| Title: | Transforms Institutional Data into Text Files for IPEDS Automated Import/Upload |
|---|---|
| Description: | Starting from user-supplied institutional data, these scripts transform, aggregate, and reshape the information to produce key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes. Final files are saved locally to a location of the user's choice. User-friendly readable files can also be produced for purposes of data review and validation. |
| Authors: | Alison Lanski [aut, cre], Shiloh Fling [aut] |
| Maintainer: | Alison Lanski <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 2.10.0 |
| Built: | 2024-12-09 00:00:59 UTC |
| Source: | CRAN |
Shortcut function to turn a dataframe into key-value pairs
apply_upload_format(df)apply_upload_format(df)
df |
dataframe with upload-compatible column names in upload-compatible order |
a dataframe with one column and upload-compatible rows
Contains sample values for extra cip codes
com_cipscom_cips
A data frame with 3 rows and 10 columns
See complete information by running ?create_dummy_data_com.R
Contains sample values for students
com_studentscom_students
A data frame with 105 rows and 13 columns
See complete information by running ?create_dummy_data_com.R
Creates a prepared dataframe to test scripts related to IPEDS Completions reporting. Produces either a student/degree dataframe or a dataframe of cips previously reported but not in the current student data, depending on the argument you select
create_dummy_data_com(df_type = "student")create_dummy_data_com(df_type = "student")
df_type |
a string: "student" to get the main df needed, "cip" to get extracips |
a dataframe ready for the rest of the comp scripts
The final dataset has 60 students with 105 majors. Students 100-130, 140, 150 have 1 major for 1 degree (journalism) Students 131-139 have 2 majors for 1 degree (journalism + parks) Students 141-149 have 3 majors for 1 degree (journalism, parks, linguistics) Students 151-159 have 3 majors for 2 degrees (1 degree with journalism/parks, 1 MBA degree) Note: 1 student has a faulty birthdate; this will show the warning "1 failed to parse"
Two rows (level 18 linguistics) are flagged as distance education
To fully process completions, we will need to include an example of a CIP code that is a possible major but has no completers and a CIP code in an award level that is possible but has no completers This is the second piece of dummy df produced
set.seed(1892) # one date fails to parse: # this is to provide an example of missing # data which is acceptable to IPEDS students <- create_dummy_data_com() additional_cips <- create_dummy_data_com(df_type = "cip")set.seed(1892) # one date fails to parse: # this is to provide an example of missing # data which is acceptable to IPEDS students <- create_dummy_data_com() additional_cips <- create_dummy_data_com(df_type = "cip")
Creates a prepared dataframe to test scripts related to IPEDS 12 Month Enrollment reporting. Produces either a student dataframe or a dataframe of instructional activity, depending on the argument you select
create_dummy_data_e1d(df_type = "student")create_dummy_data_e1d(df_type = "student")
df_type |
a string: "student" to get the main df needed, "instr" to get instructionalactivity |
a dataframe ready for the rest of the e1d scripts
The final dataset has 100 students 60 UG students (40 FT, 20 PT; 26 seeking degrees, 34 not) UG include: 20 first time, 20 transfer, 20 continuing/returning; 40 Grad Students (10 FT, 30 PT; 24 seeking degrees, 16 not)
For simplicity, only 1 race-ethnicity category is used 5 UG and 5 Grad are set to be fully distance ed 10 UG are set to be partially distance ed
set.seed(1892) student_df <- create_dummy_data_e1d() instr_df <- create_dummy_data_e1d(df_type = "instr")set.seed(1892) student_df <- create_dummy_data_e1d() instr_df <- create_dummy_data_e1d(df_type = "instr")
Creates students and retention dataframes for use in parts A, B, C, D, E, G, H. Student-faculty ratio (part G) will ask for a number when the function is run and does not need to exist here. To create both dataframes, run the function twice with different arguments, and save results into separate objects.
