midfieldr: Tools and Methods for Working with MIDFIELD Data in 'R'

Description

Provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in 'R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. 'midfieldr' interacts with practice data provided in 'midfielddata', an R data package available at https://midfieldr.github.io/midfielddata/. 'midfieldr' also interacts with the full MIDFIELD database for users who have access. This work is supported by the US National Science Foundation through grant numbers 1545667 and 2142087.

Author(s)

Maintainer: Richard Layton graphdoctor@gmail.com [copyright holder]

Authors:

• Russell Long ralong@purdue.edu [copyright holder, data manager]

• Matthew Ohland ohland@purdue.edu [copyright holder]

• Marisa Orr marisak@clemson.edu [copyright holder]

• Susan Lord slord@sandiego.edu [copyright holder]

See Also

Useful links:

• https://midfieldr.github.io/midfieldr/

• Report bugs at https://github.com/MIDFIELDR/midfieldr/issues

Determine completion status for every student

Description

Add columns to a data frame of student-level records that indicate whether a student completed a degree, and if so, whether their completion was timely.

Usage

add_completion_status(dframe, midfield_degree = degree)

Arguments

Details

By "completion" we mean an undergraduate earning their first baccalaureate degree (or degrees, for students earning more than one degree in the same term). Additional degrees, if any, earned later than the term of the first degree are ignored.

In many studies, students must complete a degree in a specified time span, for example 4-, 6-, or 8-years after admission. If they do, their completion is timely; if not, their completion is late and they are grouped with the non-completers when computing a metric such as graduation rate.

Completion status is "timely" for students completing a degree no later than their timely completion terms. See also add_timely_term().

Value

A data frame in data.table format with the following properties: rows are preserved; columns are preserved with the exception that columns added by the function overwrite existing columns of the same name (if any); grouping structures are not preserved. The added columns are:

term_degree

Character. Term in which the first degree(s) are completed. Encoded YYYYT. Joined from midfield_degree data table.

completion_status

Character. Label each observation to indicate completion status. Possible values are: "timely", indicating completion no later than the timely completion term; "late", indicating completion after the timely completion term; and "NA" indicating non-completion.

See Also

Other add_*: add_data_sufficiency(), add_timely_term()

Examples

# Start with an excerpt from the student data set 
dframe <- toy_student[1:10, .(mcid)]

# Timely term column is required to add completion status column
dframe <- add_timely_term(dframe, toy_term)

# Add completion status column
add_completion_status(dframe, toy_degree)

# Existing completion_status column, if any, is overwritten
dframe[, completion_status := NA_character_]
add_completion_status(dframe, toy_degree)

Determine data sufficiency for every student

Description

Add a column to a data frame of student-level records that labels each row for inclusion or exclusion based on data sufficiency near the upper and lower bounds of an institution's data range.

Usage

add_data_sufficiency(dframe, midfield_term = term)

Arguments

Details

The time span of MIDFIELD term data varies by institution, each having their own lower and upper bounds. For some student records, being at or near these bounds creates unavoidable ambiguity when trying to assess degree completion. Such records must be identified and in most cases excluded to prevent false summary counts.

Value

A data frame in data.table format with the following properties: rows are preserved; columns are preserved with the exception that columns added by the function overwrite existing columns of the same name (if any); grouping structures are not preserved. The added columns are:

term_i

Character. Initial term of a student's longitudinal record, encoded YYYYT. Not overwritten if present in dframe.

lower_limit

Character. Initial term of an institution's data range, encoded YYYYT

upper_limit

Character. Final term of an institution's data range, encoded YYYYT

data_sufficiency

Character. Label each observation for inclusion or exclusion based on data sufficiency. Possible values are: include, indicating that available data are sufficient for estimating timely completion; exclude-upper, indicating that data are insufficient at the upper limit of a data range; and exclude-lower, indicating that data are insufficient at the lower limit.

