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Determine completion status

Source: R/completion_status.R
completion_status.Rd

Determine the completion status for each student in a data frame and add columns that support the findings.

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.

  • Columns with names different from the new columns (named below) are not modified; columns with matching names are replaced. The new columns added are:

    • completion_term   Equal to term_degree from midf_table.

    • completion_status   Character. 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.

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

# Add the required columns from timely_term()
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 completion_term completion_status
#>             <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>

# If you repeat, the new columns are overwritten
completion_status(x, midf_table = degree)
#>               mcid timely_term completion_term completion_status
#>             <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>

# Typical application retains "timely" rows only
x[completion_status == "timely"]
#>               mcid timely_term completion_term completion_status
#>             <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