create_dummy_data_ef1(df_type = "students", n = 100)create_dummy_data_ef1(df_type = "students", n = 100)
df_type |
A string with the dummy data requested ("students" for parts A-D & G-H or "retention" for part E) |
n |
A number |
A dataframe ready for the rest of the ef1 scripts
set.seed(1234) #default creates 100 students students <- create_dummy_data_ef1() #change the dataframe retention <- create_dummy_data_ef1(df_type = "retention") #change the population size more_students <- create_dummy_data_ef1(df_type = "students", n = 250)set.seed(1234) #default creates 100 students students <- create_dummy_data_ef1() #change the dataframe retention <- create_dummy_data_ef1(df_type = "retention") #change the population size more_students <- create_dummy_data_ef1(df_type = "students", n = 250)
Creates dummy data for testing the Grad Rates functions
create_dummy_data_gr(n = 100)create_dummy_data_gr(n = 100)
n |
Number of rows of data to synthesize |
a dataframe ready for the rest of the Grad Rates functions
#use this seed to reproduce the dummy data saved to the package set.seed(4567) #default makes 100 students graduated <- create_dummy_data_gr() more_graduated <- create_dummy_data_gr(n = 500)#use this seed to reproduce the dummy data saved to the package set.seed(4567) #default makes 100 students graduated <- create_dummy_data_gr() more_graduated <- create_dummy_data_gr(n = 500)
Dummy data for Grad Rates 200 testing
create_dummy_data_gr200(n = 1000)create_dummy_data_gr200(n = 1000)
n |
A number that will be used as the length of the data frame |
a dataframe ready for the rest of the Grad Rates 200 functions
set.seed(4567) #default creates 1000 students graduates <- create_dummy_data_gr200() more_graduates <- create_dummy_data_gr200(n = 100)set.seed(4567) #default creates 1000 students graduates <- create_dummy_data_gr200() more_graduates <- create_dummy_data_gr200(n = 100)
to do: save this out into the package and make it accessible as package data
create_dummy_data_hr()create_dummy_data_hr()
a dataframe ready for the rest of the hr scripts
set.seed(4567) hr_pop <- create_dummy_data_hr()set.seed(4567) hr_pop <- create_dummy_data_hr()
Creates a prepared dataframe to test scripts related to IPEDS Outcome Measures reporting. Produces either a student/status dataframe
create_dummy_data_om()create_dummy_data_om()
remember: want to save this data out into the package so it's available
a dataframe ready for the rest of the om scripts
The final dataset has 20 students covering most statuses
#creates a very specific population #function does not allow for anything to be updated at time of run #in other words: will always create a fixed-value dataframe dat <- create_dummy_data_om()#creates a very specific population #function does not allow for anything to be updated at time of run #in other words: will always create a fixed-value dataframe dat <- create_dummy_data_om()
Contains sample values for credit hours generated and doctors-professional FTE
e1d_instre1d_instr
A data frame with 1 row and 5 columns
See complete information by running ?create_dummy_data_e1d.R
Contains 100 fictional student records with all required data
e1d_studentse1d_students
A data frame with 100 rows (students) and 14 columns
See complete information by running ?create_dummy_data_e1d.R
This data provides aggregated counts in a dataframe suitable for use in the retention component of the Fall Enrollment survey.
ef1_retentionef1_retention
A data frame with 2 rows and 6 columns
Using the default number of students, this data provides a population that touches most available categories of student reporting. Some columns use only a selection of possible values to reduce complexity.
ef1_studentsef1_students
A data frame with 100 rows and 25 columns
To recreate the saved dataframe exactly, use seed 1234 with 100 students.
