Overview
Provides tools for working with undergraduate student-level records (registrar’s data) in R, supporting longitudinal studies in particular.
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select_basic_cols()chooses columns required by midfieldr functions.
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add_post_bacc()identifies rows of post-baccalaureate terms to exclude. -
filter_cip()chooses rows of CIP data based on search terms.
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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.
Frequently used packages.
- data.table for manipulating data (Barrett et al. 2026)
- ggplot2 for charts (Wickham 2016)
Other packages, used infrequently.
- mice for imputing starting majors of First-Year Engineering students (van Buuren and Groothuis-Oudshoorn 2011).
- stringr for manipulation of character strings (Wickham 2025).
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 interacts with practice data provided in the midfielddata package (Layton et al. 2026) or with any data structure modeled on MIDFIELD (Ohland 2023), a database managed since 2023 by the American Society for Engineering Education. midfielddata is installed from the MIDFIELDR drat repository with:
install.packages("midfielddata",
repos = "https://MIDFIELDR.github.io/drat/",
type = "source"
)The installed size of midfielddata is about 24 Mb, so installation will take longer than that of a conventional CRAN package.
Usage
We illustrate usage with the “toy” data sets included in midfieldr. These are small data sets of 150 unique students used in examples.
# Initialize the working data frame using toy_term
DT <- toy_term[, .(mcid)]
DT <- unique(DT)
DT
#> mcid
#> <char>
#> 1: MCID3111158953
#> 2: MCID3111159270
#> 3: MCID3111160513
#> 4: MCID3111162677
#> 5: MCID3111164287
#> 6: MCID3111171205
#> 7: MCID3111172083
#> 8: MCID3111213943
#> 9: MCID3111248941
#> 10: MCID3111250695
#> ---
#> 141: MCID3112802165
#> 142: MCID3112803670
#> 143: MCID3112804805
#> 144: MCID3112839822
#> 145: MCID3112845932
#> 146: MCID3112846308
#> 147: MCID3112877403
#> 148: MCID3112881399
#> 149: MCID3112882995
#> 150: MCID3112884375
# Add variables relating to the timely term
DT <- add_timely_term(DT, toy_term)
# Order the rows for viewing
DT <- DT[order(timely_term)]
DT
#> mcid term_i level_i adj_span timely_term
#> <char> <char> <char> <num> <char>
#> 1: MCID3111158953 19881 01 First-year 6 19933
#> 2: MCID3111159270 19881 01 First-year 6 19933
#> 3: MCID3111160513 19881 01 First-year 6 19933
#> 4: MCID3111162677 19881 01 First-year 6 19933
#> 5: MCID3111164287 19881 01 First-year 6 19933
#> 6: MCID3111171205 19881 01 First-year 6 19933
#> 7: MCID3111172083 19881 01 First-year 6 19933
#> 8: MCID3111213943 19891 01 First-year 6 19943
#> 9: MCID3111248941 19901 01 First-year 6 19953
#> 10: MCID3111250695 19901 01 First-year 6 19953
#> ---
#> 141: MCID3112802165 20161 01 First-year 6 20213
#> 142: MCID3112803670 20161 01 First-year 6 20213
#> 143: MCID3112804805 20161 01 First-year 6 20213
#> 144: MCID3112839822 20171 01 First-year 6 20223
#> 145: MCID3112846308 20171 01 First-year 6 20223
#> 146: MCID3112845932 20173 01 First-year 6 20231
#> 147: MCID3112877403 20181 01 First-year 6 20233
#> 148: MCID3112881399 20181 01 First-year 6 20233
#> 149: MCID3112882995 20181 01 First-year 6 20233
#> 150: MCID3112884375 20181 01 First-year 6 20233
