Assignment 3

Author

Connor Watt

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…

There’s one NA value listed in the table specifically under Frye Island in top5. Looking at the data its because no value was entered for Frye Island’s top5.

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.

What town has the most people? Portland at 68,280

What town has the highest median age? Frye Island at 68.7

What town has the highest median income? Cape Elizabeth at 144,250

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? Baldwin has the lowest percentage of graduate degrees for people over 25 at 9.49%

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") +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

Cape Elizabeth, Cumberland, and Falmouth all have the highest median income while also having the highest graduate degrees. This makes sense due to a higher education typically leading to more income. This does not work for every town however due to the fact that there can be a large difference in population sizes, like how Frye Island and Portland have similarities with grad degrees over 25 but their median income is different due to a greater sample size which can bring down the median overall.

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.

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