Overview

This document contains analysis from the IPEDS module of “Completions”, which includes the number of awards by level of award (certificate or degree), first or second major and by race/ethnicity and gender. Type of program is categorized according to the Classification of Instructional Programs (CIP), a detailed coding system for postsecondary instructional programs. Data covers all awards granted between July 1, 2010 and June 30, 2022.

From the National Center for Education Statistics

“The CIP titles and program descriptions are intended to be general categories into which program completions data can be placed, not exact duplicates of a specific major or field of study titles used by individual institutions. CIP codes are standard statistical coding tools that reflect current practice and are not a prescriptive list of officially recognized or permitted programs. The CIP is not intended to be a regulatory device.

CIP codes, for the most part, are not intended to correspond exclusively to any specific degree or program level. In most cases, any given instructional program may be offered at various levels, and CIP codes are intended to capture all such data. The vast majority of CIP titles correspond to academic and occupational instructional programs offered for credit at the postsecondary level. These programs result in recognized completion points and awards, including degrees, certificates, and other formal awards.”

Figures

All Fields of Study

total_awards <- complete %>%
  filter(`CIP Code` == 99.00) %>% 
  filter(`Award Level Combined` != "Degrees/certificates total", `Award Level Combined` != "Degrees total")

ggplot(total_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Award Level - All Fields of Study", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

complete %>%
  filter(`CIP Code` == 99.00) %>% 
  filter(`Award Level Combined` == "Degrees total") %>% 
  ggplot(., aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Total Degree Completion Counts by Gender and Year - All Fields of Study", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

Health Services/Allied Health/Health Sciences, General

Health Services/Allied Health/Health Sciences, General includes general, introductory, undifferentiated, or joint program in health services occupations that prepares individuals for either entry into specialized training programs or for a variety of concentrations in the allied health area. Includes instruction in the basic sciences, research and clinical procedures, and aspects of the subject matter related to various health occupations.

“Allied Health professionals are involved with the delivery of health or related services pertaining to the identification, evaluation and prevention of diseases and disorders; dietary and nutrition services; rehabilitation and health systems management, among others. Allied health professionals, to name a few, include dental hygienists, diagnostic medical sonographers, dietitians, medical technologists, occupational therapists, physical therapists, radiographers, respiratory therapists, and speech language pathologists.” - Association of Schools Advancing Health Professions

hs_awards <- complete %>%
  filter(`CIP Title` == "Health Services/Allied Health/Health Sciences, General") %>% 
  filter(`Award Level Combined` != "Degrees/certificates total", `Award Level Combined` != "Degrees total")

ggplot(hs_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Award Level - Health Services/Allied Health/Health Sciences, General", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

complete %>%
  filter(`CIP Title` == "Health Professions and Related Programs") %>% 
  filter(`Award Level Combined` == "Degrees total") %>% 
  ggplot(., aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Total Degree Completion Counts by Gender and Year - Health Services/Allied Health/Health Sciences, General", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

M.D. Degrees

Note: Only Doctor degrees appear, which is a good gut check! This CIP Code does not include Associate’s, Bachelor’s, nor Master’s degrees. CIP Code 51.12 is different than 51.14 - Medical Clinical Sciences/Graduate Medical Studies.

51.14 is described as an undifferentiated clinical science program that prepares clinicians to conduct clinical and translational research in various areas. Note: programs that prepare clinicians to conduct research in specific scientific fields should report under the relevant CIP code series (e.g., Series 26 Biological and Biomedical Sciences).

md_awards <- complete %>%
  filter(`CIP Code` == "51.12") %>% 
  filter(`Award Level Combined` == "Degrees total")

ggplot(md_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Year - Medicine (M.D.)", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

kable(md_awards, format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
Award Level Combined CIP Title CIP Code Year Total Men Women
Degrees total Medicine 51.12 2010-2011 16863 8701 8162
Degrees total Medicine 51.12 2011-2012 16927 8809 8118
Degrees total Medicine 51.12 2012-2013 17264 8976 8288
Degrees total Medicine 51.12 2013-2014 17604 9232 8372
Degrees total Medicine 51.12 2014-2015 18302 9558 8744
Degrees total Medicine 51.12 2015-2016 18409 9852 8557
Degrees total Medicine 51.12 2016-2017 18698 9834 8864
Degrees total Medicine 51.12 2017-2018 19142 10049 9093
Degrees total Medicine 51.12 2018-2019 19423 10069 9354
Degrees total Medicine 51.12 2019-2020 27264 14084 13180
Degrees total Medicine 51.12 2020-2021 28215 14315 13900
Degrees total Medicine 51.12 2021-2022 28523 14136 14387

Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing

Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing category includes:

  • 51.3801) Nursing/Registered Nurse (RN, ASN, BSN, MSN).
  • 51.3802) Nursing Administration (MSN, MS, PhD).
  • 51.3803) Adult Health Nurse/Nursing.
  • 51.3804) Nurse Anesthetist.
  • 51.3805) Family Practice Nurse/Nurse Practitioner.
  • 51.3806) Maternal/Child Health and Neonatal Nurse/Nursing.
  • 51.3807) Nurse Midwife/Nursing Midwifery.
  • 51.3808) Nursing Science (MS, PhD).
  • 51.3809) Pediatric Nurse/Nursing.
  • 51.3810) Psychiatric/Mental Health Nurse/Nursing.
  • 51.3811) Public Health/Community Nurse/Nursing.
  • 51.3812) Perioperative/Operating Room and Surgical Nurse/Nursing.
  • 51.3813) Clinical Nurse Specialist.
  • 51.3814) Critical Care Nursing.
  • 51.3815) Occupational and Environmental Health Nursing.
  • 51.3816) Emergency Room/Trauma Nursing.
  • 51.3817) Nursing Education.
  • 51.3818) Nursing Practice.
  • 51.3819) Palliative Care Nursing.
  • 51.3820) Clinical Nurse Leader.
  • 51.3821) Geriatric Nurse/Nursing.
  • 51.3822) Women’s Health Nurse/Nursing.
  • 51.3899) Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing, Other.
rn_awards <- complete %>%
  filter(`CIP Code` == "51.38") %>% 
  filter(`Award Level Combined` != "Degrees/certificates total", `Award Level Combined` != "Degrees total")


ggplot(rn_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Award Level - Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing.", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

complete %>%
  filter(`CIP Code` == "51.38") %>% 
  filter(`Award Level Combined` == "Degrees total") %>% 
  ggplot(., aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Total Degree Completion Counts by Gender and Year - Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

rn_combined <- complete %>%
  filter(`CIP Code` == "51.38") %>%
  filter(`Award Level Combined` == "Degrees total") 


rn_combined %>% 
  kable(format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
Award Level Combined CIP Title CIP Code Year Total Men Women
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2010-2011 195714 23667 172047
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2011-2012 212851 26753 186098
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2012-2013 227404 29148 198256
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2013-2014 241362 30966 210396
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2014-2015 253139 31940 221199
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2015-2016 260466 33216 227250
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2016-2017 269275 34901 234374
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2017-2018 279272 36324 242948
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursin 51.38 2018-2019 290612 38350 252262
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing 51.38 2019-2020 298600 39621 258979
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing 51.38 2020-2021 307158 40392 266766
Degrees total Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing 51.38 2021-2022 304358 39899 264459

Practical Nursing, Vocational Nursing and Nursing Assistants

Practical Nursing, Vocational Nursing and Nursing Assistants includes:

  • 51.3901) Licensed Practical/Vocational Nurse Training (LPN, LVN, Cert., Dipl, AAS)
  • 51.3902) Nurse/Nursing Assistant/Aide and Patient Care Assistant.
  • 51.3999) Practical Nursing, Vocational Nursing and Nursing Assistants, Other.

Note: This category appears to be heavily Associate’s degree focused. Only this award is shown, and Bachelor’s, Master’s and Doctor’s are excluded.

lpn_awards <- complete %>%
  filter(`CIP Code` == "51.39") %>% 
  filter(`Award Level Combined` == "Associate's degree")


ggplot(lpn_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Year - Practical Nursing, Vocational Nursing and Nursing Assistants", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

lpn_combined <- complete %>%
  filter(`CIP Code` == "51.39") %>%
  filter(`Award Level Combined` == "Degrees total") 


lpn_combined %>% 
  kable(format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
Award Level Combined CIP Title CIP Code Year Total Men Women
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2010-2011 2264 245 2019
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2011-2012 2501 253 2248
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2012-2013 2509 266 2243
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2013-2014 2394 230 2164
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2014-2015 2013 193 1820
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2015-2016 1484 167 1317
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2016-2017 1476 127 1349
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2017-2018 1212 113 1099
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2018-2019 1340 163 1177
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2019-2020 1376 155 1221
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2020-2021 1722 164 1558
Degrees total Practical Nursing, Vocational Nursing and Nursing Assistants 51.39 2021-2022 1939 223 1716