Grab institution's UNITID from supplied data to populate missing-data rows
get_ipeds_unitid(df)get_ipeds_unitid(df)
df |
a dataframe with ipeds data and one unitid |
a character unitid
Dummy student data for the Graduation Rates scripts
gr_studentsgr_students
A data frame with 101 rows and 13 columns
Includes only 3 Race/Ethnicity categories [6, 7, 8] for simpler code; one student (a program-switcher) has a 4th category [1] for easy tracking
Contains sample values for students
gr200_studentsgr200_students
A data frame with 1000 rows and 5 columns
See complete information by running ?create_dummy_data_gr200.R
Contains sample values for staff
hr_staffhr_staff
A data frame with 3600 rows and 13 columns
See complete information by running ?create_dummy_data_hr.R
IPEDSuploadables packageTools to assist data formatting for upload to IPEDS surveys
See the README on GitHub or view documentation at the pkgdown site
Make Completions Part A
make_com_part_A(df, extracips = NULL)make_com_part_A(df, extracips = NULL)
df |
A dataframe of student/degree information |
extracips |
A dataframe of cips offered by the institution but not in |
A dataframe with the required IPEDS structure for this survey part
Make Completions Part B
make_com_part_B(df, extracips = NULL)make_com_part_B(df, extracips = NULL)
df |
A dataframe of student/degree information |
extracips |
A dataframe of cips offered by the institution but not in |
A dataframe with the required IPEDS structure for this survey part
Make Completions Part C
make_com_part_C(df)make_com_part_C(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Make Completions Part D
make_com_part_D(df, extracips = NULL)make_com_part_D(df, extracips = NULL)
df |
A dataframe of student/degree information |
extracips |
A dataframe of cips offered by the institution but not in |
A dataframe with the required IPEDS structure for this survey part
Make Completions Part E (gender details)
make_com_part_E(df, ugender, ggender)make_com_part_E(df, ugender, ggender)
df |
A dataframe of student/degree information |
ugender |
A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
ggender |
A boolean: TRUE means you are collecting and able to report "another gender" for graduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
A dataframe with the required IPEDS structure for this survey part
Make 12 Month Enrollment Part A
make_e1d_part_A(df)make_e1d_part_A(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Make 12 Month Enrollment Part B
make_e1d_part_B(df)make_e1d_part_B(df)
df |
A dataframe with summarized credit hours and student information |
A dataframe with the required IPEDS structure for this survey part
Make 12 Month Enrollment Part C
make_e1d_part_C(df)make_e1d_part_C(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Make 12 Month Enrollment Part D (gender details)
make_e1d_part_D(df, ugender, ggender)make_e1d_part_D(df, ugender, ggender)
df |
A dataframe of student/degree information |
ugender |
A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate students, even if you have no (or few) such students. Set as FALSE if necessary |
ggender |
A boolean: TRUE means you are collecting and able to report "another gender" for graduate students, even if you have no (or few) such students. Set as FALSE if necessary |
A dataframe with the required IPEDS structure for this survey part
R/E and Gender counts for dual enrollment (high school students)
make_e1d_part_E(df)make_e1d_part_E(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Flag questions about high school students enrolled for credit
make_e1d_part_F(df)make_e1d_part_F(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Breakdown of students level and demographics; also by designated CIPs in required years
make_ef1_part_A(df, cips = TRUE)make_ef1_part_A(df, cips = TRUE)
df |
A dataframe of student information |
cips |
A logical indicating if part A needs to provide breakdowns by particular CIPs |
A dataframe with the required IPEDS structure for this survey part
Student Counts by Age/gender
make_ef1_part_B(df)make_ef1_part_B(df)
df |
A dataframe of student information |
A dataframe with the required IPEDS structure for this survey part
State of origin for first time students
make_ef1_part_C(df)make_ef1_part_C(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Count of new non-degree students
make_ef1_part_D(df)make_ef1_part_D(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Retention counts
make_ef1_part_E(df)make_ef1_part_E(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Student Faculty Ratio
make_ef1_part_F(df)make_ef1_part_F(df)
df |
A dataframe (either "students" or "retention") as a unitid source |
A dataframe with the required IPEDS structure for this survey part
Distance Ed counts
make_ef1_part_G(df)make_ef1_part_G(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Make Fall Enrollment Part H (gender details)
make_ef1_part_H(df, ugender, ggender)make_ef1_part_H(df, ugender, ggender)
df |
A dataframe of student enrollment information |
ugender |
A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
ggender |
A boolean: TRUE means you are collecting and able to report "another gender" for graduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
A dataframe with the required IPEDS structure for this survey part
Make Graduation Rates Part B
make_gr_part_B(df)make_gr_part_B(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Make Graduation Rates Part C
make_gr_part_C(df)make_gr_part_C(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Make Graduation Rates 200
make_gr200(df)make_gr200(df)
df |
A dataframe of student/degree information |
