Lab5-Part A & B
Diena Dogom
2026-04-06
Introduction
In this lab, I used U.S. Census Bureau American Community Survey (ACS) data to explore social and economic patterns across counties in Mississippi. The goal of this analysis is to understand differences in graduate degree and employment across counties, and to visualize these patterns using both static and interactive graphics. This project also demonstrates how different types of data (percentages vs. raw counts) require different visualization approaches. I created three main outputs: a static margin-of-error plot, an interactive version of the same plot, and a map visualization to show geographic patterns in employment.
Data Preparation
To begin, I used the tidycensus package to access ACS data. Before retrieving the data, I got my Census API key so that I could download data directly from the Census website.
# Get Census API key
census_api_key("7712f4050792f95cda2b3af671fd0eaf1f8129c4", 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] "7712f4050792f95cda2b3af671fd0eaf1f8129c4"Next, I retrieved data for the percentage of the population with a graduate degree using the variable DP02_0066P. This variable represents a percentage, meaning it is already standardized and allows for fair comparisons across counties.
## Getting data from the 2017-2021 5-year ACS## Using the ACS Data ProfileI also explored the dataset using View() to better understand its structure, including the estimate (value) and margin of error (moe).To identify counties with the highest and lowest values, I separated the county and state names and filtered the data:
View(grad_degree_pct)grad_degree_pct_table <- get_acs(
geography = "county",
state = "MS",
variables = "DP02_0066P",
year = 2021
)## Getting data from the 2017-2021 5-year ACS## Using the ACS Data Profilegrad_degree_pct_table## # A tibble: 82 × 5
## GEOID NAME variable estimate moe
## <chr> <chr> <chr> <dbl> <dbl>
## 1 28001 Adams County, Mississippi DP02_0066P 8 1.6
## 2 28003 Alcorn County, Mississippi DP02_0066P 6.4 1.5
## 3 28005 Amite County, Mississippi DP02_0066P 4.8 1.5
## 4 28007 Attala County, Mississippi DP02_0066P 3.8 1.1
## 5 28009 Benton County, Mississippi DP02_0066P 5 2.2
## 6 28011 Bolivar County, Mississippi DP02_0066P 11.2 1.8
## 7 28013 Calhoun County, Mississippi DP02_0066P 4.8 1.2
## 8 28015 Carroll County, Mississippi DP02_0066P 3.8 2
## 9 28017 Chickasaw County, Mississippi DP02_0066P 3.1 0.9
## 10 28019 Choctaw County, Mississippi DP02_0066P 8.3 1.9
## # ℹ 72 more rows# Find highest value
highest_grad_pct <- grad_degree_pct %>%
separate(NAME, into = c("county", "state"), sep = ", ") %>%
group_by(state) %>%
filter(estimate == max(estimate, na.rm = TRUE))
# Find lowest value
lowest_grad_pct <- grad_degree_pct %>%
separate(NAME, into = c("county", "state"), sep = ", ") %>%
group_by(state) %>%
filter(estimate == min(estimate, na.rm = TRUE))highest_grad_pct## # A tibble: 1 × 6
## # Groups: state [1]
## GEOID county state variable estimate moe
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 28105 Oktibbeha County Mississippi DP02_0066P 22.5 2Oktibbeha County is highest
lowest_grad_pct## # A tibble: 1 × 6
## # Groups: state [1]
## GEOID county state variable estimate moe
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 28103 Noxubee County Mississippi DP02_0066P 2.7 1.4Noxubee County is the lowest
##Margin of Error Plot
I created a margin of error plot to show the percentage of the population with a graduate degree across counties in Mississippi, including the uncertainty around each estimate. The points represent the estimates, and the horizontal lines show the margins of error from the ACS data.
This method is useful for comparing counties and understanding data reliability. However, because Mississippi has many counties, the plot becomes crowded and harder to read. I think this method works, but it is less effective for states with a large number of geographic units.
