Overview
Provides tools in R for working with undergraduate, longitudinal, student-level records modeled on the MIDFIELD database.
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filter_programs()chooses rows of program data based on search terms. -
select_records()chooses columns required by midfieldr functions.
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post_bacc_terms()identifies rows of post-baccalaureate terms to exclude. -
timely_term()estimates a student’s timely graduation term. -
data_sufficiency()identifies rows to exclude due to insufficient data. -
completion_status()determines if a graduation is timely or late. -
prep_fye_mice()conditions data for imputing starting majors of FYE students.
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order_multiway()conditions data for Cleveland multiway charts.
Installation
Install from CRAN with:
install.packages("midfieldr")To get a bug fix or use a new feature, you can install the development version from GitHub.
# install.packages("pak")
pak::pak("MIDFIELDR/midfieldr")midfieldr is designed to work with data from MIDFIELD (Ohland 2023) or any other database with a similar structure. A sample of these data (with 98,000 students) is provided in midfielddata, an R data package you can install 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 150-student sample that loads with midfieldr. These “toy” data frames—toy_student, toy_term, and toy_degree—have the same structure as the data frames student, term, and degree in midfielddata that we use in package articles.
library(midfieldr)
library(data.table)
# Choose a minimum set of columns
student <- select_records(toy_student)
term <- select_records(toy_term)
degree <- select_records(toy_degree)
# Display one representative data frame
term
#> mcid term cip6 institution level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 19881 240102 Institution J 01 First-year
#> 2: MCID3111158953 19883 240102 Institution J 01 First-year
#> 3: MCID3111158953 19891 240102 Institution J 02 Second-year
#> ---
#> 1093: MCID3112881399 20181 260901 Institution B 01 First-year
#> 1094: MCID3112882995 20181 141901 Institution B 01 First-year
#> 1095: MCID3112884375 20181 520201 Institution B 01 First-year
# Identify post-baccalaureate terms
term <- post_bacc_terms(term)
degree <- post_bacc_terms(degree)
term
#> mcid term cip6 institution level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 19881 240102 Institution J 01 First-year
#> 2: MCID3111158953 19883 240102 Institution J 01 First-year
#> 3: MCID3111158953 19891 240102 Institution J 02 Second-year
#> ---
#> 1093: MCID3112881399 20181 260901 Institution B 01 First-year
#> 1094: MCID3112882995 20181 141901 Institution B 01 First-year
#> 1095: MCID3112884375 20181 520201 Institution B 01 First-year
#> first_degree_term term_cluster
#> <char> <char>
#> 1: <NA> pre-degree
#> 2: <NA> pre-degree
#> 3: <NA> pre-degree
#> ---
#> 1093: <NA> pre-degree
#> 1094: <NA> pre-degree
#> 1095: <NA> pre-degree
# Exclude rows after the first degree term
term <- term[term_cluster != "post-first-degree"]
term <- select_records(term)
degree <- degree[term_cluster != "post-first-degree"]
degree <- select_records(degree)
term
#> mcid term cip6 institution level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 19881 240102 Institution J 01 First-year
#> 2: MCID3111158953 19883 240102 Institution J 01 First-year
#> 3: MCID3111158953 19891 240102 Institution J 02 Second-year
#> ---
#> 1068: MCID3112881399 20181 260901 Institution B 01 First-year
#> 1069: MCID3112882995 20181 141901 Institution B 01 First-year
#> 1070: MCID3112884375 20181 520201 Institution B 01 First-year
# Data frame of IDs for refining the ppulation
DT <- term[, .(mcid)]
DT <- unique(DT)
DT
#> mcid
#> <char>
#> 1: MCID3111158953
#> 2: MCID3111159270
#> 3: MCID3111160513
#> ---
#> 148: MCID3112881399
#> 149: MCID3112882995
#> 150: MCID3112884375
# Determine data sufficiency
DT <- timely_term(DT)
DT <- data_sufficiency(DT)
DT
#> mcid term_i timely_term institution lower_limit upper_limit
#> <char> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 19881 19933 Institution J 19881 20096
#> 2: MCID3111158953 19881 19933 Institution J 19881 20096
#> 3: MCID3111158953 19881 19933 Institution J 19881 20096
#> ---
#> 1068: MCID3112881399 20181 20233 Institution B 19881 20181
#> 1069: MCID3112882995 20181 20233 Institution B 19881 20181
#> 1070: MCID3112884375 20181 20233 Institution B 19881 20181
#> data_sufficiency
#> <char>
#> 1: exclude-lower
#> 2: exclude-lower
#> 3: exclude-lower
#> ---
#> 1068: exclude-upper
#> 1069: exclude-upper
#> 1070: exclude-upper
# Retain rows with sufficient institutional data
population <- DT[data_sufficiency == "include", .(mcid)]
population <- unique(population)
population
#> mcid
#> <char>
#> 1: MCID3111213943
#> 2: MCID3111248941
#> 3: MCID3111250695
#> ---
#> 100: MCID3112409179
#> 101: MCID3112411629
#> 102: MCID3112498796
# Inner join to retain IDs in the population
student <- population[student, on = "mcid", nomatch = NULL]
term <- population[term, on = "mcid", nomatch = NULL]
degree <- population[degree, on = "mcid", nomatch = NULL]
# Drop temporary columns, records ready for further analysis
select_records(student)
#> mcid race sex institution
#> <char> <char> <char> <char>
#> 1: MCID3111213943 White Male Institution B
#> 2: MCID3111248941 White Male Institution J
#> 3: MCID3111250695 White Male Institution J
#> ---
#> 100: MCID3112409179 Asian Male Institution B
#> 101: MCID3112411629 White Male Institution B
#> 102: MCID3112498796 White Female Institution B
select_records(term)
#> mcid term cip6 institution level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111213943 19891 420101 Institution B 01 First-year
#> 2: MCID3111213943 19893 420101 Institution B 01 First-year
#> 3: MCID3111213943 19901 420101 Institution B 02 Second-year
#> ---
#> 806: MCID3112498796 20131 090101 Institution B 03 Third-year
#> 807: MCID3112498796 20133 090101 Institution B 04 Fourth-year
#> 808: MCID3112498796 20134 090101 Institution B 04 Fourth-year
select_records(degree)
#> mcid term_degree cip6 institution
#> <char> <char> <char> <char>
#> 1: MCID3111213943 19903 420101 Institution B
#> 2: MCID3111248941 19943 140901 Institution J
#> 3: MCID3111253227 19951 141901 Institution J
#> ---
#> 76: MCID3112409179 20123 090401 Institution B
#> 77: MCID3112411629 20124 500601 Institution B
#> 78: MCID3112498796 20143 090101 Institution BAcknowledgments
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/.