Provides a tidied subset of the US College Scorecard dataset, containing institutional characteristics, enrollment, student aid, costs, and student outcomes at institutions of higher education in the United States.
collegeScorecard provides a tidied subset of the US College Scorecard dataset, containing institutional characteristics, enrollment, student aid, costs, and student outcomes at institutions of higher education in the United States.
Install collegeScorecard from CRAN with:
install.packages("collegeScorecard")
Or install the development version from r-universe
install.packages(
"collegeScorecard",
repos = c("https://gadenbuie.r-universe.dev", "https://cloud.r-project.org")
)
or from Github
# install.packages("pak")
pak::pak("gadenbuie/scorecard-db/pkg")
Loading collegeScorecard gives you access to school and scorecard
tables, which are tibbles with information about institutions and yearly
costs and admissions data, respectively.
library(collegeScorecard)
skimr::skim_tee(school)
#> ── Data Summary ────────────────────────
#> Values
#> Name data
#> Number of rows 11465
#> Number of columns 25
#> _______________________
#> Column type frequency:
#> character 5
#> factor 7
#> logical 10
#> numeric 3
#> ________________________
#> Group variables None
#>
#> ── Variable type: character ────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate min max empty n_unique whitespace
#> 1 name 0 1 3 93 0 10687 0
#> 2 city 0 1 3 23 0 2935 0
#> 3 state 0 1 2 2 0 59 0
#> 4 zip 0 1 5 10 0 8918 0
#> 5 url 5332 0.535 15 136 0 5204 0
#>
#> ── Variable type: factor ───────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate ordered n_unique
#> 1 deg_predominant 1066 0.907 FALSE 4
#> 2 deg_highest 1292 0.887 FALSE 4
#> 3 control 1 1.000 FALSE 3
#> 4 locale_type 5860 0.489 FALSE 4
#> 5 locale_size 5860 0.489 FALSE 6
#> 6 adm_req_test 10744 0.0629 FALSE 4
#> 7 religious_affiliation 10641 0.0719 FALSE 60
#> top_counts
#> 1 Cer: 5618, Bac: 2533, Ass: 1798, Gra: 450
#> 2 Cer: 4291, Gra: 2559, Ass: 2104, Bac: 1219
#> 3 For: 6015, Non: 2799, Pub: 2650
#> 4 Cit: 2717, Sub: 1607, Tow: 757, Rur: 524
#> 5 Lar: 2665, Sma: 834, Mid: 825, Dis: 516
#> 6 Not: 406, Req: 153, Rec: 122, Con: 40
#> 7 Rom: 223, Uni: 83, Bap: 54, Pre: 50
#>
#> ── Variable type: logical ──────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate mean count
#> 1 is_hbcu 5789 0.495 0.0176 FAL: 5576, TRU: 100
#> 2 is_pbi 5789 0.495 0.0115 FAL: 5611, TRU: 65
#> 3 is_annhi 5789 0.495 0.00300 FAL: 5659, TRU: 17
#> 4 is_tribal 5789 0.495 0.00617 FAL: 5641, TRU: 35
#> 5 is_aanapii 5789 0.495 0.0365 FAL: 5469, TRU: 207
#> 6 is_hsi 5789 0.495 0.106 FAL: 5074, TRU: 602
#> 7 is_nanti 5789 0.495 0.00599 FAL: 5642, TRU: 34
#> 8 is_only_men 5789 0.495 0.0106 FAL: 5616, TRU: 60
#> 9 is_only_women 5789 0.495 0.00529 FAL: 5646, TRU: 30
#> 10 is_only_distance 2900 0.747 0.00829 FAL: 8494, TRU: 71
#>
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate mean sd p0 p25
#> 1 id 0 1 2698142. 8635097. 100654 183859
#> 2 latitude 5857 0.489 37.3 5.86 -14.3 33.9
#> 3 longitude 5857 0.489 -90.4 18.2 -171. -97.5
#> p50 p75 p100 hist
#> 1 372772 457679 49664501 ▇▁▁▁▁
#> 2 38.6 41.2 71.3 ▁▁▆▇▁
#> 3 -86.4 -78.9 171. ▂▇▁▁▁
skimr::skim_tee(scorecard)
#> ── Data Summary ────────────────────────
#> Values
#> Name data
#> Number of rows 200699
#> Number of columns 24
#> _______________________
#> Column type frequency:
#> character 1
#> numeric 23
#> ________________________
#> Group variables None
#>
#> ── Variable type: character ────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate min max empty n_unique whitespace
#> 1 academic_year 0 1 7 7 0 30 0
#>
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate mean sd
#> 1 id 0 1 1274554. 5631724.
#> 2 n_undergrads 28055 0.860 2316. 5205.
#> 3 cost_tuition_in 100818 0.498 12800. 11530.
#> 4 cost_tuition_out 103352 0.485 15347. 10940.
#> 5 cost_books 103638 0.484 1112. 628.
#> 6 cost_room_board_on 149774 0.254 8268. 3558.
#> 7 cost_room_board_off 108735 0.458 8285. 3696.
