# Introduction

Ever wondered what type of majors are the most popular for undergraduates across America? Ever wondered how much more money a certain degree can help you earn? Or ever thought about what the likelihood of finding employment after college with your degree is? Ever wondered if either male or female earn more? This vignette is based on a collection of 173 majors in 16 different major categories and their employment, unemployment, and median salaries based on majors. The following information was gathered and compiled by the American Community Survey. In this vignette we take a look at several graphs that break down and answer the questions listed above. With each graph there will be a comprehensive discussion explaining why the graphs look the way that they look.

This vignette is based on data collected for the 538 story entitled “This Economic Guide to Picking a College Major” by Ben Casselman available here.

First, we loaded the required packages to reproduce the analysis.

library(fivethirtyeight)
library(dplyr)
library(ggplot2)
library(knitr)
library(forcats)

Next, we loaded data which categorizes different major_category’s into divisions and then joined with this data.

divisions <- read_csv("data/divisions.csv")
college_with_divisions <- inner_join(x = college_all_ages,
y = divisions,
by = "major_category")

To avoid confusion, we also rename the median variable in college_with_divisions as q2 since median() is a function in R:

college_with_divisions <- college_with_divisions %>%
rename(q2 = median)

# Total Number of Majors in Universities Across America

In this first graph we are looking at the major categories within the college_all_ages data frame and how many majors are included in each grouping. We use the fct_infreq() and fct_rev functions in the forcats package to sort the bars in order of count.

college_all_ages <- college_all_ages %>%
mutate(major_category = major_category %>% fct_infreq() %>% fct_rev())
ggplot(data = college_all_ages,
mapping = aes(x = major_category)) +
geom_bar(fill = "red", color = "black") +
coord_flip() +
labs(x = "Major Category", y = "Number of Majors")

It is interesting to think about there being so many different kinds of engineering majors but not very many majors in the fields at the bottom.

In the next graph, we are still looking at the college_all_ages data frame and taking an in depth look of the list of most popular majors to the least popular majors. Why might some majors be more popular than others? Let’s take a look at why engineering might be popular. Based on the fivethirtyeight article, a reason why this major category might be so popular is due to the variety of majors offered within that major category itself. For example, Engineering is a major category that offers Petroleum Engineering, Mechanical Engineering, Mining & Mineral Engineering, and Nuclear Engineering just to name a few. Whereas, the least picked major category, Interdisciplinary has a low variety of majors that students can choose from. Therefore, we can guess that major categories which are the most popular tend to be the ones that have a variety of disciplines within the major to choose from.

group_major <- college_all_ages %>%
group_by(major_category) %>%
summarize(total_num = sum(total) / 1000) %>%
mutate(major_category = fct_reorder(major_category, total_num))
ggplot(data = group_major, mapping = aes(x = major_category,
y = total_num)) +
geom_col(fill = "red", color = "black") +
coord_flip() +
labs(x = "Major Category", y = "Number of Students with Major (Thousands)")

# Median Employment Rates By Major Categories

employment_rate <- college_with_divisions %>%
mutate(employed_rate = employed/total) %>%
group_by(major_category) %>%
summarize(median_employment_rate = mean(employed_rate)) %>%
mutate(major_category = fct_reorder(major_category, median_employment_rate))
ggplot(data = employment_rate,
mapping = aes(x = major_category, y = median_employment_rate)) +
geom_col() +
coord_flip() +
labs(x = "Major Category", y = "Median Percentage Employed")

This graph shows the mean rate of employment pertaining to each major category within the college_with_divisions data frame. Computers & Mathematics, Interdisciplinary Studies, Communications & Journalism, and Business major categories all represent the highest mean rate of employment across universities in America. It is interesting to see the link between some of the least chosen majors such as Communications & Journalism and Interdisciplinary Studies, also have some of the highest rates of employment.

One may hypotheses that majors such as Journalism, Mass Media, Communications and Advertising and Public Relations, within the Communications & Journalism major category, lead to jobs that are highly useful in our society. Media and advertising are a part of our everyday lives, therefore creating more demand and job opportunities leading to higher rates of employment for those who graduate with degrees in Communications & Journalism. With media and advertising comes the demand of technology and mathematics that are used to create it. The use of technology is heavily integrated into our society today on a social level, as well as to create modern innovations with computers and mathematics. Skills from those who major in Computers & Mathematics such as Communication Technologies, Computer And Information Systems, Computer Programming And Data Processing, and Computer Networking and Telecommunications, are highly demanded and valued, creating more employment opportunities.