# Optional: reduce number of columns
DT <- DT[, .(mcid, timely_term)]
# Add variables relating to data sufficiency
DT <- add_data_sufficiency(DT, toy_term)
DT
#> mcid timely_term term_i lower_limit upper_limit data_sufficiency
#> <char> <char> <char> <char> <char> <char>
#> 1: MCID3111158953 19933 19881 19881 20096 exclude-lower
#> 2: MCID3111159270 19933 19881 19881 20096 exclude-lower
#> 3: MCID3111160513 19933 19881 19881 20096 exclude-lower
#> 4: MCID3111162677 19933 19881 19881 20096 exclude-lower
#> 5: MCID3111164287 19933 19881 19881 20096 exclude-lower
#> 6: MCID3111171205 19933 19881 19881 20181 exclude-lower
#> 7: MCID3111172083 19933 19881 19881 20181 exclude-lower
#> 8: MCID3111213943 19943 19891 19881 20181 include
#> 9: MCID3111248941 19953 19901 19881 20096 include
#> 10: MCID3111250695 19953 19901 19881 20096 include
#> ---
#> 141: MCID3112802165 20213 20161 19881 20181 exclude-upper
#> 142: MCID3112803670 20213 20161 19881 20181 exclude-upper
#> 143: MCID3112804805 20213 20161 19881 20181 exclude-upper
#> 144: MCID3112839822 20223 20171 19881 20181 exclude-upper
#> 145: MCID3112845932 20231 20173 19881 20181 exclude-upper
#> 146: MCID3112846308 20223 20171 19881 20181 exclude-upper
#> 147: MCID3112877403 20233 20181 19881 20181 exclude-upper
#> 148: MCID3112881399 20233 20181 19881 20181 exclude-upper
#> 149: MCID3112882995 20233 20181 19881 20181 exclude-upper
#> 150: MCID3112884375 20233 20181 19881 20181 exclude-upper
# Filter for data sufficiency
DT <- DT[data_sufficiency %chin% "include"]
DT
#> mcid timely_term term_i lower_limit upper_limit data_sufficiency
#> <char> <char> <char> <char> <char> <char>
#> 1: MCID3111213943 19943 19891 19881 20181 include
#> 2: MCID3111248941 19953 19901 19881 20096 include
#> 3: MCID3111250695 19953 19901 19881 20096 include
#> 4: MCID3111253227 19953 19901 19881 20096 include
#> 5: MCID3111258790 19953 19901 19881 20181 include
#> 6: MCID3111263510 19953 19901 19881 20181 include
#> 7: MCID3111282492 19963 19904 19901 20153 include
#> 8: MCID3111304195 19963 19911 19881 20096 include
#> 9: MCID3111315508 19963 19911 19901 20153 include
#> 10: MCID3111316435 19963 19911 19901 20153 include
#> ---
#> 96: MCID3112317359 20123 20071 19881 20181 include
#> 97: MCID3112352869 20133 20081 19881 20181 include
#> 98: MCID3112354970 20133 20081 19881 20181 include
#> 99: MCID3112406332 20143 20091 19881 20181 include
#> 100: MCID3112409179 20143 20091 19881 20181 include
#> 101: MCID3112411629 20143 20091 19881 20181 include
#> 102: MCID3112412904 20143 20091 19901 20153 include
#> 103: MCID3112413521 20143 20091 19901 20153 include
#> 104: MCID3112471930 20153 20101 19901 20153 include
#> 105: MCID3112498796 20153 20101 19881 20181 include
# Optional: reduce number of columns
DT <- DT[, .(mcid, timely_term)]
# Add variables relating to completion status
DT <- add_completion_status(DT, toy_degree)
DT
#> mcid timely_term term_degree completion_status
#> <char> <char> <char> <char>
#> 1: MCID3111213943 19943 19903 timely
#> 2: MCID3111248941 19953 19943 timely
#> 3: MCID3111250695 19953 <NA> <NA>
#> 4: MCID3111253227 19953 19951 timely
#> 5: MCID3111258790 19953 19954 late
#> 6: MCID3111263510 19953 19933 timely
#> 7: MCID3111282492 19963 19991 late
#> 8: MCID3111304195 19963 <NA> <NA>
#> 9: MCID3111315508 19963 19961 timely
#> 10: MCID3111316435 19963 19964 late
#> ---
#> 96: MCID3112317359 20123 20111 timely
#> 97: MCID3112352869 20133 20121 timely