See Also

Other add_*: add_completion_status(), add_timely_term()

Examples

# Start with an excerpt from the student data set 
dframe <- toy_student[1:10, .(mcid)]

# Timely term column is required to add data sufficiency column
dframe <- add_timely_term(dframe, midfield_term = toy_term)

# Add data sufficiency column
add_data_sufficiency(dframe, midfield_term = toy_term)

# Existing data_sufficiency column, if any, is overwritten
dframe[, data_sufficiency := NA_character_]
add_data_sufficiency(dframe, midfield_term = toy_term)

Calculate a timely completion term for every student

Description

Add a column to a data frame of student-level records that indicates the latest term by which degree completion would be considered timely for every student.

Usage

add_timely_term(
  dframe,
  midfield_term = term,
  ...,
  span = NULL,
  sched_span = NULL
)

Arguments

Details

By "completion" we mean an undergraduate earning their first baccalaureate degree (or degrees, for students earning more than one degree in the same term).

In many studies, students must complete their programs in a specified time span, for example 4-, 6-, or 8-years after admission. If they do, their completion is timely; if not, their completion is late and they are grouped with the non-completers when computing a metric such as graduation rate.

Our heuristic assigns span number of years (default is 6 years) to every student. For students admitted at second-year level or higher, the span is reduced by one year for each full year the student is assumed to have completed. For example, a student admitted at the second-year level is assumed to have completed one year of a program, so their span is reduced by one year.

The adjusted span is added to the initial term to create the timely completion term in the timely_term column.

Value

A data frame in data.table format with the following properties: rows are preserved; columns are preserved with the exception that columns added by the function overwrite existing columns of the same name (if any); grouping structures are not preserved. The added columns are:

term_i

Character. Initial term of a student's longitudinal record, encoded YYYYT

level_i

Character. Student level (01 Freshman, 02 Sophomore, etc.) in their initial term

adj_span

Numeric. Integer span of years for timely completion adjusted for a student's initial level.

timely_term

Character. Latest term by which program completion would be considered timely for every student. Encoded YYYYT.

See Also

Other add_*: add_completion_status(), add_data_sufficiency()

Examples

# Start with an excerpt from the student data set 
dframe <- toy_student[1:10, .(mcid)]

# Add timely completion term column
add_timely_term(dframe, midfield_term = toy_term)

# Define timely completion as 200% of scheduled span (8 years)
add_timely_term(dframe, midfield_term = toy_term, span = 8)

# Existing timely_term column, if any, is overwritten
dframe[, timely_term := NA_character_]
add_timely_term(dframe, midfield_term = toy_term)

Baseline ID bloc to start a typical analysis

Description

Data frame of IDs after processing the practice data for data sufficiency and degree seeking. Provides a convenient bloc to start many of the analysis illustrated in the package articles.

Usage

baseline_mcid

Format

data.table with 76875 rows and 1 column:

mcid

Character, de-identified student ID.

See Also

Other case-study-data: study_observations, study_programs, study_results

Table of academic programs

Description

A data table based on the US National Center for Education Statistics (NCES), Integrated Postsecondary Education Data System (IPEDS), 2010 CIP, http://nces.ed.gov/ipeds/cipcode/. The data are codes and names for 1582 instructional programs organized on three levels: a 2-digit series, a 4-digit series, and a 6-digit series.

Usage

cip

Format

data.table with 1582 rows and 6 columns keyed by the 6-digit CIP code:

cip6

Character 6-digit code representing "specific instructional programs" (US National Center for Education Statistics).

cip6name

Character program name at the 6-digit level

cip4

Character 4-digit code (the first 4 digits of cip6) representing "intermediate groupings of programs that have comparable content and objectives."

cip4name

Character program name at the 4-digit level.

cip2

Character 2-digit code (the first 2 digits of cip6) representing "the most general groupings of related programs."

cip2name

Character program name at the 2-digit level.

