midfieldr

Overview

midfieldr is an R package that supplies tools for working with longitudinal undergraduate records from the MIDFIELD database (Ohland 2023) or similarly structured data tables. These tools help you develop credible populations, subset records to calculate quantitative metrics, and prepare results for dissemination.

• completion_status() Identifies students completing a program in a timely manner.
• data_sufficiency() Identifies records to exclude due to insufficient data.
• filter_programs() Helps in finding 6-digit program codes.
• initialize_fye_proxies() Conditions data for imputing starting majors of First-Year Engineering (FYE) students.
• order_multiway() Conditions multiway data for Cleveland multiway charts.
• timely_term() Determines the latest term by which program completion would be considered timely.
• undergrad_term_id() Distinguishes between undergraduate terms (to retain) and post-baccalaureate terms (to exclude).

Installation

# Install from CRAN:
install.packages("midfieldr")

# Or install the development version from GitHub:
pak::pak("MIDFIELDR/midfieldr")

# Also install the midfielddata package for practice data
install.packages("midfielddata",
  repos = "https://MIDFIELDR.github.io/drat/",
  type = "source"
)

Usage

Data that load with midfieldr include cip for program codes and small samples of the four data tables (prefix toy_) for terse examples.

library("midfieldr")
library("data.table")

# Assign preferred names to example tables
student <- copy(toy_student)
term <- copy(toy_term)
course <- copy(toy_course)
degree <- copy(toy_degree)

# Pull IDs of degree-seeking students
DT <- student[, .(mcid)]
DT
#>                mcid
#>              <char>
#>   1: MCID3111142897
#>   2: MCID3111157634
#>   3: MCID3111158724
#>  ---               
#> 349: MCID3112868072
#> 350: MCID3112869843
#> 351: MCID3112885339

# Categorize by data sufficiency
DT <- timely_term(DT, midf_table = term)
DT <- data_sufficiency(DT, midf_table = term)
# -- result summary
DT[, .N, by = "sufficiency"][order(-N)]
#>    sufficiency     N
#>         <char> <int>
#> 1:   satisfied   240
#> 2:  fail-upper    99
#> 3:  fail-lower    12

# Subset to obtain baseline population
population <- DT[sufficiency == "satisfied", .(mcid)]
population
#>                mcid
#>              <char>
#>   1: MCID3111198701
#>   2: MCID3111208924
#>   3: MCID3111213539
#>  ---               
#> 238: MCID3112592592
#> 239: MCID3112593368
#> 240: MCID3112617577

# Inner join to filter records to match the 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]

# Identify post-baccalaureate terms to exclude
term <- undergrad_term_id(term, midf_table = degree)
course <- undergrad_term_id(course, midf_table = degree)
degree <- undergrad_term_id(degree, midf_table = degree)
# -- result summary
term[, .N, by = "term_id"][order(-term_id)]
#>      term_id     N
#>       <char> <int>
#> 1: undergrad  1330
#> 2: post-bacc    17

# Subset to obtain baseline records
term <- term[term_id == "undergrad"]
course <- course[term_id == "undergrad"]
degree <- degree[term_id == "undergrad"]

# Obtain 6-digit CIP codes for Engineering (14), Psychology (42),
# and Business (52)
programs <- filter_programs(cip, c("^14", "^42", "^52"))
programs <- programs[, .(cip6name, cip6)]

# Construct the programs table
programs[, program := fcase(
  cip6 %like% "^14", "Engineering",
  cip6 %like% "^42", "Psychology",
  cip6 %like% "^52", "Business"
)]
programs <- programs[, .(cip6, program)]
programs
#>        cip6     program
#>      <char>      <char>
#>   1: 140101 Engineering
#>   2: 140102 Engineering
#>   3: 140201 Engineering
#>  ---                   
#> 173: 522001    Business
#> 174: 522101    Business
#> 175: 529999    Business

# Categorize completion status
pop <- copy(population)
pop <- timely_term(pop, midf_table = term)
pop <- completion_status(pop, midf_table = degree)
# -- result summary
pop[, .N, by = "completion"][order(-N)]
#>    completion     N
#>        <char> <int>
#> 1:     timely   161
#> 2:       <NA>    71
#> 3:       late     8

# Filter population for timely completion
pop <- unique(pop[completion == "timely", .(mcid)])
pop
#>                mcid
#>              <char>
#>   1: MCID3111213539
#>   2: MCID3111213856
#>   3: MCID3111254225
#>  ---               
#> 159: MCID3112587501
#> 160: MCID3112592592
#> 161: MCID3112593368

# Join degree CIP codes
DT <- degree[, .(mcid, cip6)][pop, on = "mcid"]

# Inner join to filter graduates by program
DT <- programs[, .(cip6, program)][DT, on = "cip6", nomatch = NULL]
DT <- DT[, .(mcid, program, cip6 = NULL)]
DT
#>               mcid     program
#>             <char>      <char>
#>  1: MCID3111254412 Engineering
#>  2: MCID3111262210 Engineering
#>  3: MCID3111265287  Psychology
#> ---                           
#> 53: MCID3112467463  Psychology
#> 54: MCID3112587501  Psychology
#> 55: MCID3112592592    Business

# Join demographics
DT <- student[, .(mcid, sex)][DT, on = "mcid"]

# Group and summarize timely graduates
DT <- DT[, .(grad = .N), by = c("program", "sex")]
DT[order(program, sex)]
#>        program    sex  grad
#>         <char> <char> <int>
#> 1:    Business Female     8
#> 2:    Business   Male    12
#> 3: Engineering Female     3
#> 4: Engineering   Male    16
#> 5:  Psychology Female    12
#> 6:  Psychology   Male     4

Acknowledgments

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/.