Introduction to midfieldr • midfieldr

When working with student-level records to develop quantitative metrics, midfieldr helps you refine and shape your data in these areas:

Programs. Collect 6-digit program codes.
Records. Credibly subset source data and refine the population to suit your study.
Blocs. Construct group of records for computing metrics.

This document introduces you to midfieldr’s basic set of tools.

library("midfieldr")
library("midfielddata")
library("data.table")

Before you start:

On syntax: We use data.table syntax for data manipulation throughout midfieldr and all data frames are of the data.table class. However, if you happen to prefer tidyverse syntax, midfieldr functions do attempt to preserve data frame attributes such as the tbl_df class.
On functions: In getting started, we provide a brief introduction only. Details are discussed at length in subsequent articles. You can always access the documentation, e.g., ?function_name, for more information.

midfieldr functions

The major functions can be categorized based on their contribution to a typical workflow:

Programs

filter_programs() helps you find program names and CIP codes.

Records and population

timely_term() estimates timely completion terms.
data_sufficiency() identifies IDs to exclude due to insufficient data.
post_bacc_terms() identifies post-baccalaureate terms to exclude.

Blocs

select_basic_cols() chooses those columns required by other midfieldr functions.
completion_status() labels program completion as timely, late, or NA.

Special conditioning

prep_fye_mice() conditions data for imputing starting majors of FYE students.
order_multiway() conditions data for Cleveland multiway charts.

Program data

Collecting and labeling 6-digit program codes.

The Classification of Instructional Programs (CIP) is a taxonomy of academic programs, encoded by 6-digit numeric codes curated by the US Department of Education (NCES 2010).

The cip data set, loaded with midfieldr, is a subset of the NCES CIP2010 data that contains codes and names for 1582 instructional programs organized on three levels—a 6-digit series, a 4-digit series, and a 2-digit series—keyed by the cip6 variable.

cip
#>                                             cip6name   cip6
#>                                               <char> <char>
#>    1:                           Agriculture, General 010000
#>    2:  Agricultural Business and Management, General 010101
#>    3: Agribusiness, Agricultural Business Operations 010102
#>   ---                                                      
#> 1580:                               Military History 540108
#> 1581:                                 History, Other 540199
#> 1582:              NonIPEDS - Undecided, Unspecified 999999
#>                                   cip4name   cip4
#>                                     <char> <char>
#>    1:                 Agriculture, General   0100
#>    2: Agricultural Business and Management   0101
#>    3: Agricultural Business and Management   0101
#>   ---                                            
#> 1580:                              History   5401
#> 1581:                              History   5401
#> 1582:    NonIPEDS - Undecided, Unspecified   9999
#>                                                        cip2name   cip2
#>                                                          <char> <char>
#>    1: Agriculture, Agricultural Operations and Related Sciences     01
#>    2: Agriculture, Agricultural Operations and Related Sciences     01
#>    3: Agriculture, Agricultural Operations and Related Sciences     01
#>   ---                                                                 
#> 1580:                                                   History     54
#> 1581:                                                   History     54
#> 1582:                         NonIPEDS - Undecided, Unspecified     99

`filter_programs()`

Chooses rows of CIP data based on search terms.

filter_programs() acts on the data frame assigned to its dframe argument to select rows that match or partially match search strings. Search strings are case-independent and can include regular expressions.

filter_programs(cip, "music")
#>                     cip6name   cip6
#>                       <char> <char>
#>  1:  Music Teacher Education 131312
#>  2:                    Music 360115
#>  3:  Religious, Sacred Music 390501
#> ---                                
#> 23:             Music, Other 500999
#> 24:         Music Management 501003
#> 25: Music Therapy, Therapist 512305
#>                                                                   cip4name
#>                                                                     <char>
#>  1: Teacher Education and Professional Development, Specific Subject Areas
#>  2:                                    Leisure and Recreational Activities
#>  3:                                                Religious, Sacred Music
#> ---                                                                       
#> 23:                                                                  Music
#> 24:                               Arts, Entertainment and Media Management
#> 25:                             Rehabilitation and Therapeutic Professions
#>       cip4                                         cip2name   cip2
#>     <char>                                           <char> <char>
#>  1:   1313                                        Education     13
#>  2:   3601              Leisure and Recreational Activities     36
#>  3:   3905      Theological Studies and Religious Vocations     39
#> ---                                                               
#> 23:   5009                       Visual and Performing Arts     50
#> 24:   5010                       Visual and Performing Arts     50
#> 25:   5123 Health Professions and Related Clinical Sciences     51