A dataframe with the required IPEDS structure for this survey part
Part A1 — COUNT of FT INSTRUCTIONAL staff by tenure status, academic rank, and race/ethnicity/gender
make_hr_part_A1(df)make_hr_part_A1(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part A2 — COUNT of FT instructional staff by tenure status, medical school, and function
make_hr_part_A2(df)make_hr_part_A2(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
HR Part B1 — COUNT of FT Non-instructional staff by occupational category
make_hr_part_B1(df)make_hr_part_B1(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part B2 — Full-time non-instructional staff by tenure, medical school, and occupational category
make_hr_part_B2(df)make_hr_part_B2(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part B3 — Full-time non-instructional staff by medical school, and occupational category
make_hr_part_B3(df)make_hr_part_B3(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part D1 — Part-time staff by occupational category
make_hr_part_D1(df)make_hr_part_D1(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part D2 — Graduate assistants by occupational category and race/ethnicity/gender
make_hr_part_D2(df)make_hr_part_D2(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part D3 — Part-time staff by tenure, medical school, and occupational category
make_hr_part_D3(df)make_hr_part_D3(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part D4 — Part-time Non-instructional staff by medical school, and occupational category
make_hr_part_D4(df)make_hr_part_D4(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part G1 — Salaries of INSTRUCTIONAL staff
make_hr_part_G1(df)make_hr_part_G1(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part G2 — Salaries of non-instructional staff
make_hr_part_G2(df)make_hr_part_G2(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part H1 — Full-time new hire instructional staff by tenure status and race/ethnicity/gender
make_hr_part_H1(df)make_hr_part_H1(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Part H2 — New hires by occupational category, Race/Ethnicity/Gender
make_hr_part_H2(df)make_hr_part_H2(df)
df |
a dataframe |
A dataframe with the required IPEDS structure for this survey part
Establishing the Outcome Measures cohorts
make_om_part_A(df)make_om_part_A(df)
df |
A dataframe of student statuses |
A dataframe with the required IPEDS structure for this survey part
Award Status at Four Years after Entry
make_om_part_B(df)make_om_part_B(df)
df |
A dataframe of student statuses |
A dataframe with the required IPEDS structure for this survey part
Award Status at Six Years after Entry
make_om_part_C(df)make_om_part_C(df)
df |
A dataframe of student statuses |
A dataframe with the required IPEDS structure for this survey part
Award Status and Enrollment at Eight Years after Entry
make_om_part_D(df)make_om_part_D(df)
df |
A dataframe of student statuses |
A dataframe with the required IPEDS structure for this survey part
Contains sample values for students
om_studentsom_students
A data frame with 20 rows and 9 columns
See complete information by running ?create_dummy_data_om.R
Some initial recoding for Completions
prep_com_data_frame(df)prep_com_data_frame(df)
df |
a dataframe of student level data or cip information |
A dataframe ready for the make_com scripts
Some initial recoding for Fall Enrollment
prep_ef1_data_frame(df)prep_ef1_data_frame(df)
df |
a dataframe of student level data |
A dataframe ready for the make_ef1 scripts
Some initial recoding for Human Resources
prep_hr_data_frame(df)prep_hr_data_frame(df)
df |
a dataframe |
A dataframe ready for the make_hr scripts
Select correct year, ensure all award levels end up with a column
prep_om_awards(df, award)prep_om_awards(df, award)
df |
A dataframe of student statuses |
award |
A string with the df column to use for processing depending on the OM part |
A dataframe pivoted and prepared for use within the make_om_part functions B-D
Some initial recoding for OutcomeMeasures
prep_om_data_frame(df)prep_om_data_frame(df)
df |
a dataframe of student level data |
A dataframe ready for the make_om scripts
Shortcut function with all steps to provide a Completions report
produce_com_report( df, extracips = NULL, part = "ALL", format = "uploadable", ugender = TRUE, ggender = TRUE )produce_com_report( df, extracips = NULL, part = "ALL", format = "uploadable", ugender = TRUE, ggender = TRUE )
df |
A dataframe set up according to the readme |
extracips |
A dataframe set up according to the readme (optional) |
part |
A string with what part of the report you want to produce: 'all', 'A', etc. |
format |
A string ( |
ugender |
A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
ggender |
A boolean: TRUE means you are collecting and able to report "another gender" for graduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
A txt or csv file at the path of your choice
#entire report produce_com_report(com_students, com_cips) #one part as csv instead of key-value produce_com_report(com_students, com_cips, part = "A", format = "readable")#entire report produce_com_report(com_students, com_cips) #one part as csv instead of key-value produce_com_report(com_students, com_cips, part = "A", format = "readable")
Shortcut function with all steps to provide a 12 Month Enrollment report
produce_e1d_report( df, hrs, part = "ALL", format = "uploadable", ugender = TRUE, ggender = TRUE )produce_e1d_report( df, hrs, part = "ALL", format = "uploadable", ugender = TRUE, ggender = TRUE )
df |
A dataframe set up according to the readme for students |
hrs |
A dataframe set up according to the readme for instructional activity |
