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How to produce other key-value uploads

When to use this How-To

Follow the instructions if you want to …

If you want the package to help you with aggregation and (some) recoding, look at the “setup” vignettes for supported uploads

Note: the produce_other_report function can be used to prepare any key-value pair file for an automated submission process (IPEDS or non-IPEDS)

General Process

If you need assistance understanding what goes into the upload file, contact IPEDS for advice.

Data prep example: Admissions

For a school WITH the ability to report “Another Gender” in the 2023-2024 reporting cycle
Read code comments in Part B for changes to make if you cannot report “Another Gender”

This provides a start-to-finish example of preparing an admissions submission based on sample data. Always check your results after you upload your txt file to the IPEDS submission portal.

Start with institutional data for applicants

#load packages
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.4.1
library(magrittr)
#> Warning: package 'magrittr' was built under R version 4.4.1
library(IPEDSuploadables)
#create data
adm_dat <- data.frame(StudentId = seq(1:24),
                      FtPt = c(rep('FT', 23), 'PT'),
                      Sex = rep(c("M", "F"), 12),
                      GenderDetail = c(rep(c("M", "F"), 11), "U", "A"),
                      Admit = c(rep(1, 16), rep(0, 8)),
                      Enroll = c(rep(1, 12), rep(0, 12)),
                      SAT = c(rep(1, 8), rep(0, 16)),
                      SAT_V = c(500, 560, 600, 660, 700, 760, 800, 800, rep(NA, 16)),
                      SAT_M = c(400, 460, 500, 560, 600, 660, 700, 700, rep(NA, 16)),
                      ACT = c(rep(0, 8), rep(1, 16)),
                      ACT_CMP = c(rep(NA, 8), 32, 32, 31, 31, 30, 30, 29, 29, 28, 28, 27, 27, 26, 26, 25, 25)
                      )
StudentId FtPt Sex GenderDetail Admit Enroll SAT SAT_V SAT_M ACT ACT_CMP
1 FT M M 1 1 1 500 400 0 NA
2 FT F F 1 1 1 560 460 0 NA
3 FT M M 1 1 1 600 500 0 NA
4 FT F F 1 1 1 660 560 0 NA
5 FT M M 1 1 1 700 600 0 NA
6 FT F F 1 1 1 760 660 0 NA
7 FT M M 1 1 1 800 700 0 NA
8 FT F F 1 1 1 800 700 0 NA
9 FT M M 1 1 0 NA NA 1 32
10 FT F F 1 1 0 NA NA 1 32
11 FT M M 1 1 0 NA NA 1 31
12 FT F F 1 1 0 NA NA 1 31
13 FT M M 1 0 0 NA NA 1 30
14 FT F F 1 0 0 NA NA 1 30
15 FT M M 1 0 0 NA NA 1 29
16 FT F F 1 0 0 NA NA 1 29
17 FT M M 0 0 0 NA NA 1 28
18 FT F F 0 0 0 NA NA 1 28
19 FT M M 0 0 0 NA NA 1 27
20 FT F F 0 0 0 NA NA 1 27
21 FT M M 0 0 0 NA NA 1 26
22 FT F F 0 0 0 NA NA 1 26
23 FT M U 0 0 0 NA NA 1 25
24 PT F A 0 0 0 NA NA 1 25

Prepare Part A - General Questions (not from sample data)

#### PART A: General Admissions Criteria
partA <- data.frame(UNITID = 999999,
                    SURVSECT = 'ADM',
                    PART = 'A',
                    ADMCON1 = 2, #GPA
                    ADMCON2 = 1, #Rank
                    ADMCON3 = 1, #Record
                    ADMCON4 = 2, #HS grad
                    ADMCON5 = 1, #Recs
                    ADMCON6 = 3, #Portfolio
                    ADMCON7 = 5, #SAT/ACT  #1 or 5 = have to do part C
                    ADMCON8 = 2, #TOEFL
                    ADMCON9 = 3, #other test
                    ADMCON10 = 2, #work exp
                    ADMCON11 = 1, #personal statement
                    ADMCON12 = 3 #legacy
                    )
UNITID SURVSECT PART ADMCON1 ADMCON2 ADMCON3 ADMCON4 ADMCON5 ADMCON6 ADMCON7 ADMCON8 ADMCON9 ADMCON10 ADMCON11 ADMCON12
999999 ADM A 2 1 1 2 1 3 5 2 3 2 1 3

