In an academic institution, many people participate in decision-making: the governing board, administrators, faculty, staff, students, and others. See AAUP’s FAQ for more information. Good decisions come from informed people, and yet sometimes information is not available to everyone involved or is heavily curated before release. This package aggregates information from the federal IPEDS databases, College Scorecard, and other sources, generally as organized by the collegetables.info site (which I also run), and organizes it in ways that lets community members all have free access to it. A common use case will be when there are discussions on the need to close departments. Faculty arguments often focus on qualitative factors, which can be powerful (“what does it mean for how much we value STEM if one cannot get a PhD in math anywhere in the state?”) but having quantitative arguments can also help. Alternatively, a broader look at the data could argue that some closures do make sense.
Note I am doing this as a hobby on my own time – nothing here reflects the views or work for any past, current, or future employer, nor of any organization. I’m just trying to save colleagues work when a new question arises.
Some caveats: this uses federal data and thus reflects the federal government’s views on demographic categories. The data are mostly analyzed with R scripts at https://github.com/bomeara/collegetables_source with some minor tweaking in this package – I’ve tried to be careful with all the coding, but there could be errors, especially comparing across years (category names can change, for example). For much of the original data, go to https://nces.ed.gov/ipeds/ and https://collegescorecard.ed.gov/data/. For comparisons to a focal school, I use the schools that focal school chooses as comparisons and the schools NCES uses as comparisons. If you see any issues, please add them to https://github.com/bomeara/sharedgovernance/issues or, even better, fix them and use a pull request.
Installation
You can install the development version of sharedgovernance from GitHub with:
# install.packages("pak")
pak::pak("bomeara/sharedgovernance")This will take an unusually long time to install (10 minutes on my computer) – just be patient. Note that some of the functions may take some time to run, too – it can be doing operations across hundreds of thousands of rows (all majors by all degrees by all years by all institutions makes for a lot of data). But the advantage is that all the data are local, so you don’t have to wait for web requests each time.
Overview of functions
The main workhorse functions are:
-
sg_find_college(college_name, verbose=TRUE): finds the institution ID number for a college given its name (or part of its name). Returns the UNITID number used in federal data. -
sg_compare_field_salaries(institution_id, focal_only=FALSE): returns a data.frame with average salaries by field and degree for the focal institution and its comparison institutions. -
sg_return_graduates(institution_id, focal_only=FALSE): returns a data.frame with number of degrees awarded by field, degree, year, and institution for the focal institution and its comparison institutions. -
sg_return_college_summary(institution_id, categories=c("Basic"), focal_only=FALSE): returns a data.frame with summary data from the comparison table for the focal institution and its comparisons. Categories can be found in thecategories_and_fieldsdataset: seetable(categories_and_fields$Category)for an overview.
You can also open the collegetables.info site for a given institution with sg_open_collegetables(institution_id).
Examples
Imagine we wanted to look into U. of Nebraska, Lincoln. First we have to find its ID number in the federal data. We find names that are closest to it (in this case, the punctuation used is slightly different, but it still finds it). Make sure to use the full name: “University of Nebraska, Lincoln” rather than (“U Nebraska Lincoln” or “UNL”). By default it prints out the top ten matches.
library(sharedgovernance)
id <- sg_find_college("University of Nebraska, Lincoln")
#> Institution entity name distances
#> 181464 University of Nebraska-Lincoln 2
#> 181394 University of Nebraska at Omaha 9
#> 181215 University of Nebraska at Kearney 10
#> 182290 University of Nevada-Reno 11
#> 102553 University of Alaska Anchorage 12
#> 122436 University of San Diego 12
#> 154095 University of Northern Iowa 12
#> 181428 University of Nebraska Medical Center 12
#> 215929 University of Scranton 12
#> 232681 University of Mary Washington 12We can look at average salaries of graduates for the focal institution (listed first by default) and its comparison institutions.
grad_salaries <- sg_compare_field_salaries(id)
print(head(grad_salaries))
#> UNITID Institution
#> 101728 181464 University of Nebraska-Lincoln
#> 101729 181464 University of Nebraska-Lincoln
#> 101731 181464 University of Nebraska-Lincoln
#> 101734 181464 University of Nebraska-Lincoln
#> 101737 181464 University of Nebraska-Lincoln
#> 101740 181464 University of Nebraska-Lincoln
#> Field Degree
#> 101728 Agriculture Bachelors
#> 101729 Agriculture Masters
#> 101731 Agricultural Business and Management Bachelors
#> 101734 Agricultural Mechanization Bachelors
#> 101737 Agricultural Production Operations Associates
#> 101740 Applied Horticulture and Horticultural Business Services Bachelors
#> Earnings.1.year.all Earnings.5.year.all Earnings.1.year.men
#> 101728 43669 NA NA
#> 101729 50894 NA NA
#> 101731 53229 60418 54837
#> 101734 59052 74776 NA
#> 101737 24427 41932 NA
#> 101740 46289 56741 NA
#> Earnings.1.year.women Earnings.5.year.men Earnings.5.year.women focal
#> 101728 NA NA NA TRUE
#> 101729 NA NA NA TRUE
#> 101731 46235 65132 55662 TRUE
#> 101734 NA NA NA TRUE
#> 101737 NA NA NA TRUE
#> 101740 NA NA NA TRUEWe can also look at number of degrees awarded over time.
