Familiarity with the MIDFIELD data structure is a prerequisite for working with midfieldr functions so we start by inspecting the practice data in midfielddata, a companion R package that provides anonymized student-level records for 98,000 undergraduates at three US institutions from 1988 through 2018. (If you haven’t yet installed midfielddata, see the Installation instructions.)
The practice data are organized in four datasets keyed by student ID.
| Dataset | Each row is | N students | N rows | N columns | Memory |
|---|---|---|---|---|---|
| course | one student per course & term | 97,555 | 3,289,532 | 12 | 324.3 MB |
| term | one student per term | 97,555 | 639,915 | 13 | 72.8 MB |
| student | one student | 97,555 | 97,555 | 13 | 17.3 MB |
| degree | one student per degree | 49,543 | 49,665 | 5 | 5.2 MB |
Definitions
- student-level data
-
Data at the “student-level” refers to information about individual students including, for example, demographics, programs, academic standing, courses, grades, and degrees. Also called Student Unit Records (SURs).
-
MIDFIELD student-level data are provided in four data tables (
student,course,term, anddegree) that were compiled by institutions and anonymized and curated by the MIDFIELD data steward. - observation
-
Row of a data frame (
student,course,term,degree) keyed by student ID. - variable
-
Column of a data frame
Method
In this article:
- Overview of each dataset
- Summary of variables typically encountered when using midfieldr functions
- Closer look: For one student, all records in all datasets
- Introduce helper function
select_required()and wraprcheck_equiv_frames()
Reminder. midfielddata is suitable for learning to work with student-level data but not for drawing inferences about program attributes or student experiences. midfielddata supplies practice data, not research data.
Load data
Start. If you are writing your own script to follow along, we use these packages in this article:
Load data tables. Data tables can be loaded individually
or collectively as needed. View data dictionaries via
?student, ?course, ?term, or
?degree.
student
Contains one observation per student. Data are assumed to be current at the time the student was admitted to their institution.
student
#> mcid institution transfer hours_transfer
#> <char> <char> <char> <num>
#> 1: MCID3111142225 Institution B First-Time Transfer NA
#> 2: MCID3111142283 Institution J First-Time Transfer NA
#> 3: MCID3111142290 Institution J First-Time Transfer NA
#> ---
#> 97553: MCID3112898894 Institution B First-Time in College NA
#> 97554: MCID3112898895 Institution B First-Time in College NA
#> 97555: MCID3112898940 Institution B First-Time in College NA
#> race sex age_desc us_citizen home_zip high_school sat_math
#> <char> <char> <char> <char> <char> <char> <num>
#> 1: Asian Male Under 25 Yes <NA> <NA> NA
#> 2: Asian Female Under 25 Yes 22020 <NA> 560
#> 3: Asian Male Under 25 Yes 23233 471872 510
#> ---
#> 97553: White Female Under 25 Yes 53716 501160 510
#> 97554: White Female Under 25 Yes 53029 500853 420
#> 97555: Other/Unknown Male Under 25 Yes 20016 090073 470
#> sat_verbal act_comp
#> <num> <num>
#> 1: NA NA
#> 2: 230 NA
#> 3: 380 NA
#> ---
#> 97553: 590 24
#> 97554: 590 32
#> 97555: 540 32Student IDs and institution names have been anonymized to remove identifiable information.
# Anonymized IDs
sample(student$mcid, 8)
#> [1] "MCID3111478315" "MCID3111363338" "MCID3111216590" "MCID3111876789"
#> [5] "MCID3112383444" "MCID3111948721" "MCID3111381575" "MCID3112827958"
# Anonymized institutions
sort(unique(student$institution))
#> [1] "Institution B" "Institution C" "Institution J"Race/ethnicity and sex are often used as grouping variables.
# Possible values
sort(unique(student$race))
#> [1] "Asian" "Black" "International" "Latine"
#> [5] "Native American" "Other/Unknown" "White"
# Possible values
sort(unique(student$sex))
#> [1] "Female" "Male" "Unknown"Counts in each category.