Health/Medical Preparatory Programs

Health/Medical Preparatory Programs includes:

  • 51.1101) Pre-Dentistry Studies.
  • 51.1102) Pre-Medicine/Pre-Medical Studies.
  • 51.1103) Pre-Pharmacy Studies.
  • 51.1104) Pre-Veterinary Studies.
  • 51.1105) Pre-Nursing Studies.
  • 51.1106) Pre-Chiropractic Studies.
  • 51.1107) Pre-Occupational Therapy Studies.
  • 51.1108) Pre-Optometry Studies.
  • 51.1109) Pre-Physical Therapy Studies.
  • 51.1199) Health/Medical Preparatory Programs, Other.

Note: This category appears to be heavily Associate’s and Bachelor’s degree focused. Only these awards are shown, and Master’s and Doctor’s are excluded.

prep_awards <- complete %>%
  filter(`CIP Code` == "51.11") %>% 
  filter(`Award Level Combined` == "Associate's degree"|`Award Level Combined` == "Bachelor's degree")


ggplot(prep_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Award Level - Health/Medical Preparatory Programs", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

complete %>%
  filter(`CIP Code` == "51.11") %>% 
  filter(`Award Level Combined` == "Degrees total") %>% 
  ggplot(., aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Total Degree Completion Counts by Gender and Year - Health/Medical Preparatory Programs", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

Health and Medical Administrative Services

Health and Medical Administrative Services includes:

  • 51.0701) Health/Health Care Administration/Management.
  • 51.0702) Hospital and Health Care Facilities Administration/Management.
  • 51.0703) Health Unit Coordinator/Ward Clerk.
  • 51.0704) Health Unit Manager/Ward Supervisor.
  • 51.0705) Medical Office Management/Administration.
  • 51.0706) Health Information/Medical Records Administration/Administrator.
  • 51.0707) Health Information/Medical Records Technology/Technician.
  • 51.0708) Medical Transcription/Transcriptionist.
  • 51.0709) Medical Office Computer Specialist/Assistant.
  • 51.0710) Medical Office Assistant/Specialist.
  • 51.0711) Medical/Health Management and Clinical Assistant/Specialist.
  • 51.0712) Medical Reception/Receptionist.
  • 51.0713) Medical Insurance Coding Specialist/Coder.
  • 51.0714) Medical Insurance Specialist/Medical Biller.
  • 51.0715) Health/Medical Claims Examiner.
  • 51.0716) Medical Administrative/Executive Assistant and Medical Secretary.
  • 51.0717) Medical Staff Services Technology/Technician.
  • 51.0718) Long Term Care Administration/Management.
  • 51.0719) Clinical Research Coordinator.
  • 51.0799) Health and Medical Administrative Services, Other.
admin_awards <- complete %>%
  filter(`CIP Code` == "51.07") %>% 
  filter(`Award Level Combined` != "Doctor's degree", `Award Level Combined` != "Degrees/certificates total", `Award Level Combined` != "Degrees total")


ggplot(admin_awards, aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  facet_wrap(~ `Award Level Combined`) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Degree Completion Counts by Gender and Award Level - Health and Medical Administrative Services.", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

complete %>%
  filter(`CIP Code` == "51.07") %>% 
  filter(`Award Level Combined` == "Degrees total") %>% 
  ggplot(., aes(x = Year)) +
  geom_line(aes(y = Total, color = "Total", group = 1)) +
  geom_point(aes(y = Total, color = "Total")) +
  geom_line(aes(y = Men, color = "Men",  group = 1)) +
  geom_point(aes(y = Men, color = "Men")) +
  geom_line(aes(y = Women, color = "Women",  group = 1)) +
  geom_point(aes(y = Women, color = "Women")) +
  scale_x_discrete(breaks = c("2010-2011", "2012-2013", "2014-2015", "2016-2017", "2018-2019", "2020-2021")) +
  labs(title = "Total Degree Completion Counts by Gender and Year - Health and Medical Administrative Services", 
       x = "Year", 
       y = "Count",
       color = "Gender") +
  scale_color_manual(values = c("Total" = "#7CAE00", "Women" = "#F8766D", "Men" = "#00Bfc4"),
                     breaks = c("Total","Men", "Women")) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()