```{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
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 <- left_join(cumberland_places, cumberland_edu, by = "town")
# Check for NA values
summary(maine_data) town county.x state.x 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 county.y
Min. :212856 Min. : 55020 Min. :36.50 Length:28
1st Qu.:321835 1st Qu.: 71432 1st Qu.:41.30 Class :character
Median :418666 Median : 91408 Median :45.20 Mode :character
Mean :482965 Mean : 91901 Mean :46.34
3rd Qu.:588422 3rd Qu.:101726 3rd Qu.:49.02
Max. :998035 Max. :144250 Max. :68.70
NA's :1
state.y n_pop_over_25 n_masters_above
Length:28 Min. : 20 Min. : 4.0
Class :character 1st Qu.: 2785 1st Qu.: 363.2
Mode :character Median : 4997 Median : 752.5
Mean : 7939 Mean : 1621.0
3rd Qu.: 9447 3rd Qu.: 1983.5
Max. :51898 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?
What town has the highest median age?
What town has the highest median income?
# Town with the highest population
maine_data %>% arrange(desc(pop)) %>% select(town, pop) %>% head(1)# A tibble: 1 × 2
town pop
<chr> <dbl>
1 Portland 68280# Town with 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 68.7# Town with 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)
maine_data %>% select(town, pct_grad_degree) %>% arrange(desc(pct_grad_degree))# A tibble: 28 × 2
town pct_grad_degree
<chr> <dbl>
1 Cape Elizabeth 41.2
2 Cumberland 38.4
3 Falmouth 33.6
4 Freeport 29.2
5 Yarmouth 29.2
6 Chebeague Island 29.1
7 Harpswell 26.1
8 Brunswick 24.3
9 Portland 23.6
10 North Yarmouth 21.6
# ℹ 18 more rows1.4 Exercise 4
What town has the lowest percentage of graduate degrees for people over 25?
maine_data %>% 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
ggplot(maine_data, aes(x = median_income, y = pct_grad_degree, label = town)) +
geom_point() +
geom_label_repel() +
labs(
title = "Graduate Degree Percentage vs Median Income",
x = "Median Income",
y = "Percentage of Graduate Degrees"
) +
theme_minimal()
Replicate this graph. Note: use geom_label_repel() just like you would use geom_label() Discuss any patterns you see in the narrative.
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. |