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Determine completion status

Source: R/completion_status.R
completion_status.Rd

Identifies students completing a program in a timely manner. Adds columns to the data frame that support the finding.

Usage

completion_status(dframe, midf_table = degree)

Arguments

dframe

Data frame or data frame extension (e.g., data.table or tibble) with required variables {mcid, timely_term}.

midf_table

degree data frame with required variables {mcid, term_degree}.

Value

Data frame with the following properties:

  • Data frame class is preserved. Groups and keys are not preserved.

  • Row order is preserved. Rows with NA values in any of the required variables are removed. Duplicated rows are removed.

  • New columns are added unless they are redundant (see Details). The new variables are:

    • bacc_term   Character. Term of a student's first baccalaureate, encoded YYYYT or, if no degree recorded, NA. Joined from the term_degree variable in midf_table.

    • completion   Character. Completion status, possible values of "timely", "late", and "NA".

Details

If a population has been filtered for data sufficiency, then determining every student's completion status is feasible. Completing an academic program in a timely manner means that a student completes the requirements for a degree within a set time span, typically 4, 6, or 8 years after admission depending on the definition adopted in a particular study. The term at the end of that span is the timely completion term.

If the student's degree term is no later than their timely completion term, then their completion status is "timely"; if later, their status is "late". For students with no degree, completion status is NA.

Redundant columns. To prevent overwriting, a variable such as bacc_term is not added to the data frame if it duplicates an existing variable. The test for redundancy is managed internally by calling rm_redundant_cols() before the final data frame is returned. For documentation, see ?rm_redundant_cols.

Examples

# Assign toy data sets
student <- toy_student
term <- toy_term
degree <- toy_degree

# Start with a selected population
x <- student[c(9:11, 21:30, 344:345), .(mcid)]
x
#>               mcid
#>             <char>
#>  1: MCID3111169729
#>  2: MCID3111170852
#>  3: MCID3111173999
#>  4: MCID3111257807
#>  5: MCID3111258275
#>  6: MCID3111258347
#>  7: MCID3111259642
#>  8: MCID3111262210
#>  9: MCID3111265287
#> 10: MCID3111269576
#> 11: MCID3111272691
#> 12: MCID3111272880
#> 13: MCID3111277081
#> 14: MCID3112751130
#> 15: MCID3112754537

# timely_term() to add required columns
x <- timely_term(x, midf_table = term)
x <- x[, .(mcid, timely_term)]
x
#>               mcid timely_term
#>             <char>      <char>
#>  1: MCID3111169729       19933
#>  2: MCID3111170852       19933
#>  3: MCID3111173999       19933
#>  4: MCID3111257807       19953
#>  5: MCID3111258275       19953
#>  6: MCID3111258347       19953
#>  7: MCID3111259642       19953
#>  8: MCID3111262210       19953
#>  9: MCID3111265287       19953
#> 10: MCID3111269576       19953
#> 11: MCID3111272691       19953
#> 12: MCID3111272880       19953
#> 13: MCID3111277081       19961
#> 14: MCID3112751130       20203
#> 15: MCID3112754537       20203

# Add completion status columns
x <- completion_status(x, midf_table = degree)
x
#>               mcid timely_term bacc_term completion
#>             <char>      <char>    <char>     <char>
#>  1: MCID3111169729       19933     19901     timely
#>  2: MCID3111170852       19933      <NA>       <NA>
#>  3: MCID3111173999       19933      <NA>       <NA>
#>  4: MCID3111257807       19953     19964       late
#>  5: MCID3111258275       19953     19921     timely
#>  6: MCID3111258347       19953     19923     timely
#>  7: MCID3111259642       19953     19934     timely
#>  8: MCID3111262210       19953     19951     timely
#>  9: MCID3111265287       19953     19904     timely
#> 10: MCID3111269576       19953     19943     timely
#> 11: MCID3111272691       19953     19914     timely
#> 12: MCID3111272880       19953     19934     timely
#> 13: MCID3111277081       19961     19963       late
#> 14: MCID3112751130       20203     20171     timely
#> 15: MCID3112754537       20203      <NA>       <NA>

# No change if added columns duplicate existing
y <- completion_status(x, midf_table = degree)
check_equiv_frames(x, y)
#> [1] TRUE

# Filter to retain "timely" rows only
x[completion == "timely"]
#>               mcid timely_term bacc_term completion
#>             <char>      <char>    <char>     <char>
#>  1: MCID3111169729       19933     19901     timely
#>  2: MCID3111258275       19953     19921     timely
#>  3: MCID3111258347       19953     19923     timely
#>  4: MCID3111259642       19953     19934     timely
#>  5: MCID3111262210       19953     19951     timely
#>  6: MCID3111265287       19953     19904     timely
#>  7: MCID3111269576       19953     19943     timely
#>  8: MCID3111272691       19953     19914     timely
#>  9: MCID3111272880       19953     19934     timely
#> 10: MCID3112751130       20203     20171     timely