Details

The midfielddata taxonomy includes one non-IPEDS code (999999) for Undecided or Unspecified, instances in which institutions reported no program information or that students were not enrolled in a program.

The MIDFIELD research database include CIPs for undergraduate pre-majors such as pre-med (511102), pre-law (220001), and pre-vet (511104).

See Also

Other cip-data: fye_proxy

Subset rows that include matches to search strings

Description

Subset a CIP data frame, retaining rows that match or partially match a vector of character strings. Columns are not subset unless selected in an optional argument.

Usage

filter_cip(keep_text = NULL, ..., drop_text = NULL, cip = NULL, select = NULL)

Arguments

Details

Search terms can include regular expressions. Uses grepl(), therefore non-character columns (if any) that can be coerced to character are also searched for matches. Columns are subset by the values in select after the search concludes.

If none of the optional arguments are specified, the function returns the original data frame.

Value

A data frame in data.table format, a subset of cip, with the following properties: exclude rows that match elements of drop_text; of the remaining rows, include those that match elements of keep_text; if select is empty, all columns are preserved, otherwise only columns included in select are retained; grouping structures are not preserved.

Examples

# Subset using keywords
filter_cip(keep_text = "engineering")


    # Multiple passes to narrow the results
    first_pass <- filter_cip("civil")
    second_pass <- filter_cip("engineering", cip = first_pass)
    filter_cip(drop_text = "technology", cip = second_pass)
    
    # drop_text argument, when used, must be named
    filter_cip("civil engineering", drop_text = "technology")
    
    # Subset using numerical codes
    filter_cip(keep_text = c("050125", "160501"))
    
    # Subset using regular expressions
    filter_cip(keep_text = "^54")
    filter_cip(keep_text = c("^1407", "^1408"))
    
    # Select columns
    filter_cip(keep_text = "^54", select = c("cip6", "cip4name"))

Starting program proxies for FYE students

Description

Proxies are the degree-granting engineering programs we estimate that First-Year Engineering (FYE) students would have declared had they not been required to enroll in FYE. Keyed by student ID. Proxies are provided for all students in the midfielddata practice data who enroll in FYE in their first term.

Usage

fye_proxy

Format

data.table with 4623 rows and 2 columns keyed by student ID:

mcid

Character, de-identified student ID.

proxy

Character, 6-digit CIP code of the estimated proxy program.

Details

The proxy variable contains 6-digit CIP codes of degree-granting engineering programs, e.g., Electrical Engineering, Mechanical Engineering, etc., that are substituted for the FYE CIP code when an analysis requires degree-granting starting programs. The most common application is a graduation rate calculation.

The estimation is based on students' first post-FYE programs and a multiple imputation suitable for categorical variables using the mice package. The predictor variables are institution, race, and sex. The estimated variable is the 6-digit CIP code of a degree-granting engineering program at their institution.

fye_proxy holds only for the practice data in midfielddata—these values cannot be commingled with the MIDFIELD research database.

See Also

Other cip-data: cip

Order categorical variables of multiway data

Description

Transform a data frame such that two independent categorical variables are factors with levels ordered for display in a multiway dot plot. Multiway data comprise a single quantitative value (or response) for every combination of levels of two categorical variables. The ordering of the rows and panels is crucial to the perception of effects (Cleveland, 1993).

Usage

order_multiway(
  dframe,
  quantity,
  categories,
  ...,
  method = NULL,
  ratio_of = NULL
)

Arguments

Details

In our context, "multiway" refers to the data structure and graph design defined by Cleveland (1993), not to the methods of analysis described by Kroonenberg (2008).

Multiway data comprise three variables: a categorical variable of m levels; a second independent categorical variable of n levels; and a quantitative variable (or response) of length mn that cross-classifies the categories, that is, there is a value of the response for each combination of levels of the two categorical variables.

In a multiway dot plot, one category is encoded by the panels, the second category is encoded by the rows of each panel, and the quantitative variable is encoded along identical horizontal scales.