#> 98: MCID3112354970 20133 20163 late
#> 99: MCID3112406332 20143 20131 timely
#> 100: MCID3112409179 20143 20123 timely
#> 101: MCID3112411629 20143 20124 timely
#> 102: MCID3112412904 20143 20123 timely
#> 103: MCID3112413521 20143 20173 late
#> 104: MCID3112471930 20153 <NA> <NA>
#> 105: MCID3112498796 20153 20143 timely
# Filter for timely completion
DT <- DT[completion_status %chin% "timely"]
DT
#> mcid timely_term term_degree completion_status
#> <char> <char> <char> <char>
#> 1: MCID3111213943 19943 19903 timely
#> 2: MCID3111248941 19953 19943 timely
#> 3: MCID3111253227 19953 19951 timely
#> 4: MCID3111263510 19953 19933 timely
#> 5: MCID3111315508 19963 19961 timely
#> 6: MCID3111316936 19963 19953 timely
#> 7: MCID3111354376 19973 19953 timely
#> 8: MCID3111355374 19973 19961 timely
#> 9: MCID3111356562 19973 19963 timely
#> 10: MCID3111357512 19973 19953 timely
#> ---
#> 46: MCID3112196380 20103 20083 timely
#> 47: MCID3112196966 20103 20093 timely
#> 48: MCID3112296580 20123 20103 timely
#> 49: MCID3112317359 20123 20111 timely
#> 50: MCID3112352869 20133 20121 timely
#> 51: MCID3112406332 20143 20131 timely
#> 52: MCID3112409179 20143 20123 timely
#> 53: MCID3112411629 20143 20124 timely
#> 54: MCID3112412904 20143 20123 timely
#> 55: MCID3112498796 20153 20143 timely
# Join demographic data, reduce number of columns
DT <- toy_student[DT, .(mcid, race, sex), on = "mcid"]
# Label the student bloc
DT[, bloc := "graduate"]
DT
#> mcid race sex bloc
#> <char> <char> <char> <char>
#> 1: MCID3111213943 White Male graduate
#> 2: MCID3111248941 White Male graduate
#> 3: MCID3111253227 White Male graduate
#> 4: MCID3111263510 White Male graduate
#> 5: MCID3111315508 Other/Unknown Male graduate
#> 6: MCID3111316936 White Female graduate
#> 7: MCID3111354376 White Male graduate
#> 8: MCID3111355374 White Male graduate
#> 9: MCID3111356562 White Male graduate
#> 10: MCID3111357512 White Male graduate
#> ---
#> 46: MCID3112196380 White Male graduate
#> 47: MCID3112196966 White Female graduate
#> 48: MCID3112296580 White Male graduate
#> 49: MCID3112317359 White Male graduate
#> 50: MCID3112352869 White Male graduate
#> 51: MCID3112406332 White Male graduate
#> 52: MCID3112409179 Asian Male graduate
#> 53: MCID3112411629 White Male graduate
#> 54: MCID3112412904 White Male graduate
#> 55: MCID3112498796 White Female graduateAcknowledgments
The development of midfieldr and midfielddata was supported by the US National Science Foundation through grant numbers 1545667 and 2142087.
References
Barrett, Tyson, Matt Dowle, Arun Srinivasan, et al. 2026. data.table: Extension of data.frame. R package version 1.18.4. https://CRAN.R-project.org/package=data.table.
Layton, Richard, Russell Long, Susan Lord, Matthew Ohland, and Marisa Orr. 2026. midfielddata: MIDFIELD Data Sample. R package version 0.2.3. https://midfieldr.github.io/midfielddata/.
Ohland, Matthew. 2023. MIDFIELD, 2004–2023. https://midfield.online/.
van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2011. “mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software 45 (3): 1–67. https://doi.org/10.18637/jss.v045.i03.
Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. ISBN 978-3-319-24277-4; Springer-Verlag New York. https://ggplot2.tidyverse.org.
Wickham, Hadley. 2025. stringr: Simple, consistent wrappers for common string operations. https://doi.org/10.32614/CRAN.package.stringr.