Introduction

This tutorial demonstrates how to download and analyze higher education data using the College Scorecard API. The College Scorecard provides data on college costs, student outcomes, and institutional characteristics for colleges and universities across the United States.

We’ll focus on University of Oregon data and make comparisons with other institutions.

Getting Started

Step 1: Get Your API Key

Before running this code, you need to get a free API key:

Go to: https://api.data.gov/signup/
Fill out the form (takes less than 1 minute)
Check your email for your API key
Copy your API key and paste it in the setup chunk below

Step 2: Install Required Packages

Run this code once to install the necessary packages:

# Only run this once
install.packages("rscorecard")
install.packages("tidyverse")

Setup

# Load required packages
library(rscorecard)
library(tidyverse)
library(knitr)

# Set your API key here
# Replace 'YOUR_API_KEY_HERE' with your actual key from https://api.data.gov/signup/
sc_key('krza60yel5q738DvsMBdLrVGzBJsRLIIpMAQgdJ1')

# Set chunk options
knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE,
  fig.width = 10,
  fig.height = 6
)

Basic University of Oregon Data

Let’s start by getting basic information about the University of Oregon.

# Get basic UO data - using unitid for exact match
uo_data <- sc_init() %>%
    sc_filter(unitid == 209551) %>%  # UO's specific unitid
    sc_select(unitid, instnm, stabbr, city, locale) %>%
    sc_year("latest") %>%
    sc_get()

# Display the data
kable(uo_data, caption = "Basic University of Oregon Information")

Basic University of Oregon Information
unitid	instnm	stabbr	city	locale	year
209551	University of Oregon	OR	Eugene	12	latest

University of Oregon Trends Over Time

Now let’s examine how enrollment and costs have changed at UO from 2010 to 2020.

# Get UO enrollment and cost data over time
# Need to get each year separately and combine
years_to_get <- 2010:2020

uo_trends <- map_dfr(years_to_get, function(yr) {
  sc_init() %>%
    sc_filter(unitid == 209551) %>%
    sc_select(instnm, ugds, costt4_a, tuitionfee_in, tuitionfee_out) %>%
    sc_year(yr) %>%
    sc_get()
})

# Convert year to numeric for plotting
uo_trends <- uo_trends %>%
  mutate(year_numeric = as.numeric(year))

# Display the data
kable(uo_trends %>% select(-year_numeric), 
      caption = "University of Oregon Enrollment and Cost Trends (2010-2020)",
      col.names = c("Institution", "Total Undergrads", "Avg Cost", 
                    "In-State Tuition", "Out-of-State Tuition", "Year"))

University of Oregon Enrollment and Cost Trends (2010-2020)
Institution	Total Undergrads	Avg Cost	In-State Tuition	Out-of-State Tuition	Year
University of Oregon	19219	19343	8190	25830	2010
University of Oregon	20248	20343	8789	27653	2011
University of Oregon	20464	21638	9310	28660	2012
University of Oregon	20473	22390	9763	29788	2013
University of Oregon	20252	23055	9918	30888	2014
University of Oregon	20220	23373	10289	32024	2015
University of Oregon	19775	24209	10761	33441	2016
University of Oregon	19163	24570	11931	34611	2017
University of Oregon	18923	25126	11898	35478	2018
University of Oregon	18743	26205	12720	36615	2019
University of Oregon	17972	27717	13857	39309	2020

Variable Definitions

ugds: Total number of undergraduate degree-seeking students
costt4_a: Average annual cost of attendance
tuitionfee_in: In-state tuition and fees
tuitionfee_out: Out-of-state tuition and fees

Enrollment Visualization

ggplot(uo_trends, aes(x = year, y = ugds)) +
    geom_line(size = 1.5, color = "#154733") +  # UO green
    geom_point(size = 3, color = "#FEE123") +    # UO yellow
    labs(title = "University of Oregon Undergraduate Enrollment Trends",
         subtitle = "2010-2020",
         x = "Year",
         y = "Total Undergraduate Enrollment") +
    scale_x_continuous(breaks = 2010:2020) +
    scale_y_continuous(labels = scales::comma) +
    theme_minimal() +
    theme(plot.title = element_text(face = "bold", size = 16),
          axis.text.x = element_text(angle = 45, hjust = 1))

Tuition Trends Visualization

# Reshape data for plotting
uo_tuition_long <- uo_trends %>%
  select(year, tuitionfee_in, tuitionfee_out) %>%
  pivot_longer(cols = c(tuitionfee_in, tuitionfee_out),
               names_to = "tuition_type",
               values_to = "amount") %>%
  mutate(tuition_type = recode(tuition_type,
                                tuitionfee_in = "In-State",
                                tuitionfee_out = "Out-of-State"))