# N by institution
student[order(institution), .N, by = "institution"]
#> institution N
#> <char> <int>
#> 1: Institution B 45660
#> 2: Institution C 26712
#> 3: Institution J 25183
# N by race
student[order(race), .N, by = "race"]
#> race N
#> <char> <int>
#> 1: Asian 4193
#> 2: Black 1860
#> 3: International 7354
#> 4: Latine 5386
#> 5: Native American 403
#> 6: Other/Unknown 4509
#> 7: White 73850
# N by sex
student[order(sex), .N, by = "sex"]
#> sex N
#> <char> <int>
#> 1: Female 46403
#> 2: Male 51151
#> 3: Unknown 1
course
Contains one observation per student per course.
course
#> mcid institution term_course course
#> <char> <char> <char> <char>
#> 1: MCID3111142225 Institution B 19881 Microprocessor Lab
#> 2: MCID3111142225 Institution B 19881 Neural Signals
#> 3: MCID3111142225 Institution B 19881 Engineering Economy
#> ---
#> 3289530: MCID3112898940 Institution B 20181 Beginning Japanese 1
#> 3289531: MCID3112898940 Institution B 20181 Precalculus Mathematics
#> 3289532: MCID3112898940 Institution B 20181 Deviance In U S Society
#> abbrev number section type faculty_rank hours_course grade
#> <char> <char> <char> <char> <char> <num> <char>
#> 1: ECEN 2230 005 <NA> <NA> 1 C
#> 2: ECEN 4811 001 <NA> <NA> 3 C
#> 3: MCEN 4147 001 <NA> <NA> 3 B+
#> ---
#> 3289530: JPNS 1010 009 Face-to-Face Lecturer 5 C
#> 3289531: MATH 1150 012 Face-to-Face Lecturer 4 C-
#> 3289532: SOCY 1004 100 Face-to-Face Instructor 3 B
#> discipline_midfield
#> <char>
#> 1: Engineering: Electrical and Computer
#> 2: Engineering: Electrical and Computer
#> 3: Engineering: Mechanical
#> ---
#> 3289530: Language and Literature: Japanese
#> 3289531: Mathematics
#> 3289532: Social Sciences: SociologyThe abbrev, number, and
discipline_midfield columns have no NA values, so they
might be useful if one is filtering for specific course types. The
course column, on the other hand, has a high number of NA
values.
term
Contains one observation per student per term.
term
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111142225 Institution B 19881 140901 01 First-year
#> 2: MCID3111142283 Institution J 19881 240102 01 First-year
#> 3: MCID3111142283 Institution J 19883 240102 01 First-year
#> ---
#> 639913: MCID3112898894 Institution B 20181 451001 01 First-year
#> 639914: MCID3112898895 Institution B 20181 302001 01 First-year
#> 639915: MCID3112898940 Institution B 20181 050103 01 First-year
#> standing coop hours_term hours_term_attempt hours_cumul
#> <char> <char> <num> <num> <num>
#> 1: Good Standing No 7 7 7
#> 2: Academic Probation No 6 6 6
#> 3: Academic Probation No 12 12 18
#> ---
#> 639913: Good Standing No 13 13 13
#> 639914: Good Standing No 18 18 18
#> 639915: Good Standing No 15 15 15
#> hours_cumul_attempt gpa_term gpa_cumul
#> <num> <num> <num>
#> 1: 7 2.56 2.56
#> 2: 6 1.85 1.85
#> 3: 18 1.93 1.90
#> ---
#> 639913: 13 3.52 3.52
#> 639914: 18 3.50 3.50
#> 639915: 15 2.18 2.18Terms are encoded YYYYT, where
-
YYYYis the year at the start of the academic year, and
-
Tencodes the semester or quarter—Fall (1), Winter (2), Spring (3), and Summer (4,5, and6)—within an academic year
For example, for academic year 1995–96,
-
19951encodes Fall 95–96 -
19953encodes Spring 95–96 -
19954encodes Summer 95–96 (first session)
Different institutions supply data over different time spans.