To refine our results, we can assign the results of a first pass to the dframe argument of a second pass. For example, our first pass below searches the default cip dataset for “music”. Our second pass searches the results of the first pass for any line that starts with “50”.

first_pass <- filter_programs(cip, "music")
second_pass <- filter_programs(first_pass, "^50")
second_pass
#>                   cip6name   cip6                                 cip4name
#>                     <char> <char>                                   <char>
#>  1:           Digital Arts 500102            General Art and Music Studies
#>  2:        Musical Theatre 500509       Drama, Theatre Arts and Stagecraft
#>  3:         Music, General 500901                                    Music
#> ---                                                                       
#> 18: Percussion Instruments 500916                                    Music
#> 19:           Music, Other 500999                                    Music
#> 20:       Music Management 501003 Arts, Entertainment and Media Management
#>       cip4                   cip2name   cip2
#>     <char>                     <char> <char>
#>  1:   5001 Visual and Performing Arts     50
#>  2:   5005 Visual and Performing Arts     50
#>  3:   5009 Visual and Performing Arts     50
#> ---                                         
#> 18:   5009 Visual and Performing Arts     50
#> 19:   5009 Visual and Performing Arts     50
#> 20:   5010 Visual and Performing Arts     50

Alternatively, one may chain the operations,

cip |>
  filter_programs("music") |>
  filter_programs("^50")
#>                   cip6name   cip6                                 cip4name
#>                     <char> <char>                                   <char>
#>  1:           Digital Arts 500102            General Art and Music Studies
#>  2:        Musical Theatre 500509       Drama, Theatre Arts and Stagecraft
#>  3:         Music, General 500901                                    Music
#> ---                                                                       
#> 18: Percussion Instruments 500916                                    Music
#> 19:           Music, Other 500999                                    Music
#> 20:       Music Management 501003 Arts, Entertainment and Media Management
#>       cip4                   cip2name   cip2
#>     <char>                     <char> <char>
#>  1:   5001 Visual and Performing Arts     50
#>  2:   5005 Visual and Performing Arts     50
#>  3:   5009 Visual and Performing Arts     50
#> ---                                         
#> 18:   5009 Visual and Performing Arts     50
#> 19:   5009 Visual and Performing Arts     50
#> 20:   5010 Visual and Performing Arts     50

Assuming we are looking for programs in a School of Music, the 4-digit code “5009” appears to be the correct finding. Our third pass searches the results of the second pass for any line that starts with “5009”.

third_pass <- filter_programs(second_pass, "^5009")
third_pass
#>                                 cip6name   cip6 cip4name   cip4
#>                                   <char> <char>   <char> <char>
#>  1:                       Music, General 500901    Music   5009
#>  2: Music History, Literature and Theory 500902    Music   5009
#>  3:           Music Performance, General 500903    Music   5009
#> ---                                                            
#> 15:                 Woodwind Instruments 500915    Music   5009
#> 16:               Percussion Instruments 500916    Music   5009
#> 17:                         Music, Other 500999    Music   5009
#>                       cip2name   cip2
#>                         <char> <char>
#>  1: Visual and Performing Arts     50
#>  2: Visual and Performing Arts     50
#>  3: Visual and Performing Arts     50
#> ---                                  
#> 15: Visual and Performing Arts     50
#> 16: Visual and Performing Arts     50
#> 17: Visual and Performing Arts     50

If these were part of our study programs, we would save the 6-digit codes and names and add a program variable to label each row as needed to suit our goals. Here, we insert a placeholder value “label_TBD”. We might save the data frame as our programs data frame.

programs <- third_pass[, .(cip6name, cip6, program = "label_TBD")]
programs
#>                                                 cip6name   cip6   program
#>                                                   <char> <char>    <char>
#>  1:                                       Music, General 500901 label_TBD
#>  2:                 Music History, Literature and Theory 500902 label_TBD
#>  3:                           Music Performance, General 500903 label_TBD
#>  4:                         Music Theory and Composition 500904 label_TBD
#>  5:                       Musicology and Ethnomusicology 500905 label_TBD
#>  6:                                           Conducting 500906 label_TBD
#>  7:                                      Piano and Organ 500907 label_TBD
#>  8:                                      Voice and Opera 500908 label_TBD
#>  9:                   Music Management and Merchandising 500909 label_TBD
#> 10:                                   Jazz, Jazz Studies 500910 label_TBD
#> 11: Violin, Viola, Guitar and Other Stringed Instruments 500911 label_TBD
#> 12:                                       Music Pedagogy 500912 label_TBD
#> 13:                                     Music Technology 500913 label_TBD
#> 14:                                    Brass Instruments 500914 label_TBD
#> 15:                                 Woodwind Instruments 500915 label_TBD
#> 16:                               Percussion Instruments 500916 label_TBD
#> 17:                                         Music, Other 500999 label_TBD

