Description
US College Scorecard Data.
Description
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.
README.md
collegeScorecard
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.
Installation
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")
Example
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 11300
#> 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 10525 0
#> 2 city 0 1 3 23 0 2915 0
#> 3 state 0 1 2 2 0 59 0
#> 4 zip 0 1 5 10 0 8757 0
#> 5 url 4943 0.563 15 123 0 5435 0
#>
#> ── Variable type: factor ───────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate ordered n_unique
#> 1 deg_predominant 998 0.912 FALSE 4
#> 2 deg_highest 1193 0.894 FALSE 4
#> 3 control 1 1.00 FALSE 3
#> 4 locale_type 5415 0.521 FALSE 4
#> 5 locale_size 5415 0.521 FALSE 6
#> 6 adm_req_test 8685 0.231 FALSE 4
#> 7 religious_affiliation 10449 0.0753 FALSE 60
#> top_counts
#> 1 Cer: 5483, Bac: 2549, Ass: 1819, Gra: 451
#> 2 Cer: 4287, Gra: 2517, Ass: 2111, Bac: 1192
#> 3 For: 5908, Non: 2760, Pub: 2631
#> 4 Cit: 2812, Sub: 1750, Tow: 822, Rur: 501
#> 5 Lar: 2809, Sma: 887, Mid: 866, Dis: 508
#> 6 Con: 1205, Not: 1015, Req: 273, Rec: 122
#> 7 Rom: 232, Uni: 85, Bap: 56, Pre: 54
#>
#> ── Variable type: logical ──────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate mean count
#> 1 is_hbcu 5412 0.521 0.0168 FAL: 5789, TRU: 99
#> 2 is_pbi 5412 0.521 0.0105 FAL: 5826, TRU: 62
#> 3 is_annhi 5412 0.521 0.00272 FAL: 5872, TRU: 16
#> 4 is_tribal 5412 0.521 0.00594 FAL: 5853, TRU: 35
#> 5 is_aanapii 5412 0.521 0.0350 FAL: 5682, TRU: 206
#> 6 is_hsi 5412 0.521 0.0909 FAL: 5353, TRU: 535
#> 7 is_nanti 5412 0.521 0.00543 FAL: 5856, TRU: 32
#> 8 is_only_men 5412 0.521 0.0102 FAL: 5828, TRU: 60
#> 9 is_only_women 5412 0.521 0.00510 FAL: 5858, TRU: 30
#> 10 is_only_distance 2832 0.749 0.00709 FAL: 8408, TRU: 60
#>
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate mean sd p0 p25
#> 1 id 0 1 2550768. 8357052. 100654 182632.
#> 2 latitude 5412 0.521 37.3 5.87 -14.3 33.9
#> 3 longitude 5412 0.521 -90.4 18.2 -171. -97.5
#> p50 p75 p100 hist
#> 1 367422 455666. 49664501 ▇▁▁▁▁
#> 2 38.6 41.2 71.3 ▁▁▆▇▁
#> 3 -86.3 -78.9 171. ▂▇▁▁▁
skimr::skim_tee(scorecard)
#> ── Data Summary ────────────────────────
#> Values
#> Name data
#> Number of rows 183306
#> 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 27 0
#>
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#> skim_variable n_missing complete_rate mean sd
#> 1 id 0 1 1163825. 5311085.
#> 2 n_undergrads 20563 0.888 2285. 5101.
#> 3 cost_tuition_in 90259 0.508 12429. 10992.
#> 4 cost_tuition_out 92793 0.494 14882. 10390.
#> 5 cost_books 92332 0.496 1101. 623.
#> 6 cost_room_board_on 136207 0.257 7961. 3322.
#> 7 cost_room_board_off 97476 0.468 7963. 3392.
#> 8 cost_avg 104675 0.429 15809. 8216.
#> 9 cost_avg_income_0_30k 105507 0.424 13996. 7669.
#> 10 cost_avg_income_30_48k 115448 0.370 14736. 7723.
#> 11 cost_avg_income_48_75k 120294 0.344 16807. 7750.
#> 12 cost_avg_income_75_110k 129900 0.291 19224. 7843.
#> 13 cost_avg_income_110k_plus 138593 0.244 21352. 9036.
#> 14 amnt_earnings_med_10y 143433 0.218 35228. 15046.
#> 15 rate_completion 134721 0.265 0.333 0.237
#> 16 rate_admissions 126465 0.310 0.698 0.210
#> 17 score_sat_avg 153731 0.161 1076. 135.
#> 18 score_act_p25 156788 0.145 20.2 3.69
#> 19 score_act_p75 156794 0.145 25.4 3.55
#> 20 score_sat_verbal_p25 156906 0.144 484. 72.3
#> 21 score_sat_verbal_p75 156905 0.144 592. 69.3
#> 22 score_sat_math_p25 156771 0.145 486. 75.7
#> 23 score_sat_math_p75 156773 0.145 594. 72.3
#> p0 p25 p50 p75 p100 hist
#> 1 100654 164562 213987 416670 49664501 ▇▁▁▁▁
#> 2 0 117 490 2050 253594 ▇▁▁▁▁
#> 3 0 4053 9556 16888 74787 ▇▂▁▁▁
#> 4 0 7475 12336 18810 74787 ▇▃▁▁▁
#> 5 0 800 1000 1354. 28000 ▇▁▁▁▁
#> 6 0 5496 7560 10008 27000 ▃▇▂▁▁
#> 7 0 5725 7474 9728 106962 ▇▁▁▁▁
#> 8 -103168 9319 15281 21090. 112050 ▁▁▇▁▁
#> 9 -117833 7952 13336 19036. 111962 ▁▁▇▂▁
#> 10 -44508 8618. 14055 19740 113384 ▁▇▃▁▁
#> 11 -17804 10711. 16289 21716 113427 ▂▇▁▁▁
#> 12 -18045 13149. 18933 24285. 114298 ▁▇▁▁▁
#> 13 -17487 14415 20448 26533 113314 ▁▇▁▁▁
#> 14 8400 25400 32900 42000 250000 ▇▁▁▁▁
#> 15 0 0.145 0.3 0.498 1 ▇▇▅▂▁
#> 16 0 0.569 0.725 0.854 1 ▁▂▅▇▇
#> 17 514 989 1056 1145 1599 ▁▂▇▂▁
#> 18 1 18 20 22 35 ▁▁▇▃▁
#> 19 2 23 25 27 36 ▁▁▂▇▂
#> 20 100 440 480 520 799 ▁▁▇▃▁
#> 21 100 540 590 630 800 ▁▁▂▇▂
#> 22 100 440 475 520 799 ▁▁▇▃▁
#> 23 100 550 588 630 800 ▁▁▂▇▂
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