To install the latest development version from Github use the `remotes`` package:
remotes::install_github('jbryer/ipeds')This package requires mdbtools.
The following commands will install mdbtools on Mac:
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" < /dev/null 2> /dev/null \n')
brew install mdbtools
The vignette also has useful information for getting started:
vignette('ipeds')The available_ipeds will return a data.frame indicating which
databases are available for download and my have already been
downloaded.
ipeds::available_ipeds()## IPEDS data files will be downloaded to /Users/jbryer/Dropbox (Personal)/Projects/ipeds/data/downloaded/
## Use options("ipeds.download.dir" = "/PATH/TO/DOWNLOAD") to override this.
## year year_string final provisional downloaded download_date download_size
## 1 2007 2006-07 TRUE FALSE FALSE <NA> <NA>
## 2 2008 2007-08 TRUE FALSE FALSE <NA> <NA>
## 3 2009 2008-09 TRUE FALSE FALSE <NA> <NA>
## 4 2010 2009-10 TRUE FALSE FALSE <NA> <NA>
## 5 2011 2010-11 TRUE FALSE FALSE <NA> <NA>
## 6 2012 2011-12 TRUE FALSE FALSE <NA> <NA>
## 7 2013 2012-13 TRUE FALSE FALSE <NA> <NA>
## 8 2014 2013-14 TRUE FALSE FALSE <NA> <NA>
## 9 2015 2014-15 TRUE FALSE FALSE <NA> <NA>
## 10 2016 2015-16 TRUE TRUE FALSE <NA> <NA>
## 11 2017 2016-17 TRUE FALSE FALSE <NA> <NA>
## 12 2018 2017-18 TRUE FALSE FALSE <NA> <NA>
## 13 2019 2018-19 TRUE FALSE FALSE <NA> <NA>
## 14 2020 2019-20 TRUE FALSE FALSE <NA> <NA>
## 15 2021 2020-21 TRUE TRUE TRUE 2023-02-15 55.6 MB
## 16 2022 2021-22 FALSE TRUE FALSE <NA> <NA>
## 17 2023 2022-23 FALSE FALSE FALSE <NA> <NA>
The download_ipeds will download an IPEDS database for a given year.
ipeds::download_ipeds(2021)## Zip file already downloaded. Set force=TRUE to redownload.
## 34 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 41 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
## 87 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87
## 120 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 13 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13
## 35 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## 13 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13
## 34 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 111 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111
## 36 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 115 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115
## 34 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
## 35 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## 35 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## 56 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
## 248 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248
## 36 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 28 variables; Processing variable:1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
The load_ipeds will return a list of all the survey tables (as
data.frames) for the given year.
ipeds2021 <- ipeds::load_ipeds(2021)
names(ipeds2021)## [1] "ADM2020" "C2020_A" "C2020_B" "C2020_C"
## [5] "C2020DEP" "CUSTOMCGIDS2020" "DRVAL2020" "DRVC2020"
## [9] "DRVEF122020" "DRVEF2020" "DRVF2020" "DRVGR2020"
## [13] "DRVIC2020" "DRVOM2020" "EAP2020" "EF2020"
## [17] "EF2020A" "EF2020A_DIST" "EF2020B" "EF2020C"
## [21] "EF2020D" "EFFY2020" "EFFY2020_DIST" "EFIA2020"
## [25] "F1920_F1A" "F1920_F3" "Filenames20" "FLAGS2020"
## [29] "GR200_20" "GR2020" "GR2020_L2" "HD2020"
## [33] "IC2020" "IC2020_AY" "IC2020_PY" "IC2020Mission"
## [37] "S2020_IS" "S2020_NH" "S2020_OC" "S2020_SIS"
## [41] "SAL2020_IS" "sectiontable20" "SFA1920_P1" "SFA1920_P2"
## [45] "SFAV1920" "Tables20" "valuesets20" "vartable20"
## [49] "AL2020" "DRVADM2020" "DRVHR2020" "EF2020CP"
## [53] "F1920_F2" "GR2020_PELL_SSL" "OM2020" "SAL2020_NIS"
The ipeds_help function will return the data dictionary for the given
year.
View(ipeds_help(2021))If the table parameter is specified, then the data dictionary for the
given survey is returned (i.e. the variables in that table, see
data(surveys) for the available survey IDs).