# Range of data by institution
term[, .(min_term = min(term), max_term = max(term)), by = "institution"]
#> institution min_term max_term
#> <char> <char> <char>
#> 1: Institution B 19881 20181
#> 2: Institution J 19881 20096
#> 3: Institution C 19901 20154Programs are encoded in the cip6 variable, a 6-digit
character based on the 2010 Classification of Instructional Programs
(CIP) (NCES
2010).
# A sample of cip6 values
sort(unique(sample(term$cip6, 8)))
#> [1] "110101" "140102" "240102" "520201" "521401"Student level is used when determining timely completion terms of transfer students.
degree
Contains one observation per student per degree.
This dataset contains records for graduates only, thus the number of
observations in degree (49,665) is less than the number of
observations in student (97,555). The
term_degree and cip6 variables indicate when
and from which program a student graduates.
degree
#> mcid institution term_degree cip6
#> <char> <char> <char> <char>
#> 1: MCID3111142225 Institution B 19881 141001
#> 2: MCID3111142290 Institution J 19921 141001
#> 3: MCID3111142294 Institution J 19903 141001
#> ---
#> 49663: MCID3112839623 Institution B 20181 160102
#> 49664: MCID3112845220 Institution B 20181 270101
#> 49665: MCID3112845673 Institution B 20174 090101
#> degree
#> <char>
#> 1: Bachelor of Science in Electrical Engineering
#> 2: Bachelor of Science in Electrical Engineering
#> 3: Bachelor of Science in Electrical Engineering
#> ---
#> 49663: Bachelor of Science in Linguistics
#> 49664: Bachelor of Science in Mathematics
#> 49665: Bachelor of Science in Speech Communication and RhetoricNumber of degrees earned per student.
# Count students by number of degrees
by_id <- degree[, .(degree_count = .N), by = "mcid"]
by_id[, .(N_students = .N), by = "degree_count"]
#> degree_count N_students
#> <int> <int>
#> 1: 1 49421
#> 2: 2 122Closer look
We display the records for one specific student, using their ID to subset each dataset.
# One student ID
id_we_want <- "MCID3112192438"Student. As expected, student yields one row
per student.
# Observations for a selected ID
student[mcid == id_we_want]
#> mcid institution transfer hours_transfer race
#> <char> <char> <char> <num> <char>
#> 1: MCID3112192438 Institution C First-Time in College NA White
#> sex age_desc us_citizen home_zip high_school sat_math sat_verbal act_comp
#> <char> <char> <char> <char> <char> <num> <num> <num>
#> 1: Female Under 25 Yes 80521 <NA> 580 390 27Course. For this student, the records span 47 rows, one row per course.
# Observations for a selected ID
course[mcid == id_we_want]
#> mcid institution term_course course
#> <char> <char> <char> <char>
#> 1: MCID3112192438 Institution C 20051 Key Academic Community Seminar
#> 2: MCID3112192438 Institution C 20051 Humans and Other Animals
#> 3: MCID3112192438 Institution C 20051 Health and Wellness
#> ---
#> 45: MCID3112192438 Institution C 20093 Health and the Mind
#> 46: MCID3112192438 Institution C 20093 Social Psychology Laboratory
#> 47: MCID3112192438 Institution C 20093 Group Study
#> abbrev number section type faculty_rank hours_course
#> <char> <char> <char> <char> <char> <num>
#> 1: KA 192 009 <NA> Instructor 3
#> 2: BZCC 101 002 <NA> Assistant Professor 3
#> 3: EXCC 145 004 <NA> Non-Academic Professional 3
#> ---
#> 45: PSY 121 001 Face-to-Face Non-Academic Professional 1
#> 46: PSY 317 L02 Face-to-Face Graduate Assistant 2
#> 47: PSY 496 004 Face-to-Face Instructor 3
#> grade discipline_midfield
#> <char> <char>
#> 1: A Academic Support
#> 2: B Biological and Biomedical Sciences: Botany
#> 3: A Education: Physical and Coaching
#> ---
#> 45: A+ Psychology
#> 46: A Psychology
#> 47: A+ PsychologyTerm. Here, the records span 10 rows, one row per term.