completions <- sg_return_graduates(id)
print(head(dplyr::select(completions, Institution, Classification, Degree, `Year`, `Grand total`),40))
#> Institution Classification Degree Year
#> 86 University of Nebraska-Lincoln Accounting Bachelors 2014
#> 87 University of Nebraska-Lincoln Accounting Bachelors 2014
#> 146 University of Nebraska-Lincoln Accounting Bachelors 2015
#> 147 University of Nebraska-Lincoln Accounting Bachelors 2015
#> 207 University of Nebraska-Lincoln Accounting Bachelors 2016
#> 208 University of Nebraska-Lincoln Accounting Bachelors 2016
#> 269 University of Nebraska-Lincoln Accounting Bachelors 2017
#> 270 University of Nebraska-Lincoln Accounting Bachelors 2017
#> 330 University of Nebraska-Lincoln Accounting Bachelors 2018
#> 331 University of Nebraska-Lincoln Accounting Bachelors 2018
#> 390 University of Nebraska-Lincoln Accounting Bachelors 2019
#> 391 University of Nebraska-Lincoln Accounting Bachelors 2019
#> 454 University of Nebraska-Lincoln Accounting Bachelors 2020
#> 455 University of Nebraska-Lincoln Accounting Bachelors 2020
#> 518 University of Nebraska-Lincoln Accounting Bachelors 2021
#> 519 University of Nebraska-Lincoln Accounting Bachelors 2021
#> 581 University of Nebraska-Lincoln Accounting Bachelors 2022
#> 582 University of Nebraska-Lincoln Accounting Bachelors 2022
#> 641 University of Nebraska-Lincoln Accounting Bachelors 2023
#> 642 University of Nebraska-Lincoln Accounting Bachelors 2023
#> 702 University of Nebraska-Lincoln Accounting Bachelors 2024
#> 934 University of Nebraska-Lincoln Accounting Masters 2014
#> 977 University of Nebraska-Lincoln Accounting Masters 2015
#> 1022 University of Nebraska-Lincoln Accounting Masters 2016
#> 1067 University of Nebraska-Lincoln Accounting Masters 2017
#> 1112 University of Nebraska-Lincoln Accounting Masters 2018
#> 1156 University of Nebraska-Lincoln Accounting Masters 2019
#> 1199 University of Nebraska-Lincoln Accounting Masters 2020
#> 1242 University of Nebraska-Lincoln Accounting Masters 2021
#> 1286 University of Nebraska-Lincoln Accounting Masters 2022
#> 1328 University of Nebraska-Lincoln Accounting Masters 2023
#> 1368 University of Nebraska-Lincoln Accounting Masters 2024
#> 1556 University of Nebraska-Lincoln Acting Bachelors 2022
#> 1563 University of Nebraska-Lincoln Acting Bachelors 2023
#> 1570 University of Nebraska-Lincoln Acting Bachelors 2024
#> 1602 University of Nebraska-Lincoln Actuarial Science Bachelors 2014
#> 1603 University of Nebraska-Lincoln Actuarial Science Bachelors 2014
#> 1617 University of Nebraska-Lincoln Actuarial Science Bachelors 2015
#> 1618 University of Nebraska-Lincoln Actuarial Science Bachelors 2015
#> 1634 University of Nebraska-Lincoln Actuarial Science Bachelors 2016
#> Grand total
#> 86 111
#> 87 10
#> 146 99
#> 147 12
#> 207 108
#> 208 11
#> 269 143
#> 270 12
#> 330 135
#> 331 9
#> 390 134
#> 391 11
#> 454 131
#> 455 9
#> 518 115
#> 519 7
#> 581 122
#> 582 10
#> 641 123
#> 642 15
#> 702 43
#> 934 31
#> 977 34
#> 1022 36
#> 1067 47
#> 1112 42
#> 1156 46
#> 1199 45
#> 1242 31
#> 1286 39
#> 1328 45
#> 1368 14
#> 1556 0
#> 1563 0
#> 1570 0
#> 1602 48
#> 1603 0
#> 1617 51
#> 1618 0
#> 1634 72These are just data.frames. We can export them to CSV files (i.e., write.csv(completions, file="completions.csv")) if analyzing them in Excel, Google Sheets, or another program is preferred. But we can also analyze them in R. For example, we can plot the number of Bachelors’ degrees in ‘Biology/Biological Sciences, General’ over time for the focal institution and its comparisons.
library(ggplot2)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
bio_degrees <- completions |> dplyr::filter(Classification=="Biology/Biological Sciences, General", Degree=="Bachelors", focal==TRUE)
g <- ggplot(bio_degrees, aes(x=`Year`, y=`Grand total`, color=`First or Second Major`)) +
geom_line() +
geom_point() +
labs(title="Biology/Biological Sciences, General Bachelors' Degrees Awarded Over Time",
y="Number of Degrees Awarded",
x="IPEDS Year")
print(g)
If we want basic info on this college, we can also get a summary table.
summary_data <- sg_return_college_summary(id, categories=c("Basic", "Institution_Finances", "Enrollment", "Library"), focal_only=TRUE)
print(summary_data[, c("Year", "Undergrad full time", "Revenue minus expenses", "Number of physical books")])
#> Year Undergrad full time Revenue minus expenses Number of physical books
#> 1507 2024 <NA> NA <NA>
#> 5489 2023 18449 118853325 1425365
#> 9529 2022 18175 101907312 1421525
#> 13611 2021 18437 137521518 1757277
#> 17673 2020 18949 -77948359 1542633
#> 21781 2019 19132 8147535 1558340
#> 25981 2018 19466 31208591 1827478
#> 30378 2017 19534 76475449 1613798
#> 34909 2016 19381 70067430 1743769
#> 39532 2015 18817 75761674 2998584
#> 44328 2014 18660 98950888 3791910To see what categories are available, we can look at the categories_and_fields dataset.