# Plot
md_plot_errorbar <- ggplot(grad_degree_pct,
aes(x = estimate,
y = reorder(NAME, estimate))) +
# Margin of error bars
geom_errorbar(aes(xmin = estimate - moe,
xmax = estimate + moe),
width = 0.2,
linewidth = 0.4,
color = "gray50") +
# Points
geom_point(color = "darkred", size = 2) +
# Format x-axis as percentage
scale_x_continuous(labels = function(x) paste0(x, "%")) +
# Clean county names
scale_y_discrete(labels = function(x)
str_remove(x, " County, Mississippi|, Mississippi")) +
# Labels
labs(
title = "Graduate Degree Percentage by County (2021 ACS)",
subtitle = "Mississippi (2021 American Community Survey)",
caption = "Points show estimates; horizontal lines indicate margins of error",
x = "Percent of population with a graduate degree",
y = NULL
) +
# Theme
theme_minimal(base_size = 11) +
theme(
axis.text.y = element_text(size = 5), # smaller text to reduce clutter
plot.title = element_text(face = "bold"),
panel.grid.minor = element_blank()
)Interactive Plots.
Use the plotly package to convert the plot to an interactive chart.
md_plot_errorbar
Use the ggiraph package to make the plot interactive chart.
# Interactive Plot
ggplotly(md_plot_errorbar, tooltip = "x")md_plot_ggiraph <- md_plot_errorbar
girafe(ggobj = md_plot_ggiraph) %>%
girafe_options(opts_hover(css = "fill:cyan;"))Use the load_variables() function to find a variable that interests me the most which is the EMPLOYMENT STATUS FOR THE POPULATION 16 YEARS AND OLDER.
vars <- load_variables(2021, "acs5")
View(vars)
#B23025_003Interactive Map (mapview)
This map displays employment counts across counties. Because the data represents raw counts.
emplyment_16andOlder <- get_acs(
geography = "county",
state = "MS",
variables = "B23025_003",
year = 2021,
geometry = TRUE
)## Getting data from the 2017-2021 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== 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|==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%centroids <- st_centroid(emplyment_16andOlder)## Warning: st_centroid assumes attributes are constant over geometriesemplyment_16andOlder## Simple feature collection with 82 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -91.65501 ymin: 30.17394 xmax: -88.09789 ymax: 34.99605
## Geodetic CRS: NAD83
## First 10 features:
## GEOID NAME variable estimate moe
## 1 28079 Leake County, Mississippi B23025_003 8688 509
## 2 28121 Rankin County, Mississippi B23025_003 80190 1560
## 3 28001 Adams County, Mississippi B23025_003 11437 682
## 4 28145 Union County, Mississippi B23025_003 12915 450
## 5 28051 Holmes County, Mississippi B23025_003 5819 432
## 6 28073 Lamar County, Mississippi B23025_003 31298 1060
## 7 28131 Stone County, Mississippi B23025_003 7708 585
## 8 28143 Tunica County, Mississippi B23025_003 4672 258
## 9 28083 Leflore County, Mississippi B23025_003 11205 578
## 10 28013 Calhoun County, Mississippi B23025_003 5656 294
## geometry
## 1 MULTIPOLYGON (((-89.7303 32...
## 2 MULTIPOLYGON (((-90.25346 3...
## 3 MULTIPOLYGON (((-91.65501 3...
## 4 MULTIPOLYGON (((-89.24626 3...
## 5 MULTIPOLYGON (((-90.43168 3...
## 6 MULTIPOLYGON (((-89.65421 3...
## 7 MULTIPOLYGON (((-89.34139 3...
## 8 MULTIPOLYGON (((-90.59178 3...
## 9 MULTIPOLYGON (((-90.45376 3...
## 10 MULTIPOLYGON (((-89.50975 3...emplymentCount<-emplyment_16andOlder
mapview(emplymentCount, zcol = "estimate")Static Map (ggplot2 – Graduated Symbols)
To better represent raw counts, I created a graduated symbols map where circle size reflects employment totals
ggplot(emplyment_16andOlder) +
geom_sf(aes(fill = estimate), color = "black", fill = "lightgrey") +
geom_sf(data = centroids, aes(size = estimate),
alpha = 0.7, color = "navy") +
theme_void() +
labs(
title = "Total Employment (Age 16+) by County",
subtitle = "Mississippi- 2021 ACS",
caption = "Source: U.S. Census Bureau ACS (5-Year Estimates)"
) +
theme_minimal() +
scale_size_area(max_size = 6)