Prepare Part B - Student Counts (from sample data)

##### PART B: Admission Counts; FirstTime UG only
partB <- data.frame(UNITID = 999999,
                    SURVSECT = 'ADM',
                    PART = 'B',
                    APPLCNM = nrow(adm_dat[adm_dat$GenderDetail == 'M', ]),
                    APPLCNW = nrow(adm_dat[adm_dat$GenderDetail == 'F', ]),
                    APPLCNT = nrow(adm_dat),
                    ADMSSNM = nrow(adm_dat[adm_dat$GenderDetail == 'M' & 
                                             adm_dat$Admit == 1,]),
                    ADMSSNW = nrow(adm_dat[adm_dat$GenderDetail == 'F' & 
                                             adm_dat$Admit == 1,]),
                    ADMSSNT = nrow(adm_dat[adm_dat$Admit == 1,]),
                    ENRLFTM = nrow(adm_dat[adm_dat$GenderDetail == 'M' & 
                                             adm_dat$Enroll == 1 & 
                                             adm_dat$FtPt == 'FT', ]),
                    ENRLFTW = nrow(adm_dat[adm_dat$GenderDetail == 'F' & 
                                             adm_dat$Enroll == 1 & 
                                             adm_dat$FtPt == 'FT', ]),
                    ENRLFTT = nrow(adm_dat[adm_dat$Enroll == 1 & 
                                             adm_dat$FtPt == 'FT', ]),
                    ENRLPTM = nrow(adm_dat[adm_dat$GenderDetail == 'M' & 
                                             adm_dat$Enroll == 1 & 
                                             adm_dat$FtPt == 'PT', ]),
                    ENRLPTW = nrow(adm_dat[adm_dat$GenderDetail == 'F' & 
                                             adm_dat$Enroll == 1 & 
                                             adm_dat$FtPt == 'PT', ]),
                    ENRLPTT = nrow(adm_dat[adm_dat$Enroll == 1 & 
                                             adm_dat$FtPt == 'PT', ]),
                    #can you report another gender? 1 = yes, 2 = no
                    ADMGU01 = 1,
                    #if you said 1, keep the code below as-is
                    #if you said 2, remove code, and assign -2 to all 4 columns
                    APPLCNAG = nrow(adm_dat[adm_dat$GenderDetail == 'A', ]),
                    ADMSSNAG = nrow(adm_dat[adm_dat$GenderDetail == 'A' & 
                                              adm_dat$Admit == 1, ]),
                    ENRLFTAG = nrow(adm_dat[adm_dat$GenderDetail == 'A' & 
                                              adm_dat$Enroll == 1 & 
                                              adm_dat$FtPt == 'FT', ]),
                    ENRLPTAG = nrow(adm_dat[adm_dat$GenderDetail == 'A' & 
                                              adm_dat$Enroll == 1 & 
                                              adm_dat$FtPt == 'PT', ])
                    )

#mask data if you ARE able to report "Another Gender", 
# but the count is below 5 in any category
#if you are NOT able to report "Another Gender", 
# this code will not change your data, even if you run it
if((partB$APPLCNAG < 5 | partB$ADMSSNAG < 5 | 
    partB$ENRLFTAG < 5 | partB$ENRLPTAG < 5) & partB$ADMGU01 == 1){
  partB$ADMGU01 <- 3
  partB$APPLCNAG <- -2
  partB$ADMSSNAG <- -2
  partB$ENRLFTAG <- -2
  partB$ENRLPTAG <- -2
}
UNITID SURVSECT PART APPLCNM APPLCNW APPLCNT ADMSSNM ADMSSNW ADMSSNT ENRLFTM ENRLFTW ENRLFTT ENRLPTM ENRLPTW ENRLPTT ADMGU01 APPLCNAG ADMSSNAG ENRLFTAG ENRLPTAG
999999 ADM B 11 11 24 8 8 16 6 6 12 0 0 0 3 -2 -2 -2 -2

Part C: Test Scores (from sample data)

#### PART C: Test Scores

adm_enr <- adm_dat %>%
  filter(Enroll == 1)