Value

A data frame in data.table format with the following properties: rows are preserved; columns specified by categories are converted to factors and ordered; the column specified by quantity is converted to type double; other columns are preserved with the exception that columns added by the function overwrite existing columns of the same name (if any); grouping structures are not preserved. The added columns are:

CATEGORY_median columns (when ordering method is "median")

Numeric. Two columns of medians of the quantitative variable grouped by the categorical variables. The CATEGORY placeholder in the column name is replaced by a category name from the categories argument. For example, suppose categories = c("program", "people") and method = "median". The two new column names would be program_median and people_median.

CATEGORY_QUANTITY columns (when ordering method is "percent")

Numeric. Two columns of percentages based on the same ratio that produces the quantitative variable except grouped by the categorical variables. The CATEGORY placeholder in the column name is replaced by a category name from the categories argument; the QUANTITY placeholder is replaced by the quantitative variable name in the quantity argument. For example, suppose categories = c("program", "people"), and quantity = "grad_rate", and method = "percent". The two new column names would be program_grad_rate and people_grad_rate.

References

Cleveland WS (1993). Visualizing Data. Hobart Press, Summit, NJ.

Kroonenberg PM (2008). Applied Multiway Data Analysis. Wiley, Hoboken, NJ.

Examples

# Subset of built-in data set
dframe <- study_results[program == "EE" | program == "ME"]
dframe[, people := paste(race, sex)]
dframe[, c("race", "sex") := NULL]
data.table::setcolorder(dframe, c("program", "people"))

# Class before ordering
class(dframe$program)
class(dframe$people)

# Class and levels after ordering
mw1 <- order_multiway(dframe, 
                      quantity = "stickiness", 
                      categories = c("program", "people"))
class(mw1$program)
levels(mw1$program)
class(mw1$people)
levels(mw1$people)

# Display category medians 
mw1

# Existing factors (if any) are re-ordered
mw2 <- dframe
mw2$program <- factor(mw2$program, levels = c("ME", "EE"))

# Levels before conditioning
levels(mw2$program) 

# Levels after conditioning
mw2 <- order_multiway(dframe, 
                      quantity = "stickiness", 
                      categories = c("program", "people"))
levels(mw2$program) 

# Ordering using percent method
order_multiway(dframe, 
               quantity = "stickiness", 
               categories = c("program", "people"), 
               method = "percent", 
               ratio_of = c("graduates", "ever_enrolled"))

Prepare FYE data for multiple imputation

Description

Constructs a data frame of student-level records of First-Year Engineering (FYE) programs and conditions the data for later use as an input to the mice R package for multiple imputation. Sets up three variables as predictors (institution, race/ethnicity, and sex) and one variable to be estimated (program CIP code).

Usage

prep_fye_mice(
  midfield_student = student,
  midfield_term = term,
  ...,
  fye_codes = NULL
)

Arguments

Details

At some US institutions, engineering students are required to complete a First-Year Engineering (FYE) program as a prerequisite for declaring an engineering major. Administratively, degree-granting engineering programs such as Electrical Engineering or Mechanical Engineering treat their incoming post-FYE students as their "starting" cohorts. However, when computing a metric that requires a count of starters—graduation rate, for example—FYE records must be treated with special care to avoid a miscount.

To illustrate the potential for miscounting starters, suppose we wish to calculate a Mechanical Engineering (ME) graduation rate. Students starting in ME constitute the starting pool and the fraction of that pool graduating in ME is the graduation rate. At FYE institutions, an ME program would typically define their starting pool as the post-FYE cohort entering their program. This may be the best information available, but it invariably undercounts starters by failing to account for FYE students who do not transition (post-FYE) to degree-granting engineering programs—students who may have left the institution or switched to non-engineering majors. In either case, in the absence of the FYE requirement, some of these students would have been ME starters. By neglecting these students, the count of ME starters is artificially low resulting in an ME graduation rate that is artificially high. The same is true for every degree-granting engineering discipline in an FYE institution.