# Observations for a selected ID
term[mcid == id_we_want]
#> mcid institution term cip6 level standing
#> <char> <char> <char> <char> <char> <char>
#> 1: MCID3112192438 Institution C 20051 451101 01 First-year Good Standing
#> 2: MCID3112192438 Institution C 20053 190701 01 First-year Good Standing
#> 3: MCID3112192438 Institution C 20061 451101 02 Second-year Good Standing
#> 4: MCID3112192438 Institution C 20063 451101 02 Second-year Good Standing
#> 5: MCID3112192438 Institution C 20071 451101 03 Third-year Good Standing
#> 6: MCID3112192438 Institution C 20073 451101 03 Third-year Good Standing
#> 7: MCID3112192438 Institution C 20081 451101 03 Third-year Good Standing
#> 8: MCID3112192438 Institution C 20083 451101 04 Fourth-year Good Standing
#> 9: MCID3112192438 Institution C 20091 451101 04 Fourth-year Good Standing
#> 10: MCID3112192438 Institution C 20093 451101 05 Fifth-year Plus Good Standing
#> coop hours_term hours_term_attempt hours_cumul hours_cumul_attempt
#> <char> <num> <num> <num> <num>
#> 1: No 15 15 15 15
#> 2: No 11 11 26 26
#> 3: No 16 16 42 42
#> 4: No 8 8 50 50
#> 5: No 12 12 62 62
#> 6: No 13 13 75 75
#> 7: Yes 14 14 89 89
#> 8: No 16 16 105 105
#> 9: No 13 13 118 118
#> 10: No 12 12 130 130
#> gpa_term gpa_cumul
#> <num> <num>
#> 1: 3.80 3.80
#> 2: 3.40 3.63
#> 3: 3.25 3.49
#> 4: 3.81 3.54
#> 5: 3.75 3.58
#> 6: 3.38 3.54
#> 7: 3.79 3.58
#> 8: 3.75 3.61
#> 9: 4.00 3.65
#> 10: 4.00 3.68Degree. In this example, the records span 2 rows, one row per degree. The degrees were earned in the same term, Spring 2009.
# Observations for a selected ID
degree[mcid == id_we_want]
#> mcid institution term_degree cip6
#> <char> <char> <char> <char>
#> 1: MCID3112192438 Institution C 20093 420101
#> 2: MCID3112192438 Institution C 20093 451101
#> degree
#> <char>
#> 1: Bachelor of Science in Psychology
#> 2: Bachelor of Arts in SociologyNot all students with more than one degree earn them in the same term. For example, the next student earned a degree in 1996 and a second degree in 1999. In most analyses, only the first baccalaureate degree would be used.
# Observations for a different ID
degree[mcid == "MCID3111315508"]
#> mcid institution term_degree cip6
#> <char> <char> <char> <char>
#> 1: MCID3111315508 Institution C 19961 260101
#> 2: MCID3111315508 Institution C 19994 260701
#> degree
#> <char>
#> 1: Bachelor of Science in Biological Sciences
#> 2: Bachelor of Science in Animal Biology
select_required()
A midfieldr convenience function to reduce the number of columns of a MIDFIELD data table after loading. Using this function is optional.
select_required() selects only those columns typically
required by other midfieldr functions. Operates on a data frame to
retain columns having names that match or partially match search terms.
Rows are unaffected.
The primary benefit is reducing screen clutter when viewing data
frames during an interactive session. The disadvantage is that the
deleted columns are unavailable unless you first set aside a copy of the
source file or reload it using data() when you need it.
Arguments.
midfield_xMIDFIELD data frame, typicallystudent,term, ordegree.select_addOptional character vector of search terms to add to the default vector given byc("mcid", "institution", "race", "sex", "^term", "cip6", "level"). Argument, if used, must be used by name.
For example, term records are significantly more compact if we select this minimum set of columns.
# Select variables required by midfieldr functions
select_required(term)
#> mcid institution term cip6 level
#> <char> <char> <char> <char> <char>
#> 1: MCID3111142225 Institution B 19881 140901 01 First-year
#> 2: MCID3111142283 Institution J 19881 240102 01 First-year
#> 3: MCID3111142283 Institution J 19883 240102 01 First-year
#> ---
#> 639913: MCID3112898894 Institution B 20181 451001 01 First-year
#> 639914: MCID3112898895 Institution B 20181 302001 01 First-year
#> 639915: MCID3112898940 Institution B 20181 050103 01 First-yearWe can add columns if we need them.