part |
A string with what part of the report you want to produce: 'all', 'A', etc. |
format |
A string ( |
ugender |
A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate students, even if you have no (or few) such students. Set as FALSE if necessary |
ggender |
A boolean: TRUE means you are collecting and able to report "another gender" for graduate students, even if you have no (or few) such students. Set as FALSE if necessary |
A txt or csv file at the path of your choice
#entire report produce_e1d_report(e1d_students, e1d_instr) #one part, as csv instead of key-value file produce_e1d_report(e1d_students, part = "A", format = "readable")#entire report produce_e1d_report(e1d_students, e1d_instr) #one part, as csv instead of key-value file produce_e1d_report(e1d_students, part = "A", format = "readable")
Shortcut function with all steps to provide a Fall Enrollment report
produce_ef1_report( students, retention, part = "ALL", include_optional = FALSE, format = "uploadable", ugender = TRUE, ggender = TRUE )produce_ef1_report( students, retention, part = "ALL", include_optional = FALSE, format = "uploadable", ugender = TRUE, ggender = TRUE )
students |
A dataframe set up according to the readme with student data |
retention |
A dataframe set up according to the readme with retention data |
part |
A string with what part of the report you want to produce: 'all', 'A', etc. |
include_optional |
A boolean flag for whether optional parts should be included |
format |
A string ( |
ugender |
A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
ggender |
A boolean: TRUE means you are collecting and able to report "another gender" for graduate completers, even if you have no (or few) such students. Set as FALSE if necessary |
A txt or csv file at the path of your choice
#entire report produce_ef1_report(ef1_students, ef1_retention) #entire report with optional sections produce_ef1_report(ef1_students, ef1_retention, include_optional = TRUE) #one part as csv instead of key-value produce_ef1_report(ef1_students, part = 'D', format = 'readable')#entire report produce_ef1_report(ef1_students, ef1_retention) #entire report with optional sections produce_ef1_report(ef1_students, ef1_retention, include_optional = TRUE) #one part as csv instead of key-value produce_ef1_report(ef1_students, part = 'D', format = 'readable')
Shortcut function with all steps to provide a Graduation Rates report
produce_gr_report( df, part = "ALL", format = "uploadable", ugender = lifecycle::deprecated() )produce_gr_report( df, part = "ALL", format = "uploadable", ugender = lifecycle::deprecated() )
df |
a dataframe set up according to the readme |
part |
a string with what part of the report you want to produce "all", "A1", etc. |
format |
A string ( |
ugender |
'r lifecycle::badge("deprecated")' A boolean: TRUE means you are collecting and able to report "another gender" for undergraduate students, even if you have no (or few) such students. Set as FALSE if necessary. **Starting in 2024-2025, this argument will be ignored by later code.** |
A txt or csv file at the path of your choice
#entire report produce_gr_report(gr_students) #one part in csv format instead of key-value produce_gr_report(gr_students, part = "B", format = "readable")#entire report produce_gr_report(gr_students) #one part in csv format instead of key-value produce_gr_report(gr_students, part = "B", format = "readable")
Shortcut function with all steps to provide a Grad Rates 200 report
produce_gr200_report(df, format = "uploadable")produce_gr200_report(df, format = "uploadable")
df |
a dataframe set up according to the readme |
format |
A string ( |
A txt or csv file at the path of your choice
#entire report produce_gr200_report(gr200_students)#entire report produce_gr200_report(gr200_students)
Shortcut function with all steps to provide a Human Resources report
produce_hr_report(df, part = "all", format = "uploadable")produce_hr_report(df, part = "all", format = "uploadable")
df |
a dataframe set up according to the readme |
part |
a string with what part of the report you want to produce "all", "A1", etc. |
format |
A string ( |
A txt or csv file at the path of your choice
#entire report produce_hr_report(hr_staff) #subsection with csv output instead of key-value txt produce_hr_report(hr_staff, part = "A1", format = "readable")#entire report produce_hr_report(hr_staff) #subsection with csv output instead of key-value txt produce_hr_report(hr_staff, part = "A1", format = "readable")
Shortcut function with all steps to provide an Outcome Measures report
produce_om_report(df, part = "ALL", format = "uploadable")produce_om_report(df, part = "ALL", format = "uploadable")
df |
A dataframe set up according to the readme |
part |
A string with what part of the report you want to produce: 'all', 'A', etc. |
format |
A string ( |
A txt or csv file at the path of your choice
#entire report produce_om_report(om_students) #one part with csv output instead of key-value produce_om_report(om_students, part = 'A', format = 'readable')#entire report produce_om_report(om_students) #one part with csv output instead of key-value produce_om_report(om_students, part = 'A', format = 'readable')
Use this function to create a key-value pair uploadable file
from your own prepared dataframes, instead of using a different (provided)
produce function. Your dataframes must be prepped to match final
submission requirements as laid out by IPEDS (or whatever survey you will
use this for. Use this function for one survey at a time, and add a
separate dataframe for each part to the ... argument. See vignette
for more details.
