How to produce other key-value uploads
Source:vignettes/howto_use_autoformat.Rmd
howto_use_autoformat.RmdWhen to use this How-To
Follow the instructions if you want to …
- Do your own data recoding and aggregation for IPEDS submission
- Get help converting your data into a key-value pair format
- Get help combining parts with different numbers of columns
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
- Download/Open the IPEDS Import Spec for a particular survey and institution-type on the IPEDS Survey Materials instructions page. Between reporting cycles, this page may be down: consult the archive to see previous specs.
- Use the “Key Value Pair File” instructions to prep your data. This will be the second part of the document; the first part is for fixed-width files.
- Create one dataframe for each survey part using the exact IPEDS FieldIDs as column names and using only the values described in the table of specs and the additional information in the file’s appendix (scroll down). Follow the same order of columns and parts.
-
Use the package function
produce_other_reportwith your files.
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 |