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(readr) # Used for reading in from a spreadsheet
library(knitr)
library(forcats)
Next, we loaded data which categorizes different major_category
’s into division
s 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)
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)")
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.
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.
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 |
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.