Therefore, to avoid miscounting starters at FYE institutions, we have to estimate an "FYE proxy", that is, the 6-digit CIP codes of the degree-granting engineering programs that FYE students would have declared had they not been required to enroll in FYE. The purpose of 'prep_fye_mice()“ is to prepare the data for making that estimation.

After running prep_fye_mice() but before running mice(), one can edit variables or add variables to create a custom set of predictors. The mice package expects all predictors and the proxy variables to be factors. Do not delete the institution variable because it ensures that a student's imputed program is available at their institution.

In addition, ensure that the only missing values are in the proxy column. Other variables are expected to be complete (no NA values). A value of "unknown" in a predictor column, e.g., race/ethnicity or sex, is an acceptable value, not missing data. Observations with missing or unknown values in the ID or institution columns (if any) should be removed.

Value

A data frame in data.table format conditioned for later use as an input to the mice R package for multiple imputation. The data frame comprises one row for every FYE student, first-term and migrator. Grouping structures are not preserved. The columns returned are:

mcid

Character, anonymized student identifier. Returned as-is.

race

Factor, race/ethnicity as self-reported by the student. An imputation predictor variable.

sex

Factor, sex as self-reported by the student. An imputation predictor variable.

institution

Factor, anonymized institution name. An imputation predictor variable.

proxy

Factor, 6-digit CIP code of a student's known, post-FYE engineering program or NA representing missing values to be imputed.

Method

The function extracts all terms for all FYE students, including those who migrate to enter Engineering after their first term, and identifies the first post-FYE program in which they enroll, if any. This treatment yields two possible outcomes for values returned in the proxy column:

1. The student completes FYE and enrolls in an engineering major. For this outcome, we know that at the student's first opportunity, they enrolled in an engineering program of their choosing. The CIP code of that program is returned as the student's FYE proxy.

2. The student does not enroll post-FYE in an engineering major. Such students have no further records in the database or switched from Engineering to another program. For this outcome, the data provide no information regarding what engineering program the student would have declared originally had the institution not required them to enroll in FYE. For these students a proxy value of NA is returned. These are the data treated as missing values to be imputed by mice().

In cases where students enter FYE, change programs, and re-enter FYE, only the first group of FYE terms is considered. Any programs before FYE are ignored.

The resulting data frame is ready for use as input for the mice package, with all variables except mcid returned as factors.

Examples

# Using toy data
prep_fye_mice(toy_student, toy_term)

# Other columns, if any, are dropped
colnames(toy_student)
colnames(prep_fye_mice(toy_student, toy_term))

# Optional argument permits multiple CIP codes for FYE
prep_fye_mice(midfield_student = toy_student, 
              midfield_term =toy_term, 
              fye_codes = c("140101", "140102"))

Test for equal content between two data tables

Description

Test of data equality between data.table objects. Convenience function used in 'midfieldr' articles.

Usage

same_content(x, y)

Arguments

Details

Wrapper around all.equal() for class data.table that ignores row order, column order, and data.table keys. Both inputs must be date frames. Equivalent to:

all.equal(target, current, ignore.row.order = TRUE, ignore.col.order = TRUE)

Value

Either TRUE or a description of the differences between x and y.

Examples

# Same information and ignore row order, column order
x <- toy_student[order(mcid), .(mcid, institution)]
y <- toy_student[order(institution), .(institution, mcid)]
same_content(x, y)

# Different number of rows
x <- toy_student[1:10]
y <- toy_student[1:11]
same_content(x, y)

# Different column names
x <- toy_student[, .(mcid)]
y <- toy_student[, .(institution)]
same_content(x, y)

# Different number of columns and column names
x <- toy_student[, .(mcid)]
y <- toy_student[, .(mcid, institution)]
same_content(x, y)

# Different number of rows, number of columns, and column names
x <- toy_student
y <- toy_term
same_content(x, y)

# Different row content
x <- toy_student[1:10]
y <- toy_student[2:11]
same_content(x, y)

Select required midfieldr variables

Description

Subset a data frame, selecting columns by matching or partially matching a vector of character strings. A convenience function to reduce the dimensions of a MIDFIELD data table at the start of a session by selecting only those columns typically required by other midfieldr functions. Particularly useful in interactive sessions when viewing the data tables at various stages of an analysis.

