Pricilla Nakyazze 607-Week 1 Assignment
2025-08-26
Introduction
Choosing a college major is more than just following one’s passion. It can have a measurable impact on future earnings. Even among closely related fields, significant differences in income potential exist. For instance, actuarial science majors tend to out-earn accounting majors, and public policy majors see better earnings outcomes than history majors. Interestingly, vocational fields like court reporting may offer better returns than more traditional majors like criminology. While earning a college degree does not guarantee economic success, data clearly shows that choosing the right major can improve the odds of financial stability.
The full Article on choince of major’s impact on employment can be found here: https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)install.packages("readr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(readr)
library(ggplot2)##1.1 Load the college majors-all ages data into a data frame and preview.
majors_data <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/all-ages.csv")## Rows: 173 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Major, Major_category
## dbl (9): Major_code, Total, Employed, Employed_full_time_year_round, Unemplo...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.glimpse(majors_data)## Rows: 173
## Columns: 11
## $ Major_code <dbl> 1100, 1101, 1102, 1103, 1104, 1105, 1106…
## $ Major <chr> "GENERAL AGRICULTURE", "AGRICULTURE PROD…
## $ Major_category <chr> "Agriculture & Natural Resources", "Agri…
## $ Total <dbl> 128148, 95326, 33955, 103549, 24280, 794…
## $ Employed <dbl> 90245, 76865, 26321, 81177, 17281, 63043…
## $ Employed_full_time_year_round <dbl> 74078, 64240, 22810, 64937, 12722, 51077…
## $ Unemployed <dbl> 2423, 2266, 821, 3619, 894, 2070, 264, 2…
## $ Unemployment_rate <dbl> 0.02614711, 0.02863606, 0.03024832, 0.04…
## $ Median <dbl> 50000, 54000, 63000, 46000, 62000, 50000…
## $ P25th <dbl> 34000, 36000, 40000, 30000, 38500, 35000…
## $ P75th <dbl> 80000, 80000, 98000, 72000, 90000, 75000…Select the Major column and return it as a vector
majors <- majors_data |>
select(Major) |>
pull()
head(majors, n=12)## [1] "GENERAL AGRICULTURE"
## [2] "AGRICULTURE PRODUCTION AND MANAGEMENT"
## [3] "AGRICULTURAL ECONOMICS"
## [4] "ANIMAL SCIENCES"
## [5] "FOOD SCIENCE"
## [6] "PLANT SCIENCE AND AGRONOMY"
## [7] "SOIL SCIENCE"
## [8] "MISCELLANEOUS AGRICULTURE"
## [9] "ENVIRONMENTAL SCIENCE"
## [10] "FORESTRY"
## [11] "NATURAL RESOURCES MANAGEMENT"
## [12] "ARCHITECTURE"Identifies the majors that contain “AGRICULTURE”
str_view(majors, "AGRICULTURE")## [1] │ GENERAL <AGRICULTURE>
## [2] │ <AGRICULTURE> PRODUCTION AND MANAGEMENT
## [8] │ MISCELLANEOUS <AGRICULTURE>students must approach their college decisions with care. Choosing a major with stronger labor market outcomes not only boosts earning potential but also reduces the risk of graduating into low-income brackets. The worst-case scenario? Ending up in the bottom 25% of earners, where attending college may not have paid off financially. Psychology is considered a major with lor return on Investment. Business majors are often influenced by many variables so that even though unemployment is high the number of students that are gainfully employed after graduating is high as well.
Get top 4 majors with most unemployed graduates.
top4_unemployed_majors <- majors_data |>
dplyr::arrange(desc(Unemployed)) |>
head(4) |>
dplyr::arrange(Unemployed)
# View the result
print(top4_unemployed_majors)## # A tibble: 4 × 11
## Major_code Major Major_category Total Employed Employed_full_time_y…¹
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 6201 ACCOUNTING Business 1.78e6 1335825 1095027
## 2 5200 PSYCHOLOGY Psychology & … 1.48e6 1055854 736817
## 3 6200 GENERAL BUSI… Business 2.15e6 1580978 1304646
## 4 6203 BUSINESS MAN… Business 3.12e6 2354398 1939384
## # ℹ abbreviated name: ¹Employed_full_time_year_round
## # ℹ 5 more variables: Unemployed <dbl>, Unemployment_rate <dbl>, Median <dbl>,
## # P25th <dbl>, P75th <dbl>library(dplyr)
Get top 10 majors with most unemployed, showing specific columns, sorted in ascending order
top10_unemployed_majors <- majors_data |>
arrange(desc(Unemployed)) |>
slice_head(n = 10) |>
arrange(Unemployed) |>
select(Major, Total, Employed, Employed_full_time_year_round, Unemployed)
# View the result
print(top10_unemployed_majors)## # A tibble: 10 × 5
## Major Total Employed Employed_full_time_y…¹ Unemployed
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BIOLOGY 8.39e5 583079 422788 36757
## 2 GENERAL EDUCATION 1.44e6 843693 591863 38742
## 3 POLITICAL SCIENCE AND GOVE… 7.49e5 541630 421761 40376
## 4 MARKETING AND MARKETING RE… 1.11e6 890125 704912 51839
## 5 ENGLISH LANGUAGE AND LITER… 1.10e6 708882 482229 52248
## 6 COMMUNICATIONS 9.88e5 790696 595739 54390
## 7 ACCOUNTING 1.78e6 1335825 1095027 75379
## 8 PSYCHOLOGY 1.48e6 1055854 736817 79066
## 9 GENERAL BUSINESS 2.15e6 1580978 1304646 85626
## 10 BUSINESS MANAGEMENT AND AD… 3.12e6 2354398 1939384 147261
## # ℹ abbreviated name: ¹Employed_full_time_year_roundGet top 4 majors with highest employed graduates, sorted in ascending order
top4_employed_majors <- majors_data |>
dplyr::arrange(desc(Employed)) |>
head(4) |>
dplyr::arrange(Employed)
# View the result
print(top4_employed_majors)## # A tibble: 4 × 11
## Major_code Major Major_category Total Employed Employed_full_time_y…¹
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 6107 NURSING Health 1.77e6 1325711 947546
## 2 6201 ACCOUNTING Business 1.78e6 1335825 1095027
## 3 6200 GENERAL BUSI… Business 2.15e6 1580978 1304646
## 4 6203 BUSINESS MAN… Business 3.12e6 2354398 1939384
## # ℹ abbreviated name: ¹Employed_full_time_year_round
## # ℹ 5 more variables: Unemployed <dbl>, Unemployment_rate <dbl>, Median <dbl>,
## # P25th <dbl>, P75th <dbl>A more accurate way to tell the return on investment on a course is by percentage of students employed and unemployed.