produce_other_report(..., survey = "MySurvey", part = "AllParts")produce_other_report(..., survey = "MySurvey", part = "AllParts")
... |
dataframes (one for each survey part, in order) |
survey |
string with the survey name you'd like in your filename |
part |
string with the part name (subname) you'd like your file name |
txt file on your computer with the title [survey]_[part]_[today's date].txt
You must name the arguments for survey and part if using
non-default value. If the arguments are unnamed, the function will assume
their values are additional dataframes.
#With built-in R data produce_other_report(mtcars[1:5,], iris[1:5,], ToothGrowth[1:5,], survey = 'FakeSurvey') #Will not execute properly (argument unnamed) #produce_other_report(mtcars[1:5,], iris[1:5,], ToothGrowth[1:5,], 'FakeSurvey')#With built-in R data produce_other_report(mtcars[1:5,], iris[1:5,], ToothGrowth[1:5,], survey = 'FakeSurvey') #Will not execute properly (argument unnamed) #produce_other_report(mtcars[1:5,], iris[1:5,], ToothGrowth[1:5,], 'FakeSurvey')
Set the path for where the reports will be saved to.
set_report_path()set_report_path()
path
Table of data requirements for Completions starting dataframe
specs_COMspecs_COM
A data frame with 21 rows and 4 columns
Table of data requirements for 12 Month Enrollment starting dataframes
specs_E1Dspecs_E1D
A data frame with 19 rows and 4 columns
Table of data requirements for Fall Enrollment starting dataframes
specs_EF1specs_EF1
A data frame with 23 rows and 4 columns
Table of data requirements for Graduation Rates starting dataframe
specs_GRspecs_GR
A data frame with 13 rows and 3 columns
Table of data requirements for Grad Rates 200 starting dataframe
specs_GR200specs_GR200
A data frame with 5 rows and 3 columns
Table of data requirements for HR starting dataframe
specs_HRspecs_HR
A data frame with 13 rows and 3 columns
Table of data requirements for OM starting dataframe
specs_OMspecs_OM
A data frame with 9 rows and 3 columns
Write the prepared data to a txt file in key-value format
write_report(..., survey, part, output_path)write_report(..., survey, part, output_path)
... |
dataframes (one for each survey part, in order) |
survey |
a string (which [IPEDS] survey) |
part |
a string (which upload part of the survey) |
output_path |
a file path (where the file should be saved) |
a txt file (at the path location)
All arguments for this function are required and must be named. Dataframes must have the key as the column name (with appropriate capitalization) and the value in the cells
Write the prepared data to a csv file
write_report_csv(df, survey, part, output_path)write_report_csv(df, survey, part, output_path)
df |
a dataframe (prepared via the 'make' scripts) |
survey |
a string (which IPEDS survey) |
part |
a string (which upload part of the survey) |
output_path |
a path (which folder the report should go in) |
a csv file (at the path location)
All arguments for this function are required. The dataframe must have the key as the column name (with appropriate capitalization) and the value in the cells