# Create the plot
ggplot(uo_tuition_long, aes(x = year, y = amount, color = tuition_type)) +
    geom_line(size = 1.5) +
    geom_point(size = 3) +
    labs(title = "University of Oregon Tuition Trends",
         subtitle = "In-State vs Out-of-State Tuition & Fees (2010-2020)",
         x = "Year",
         y = "Tuition & Fees ($)",
         color = "Student Type") +
    scale_color_manual(values = c("In-State" = "#154733", 
                                   "Out-of-State" = "#FEE123")) +
    scale_x_continuous(breaks = 2010:2020) +
    scale_y_continuous(labels = scales::dollar) +
    theme_minimal() +
    theme(plot.title = element_text(face = "bold", size = 16),
          axis.text.x = element_text(angle = 45, hjust = 1),
          legend.position = "bottom")

Comparing Oregon Universities

Let’s compare UO to other major Oregon universities.

# Compare UO to Oregon State and Portland State
# Compare UO to Oregon State and Portland State using unitids
oregon_comparison <- sc_init() %>%
    sc_filter(unitid == c(209551, 209542, 209807)) %>%  # UO, OSU, PSU
    sc_select(instnm, ugds, adm_rate, sat_avg, 
              tuitionfee_in, pctfloan) %>%
    sc_year("latest") %>%
    sc_get()

# Display the comparison
kable(oregon_comparison, 
      digits = 2,
      caption = "Comparison of Major Oregon Universities",
      col.names = c("Institution", "Enrollment", "Admission Rate", 
                    "Avg SAT", "In-State Tuition", "% Federal Loans", "Year"))

Comparison of Major Oregon Universities
Institution	Enrollment	Admission Rate	Avg SAT	In-State Tuition	% Federal Loans	Year
Oregon State University	29557	0.79	NA	13494	0.33	latest
University of Oregon	19758	0.85	1257	15669	0.31	latest
Portland State University	13619	0.91	NA	11238	0.33	latest

Variable Definitions

adm_rate: Admission rate (proportion of applicants admitted)
sat_avg: Average SAT score (composite)
pctfloan: Percent of students receiving federal student loans

Visualization: Oregon Universities Comparison

# Create a comparison plot
oregon_comparison %>%
  ggplot(aes(x = reorder(instnm, -ugds), y = ugds, fill = instnm)) +
    geom_col() +
    geom_text(aes(label = scales::comma(ugds)), 
              vjust = -0.5, size = 4) +
    labs(title = "Undergraduate Enrollment at Major Oregon Universities",
         x = NULL,
         y = "Total Undergraduate Enrollment") +
    scale_y_continuous(labels = scales::comma, 
                       expand = expansion(mult = c(0, 0.1))) +
    scale_fill_manual(values = c("University of Oregon" = "#154733",
                                  "Oregon State University" = "#DC4405",
                                  "Portland State University" = "#00573F")) +
    theme_minimal() +
    theme(legend.position = "none",
          axis.text.x = element_text(angle = 15, hjust = 1),
          plot.title = element_text(face = "bold", size = 14))

Pac-12 Conference Comparison

Let’s see how UO compares to other Pac-12 universities.

# Get Pac-12 universities data using unitids
pac12_unitids <- c(209551,  # University of Oregon
                   236948,  # University of Washington-Seattle
                   110662,  # UCLA
                   110635,  # UC Berkeley
                   123961,  # USC
                   243744,  # Stanford
                   126614,  # Colorado Boulder
                   104179,  # Arizona
                   104151,  # Arizona State-Tempe
                   230764)  # Utah

pac12 <- sc_init() %>%
    sc_filter(unitid %in% pac12_unitids) %>%
    sc_select(instnm, ugds, adm_rate, sat_avg, 
              md_earn_wne_p10, grad_debt_mdn) %>%
    sc_year("latest") %>%
    sc_get()

# Display the data
kable(pac12, 
      digits = 2,
      caption = "Pac-12 Universities Comparison",
      col.names = c("Institution", "Enrollment", "Admission Rate", 
                    "Avg SAT", "Median Earnings (10yr)", 
                    "Median Debt", "Year"))

