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()
Major Median
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()
Major Unemployment_rate
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()
Major ShareWomen
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()