# Median Unemployment rates by major categories

Since the college_with_divisions data frame already includes an unemployment_rate variable, we need not create a new one using mutate() as we did with employment_rate above.

unemployment_rate <- college_with_divisions %>%
#  mutate(unemployment_rate = unemployed/total) %>%
group_by(major_category) %>%
summarize(median_unemployment_rate = median(unemployment_rate))  %>%
mutate(major_category = fct_reorder(major_category, median_unemployment_rate))
ggplot(data = unemployment_rate,
mapping = aes(x = major_category, y = median_unemployment_rate)) +
geom_col() +
coord_flip() +
labs(x = "Major Category", y = "Median Percentage Unemployed")

This graph depicts the mean unemployment rates by major category, using the college_with_divisions data frame again. The majors within the Art major category represent the highest rate of unemployment. Fine Arts, Drama And Theater Arts, Music, and Visual And Performing Arts represent some of the various majors within the Art major category. Some guesses as to potential factors as to why art has such a low mean unemployment rate are that it is often hard to find jobs and jobs that pay well in the art, theater, and music fields. Communications & Journalism, Psychology & Social work, and Humanities & Liberal Arts represent other major categories that have the highest mean of unemployment rates. These fields can often be very competitive which can create low employment rates for those who graduate with degrees in these majors.

# Summary tables of median salaries for STEM, humanities, and art majors

In the following table we have provided a table based on the college_with_divisions data frame in which we can see the breakdown of majors within major_category that are organized as a part of STEM or Humanities. The table is broken down from the highest median earnings to the lowest median earnings.

college_with_divisions %>%
filter(division %in% c("STEM", "Humanities", "Art")) %>%
mutate(median_salary = scales::dollar(q2)) %>%
select(major, major_category, division, median_salary) %>%
arrange(desc(median_salary))
major major_category division median_salary
Naval Architecture And Marine Engineering Engineering STEM $97,000 Metallurgical Engineering Engineering STEM$96,000
Nuclear Engineering Engineering STEM $95,000 Mining And Mineral Engineering Engineering STEM$92,000
Mathematics And Computer Science Computers & Mathematics STEM $92,000 Electrical Engineering Engineering STEM$88,000
Chemical Engineering Engineering STEM $86,000 Geological And Geophysical Engineering Engineering STEM$85,000
Aerospace Engineering Engineering STEM $80,000 Computer Engineering Engineering STEM$80,000
Mechanical Engineering Engineering STEM $80,000 Astronomy And Astrophysics Physical Sciences STEM$80,000
Computer Science Computers & Mathematics STEM $78,000 Architectural Engineering Engineering STEM$78,000
Civil Engineering Engineering STEM $78,000 Materials Engineering And Materials Science Engineering STEM$78,000
General Engineering Engineering STEM $75,000 Industrial And Manufacturing Engineering Engineering STEM$75,000
Materials Science Engineering STEM $75,000 Engineering And Industrial Management Engineering STEM$74,000
Environmental Engineering Engineering STEM $70,000 Miscellaneous Engineering Engineering STEM$70,000
Industrial Production Technologies Engineering STEM $70,000 Applied Mathematics Computers & Mathematics STEM$70,000
Statistics And Decision Science Computers & Mathematics STEM $70,000 Physics Physical Sciences STEM$70,000
Information Sciences Computers & Mathematics STEM $68,000 Electrical Engineering Technology Engineering STEM$67,000
Transportation Sciences And Technologies Industrial Arts & Consumer Services Art $67,000 Mathematics Computers & Mathematics STEM$66,000
Computer And Information Systems Computers & Mathematics STEM $65,000 Biomedical Engineering Engineering STEM$65,000
Engineering Mechanics Physics And Science Engineering STEM $65,000 Geology And Earth Science Physical Sciences STEM$65,000
Construction Services Industrial Arts & Consumer Services Art $65,000 Military Technologies Industrial Arts & Consumer Services Art$64,000
Agricultural Economics Agriculture & Natural Resources STEM $63,000 Soil Science Agriculture & Natural Resources STEM$63,000
Architecture Engineering STEM $63,000 Engineering Technologies Engineering STEM$63,000
Miscellaneous Engineering Technologies Engineering STEM $63,000 Food Science Agriculture & Natural Resources STEM$62,000
Biological Engineering Engineering STEM $62,000 Nuclear, Industrial Radiology, And Biological Technologies Physical Sciences STEM$62,000
Computer Programming And Data Processing Computers & Mathematics STEM $60,000 Mechanical Engineering Related Technologies Engineering STEM$60,000
Microbiology Biology & Life Science STEM $60,000 Pharmacology Biology & Life Science STEM$60,000
Physical Sciences Physical Sciences STEM $60,000 Atmospheric Sciences And Meteorology Physical Sciences STEM$60,000
Chemistry Physical Sciences STEM $59,000 Forestry Agriculture & Natural Resources STEM$58,000
Geosciences Physical Sciences STEM $57,000 Multi-Disciplinary Or General Science Physical Sciences STEM$56,000
Computer Administration Management And Security Computers & Mathematics STEM $55,000 Computer Networking And Telecommunications Computers & Mathematics STEM$55,000
Zoology Biology & Life Science STEM $55,000 Oceanography Physical Sciences STEM$55,000
Agriculture Production And Management Agriculture & Natural Resources STEM $54,000 Biochemical Sciences Biology & Life Science STEM$53,000
Cognitive Science And Biopsychology Biology & Life Science STEM $53,000 Miscellaneous Agriculture Agriculture & Natural Resources STEM$52,000
Environmental Science Biology & Life Science STEM $52,000 Natural Resources Management Agriculture & Natural Resources STEM$52,000
Miscellaneous Biology Biology & Life Science STEM $52,000 Biology Biology & Life Science STEM$51,000
General Agriculture Agriculture & Natural Resources STEM $50,000 Plant Science And Agronomy Agriculture & Natural Resources STEM$50,000
Communications Communications & Journalism Humanities $50,000 Journalism Communications & Journalism Humanities$50,000
Advertising And Public Relations Communications & Journalism Humanities $50,000 Communication Technologies Computers & Mathematics STEM$50,000
English Language And Literature Humanities & Liberal Arts Humanities $50,000 Liberal Arts Humanities & Liberal Arts Humanities$50,000
Botany Biology & Life Science STEM $50,000 Physiology Biology & Life Science STEM$50,000
History Humanities & Liberal Arts Humanities $50,000 United States History Humanities & Liberal Arts Humanities$50,000
Mass Media Communications & Journalism Humanities $48,000 Linguistics And Comparative Language And Literature Humanities & Liberal Arts Humanities$48,000
French German Latin And Other Common Foreign Language Studies Humanities & Liberal Arts Humanities $48,000 Genetics Biology & Life Science STEM$48,000
Electrical, Mechanical, And Precision Technologies And Production Industrial Arts & Consumer Services Art $48,000 Ecology Biology & Life Science STEM$47,500
Film Video And Photographic Arts Arts Art $47,000 Humanities Humanities & Liberal Arts Humanities$46,700
Commercial Art And Graphic Design Arts Art $46,600 Animal Sciences Agriculture & Natural Resources STEM$46,000
Area Ethnic And Civilization Studies Humanities & Liberal Arts Humanities $46,000 Other Foreign Languages Humanities & Liberal Arts Humanities$45,000
Molecular Biology Biology & Life Science STEM $45,000 Intercultural And International Studies Humanities & Liberal Arts Humanities$45,000
Philosophy And Religious Studies Humanities & Liberal Arts Humanities $45,000 Fine Arts Arts Art$45,000
Music Arts Art $45,000 Miscellaneous Fine Arts Arts Art$45,000
Art History And Criticism Humanities & Liberal Arts Humanities $44,500 Physical Fitness Parks Recreation And Leisure Industrial Arts & Consumer Services Art$44,000
Anthropology And Archeology Humanities & Liberal Arts Humanities $43,000 Drama And Theater Arts Arts Art$42,000
Family And Consumer Sciences Industrial Arts & Consumer Services Art $40,500 Cosmetology Services And Culinary Arts Industrial Arts & Consumer Services Art$40,000
Composition And Rhetoric Humanities & Liberal Arts Humanities $40,000 Theology And Religious Vocations Humanities & Liberal Arts Humanities$40,000
Visual And Performing Arts Arts Art $40,000 Studio Arts Arts Art$37,600
Neuroscience Biology & Life Science STEM $35,000 Petroleum Engineering Engineering STEM$125,000

