Determine completion status

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

Usage

completion_status(dframe, midfield_table = degree)

Arguments

dframe

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

midfield_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:

  • term_degree   Joined from midfield_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

term <- toy_term
degree <- toy_degree

# Start with a selected population. 
x <- toy_student[21:36, .(mcid, sex)]
x
#>               mcid    sex
#>             <char> <char>
#>  1: MCID3111257807 Female
#>  2: MCID3111258275   Male
#>  3: MCID3111258347 Female
#>  4: MCID3111259642   Male
#>  5: MCID3111262210   Male
#>  6: MCID3111265287   Male
#>  7: MCID3111269576   Male
#>  8: MCID3111272691 Female
#>  9: MCID3111272880 Female
#> 10: MCID3111277081   Male
#> 11: MCID3111278815   Male
#> 12: MCID3111282337 Female
#> 13: MCID3111296595   Male
#> 14: MCID3111301718 Female
#> 15: MCID3111310842 Female
#> 16: MCID3111311799 Female

# Add the required columns from timely_term().
x <- timely_term(x, midfield_table = term)
x <- x[, .(mcid, sex, timely_term)]
x
#>               mcid    sex timely_term
#>             <char> <char>      <char>
#>  1: MCID3111257807 Female       19953
#>  2: MCID3111258275   Male       19953
#>  3: MCID3111258347 Female       19953
#>  4: MCID3111259642   Male       19953
#>  5: MCID3111262210   Male       19953
#>  6: MCID3111265287   Male       19953
#>  7: MCID3111269576   Male       19953
#>  8: MCID3111272691 Female       19953
#>  9: MCID3111272880 Female       19953
#> 10: MCID3111277081   Male       19961
#> 11: MCID3111278815   Male       19961
#> 12: MCID3111282337 Female       19963
#> 13: MCID3111296595   Male       19963
#> 14: MCID3111301718 Female       19963
#> 15: MCID3111310842 Female       19963
#> 16: MCID3111311799 Female       19963

# Add completion status columns. Unrelated columns (sex) are unaffected.
x <- completion_status(x, midfield_table = degree)
x
#>               mcid    sex timely_term term_degree completion_status
#>             <char> <char>      <char>      <char>            <char>
#>  1: MCID3111257807 Female       19953       19964              late
#>  2: MCID3111258275   Male       19953       19921            timely
#>  3: MCID3111258347 Female       19953       19923            timely
#>  4: MCID3111259642   Male       19953       19934            timely
#>  5: MCID3111262210   Male       19953       19951            timely
#>  6: MCID3111265287   Male       19953       19904            timely
#>  7: MCID3111269576   Male       19953       19943            timely
#>  8: MCID3111272691 Female       19953       19914            timely
#>  9: MCID3111272880 Female       19953       19934            timely
#> 10: MCID3111277081   Male       19961       19963              late
#> 11: MCID3111278815   Male       19961        <NA>              <NA>
#> 12: MCID3111282337 Female       19963       19924            timely
#> 13: MCID3111296595   Male       19963        <NA>              <NA>
#> 14: MCID3111301718 Female       19963        <NA>              <NA>
#> 15: MCID3111310842 Female       19963        <NA>              <NA>
#> 16: MCID3111311799 Female       19963        <NA>              <NA>

# Repeat. New columns silently replace existing columns of the same name.
y <- completion_status(x, midfield_table = degree)
y
#>               mcid    sex timely_term term_degree completion_status
#>             <char> <char>      <char>      <char>            <char>
#>  1: MCID3111257807 Female       19953       19964              late
#>  2: MCID3111258275   Male       19953       19921            timely
#>  3: MCID3111258347 Female       19953       19923            timely
#>  4: MCID3111259642   Male       19953       19934            timely
#>  5: MCID3111262210   Male       19953       19951            timely
#>  6: MCID3111265287   Male       19953       19904            timely
#>  7: MCID3111269576   Male       19953       19943            timely
#>  8: MCID3111272691 Female       19953       19914            timely
#>  9: MCID3111272880 Female       19953       19934            timely
#> 10: MCID3111277081   Male       19961       19963              late
#> 11: MCID3111278815   Male       19961        <NA>              <NA>
#> 12: MCID3111282337 Female       19963       19924            timely
#> 13: MCID3111296595   Male       19963        <NA>              <NA>
#> 14: MCID3111301718 Female       19963        <NA>              <NA>
#> 15: MCID3111310842 Female       19963        <NA>              <NA>
#> 16: MCID3111311799 Female       19963        <NA>              <NA>