructions to receive any credit for this portion.

Other Formats