We can also break this down further to look at the median salary across major_category:

college_with_divisions %>%
filter(division %in% c("STEM", "Humanities", "Art")) %>%
group_by(major_category, division) %>%
summarize(median_salary = median(q2)) %>%
mutate(median_salary = scales::dollar(median_salary)) %>%
arrange(desc(median_salary))
major_category division median_salary
Engineering STEM $75,000 Computers & Mathematics STEM$66,000
Physical Sciences STEM $60,000 Agriculture & Natural Resources STEM$53,000
Biology & Life Science STEM $51,500 Communications & Journalism Humanities$50,000
Industrial Arts & Consumer Services Art $48,000 Humanities & Liberal Arts Humanities$46,000
Arts Art $45,000 And lastly we break this down to see the median salary across division: college_with_divisions %>% filter(division %in% c("STEM", "Humanities", "Art")) %>% group_by(division) %>% summarize(median_salary = median(q2)) %>% mutate(median_salary = scales::dollar(median_salary)) %>% arrange(desc(median_salary)) division median_salary STEM$63,000
Humanities $48,000 Art$45,000

# Conclusion

After analyzing the data which depicted the rates of employment, unemployment, and median earnings for STEM, Humanities, and Art majors, we can reason that earning a bachelor’s degree in STEM majors (science, technology, engineering, and mathematics) likely increases the chances for employment and higher earnings. The college_all_ages and college_with_divisions data frames provided us with the statistical information for us to conclude what major categories in universities across America will have a higher rates of employment and median earnings.