# Select additional columns
select_required(term, select_add = c("gpa_term"))
#> mcid institution term cip6 level gpa_term
#> <char> <char> <char> <char> <char> <num>
#> 1: MCID3111142225 Institution B 19881 140901 01 First-year 2.56
#> 2: MCID3111142283 Institution J 19881 240102 01 First-year 1.85
#> 3: MCID3111142283 Institution J 19883 240102 01 First-year 1.93
#> ---
#> 639913: MCID3112898894 Institution B 20181 451001 01 First-year 3.52
#> 639914: MCID3112898895 Institution B 20181 302001 01 First-year 3.50
#> 639915: MCID3112898940 Institution B 20181 050103 01 First-year 2.18
check_equiv_frames()
A function imported from the wrapr package that confirms two data frames are equivalent after reordering columns and rows. Accessible by loading midfieldr.
Example. Demonstrate that the following implementations of
select_required() yield identical results.
# Required argument explicitly named
x <- select_required(midfield_x = term)
# Required argument not named
y <- select_required(term)
# Optional argument, if used, must be named. NULL yields the default columns.
z <- select_required(term, select_add = NULL)
# Demonstrate equivalence
check_equiv_frames(x, y)
#> [1] TRUE
check_equiv_frames(x, z)
#> [1] TRUEDemonstrate that row and column order are ignored.
# Two columns from student, use key to order rows
x <- student[, .(mcid, institution)]
setkey(x, mcid)
x
#> Key: <mcid>
#> mcid institution
#> <char> <char>
#> 1: MCID3111142225 Institution B
#> 2: MCID3111142283 Institution J
#> 3: MCID3111142290 Institution J
#> ---
#> 97553: MCID3112898894 Institution B
#> 97554: MCID3112898895 Institution B
#> 97555: MCID3112898940 Institution B
# Same information with different row order, column order, and key
y <- student[, .(institution, mcid)]
setkey(y, institution)
y
#> Key: <institution>
#> institution mcid
#> <char> <char>
#> 1: Institution B MCID3111142225
#> 2: Institution B MCID3111142689
#> 3: Institution B MCID3111142729
#> ---
#> 97553: Institution J MCID3112447751
#> 97554: Institution J MCID3112447753
#> 97555: Institution J MCID3112447754
# Demonstrate equivalence
check_equiv_frames(x, y)
#> [1] TRUEIf the two data tables do not have the same content, the return is FALSE.
# Demonstrate non-equivalence
check_equiv_frames(student, degree)
#> [1] FALSETo explore the differences between non-equivalent data frames,
janitor::compare_df_cols() returns a comparison of column
names and class.
library(janitor)
compare_df_cols(student, degree)
#> column_name student degree
#> 1 act_comp numeric <NA>
#> 2 age_desc character <NA>
#> 3 cip6 <NA> character
#> 4 degree <NA> character
#> 5 high_school character <NA>
#> 6 home_zip character <NA>
#> 7 hours_transfer numeric <NA>
#> 8 institution character character
#> 9 mcid character character
#> 10 race character <NA>
#> 11 sat_math numeric <NA>
#> 12 sat_verbal numeric <NA>
#> 13 sex character <NA>
#> 14 term_degree <NA> character
#> 15 transfer character <NA>
#> 16 us_citizen character <NA>Reusable code
Preparation. The immediate prerequisites or “intake” required by the reusable code chunk are the source data tables.
# Load source data
data(student, term, degree)Initial data processing. A summary code chunk for ready reference.
# Optional. Copy of source files with all variables
source_student <- copy(student)
source_term <- copy(term)
source_degree <- copy(degree)
# Optional. Select variables required by midfieldr functions
student <- select_required(source_student)
term <- select_required(source_term)
degree <- select_required(source_degree)The copy() function ensures that “by-reference” changes
to student, for example, have no effect on
source_student (Dowle and Srinivasan
2022). Thus the source_* objects retain all the
original columns, if needed later.