Pac-12 Universities Comparison
Institution	Enrollment	Admission Rate	Avg SAT	Median Earnings (10yr)	Median Debt	Year
Arizona State University Campus Immersion	64398	0.90	NA	62668	19500	latest
University of Arizona	40769	0.86	1261	59979	19620	latest
University of California-Berkeley	33073	0.12	NA	92446	13000	latest
University of California-Los Angeles	33040	0.09	NA	82511	14000	latest
University of Southern California	20817	0.10	1501	92498	18000	latest
University of Colorado Boulder	31578	0.83	1353	69738	19500	latest
University of Oregon	19758	0.85	1257	61324	20139	latest
University of Utah	26041	0.87	1231	67170	19000	latest
University of Washington-Seattle Campus	31588	0.43	NA	78466	14615	latest
Stanford University	7841	0.04	1553	124080	12000	latest

Variable Definitions

md_earn_wne_p10: Median earnings of students 10 years after entry
grad_debt_mdn: Median debt for students who completed

Admission Rates Across Pac-12

pac12 %>%
  filter(!is.na(adm_rate)) %>%
  mutate(instnm_short = str_remove(instnm, "University of |University-|Campus")) %>%
  ggplot(aes(x = reorder(instnm_short, adm_rate), y = adm_rate)) +
    geom_col(aes(fill = instnm == "University of Oregon")) +
    geom_text(aes(label = scales::percent(adm_rate, accuracy = 1)), 
              hjust = -0.2, size = 3.5) +
    coord_flip() +
    labs(title = "Admission Rates at Pac-12 Universities",
         x = NULL,
         y = "Admission Rate") +
    scale_y_continuous(labels = scales::percent, 
                       expand = expansion(mult = c(0, 0.15))) +
    scale_fill_manual(values = c("TRUE" = "#154733", "FALSE" = "gray70")) +
    theme_minimal() +
    theme(legend.position = "none",
          plot.title = element_text(face = "bold", size = 14))

University of Oregon Student Demographics

Let’s examine the demographic composition of UO’s student body.

# Get UO demographic data
uo_demographics <- sc_init() %>%
    sc_filter(unitid == 209551) %>%
    sc_select(instnm, ugds, ugds_white, ugds_black, 
              ugds_hisp, ugds_asian, ugds_aian, ugds_nhpi,
              pctpell, par_ed_pct_1stgen) %>%
    sc_year("latest") %>%
    sc_get()

# Display the data
kable(uo_demographics, 
      digits = 3,
      caption = "University of Oregon Student Demographics",
      col.names = c("Institution", "Total Enrollment", "% White", 
                    "% Black", "% Hispanic", "% Asian", "% Am Indian/AK Native",
                    "% Native HI/Pac Isl", "% Pell Grant", 
                    "% First Gen", "Year"))

University of Oregon Student Demographics
Institution	Total Enrollment	% White	% Black	% Hispanic	% Asian	% Am Indian/AK Native	% Native HI/Pac Isl	% Pell Grant	% First Gen	Year
University of Oregon	19758	0.618	0.027	0.158	0.068	0.005	0.004	0.22	0.284	latest

Variable Definitions

ugds_[race]: Proportion of undergraduate students by race/ethnicity
pctpell: Percent of students receiving Pell grants (indicator of low-income students)
par_ed_pct_1stgen: Percent of students whose parents did not attend college

Demographic Composition Visualization

# Reshape demographic data for plotting
# Reshape demographic data for plotting
uo_race_data <- uo_demographics %>%
  select(starts_with("ugds_")) %>%
  pivot_longer(everything(), 
               names_to = "race_category", 
               values_to = "proportion") %>%
  filter(!is.na(proportion), proportion > 0) %>%
  mutate(race_category = recode(race_category,
                                 ugds_white = "White",
                                 ugds_black = "Black",
                                 ugds_hisp = "Hispanic",
                                 ugds_asian = "Asian",
                                 ugds_aian = "Am Indian/AK Native",
                                 ugds_nhpi = "Native HI/Pac Isl"))

# Create pie chart
ggplot(uo_race_data, aes(x = "", y = proportion, fill = race_category)) +
  geom_col(width = 1, color = "white") +
  coord_polar("y") +
  labs(title = "University of Oregon Student Body by Race/Ethnicity",
       fill = "Race/Ethnicity") +
  scale_fill_brewer(palette = "Set2") +
  theme_void() +
  theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
        legend.position = "right")

Comprehensive UO Data

Here’s a more comprehensive dataset with multiple outcome measures.