Usage

select_required(midfield_x, ..., select_add = NULL)

Arguments

Details

Several midfieldr functions are designed to operate on one or more of the MIDFIELD data tables, usually student, term, or degree. This family of functions requires only a small subset of available variables: the required columns built in to the function are mcid, institution, race, sex, ⁠^term⁠, cip6, and level. Additional column names or partial names can be included by using the select_add argument.

The column names of midfield_x are searched for matches or partial matches using grep(), thus search terms can include regular expressions. Variables with names that match or partially match the search terms are returned; all other columns are dropped. Rows are unaffected. Search terms not present are silently ignored.

One could use this function to select columns from a non-MIDFIELD data frame, but with no benefit to the user—conventional column selection syntax is better suited to that task. Here, we specialize the column selection to serve midfieldr functions.

Value

A data frame in data.table format with the following properties: rows are preserved; columns with names that match or partially match values in select_add are retained; grouping structures are not preserved.

Examples

# Default character vector for selecting columns
default_cols<- c("mcid", "institution", "race", "sex", "^term", "cip6", "level")

# Create one string separated by OR
search_pattern <- paste(default_cols, collapse = "|")

# Find names of columns matching or partially matching 
x <- select_required(toy_student) 
names(x)
grepl(search_pattern, names(x))

x <- select_required(toy_term) 
names(x)
grepl(search_pattern, names(x))

x <- select_required(toy_degree) 
names(x)
grepl(search_pattern, names(x))

x <- select_required(toy_course) 
names(x)
grepl(search_pattern, names(x))

# Adding search terms
x <- select_required(toy_course, select_add = c("abbrev", "number", "grade")) 
names(x)

Case-study observations

Description

Data table of post-processed observations of students ever enrolled in, and students graduating from, the four programs of the case study. Keyed by student ID. Provided for the convenience of vignette users.

Usage

study_observations

Format

data.table with 8917 rows and 5 columns. The variables are:

mcid

Character, de-identified student ID.

race

Character, race/ethnicity as self-reported by the student, e.g., Asian, Black, Latine, etc.

sex

Character, sex as self-reported by the student, possible values are Female, Male, and Unknown.

program

Character, academic program label.

bloc

Character, indicating the grouping (ever_enrolled or graduates) to which an observation belongs.

Details

Starting with the case-study starting pool of students ever enrolled in the four programs of the study (Civil, Electrical, Industrial/Systems, and Mechanical Engineering), we filtered the data for data sufficiency, degree seeking, program, and timely completion.

A data frame of "ever enrolled" and a data frame of "timely graduates" were bound using shared column names and are distinguished in the bloc variable. This data structure facilitates grouping and summarizing by race, sex, program, and group.

See Also

Other case-study-data: baseline_mcid, study_programs, study_results

Case-study program labels and codes

Description

Data table of program CIP codes and labels of the four programs of the case study. Keyed by 6-digit CIPs. Provided for the convenience of vignette users.

Usage

study_programs

Format

data.table with 15 rows and 2 columns. The variables are:

cip6

Character, 6-digit CIP code of program in which a student is enrolled in a term.

program

Character, abbreviated labels for four engineering programs. Values are "CE" (Civil Engineering), "EE" (Electrical Engineering), "ISE" (Industrial/Systems Engineering), and "ME" (Mechanical Engineering).

