What are the highest earning engineering majors?
grads %>%
filter(Major_category == "Engineering") %>%
mutate(Major = str_to_title(Major)) %>%
select(Major, Median) %>%
arrange(desc(Median)) %>%
kable()
Petroleum Engineering |
110000 |
Mining And Mineral Engineering |
75000 |
Metallurgical Engineering |
73000 |
Naval Architecture And Marine Engineering |
70000 |
Chemical Engineering |
65000 |
Nuclear Engineering |
65000 |
Mechanical Engineering |
60000 |
Electrical Engineering |
60000 |
Computer Engineering |
60000 |
Aerospace Engineering |
60000 |
Biomedical Engineering |
60000 |
Materials Science |
60000 |
Engineering Mechanics Physics And Science |
58000 |
Biological Engineering |
57100 |
Industrial And Manufacturing Engineering |
57000 |
General Engineering |
56000 |
Architectural Engineering |
54000 |
Electrical Engineering Technology |
52000 |
Materials Engineering And Materials Science |
52000 |
Civil Engineering |
50000 |
Miscellaneous Engineering |
50000 |
Environmental Engineering |
50000 |
Engineering Technologies |
50000 |
Geological And Geophysical Engineering |
50000 |
Industrial Production Technologies |
46000 |
Engineering And Industrial Management |
44000 |
Architecture |
40000 |
Miscellaneous Engineering Technologies |
40000 |
Mechanical Engineering Related Technologies |
40000 |
grads %>%
filter(Major_category == "Engineering") %>%
mutate(Major = str_to_title(Major)) %>%
select(Major, Median) %>%
arrange(desc(Median)) %>%
ggplot() +
geom_col(aes(y = reorder(Major, Median), x = Median)) +
labs(x = "Median income ($)",
y = "Major") +
theme_minimal()

Petroleum engineers apparently make bank!
Within the engineering majors, which ones have better employment
rates?
grads %>%
filter(Major_category == "Engineering") %>%
mutate(Major = str_to_title(Major)) %>%
select(Major, Unemployment_rate) %>%
arrange(Unemployment_rate) %>%
kable()
Engineering Mechanics Physics And Science |
0.0063343 |
Petroleum Engineering |
0.0183805 |
Materials Science |
0.0230428 |
Metallurgical Engineering |
0.0240964 |
Materials Engineering And Materials Science |
0.0277888 |
Industrial Production Technologies |
0.0283081 |
Engineering And Industrial Management |
0.0336517 |
Industrial And Manufacturing Engineering |
0.0428755 |
Naval Architecture And Marine Engineering |
0.0501253 |
Miscellaneous Engineering Technologies |
0.0525385 |
Engineering Technologies |
0.0550304 |
Mechanical Engineering Related Technologies |
0.0563571 |
Mechanical Engineering |
0.0573423 |
Electrical Engineering |
0.0591738 |
General Engineering |
0.0598242 |
Chemical Engineering |
0.0610977 |
Architectural Engineering |
0.0619308 |
Aerospace Engineering |
0.0651621 |
Computer Engineering |
0.0654093 |
Civil Engineering |
0.0706096 |
Miscellaneous Engineering |
0.0743925 |
Geological And Geophysical Engineering |
0.0750383 |
Biological Engineering |
0.0871431 |
Electrical Engineering Technology |
0.0875571 |
Biomedical Engineering |
0.0920839 |
Environmental Engineering |
0.0935886 |
Architecture |
0.1133319 |
Mining And Mineral Engineering |
0.1172414 |
Nuclear Engineering |
0.1772264 |
grads %>%
filter(Major_category == "Engineering") %>%
mutate(Major = str_to_title(Major)) %>%
select(Major, Unemployment_rate) %>%
arrange(Unemployment_rate) %>%
ggplot() +
geom_col(aes(y = reorder(Major, -Unemployment_rate), x = Unemployment_rate)) +
labs(x = "Unemployment rate",
y = "Major") +
theme_minimal()

Within the engineering majors, which ones have a better gender
balance?
grads %>%
filter(Major_category == "Engineering") %>%
mutate(Major = str_to_title(Major)) %>%
select(Major, ShareWomen) %>%
arrange(desc(ShareWomen)) %>%
kable()
Architecture |
0.4514649 |
Biomedical Engineering |
0.4378469 |
Architectural Engineering |
0.3504425 |
Industrial And Manufacturing Engineering |
0.3434732 |
Environmental Engineering |
0.3422288 |
Chemical Engineering |
0.3416305 |
Materials Engineering And Materials Science |
0.3250919 |
Geological And Geophysical Engineering |
0.3222222 |
Biological Engineering |
0.3207843 |
Materials Science |
0.3108203 |
Electrical Engineering Technology |
0.2926070 |
General Engineering |
0.2529598 |
Engineering Technologies |
0.2513889 |
Industrial Production Technologies |
0.2491902 |
Civil Engineering |
0.2271179 |
Miscellaneous Engineering Technologies |
0.2000227 |
Computer Engineering |
0.1994126 |
Electrical Engineering |
0.1964503 |
Miscellaneous Engineering |
0.1899704 |
Engineering Mechanics Physics And Science |
0.1839852 |
Engineering And Industrial Management |
0.1741225 |
Metallurgical Engineering |
0.1530374 |
Nuclear Engineering |
0.1449670 |
Aerospace Engineering |
0.1397928 |
Petroleum Engineering |
0.1205643 |
Mechanical Engineering |
0.1195589 |
Naval Architecture And Marine Engineering |
0.1073132 |
Mining And Mineral Engineering |
0.1018519 |
Mechanical Engineering Related Technologies |
0.0774530 |
grads %>%
filter(Major_category == "Engineering") %>%
mutate(Major = str_to_title(Major)) %>%
select(Major, ShareWomen) %>%
arrange(desc(ShareWomen)) %>%
ggplot() +
geom_col(aes(y = reorder(Major, ShareWomen), x = ShareWomen)) +
labs(x = "Unemployment rate",
y = "Major") +
theme_minimal()