#in this example we are not supplying ACT test percentiles by subject
partC <- data.frame(UNITID = 999999,
                    SURVSECT = 'ADM',
                    PART = 'C',
                    SATINUM = nrow(adm_enr[adm_enr$SAT == 1, ]),
                    SATIPCT = round(nrow(adm_enr[adm_enr$SAT == 1, ])*100/nrow(adm_enr), 0),
                    ACTNUM = nrow(adm_enr[adm_enr$ACT == 1,]),
                    ACTPCT = round(nrow(adm_enr[adm_enr$ACT == 1,])*100/nrow(adm_enr), 0),
                    SATVR25 = quantile(adm_enr$SAT_V[!is.na(adm_enr$SAT_V)], .25),
                    SATVR75 = quantile(adm_enr$SAT_V[!is.na(adm_enr$SAT_V)], .75),
                    SATMT25 = quantile(adm_enr$SAT_M[!is.na(adm_enr$SAT_M)], .25),
                    SATMT75 = quantile(adm_enr$SAT_M[!is.na(adm_enr$SAT_M)], .75),
                    ACTCM25 = quantile(adm_enr$ACT_CMP[!is.na(adm_enr$ACT_CMP)], .25),
                    ACTCM75 = quantile(adm_enr$ACT_CMP[!is.na(adm_enr$ACT_CMP)], .75),
                    ACTEN25 = -2,
                    ACTEN75 = -2,
                    ACTMT25 = -2,
                    ACTMT75 = -2,
                    SATVR50 = quantile(adm_enr$SAT_V[!is.na(adm_enr$SAT_V)], .5),
                    SATMT50 = quantile(adm_enr$SAT_M[!is.na(adm_enr$SAT_M)], .5),
                    ACTCM50 = quantile(adm_enr$ACT_CMP[!is.na(adm_enr$ACT_CMP)], .5),
                    ACTEN50 = -2,
                    ACTMT50 = -2)

#mask data for an exam if you have fewer than 5 students counted for it
if(partC$SATINUM < 5){
  partC <- partC %>%
    mutate(across(c("SATVR25", "SATVR75", "SATVR50",
                    "SATMT25", "SATMT75", "SATMT50"), 
                  function(x) -2))
}
if(partC$ACTNUM < 5){
  partC <- partC %>%
    mutate(across(c("ACTCM25", "ACTCM75", "ACTCM50", 
                    "ACTMT25", "ACTMT75", "ACTMT50", 
                    "ACTEN25", "ACTEN75", "ACTEN50"), 
                  function(x) -2))
}
UNITID SURVSECT PART SATINUM SATIPCT ACTNUM ACTPCT SATVR25 SATVR75 SATMT25 SATMT75 ACTCM25 ACTCM75 ACTEN25 ACTEN75 ACTMT25 ACTMT75 SATVR50 SATMT50 ACTCM50 ACTEN50 ACTMT50
999999 ADM C 8 67 4 33 590 770 490 670 -2 -2 -2 -2 -2 -2 680 580 -2 -2 -2

Use this package to convert those dataframes into a single uploadable txt file

The file format is shown below, but the package will actually save this as a txt file at the location of your choice.

produce_other_report(partA, partB, partC, survey = "Admissions")
UNITID=999999,SURVSECT=ADM,PART=A,ADMCON1=2,ADMCON2=1,ADMCON3=1,ADMCON4=2,ADMCON5=1,ADMCON6=3,ADMCON7=5,ADMCON8=2,ADMCON9=3,ADMCON10=2,ADMCON11=1,ADMCON12=3
UNITID=999999,SURVSECT=ADM,PART=B,APPLCNM=11,APPLCNW=11,APPLCNT=24,ADMSSNM=8,ADMSSNW=8,ADMSSNT=16,ENRLFTM=6,ENRLFTW=6,ENRLFTT=12,ENRLPTM=0,ENRLPTW=0,ENRLPTT=0,ADMGU01=3,APPLCNAG=-2,ADMSSNAG=-2,ENRLFTAG=-2,ENRLPTAG=-2
UNITID=999999,SURVSECT=ADM,PART=C,SATINUM=8,SATIPCT=67,ACTNUM=4,ACTPCT=33,SATVR25=590,SATVR75=770,SATMT25=490,SATMT75=670,ACTCM25=-2,ACTCM75=-2,ACTEN25=-2,ACTEN75=-2,ACTMT25=-2,ACTMT75=-2,SATVR50=680,SATMT50=580,ACTCM50=-2,ACTEN50=-2,ACTMT50=-2

Upload your final txt file to the IPEDS website

This step is no different than any other upload.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.