Overview
An R package that supplies tools for working with longitudinal undergraduate records from the MIDFIELD database—or similarly structured data tables—in the following areas.
Programs
- filter_programs() helps you find program names and CIP codes.
Records and population
- timely_term() estimates timely completion terms.
- data_sufficiency() identifies IDs to exclude due to insufficient data.
- post_bacc_terms() identifies rows with post-baccalaureate terms to exclude.
Blocs
- completion_status() labels program completion as timely, late, or NA.
Special conditioning
- prep_fye_mice() conditions data for imputing starting majors of FYE students.
- order_multiway() conditions data for Cleveland multiway charts.
Convenience
- select_basic_cols() minimizes the number of columns viewed for interactive sessions.
- look_at() wraps base::str() with our preferred arguments.
library("midfieldr")
packageVersion("midfieldr")
#> [1] '1.0.3.9021'
Sys.Date()
#> [1] "2026-07-13"Installation
Install from CRAN with:
install.packages("midfieldr")To get a bug fix or preview a new feature, you can install the development version from GitHub.
# install.packages("pak")
pak::pak("MIDFIELDR/midfieldr")midfieldr is designed to operate on the MIDFIELD database (Ohland 2023) or similarly structured data such as the MIDFIELD sample in midfielddata, an R data package you can download from GitHub.
install.packages("midfielddata",
repos = "https://MIDFIELDR.github.io/drat/",
type = "source"
)For information on accessing the MIDFIELD database for research, contact the American Society for Engineering Education (ASEE).
Usage
We illustrate usage with a small sample that loads with midfieldr for use in such examples. These data frames (toy_student, toy_term, toy_course, toy_degree) have the same structure as the tables in midfielddata.
library("midfieldr")
library("data.table")
# Assign data tables to the expected names
student <- copy(toy_student)
term <- copy(toy_term)
course <- copy(toy_course)
degree <- copy(toy_degree)
look_at(term)
#> Classes 'data.table' and 'data.frame': 1821 obs. of 13 variables:
#> $ mcid : chr "MCID3111142897" "MCID3111157634" "MCID311115763"..
#> $ term : chr "19881" "19881" "19883" "19891" ...
#> $ cip6 : chr "400801" "240102" "040201" "040201" ...
#> $ institution : chr "Institution B" "Institution J" "Institution J" "..
#> $ level : chr "01 First-year" "01 First-year" "01 First-year" "..
#> $ standing : chr "Good Standing" "Good Standing" "Good Standing" "..
#> $ coop : chr "No" "No" "No" "No" ...
#> $ hours_term : num 9 13 10 18 15 14 3 13 16 17 ...
#> $ hours_term_attempt : num 9 13 10 18 15 14 4 13 16 17 ...
#> $ hours_cumul : num 9 13 23 41 56 14 17 13 29 46 ...
#> $ hours_cumul_attempt: num 9 13 23 41 56 14 18 13 29 46 ...
#> $ gpa_term : num 3.57 2.1 2.75 2.28 1.6 2.1 2 2.16 3 2.23 ...
#> $ gpa_cumul : num 3.57 2.1 2.38 2.34 2.14 2.1 2.08 2.16 2.62 2.48 ...
# Identify post-baccalaureate terms
term <- post_bacc_terms(term, midfield_table = degree)
course <- post_bacc_terms(course, midfield_table = degree)
degree <- post_bacc_terms(degree, midfield_table = degree)
look_at(term)
#> Classes 'data.table' and 'data.frame': 1821 obs. of 15 variables:
#> $ mcid : chr "MCID3111142897" "MCID3111157634" "MCID311115763"..
#> $ term : chr "19881" "19881" "19883" "19891" ...
#> $ cip6 : chr "400801" "240102" "040201" "040201" ...
#> $ institution : chr "Institution B" "Institution J" "Institution J" "..
#> $ level : chr "01 First-year" "01 First-year" "01 First-year" "..
#> $ standing : chr "Good Standing" "Good Standing" "Good Standing" "..
#> $ coop : chr "No" "No" "No" "No" ...