# Get comprehensive UO data
uo_complete <- sc_init() %>%
    sc_filter(unitid == 209551) %>%
    sc_select(
        # Basic info
        instnm, city, stabbr, locale,
        # Enrollment
        ugds, ugds_women,
        # Admissions
        adm_rate, sat_avg, actcmmid,
        # Costs
        costt4_a, tuitionfee_in, tuitionfee_out, 
        # Aid
        pctpell, pctfloan,
        # Outcomes
        c150_4, ret_ft4, grad_debt_mdn, md_earn_wne_p10
    ) %>%
    sc_year("latest") %>%
    sc_get()

# Display the data
kable(uo_complete, 
      digits = 2,
      caption = "Comprehensive University of Oregon Data")

Comprehensive University of Oregon Data
instnm	city	stabbr	locale	ugds	ugds_women	adm_rate	sat_avg	actcmmid	costt4_a	tuitionfee_in	tuitionfee_out	pctpell	pctfloan	c150_4	ret_ft4	grad_debt_mdn	md_earn_wne_p10	year
University of Oregon	Eugene	OR	12	19758	0.56	0.85	1257	NA	32636	15669	43302	0.22	0.31	0.71	0.85	20139	61324	latest

Additional Variable Definitions

ugds_women: Proportion of undergraduate students who are women
actcmmid: Midpoint of the ACT cumulative score
c150_4: Completion rate for first-time, full-time students (150% of expected time)
ret_ft4: Retention rate of first-time, full-time students

Export Data

Save your data for further analysis or sharing.

# Create a data directory if it doesn't exist
if (!dir.exists("data")) {
  dir.create("data")
}

# Export datasets to CSV
write.csv(uo_trends, "data/uo_trends.csv", row.names = FALSE)
write.csv(oregon_comparison, "data/oregon_universities.csv", row.names = FALSE)
write.csv(pac12, "data/pac12_comparison.csv", row.names = FALSE)
write.csv(uo_demographics, "data/uo_demographics.csv", row.names = FALSE)
write.csv(uo_complete, "data/uo_comprehensive.csv", row.names = FALSE)

cat("Data files saved to the 'data' directory!\n")

Your Turn: Lab 2

Resources

College Scorecard Website: https://collegescorecard.ed.gov/
API Documentation: https://collegescorecard.ed.gov/data/api/
Data Dictionary: https://collegescorecard.ed.gov/data/data-documentation/
rscorecard Package: https://github.com/btskinner/rscorecard
Get API Key: https://api.data.gov/signup/

Session Information

sessionInfo()

## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/Los_Angeles
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] knitr_1.49        lubridate_1.9.4   forcats_1.0.0     stringr_1.5.1    
##  [5] dplyr_1.1.4       purrr_1.0.2       readr_2.1.5       tidyr_1.3.1      
##  [9] tibble_3.2.1      ggplot2_3.5.1     tidyverse_2.0.0   rscorecard_0.32.0
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6       jsonlite_1.9.0     compiler_4.4.1     tidyselect_1.2.1  
##  [5] jquerylib_0.1.4    scales_1.3.0       yaml_2.3.10        fastmap_1.2.0     
##  [9] R6_2.6.1           labeling_0.4.3     generics_0.1.3     curl_5.2.1        
## [13] munsell_0.5.1      RColorBrewer_1.1-3 bslib_0.9.0        pillar_1.10.1     
## [17] tzdb_0.4.0         rlang_1.1.4        cachem_1.1.0       stringi_1.8.4     
## [21] xfun_0.49          sass_0.4.9         lazyeval_0.2.2     timechange_0.3.0  
## [25] cli_3.6.3          withr_3.0.2        magrittr_2.0.3     digest_0.6.37     
## [29] grid_4.4.1         rstudioapi_0.16.0  hms_1.1.3          lifecycle_1.0.4   
## [33] vctrs_0.6.5        evaluate_1.0.3     glue_1.8.0         farver_2.1.2      
## [37] colorspace_2.1-1   rmarkdown_2.29     httr_1.4.7         tools_4.4.1       
## [41] pkgconfig_2.0.3    htmltools_0.5.8.1

Exploring University of Oregon Data with the College Scorecard API

Maithreyi Gopalan

2026-01-14

Introduction

Getting Started

Step 1: Get Your API Key

Step 2: Install Required Packages

Setup

Basic University of Oregon Data

University of Oregon Trends Over Time

Variable Definitions

Enrollment Visualization

Tuition Trends Visualization

Comparing Oregon Universities

Variable Definitions

Visualization: Oregon Universities Comparison

Pac-12 Conference Comparison

Variable Definitions

Admission Rates Across Pac-12

University of Oregon Student Demographics

Variable Definitions

Demographic Composition Visualization

Comprehensive UO Data

Additional Variable Definitions

Export Data

Your Turn: Lab 2

Resources

Session Information