# Calculate unemployment percentage and get top 10 majors with highest % unemployed
top10_unemployed_pct <- majors_data |>
mutate(Unemployment_Percent = (Unemployed / Employed) * 100) |>
arrange(desc(Unemployment_Percent)) |>
slice_head(n = 10) |>
arrange(Unemployment_Percent) # Ascending order for plotting
# Plot
ggplot(top10_unemployed_pct, aes(x = reorder(Major, Unemployment_Percent),
y = Unemployment_Percent,
fill = Unemployment_Percent)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%.1f%%", Unemployment_Percent)),
hjust = 1.1, color = "white", size = 3) +
scale_fill_gradient(low = "lightblue", high = "darkblue") +
coord_flip() +
labs(
title = "High Unemployment rate (%)",
x = "Major",
y = "Unemployment Rate (%)"
) +
theme_minimal() +
theme(legend.position = "none") # optional: remove legend if not needed
##PercentageEmployed = Employed / (Employed + Unemployed) * 100
# Create summary table
employment_summary <- majors_data |>
mutate(PercentageEmployed = round(Employed / (Employed + Unemployed) * 100, 2)) |>
select(Major, Employed, Unemployed, PercentageEmployed)
# View the result
print(employment_summary)## # A tibble: 173 × 4
## Major Employed Unemployed PercentageEmployed
## <chr> <dbl> <dbl> <dbl>
## 1 GENERAL AGRICULTURE 90245 2423 97.4
## 2 AGRICULTURE PRODUCTION AND MANAGEMENT 76865 2266 97.1
## 3 AGRICULTURAL ECONOMICS 26321 821 97.0
## 4 ANIMAL SCIENCES 81177 3619 95.7
## 5 FOOD SCIENCE 17281 894 95.1
## 6 PLANT SCIENCE AND AGRONOMY 63043 2070 96.8
## 7 SOIL SCIENCE 4926 264 94.9
## 8 MISCELLANEOUS AGRICULTURE 6392 261 96.1
## 9 ENVIRONMENTAL SCIENCE 87602 4736 94.9
## 10 FORESTRY 48228 2144 95.7
## # ℹ 163 more rows# Filter the 20 lowest percentage employed majors
lowest_20 <- employment_summary |>
arrange(PercentageEmployed) |>
slice_head(n = 20)
# Plot
ggplot(lowest_20, aes(x = reorder(Major, PercentageEmployed),
y = PercentageEmployed,
fill = PercentageEmployed)) +
geom_col() +
coord_flip() +
scale_fill_gradient(low = "lightblue", high = "darkblue") +
geom_text(aes(label = paste0(PercentageEmployed, "%")),
hjust = 1.1, color = "white", size = 3) +
labs(
title = "20 Majors with highest ROI",
x = "Major",
y = "Percentage Employed"
) +
theme_minimal() +
theme(legend.position = "none")
Conclusions
Despite growing doubts about the value of a college degree, research shows that a bachelor’s degree remains a worthwhile investment overall. In fact, a recent study by the Federal Reserve Bank of New York finds that the financial return on a college degree is near its historical peak,even after accounting for rising tuition costs.
That said, students must approach their college decisions with care. Choosing a major with stronger labor market outcomes not only boosts earning potential but also reduces the risk of graduating into low-income brackets. The worst-case scenario? Ending up in the bottom 25% of earners, where attending college may not have paid off financially.
Recommendations.
Evaluate return on investment when selecting a major. Use non conventional means to get an education like mentoring, internships and apprenticeships.
Research earnings data by major, ideally from reliable sources like the U.S. Census or labor market studies and Pay scale
Schools should be transparent about the return on investment for different majors and policies supported that make this kind of data more accessible to prospective students.
By combining informed decision-making with personal interests, students can pursue degrees that offer both fulfillment and financial stability.
RESOURCES
CHATGPT
https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/
https://www.geeksforgeeks.org/r-language/graph-plotting-in-r-programming/