#> $ hours_term : num 9 13 10 18 15 14 3 13 16 17 ...
#> $ hours_term_attempt : num 9 13 10 18 15 14 4 13 16 17 ...
#> $ hours_cumul : num 9 13 23 41 56 14 17 13 29 46 ...
#> $ hours_cumul_attempt: num 9 13 23 41 56 14 18 13 29 46 ...
#> $ gpa_term : num 3.57 2.1 2.75 2.28 1.6 2.1 2 2.16 3 2.23 ...
#> $ gpa_cumul : num 3.57 2.1 2.38 2.34 2.14 2.1 2.08 2.16 2.62 2.48 ...
#> $ first_degree_term : chr NA NA NA NA ...
#> $ term_cluster : chr "pre-degree" "pre-degree" "pre-degree" "pre-degr"..
# Exclude rows after the first degree term
term <- term[term_cluster != "post-first-degree"]
course <- course[term_cluster != "post-first-degree"]
degree <- degree[term_cluster != "post-first-degree"]
look_at(term)
#> Classes 'data.table' and 'data.frame': 1802 obs. of 15 variables:
#> $ mcid : chr "MCID3111142897" "MCID3111157634" "MCID311115763"..
#> $ term : chr "19881" "19881" "19883" "19891" ...
#> $ cip6 : chr "400801" "240102" "040201" "040201" ...
#> $ institution : chr "Institution B" "Institution J" "Institution J" "..
#> $ level : chr "01 First-year" "01 First-year" "01 First-year" "..
#> $ standing : chr "Good Standing" "Good Standing" "Good Standing" "..
#> $ coop : chr "No" "No" "No" "No" ...
#> $ hours_term : num 9 13 10 18 15 14 3 13 16 17 ...
#> $ hours_term_attempt : num 9 13 10 18 15 14 4 13 16 17 ...
#> $ hours_cumul : num 9 13 23 41 56 14 17 13 29 46 ...
#> $ hours_cumul_attempt: num 9 13 23 41 56 14 18 13 29 46 ...
#> $ gpa_term : num 3.57 2.1 2.75 2.28 1.6 2.1 2 2.16 3 2.23 ...
#> $ gpa_cumul : num 3.57 2.1 2.38 2.34 2.14 2.1 2.08 2.16 2.62 2.48 ...
#> $ first_degree_term : chr NA NA NA NA ...
#> $ term_cluster : chr "pre-degree" "pre-degree" "pre-degree" "pre-degr"..
# Begin refining the population
DT <- term[, .(mcid)]
DT <- unique(DT)
DT
#> mcid
#> <char>
#> 1: MCID3111142897
#> 2: MCID3111157634
#> 3: MCID3111158724
#> ---
#> 349: MCID3112868072
#> 350: MCID3112869843
#> 351: MCID3112885339
# Build data sufficiency data frame
DT <- timely_term(DT, midfield_table = term)
DT <- data_sufficiency(DT, midfield_table = term)
DT
#> mcid term_i level_i adj_span timely_term institution
#> <char> <char> <char> <num> <char> <char>
#> 1: MCID3111142897 19881 01 First-year 6 19933 Institution B
#> 2: MCID3111157634 19881 01 First-year 6 19933 Institution J
#> 3: MCID3111158724 19881 01 First-year 6 19933 Institution J
#> ---
#> 349: MCID3112868072 20171 01 First-year 6 20223 Institution B
#> 350: MCID3112869843 20173 01 First-year 6 20231 Institution B
#> 351: MCID3112885339 20181 01 First-year 6 20233 Institution B
#> lower_limit upper_limit data_sufficiency
#> <char> <char> <char>
#> 1: 19881 20181 exclude-lower
#> 2: 19881 20096 exclude-lower
#> 3: 19881 20096 exclude-lower
#> ---
#> 349: 19881 20181 exclude-upper
#> 350: 19881 20181 exclude-upper
#> 351: 19881 20181 exclude-upper
# Retain rows with sufficient institutional data
population <- DT[data_sufficiency == "include", .(mcid)]
population <- unique(population)
population
#> mcid
#> <char>
#> 1: MCID3111198701
#> 2: MCID3111208924
#> 3: MCID3111213539
#> ---
#> 238: MCID3112592592
#> 239: MCID3112593368
#> 240: MCID3112617577
# Inner join to filter data tables to match population
student <- population[student, on = "mcid", nomatch = NULL]
term <- population[term, on = "mcid", nomatch = NULL]
course <- population[course, on = "mcid", nomatch = NULL]
degree <- population[degree, on = "mcid", nomatch = NULL]
look_at(term)
#> Classes 'data.table' and 'data.frame': 1330 obs. of 15 variables:
#> $ mcid : chr "MCID3111198701" "MCID3111198701" "MCID311120892"..
