Overview
Provides tools in R for working with undergraduate, longitudinal, student-level records modeled on the MIDFIELD database.
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filter_cip_rows()chooses rows of program data based on search terms. -
select_record_cols()chooses columns required by midfieldr functions.
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add_term_cluster()identifies rows of post-baccalaureate terms to exclude. -
add_timely_term()estimates a student’s timely graduation term. -
add_data_sufficiency()identifies rows to exclude due to insufficient data. -
add_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_record_cols(toy_student, type = "s")
term <- select_record_cols(toy_term, "t")
degree <- select_record_cols(toy_degree, "d")
# Display one representative data frame
term
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 Institution J 19881 240102 01 First-year
#> 2: MCID3111158953 Institution J 19883 240102 01 First-year
#> 3: MCID3111158953 Institution J 19891 240102 02 Second-year
#> ---
#> 1093: MCID3112881399 Institution B 20181 260901 01 First-year
#> 1094: MCID3112882995 Institution B 20181 141901 01 First-year
#> 1095: MCID3112884375 Institution B 20181 520201 01 First-year
# Add a column to label term clusters
term <- add_term_cluster(term)
degree <- add_term_cluster(degree)
term
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 Institution J 19881 240102 01 First-year
#> 2: MCID3111158953 Institution J 19883 240102 01 First-year
#> 3: MCID3111158953 Institution J 19891 240102 02 Second-year
#> ---
#> 1093: MCID3112881399 Institution B 20181 260901 01 First-year
#> 1094: MCID3112882995 Institution B 20181 141901 01 First-year
#> 1095: MCID3112884375 Institution B 20181 520201 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"]
degree <- degree[term_cluster != "post-first-degree"]
term
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 Institution J 19881 240102 01 First-year
#> 2: MCID3111158953 Institution J 19883 240102 01 First-year
#> 3: MCID3111158953 Institution J 19891 240102 02 Second-year
#> ---
#> 1068: MCID3112881399 Institution B 20181 260901 01 First-year
#> 1069: MCID3112882995 Institution B 20181 141901 01 First-year
#> 1070: MCID3112884375 Institution B 20181 520201 01 First-year
#> first_degree_term term_cluster
#> <char> <char>
#> 1: <NA> pre-degree
#> 2: <NA> pre-degree
#> 3: <NA> pre-degree
#> ---
#> 1068: <NA> pre-degree
#> 1069: <NA> pre-degree
#> 1070: <NA> pre-degree
# Drop the temporary columns
term <- select_record_cols(term, type = "t")
degree <- select_record_cols(degree, type = "d")
term
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 Institution J 19881 240102 01 First-year
#> 2: MCID3111158953 Institution J 19883 240102 01 First-year
#> 3: MCID3111158953 Institution J 19891 240102 02 Second-year
#> ---
#> 1068: MCID3112881399 Institution B 20181 260901 01 First-year
#> 1069: MCID3112882995 Institution B 20181 141901 01 First-year
#> 1070: MCID3112884375 Institution B 20181 520201 01 First-year
# Population at this point, for next filter
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 <- add_timely_term(DT)
DT <- add_data_sufficiency(DT)
DT
#> mcid level_i adj_span timely_term term_i lower_limit
#> <char> <char> <num> <char> <char> <char>
#> 1: MCID3111158953 01 First-year 6 19933 19881 19881
#> 2: MCID3111159270 01 First-year 6 19933 19881 19881
#> 3: MCID3111160513 01 First-year 6 19933 19881 19881
#> ---
#> 148: MCID3112881399 01 First-year 6 20233 20181 19881
#> 149: MCID3112882995 01 First-year 6 20233 20181 19881
#> 150: MCID3112884375 01 First-year 6 20233 20181 19881
#> upper_limit data_sufficiency
#> <char> <char>
#> 1: 20096 exclude-lower
#> 2: 20096 exclude-lower
#> 3: 20096 exclude-lower
#> ---
#> 148: 20181 exclude-upper
#> 149: 20181 exclude-upper
#> 150: 20181 exclude-upper
# Retain rows with sufficient institutional data
population <- DT[data_sufficiency == "include", .(mcid)]
population
#> mcid
#> <char>
#> 1: MCID3111213943
#> 2: MCID3111248941
#> 3: MCID3111250695
#> ---
#> 100: MCID3112409179
#> 101: MCID3112411629
#> 102: MCID3112498796
# Filter records using inner join with population
student <- population[student, on = "mcid", nomatch = NULL]
term <- population[term, on = "mcid", nomatch = NULL]
degree <- population[degree, on = "mcid", nomatch = NULL]
# Records ready for grouping, summarizing, calculating metrics, etc.
student
#> mcid race sex
#> <char> <char> <char>
#> 1: MCID3111213943 White Male
#> 2: MCID3111248941 White Male
#> 3: MCID3111250695 White Male
#> ---
#> 100: MCID3112409179 Asian Male
#> 101: MCID3112411629 White Male
#> 102: MCID3112498796 White Female
term
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111213943 Institution B 19891 420101 01 First-year
#> 2: MCID3111213943 Institution B 19893 420101 01 First-year
#> 3: MCID3111213943 Institution B 19901 420101 02 Second-year
#> ---
#> 806: MCID3112498796 Institution B 20131 090101 03 Third-year
#> 807: MCID3112498796 Institution B 20133 090101 04 Fourth-year
#> 808: MCID3112498796 Institution B 20134 090101 04 Fourth-year
degree
#> mcid term_degree cip6
#> <char> <char> <char>
#> 1: MCID3111213943 19903 420101
#> 2: MCID3111248941 19943 140901
#> 3: MCID3111253227 19951 141901
#> ---
#> 76: MCID3112409179 20123 090401
#> 77: MCID3112411629 20124 500601
#> 78: MCID3112498796 20143 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/.