#> 8 cost_avg 112476 0.440 16006. 8418.
#> 9 cost_avg_income_0_30k 113458 0.435 14123. 7844.
#> 10 cost_avg_income_30_48k 124364 0.380 14808. 7889.
#> 11 cost_avg_income_48_75k 129612 0.354 16847. 7896.
#> 12 cost_avg_income_75_110k 140130 0.302 19335. 8010.
#> 13 cost_avg_income_110k_plus 149652 0.254 21643. 9263.
#> 14 amnt_earnings_med_10y 161040 0.198 35245. 15056.
#> 15 rate_completion 148362 0.261 0.338 0.237
#> 16 rate_admissions 139752 0.304 0.700 0.211
#> 17 score_sat_avg 169085 0.158 1083. 138.
#> 18 score_act_p25 172362 0.141 20.3 3.82
#> 19 score_act_p75 172368 0.141 25.6 3.63
#> 20 score_sat_verbal_p25 172407 0.141 489. 74.3
#> 21 score_sat_verbal_p75 172406 0.141 595. 70.2
#> 22 score_sat_math_p25 172273 0.142 489. 77.3
#> 23 score_sat_math_p75 172275 0.142 597. 73.3
#> p0 p25 p50 p75 p100 hist
#> 1 100654 165033 214795 418560 49664501 ▇▁▁▁▁
#> 2 0 118 498 2073 253594 ▇▁▁▁▁
#> 3 0 4170 9685 17148 74787 ▇▂▁▁▁
#> 4 0 7610 12544 19356. 74787 ▇▃▁▁▁
#> 5 0 800 1020 1386 28000 ▇▁▁▁▁
#> 6 0 5600 7811 10446 34336 ▆▇▁▁▁
#> 7 0 5840 7662. 10110 106962 ▇▁▁▁▁
#> 8 -103168 9415 15419 21310. 115636 ▁▁▇▁▁
#> 9 -117833 7992 13418 19180 115636 ▁▁▇▂▁
#> 10 -44508 8609 14085 19815 113384 ▁▇▃▁▁
#> 11 -17804 10683 16278 21788. 113427 ▂▇▁▁▁
#> 12 -18045 13169 18986 24421 114298 ▁▇▁▁▁
#> 13 -17487 14571 20674 26828. 113314 ▁▇▁▁▁
#> 14 8400 25500 32900 42000 250000 ▇▁▁▁▁
#> 15 0 0.150 0.308 0.5 1 ▇▇▅▃▁
#> 16 0 0.572 0.729 0.858 1 ▁▂▅▇▇
#> 17 514 990 1064 1155 1599 ▁▂▇▂▁
#> 18 1 18 20 22 35 ▁▁▇▃▁
#> 19 2 23 25 28 36 ▁▁▂▇▂
#> 20 100 440 480 530 799 ▁▁▇▃▁
#> 21 100 550 590 640 800 ▁▁▂▇▂
#> 22 100 440 480 530 799 ▁▁▇▃▁
#> 23 100 550 590 640 800 ▁▁▂▇▂
The help documentation of each dataset provides a short description of each column and the source of the data. Additional information is included in the packaged data dictionary.
readRDS(system.file("scorecard-data-dictionary.rds", package = "collegeScorecard"))
#> $info
#> # A tibble: 3,305 × 11
#> name_of_data_element dev_category developer_friendly_n…¹ api_data_type
#> <chr> <chr> <chr> <chr>
#> 1 Unit ID for institution root id integer
#> 2 8-digit OPE ID for institu… root ope8_id string
#> 3 6-digit OPE ID for institu… root ope6_id string
#> 4 Institution name school name autocomplete
#> 5 City school city autocomplete
#> 6 State postcode school state string
#> 7 ZIP code school zip string
#> 8 Accreditor for institution school accreditor string
#> 9 URL for institution's home… school school_url string
#> 10 URL for institution's net … school price_calculator_url string
#> # ℹ 3,295 more rows
#> # ℹ abbreviated name: ¹developer_friendly_name
#> # ℹ 7 more variables: data_type <chr>, index <chr>, variable_name <chr>,
#> # source <chr>, shown_use_on_site <chr>, notes <chr>, has_labels <lgl>
#>
#> $labels
#> # A tibble: 293 × 4
#> variable_name value label notes
#> <chr> <chr> <chr> <chr>
#> 1 MAIN 0 Not main campus <NA>
#> 2 MAIN 1 Main campus <NA>
#> 3 PREDDEG 0 Not classified <NA>
#> 4 PREDDEG 1 Predominantly certificate-degree granting <NA>
#> 5 PREDDEG 2 Predominantly associate's-degree granting <NA>
#> 6 PREDDEG 3 Predominantly bachelor's-degree granting <NA>
#> 7 PREDDEG 4 Entirely graduate-degree granting <NA>
#> 8 HIGHDEG 0 Non-degree-granting <NA>
#> 9 HIGHDEG 1 Certificate degree <NA>
#> 10 HIGHDEG 2 Associate degree <NA>
#> # ℹ 283 more rows