#> $ term : chr "19891" "19893" "19891" "19893" ...
#> $ cip6 : chr "240102" "520301" "240102" "140102" ...
#> $ institution : chr "Institution J" "Institution J" "Institution J" "..
#> $ level : chr "01 First-year" "01 First-year" "01 First-year" "..
#> $ standing : chr "Good Standing" "Academic Warning" "Good Standin"..
#> $ coop : chr "No" "No" "No" "No" ...
#> $ hours_term : num 16 10 13 6 5 5 15 14 15 17 ...
#> $ hours_term_attempt : num 16 13 13 11 5 5 15 14 15 17 ...
#> $ hours_cumul : num 16 26 13 19 24 29 15 29 44 61 ...
#> $ hours_cumul_attempt: num 16 29 13 24 24 29 15 29 44 61 ...
#> $ gpa_term : num 2.08 1.87 2 1.85 1.88 1 3 2.67 2.55 2.84 ...
#> $ gpa_cumul : num 2.08 2 2 1.95 1.94 1.78 3 2.84 2.74 2.77 ...
#> $ first_degree_term : chr NA NA NA NA ...
#> $ term_cluster : chr "pre-degree" "pre-degree" "pre-degree" "pre-degr"..
# Choose a minimum set of columns
student <- select_basic_cols(student)
term <- select_basic_cols(term)
course <- select_basic_cols(course)
degree <- select_basic_cols(degree)
student
#> mcid race sex
#> <char> <char> <char>
#> 1: MCID3111198701 White Male
#> 2: MCID3111208924 White Male
#> 3: MCID3111213539 White Female
#> ---
#> 238: MCID3112592592 White Male
#> 239: MCID3112593368 White Female
#> 240: MCID3112617577 International Female
term
#> mcid term cip6 institution level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111198701 19891 240102 Institution J 01 First-year
#> 2: MCID3111198701 19893 520301 Institution J 01 First-year
#> 3: MCID3111208924 19891 240102 Institution J 01 First-year
#> ---
#> 1328: MCID3112593368 20151 090101 Institution B 03 Third-year
#> 1329: MCID3112593368 20153 090101 Institution B 04 Fourth-year
#> 1330: MCID3112617577 20123 240199 Institution B 01 First-year
course
#> mcid term_course abbrev number
#> <char> <char> <char> <char>
#> 1: MCID3111198701 19891 ACCT 1504
#> 2: MCID3111198701 19891 CHEM 1015
#> 3: MCID3111198701 19891 CHEM 1025
#> ---
#> 6378: MCID3112617577 20123 LING 1000
#> 6379: MCID3112617577 20123 PSYC 1001
#> 6380: MCID3112617577 20123 WRTG 1150
degree
#> mcid term_degree cip6
#> <char> <char> <char>
#> 1: MCID3111213539 19923 030103
#> 2: MCID3111213856 19911 261399
#> 3: MCID3111254225 19923 270101
#> ---
#> 167: MCID3112587501 20141 420101
#> 168: MCID3112592592 20153 520201
#> 169: MCID3112593368 20153 090101Acknowledgments
The development of midfieldr and midfielddata was supported by the US National Science Foundation through grant numbers 1545667 and 2142087.
References
Ohland, Matthew. 2023. MIDFIELD, 2004–2023. https://midfield.online/.