```{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")
```Assignment 3 - Colby Chavarie
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 namecounty- Cumberland for all recordsstate- ME for all recordspop- an estimate of the populationtop5- median income for the top 5% of earners in the townmedian_income- median income for all adults in the townmedian_age- median age for the town
The second file, cumberland_edu.csv contains:
town,county,state- same values as the first filen_pop_over_25- the number of people over 25 in the townn_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
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 %>%
left_join(cumberland_edu, by = c("town", "county", "state"))
# Check for NA values
summary(maine_data) town county state pop
Length:28 Length:28 Length:28 Min. : 20
Class :character Class :character Class :character 1st Qu.: 3367
Mode :character Mode :character Mode :character Median : 6994
Mean :10834
3rd Qu.:13878
Max. :68280
top5 median_income median_age n_pop_over_25
Min. :212856 Min. : 55020 Min. :36.50 Min. : 20
1st Qu.:321835 1st Qu.: 71432 1st Qu.:41.30 1st Qu.: 2785
Median :418666 Median : 91408 Median :45.20 Median : 4997
Mean :482965 Mean : 91901 Mean :46.34 Mean : 7939
3rd Qu.:588422 3rd Qu.:101726 3rd Qu.:49.02 3rd Qu.: 9447
Max. :998035 Max. :144250 Max. :68.70 Max. :51898
NA's :1
n_masters_above
Min. : 4.0
1st Qu.: 363.2
Median : 752.5
Mean : 1621.0
3rd Qu.: 1983.5
Max. :12233.0
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?
maine_data %>%
arrange(desc(pop)) %>%
select(town, pop) %>%
head(1)# A tibble: 1 × 2
town pop
<chr> <dbl>
1 Portland 68280What town has the highest median age?
maine_data %>%
arrange(desc(median_age)) %>%
select(town, median_age) %>%
head(1)# A tibble: 1 × 2
town median_age
<chr> <dbl>
1 Frye Island 68.7What town has the highest median income?
maine_data %>%
arrange(desc(median_income)) %>%
select(town, median_income) %>%
head(1)# A tibble: 1 × 2
town median_income
<chr> <dbl>
1 Cape Elizabeth 1442501.3 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) * 100)
# View the data with the new column
head(maine_data)# A tibble: 6 × 10
town county state pop top5 median_income median_age n_pop_over_25
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Baldwin Cumbe… ME 1180 212856 68625 50.3 927
2 Bridgton Cumbe… ME 5471 315914 78546 47.6 4170
3 Brunswick Cumbe… ME 21691 418666 71236 41.3 14910
4 Cape Elizabe… Cumbe… ME 9519 833724 144250 48.6 6809
5 Casco Cumbe… ME 3657 392392 60708 51.1 3006
6 Chebeague Is… Cumbe… ME 597 668189 58571 55.6 430
# ℹ 2 more variables: n_masters_above <dbl>, pct_grad_degree <dbl>1.4 Exercise 4
What town has the lowest percentage of graduate degrees for people over 25?
maine_data %>%
filter(!is.na(pct_grad_degree)) %>%
arrange(pct_grad_degree) %>%
select(town, pct_grad_degree) %>%
head(1)# A tibble: 1 × 2
town pct_grad_degree
<chr> <dbl>
1 Baldwin 9.491.5 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.
ggplot(maine_data, aes(x = median_income, y = pct_grad_degree, label = town)) +
geom_point(color = "blue", size = 3) +
geom_label_repel() +
labs(
title = "Graduate Degree % vs. Median Income",
x = "Median Income",
y = "Percentage with Graduate Degrees"
) +
theme_minimal()
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) |
100% - flawless | 67% - minor issues | 33% - moderate issues | 0% - major issues or not attempted |
|---|---|---|---|---|
| Narrative: typos and grammatical errors (8%) |
||||
| Document formatting: correctly implemented instructions (8%) |
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Exercises (15% each) |
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Submitted properly to Brightspace (9%) |
NA | NA | You must submit according to instructions to receive any credit for this portion. |