View(ipeds_help(table = 'HD', year = 2021))To load a specific table, the ipeds_survey function will return a
data.frame with the data.
hd2021 <- ipeds::ipeds_survey(table = 'HD', year = 2021)
names(hd2021)## [1] "UNITID" "INSTNM" "IALIAS" "ADDR" "CITY" "STABBR"
## [7] "ZIP" "FIPS" "OBEREG" "CHFNM" "CHFTITLE" "GENTELE"
## [13] "EIN" "DUNS" "OPEID" "OPEFLAG" "WEBADDR" "ADMINURL"
## [19] "FAIDURL" "APPLURL" "NPRICURL" "VETURL" "ATHURL" "DISAURL"
## [25] "SECTOR" "ICLEVEL" "CONTROL" "HLOFFER" "UGOFFER" "GROFFER"
## [31] "HDEGOFR1" "DEGGRANT" "HBCU" "HOSPITAL" "MEDICAL" "TRIBAL"
## [37] "LOCALE" "OPENPUBL" "ACT" "NEWID" "DEATHYR" "CLOSEDAT"
## [43] "CYACTIVE" "POSTSEC" "PSEFLAG" "PSET4FLG" "RPTMTH" "INSTCAT"
## [49] "C18BASIC" "C18IPUG" "C18IPGRD" "C18UGPRF" "C18ENPRF" "C18SZSET"
## [55] "C15BASIC" "CCBASIC" "CARNEGIE" "LANDGRNT" "INSTSIZE" "F1SYSTYP"
## [61] "F1SYSNAM" "F1SYSCOD" "CBSA" "CBSATYPE" "CSA" "NECTA"
## [67] "COUNTYCD" "COUNTYNM" "CNGDSTCD" "LONGITUD" "LATITUDE" "DFRCGID"
## [73] "DFRCUSCG"
Mapping variable factors.
names(hd2021) <- tolower(names(hd2021))
hd2021 <- ipeds::recodeDirectory(hd2021)
IvyLeague <- c("186131","190150","166027","130794","215062","182670","217156","190415")
hd2021.ivy <- hd2021[which(hd2021$unitid %in%IvyLeague),]
p <- hd2021.ivy[, c("instnm", "webaddr", "stabbr", "control")]
names(p) <- c("Institution", "Web Address", "State", "Sector")
p## Institution Web Address State
## 638 Yale University https://www.yale.edu/ CT
## 1511 Harvard University www.harvard.edu/ MA
## 1982 Dartmouth College www.dartmouth.edu/ NH
## 2050 Princeton University www.princeton.edu/ NJ
## 2163 Columbia University in the City of New York www.columbia.edu/ NY
## 2169 Cornell University www.cornell.edu/ NY
## 2979 University of Pennsylvania www.upenn.edu/ PA
## 3058 Brown University www.brown.edu/ RI
## Sector
## 638 Private not-for-profit
## 1511 Private not-for-profit
## 1982 Private not-for-profit
## 2050 Private not-for-profit
## 2163 Private not-for-profit
## 2169 Private not-for-profit
## 2979 Private not-for-profit
## 3058 Private not-for-profit
Use the enrollment survey data.
enrollment <- ipeds::ipeds_survey(table = 'EFFY', year = 2021)
names(enrollment) <- tolower(names(enrollment))
enrollment <- enrollment[which(enrollment$unitid %in% c(IvyLeague) ),]
enrollment <- merge(enrollment, hd2021[, c("unitid", "instnm", "control")], by = "unitid", all.x = TRUE, sort = FALSE)
enrollment <- enrollment[which(enrollment[,"effylev"] == 1),] # Level 1 is Undergraduate
p <- enrollment[, c("instnm", "efytotlt", "control")]
names(p) <- c("Institution", "Total Undergraduate Enrollment", "Sector")
p## Institution Total Undergraduate Enrollment
## 1 Yale University 14910
## 28 Harvard University 41024
## 46 Dartmouth College 7177
## 69 Princeton University 8532
## 100 Columbia University in the City of New York 33882
## 110 Cornell University 24594
## 134 University of Pennsylvania 30688
## 146 Brown University 10807
## Sector
## 1 Private not-for-profit
## 28 Private not-for-profit
## 46 Private not-for-profit
## 69 Private not-for-profit
## 100 Private not-for-profit
## 110 Private not-for-profit
## 134 Private not-for-profit
## 146 Private not-for-profit