Details

Starting with the midfieldr cip data set, we extracted the CIPs of the four programs of the case study and assigned them a custom label to be used for grouping and summarizing.

See Also

Other case-study-data: baseline_mcid, study_observations, study_results

Case-study results

Description

Data table of longitudinal stickiness for the four programs of the case study (Civil, Electrical, Industrial/Systems, and Mechanical Engineering) grouped by program, race/ethnicity, and sex. Provided for the convenience of vignette users.

Usage

study_results

Format

data.table with 50 rows and 6 columns:

program

Character, academic program label.

sex

Character, sex as self-reported by the student, possible values are Female, Male, and Unknown.

race

Character, race/ethnicity as self-reported by the student, e.g., Asian, Black, Latine, etc.

ever_enrolled

Numerical, number of students ever enrolled in a program.

graduates

Numerical, number of students completing a program.

stickiness

Numerical, program stickiness, the ratio of graduates to ever_enrolled, in percent.

Details

Longitudinal stickiness is the ratio of the number of students graduating from a program to the number of students ever enrolled in the program over the time span of available data. Results are based on data that have been filtered for data sufficiency, degree seeking, and timely completion.

See Also

Other case-study-data: baseline_mcid, study_observations, study_programs

Course data for examples

Description

Selected variables modeled on those in the course practice data for use in package examples and articles. Sampled from an early version of the practice data, the toy data are not a current practice data sample.

Usage

toy_course

Format

data.table with 4616 rows and 6 columns keyed by student ID:

mcid

Character, de-identified student ID.

institution

Character, de-identified institution name, e.g., Institution A, Institution B, etc.

term

Character, academic year and term, format YYYYT.

abbrev

Character, course alphabetical identifier, e.g. ENGR, MATH, ENGL.

number

Character, course numeric identifier, e.g. 101, 3429.

grade

Character, course grade, e.g., A+, A, A-, B+, I, NG, etc.

See Also

Other toy-data: toy_degree, toy_student, toy_term

Degree data for examples

Description

Selected variables modeled on those in the degree practice data for use in package examples and articles. Sampled from an early version of the practice data, the toy data are not a current practice data sample.

Usage

toy_degree

Format

data.table with 65 rows and 4 columns keyed by student ID. The variables are:

mcid

Character, de-identified student ID.

institution

Character, de-identified institution name, e.g., Institution A, Institution B, etc.

term_degree

Character, academic year and term in which a student completes their program, format YYYYT.

cip6

Character, 6-digit CIP code of program in which a student is enrolled in a term.

See Also

Other toy-data: toy_course, toy_student, toy_term

Student data for examples

Description

Selected variables modeled on those in the student practice data for use in package examples and articles. Sampled from an early version of the practice data, the toy data are not a current practice data sample.

Usage

toy_student

Format

data.table with 99 rows and 4 columns keyed by student ID:

mcid

Character, de-identified student ID.

institution

Character, de-identified institution name, e.g., Institution A, Institution B, etc.

race

Character, race/ethnicity as self-reported by the student, e.g., Asian, Black, Latine, etc.

sex

Character, sex as self-reported by the student, possible values are Female, Male, and Unknown.

See Also

Other toy-data: toy_course, toy_degree, toy_term

Term data for examples

Description

Selected variables modeled on those in the term practice data for use in package examples and articles. Sampled from an early version of the practice data, the toy data are not a current practice data sample.

Usage

toy_term

Format

data.table with 150 rows and 5 columns keyed by student ID. The variables are:

mcid

Character, de-identified student ID.

institution

Character, de-identified institution name, e.g., Institution A, Institution B, etc.

term

Character, academic year and term, format YYYYT.

cip6

Character, 6-digit CIP code of program in which a student is enrolled in a term.

level

Character, 01 Freshman, 02 Sophomore, etc. The equivalent values in the current practice data are 01 First-Year, 02-Second Year, etc.

See Also

Other toy-data: toy_course, toy_degree, toy_student