Which specific programs we select depend on the study, of course, but in all cases the end result is a data frame like the one shown above with the 6-digit CIP codes, possibly the NCES 6-digit program name, and our own program labels.

Student-level data

Credibly subset the source data and refine the population.

For this article we load the student, term, and degree tables from midfielddata.

data(student, term, degree)

look_at() is a midfieldr convenience function that wraps base::str() using our preferred arguments. The data tables are linked by mcid, the anonymized student ID.

look_at(student)
#> Classes 'data.table' and 'data.frame':   97555 obs. of  13 variables:
#>  $ mcid          : chr  "MCID3111142225" "MCID3111142283" "MCID3111142290" "M"..
#>  $ race          : chr  "Asian" "Asian" "Asian" "Asian" ...
#>  $ sex           : chr  "Male" "Female" "Male" "Male" ...
#>  $ institution   : chr  "Institution B" "Institution J" "Institution J" "Inst"..
#>  $ transfer      : chr  "First-Time Transfer" "First-Time Transfer" "First-Ti"..
#>  $ hours_transfer: num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ age_desc      : chr  "Under 25" "Under 25" "Under 25" "Under 25" ...
#>  $ us_citizen    : chr  "Yes" "Yes" "Yes" "Yes" ...
#>  $ home_zip      : chr  NA "22020" "23233" "20853" ...
#>  $ high_school   : chr  NA NA "471872" NA ...
#>  $ sat_math      : num  NA 560 510 640 600 570 480 NA NA NA ...
#>  $ sat_verbal    : num  NA 230 380 460 500 530 530 NA NA NA ...
#>  $ act_comp      : num  NA NA NA NA NA NA NA NA NA NA ...

look_at(term)
#> Classes 'data.table' and 'data.frame':   639915 obs. of  13 variables:
#>  $ mcid               : chr  "MCID3111142225" "MCID3111142283" "MCID311114228"..
#>  $ term               : chr  "19881" "19881" "19883" "19885" ...
#>  $ cip6               : chr  "140901" "240102" "240102" "190601" ...
#>  $ institution        : chr  "Institution B" "Institution J" "Institution J" "..
#>  $ level              : chr  "01 First-year" "01 First-year" "01 First-year" "..
#>  $ standing           : chr  "Good Standing" "Academic Probation" "Academic P"..
#>  $ coop               : chr  "No" "No" "No" "No" ...
#>  $ hours_term         : num  7 6 12 6 6 6 6 18 15 14 ...
#>  $ hours_term_attempt : num  7 6 12 6 6 6 6 18 18 14 ...
#>  $ hours_cumul        : num  7 6 18 24 30 36 42 63 78 14 ...
#>  $ hours_cumul_attempt: num  7 6 18 24 30 36 42 63 81 14 ...
#>  $ gpa_term           : num  2.56 1.85 1.93 2.15 1.85 1.2 1.85 2.33 2.32 2.15 ..
#>  $ gpa_cumul          : num  2.56 1.85 1.9 1.96 1.94 1.82 1.82 1.98 2.04 2.15 ..

look_at(degree)
#> Classes 'data.table' and 'data.frame':   49665 obs. of  5 variables:
#>  $ mcid       : chr  "MCID3111142225" "MCID3111142290" "MCID3111142294" "MCID"..
#>  $ term_degree: chr  "19881" "19921" "19903" "19921" ...
#>  $ cip6       : chr  "141001" "141001" "141001" "141001" ...
#>  $ institution: chr  "Institution B" "Institution J" "Institution J" "Institu"..
#>  $ degree     : chr  "Bachelor of Science in Electrical Engineering" "Bachelo"..

As we prepare the data, we copy our “source” material under separate names (and locations in memory) at a couple of key points.

student_source <- copy(student)
term_source <- copy(term)
degree_source <- copy(degree)

Then we can use the shorter names such as term and degree as we work. The shorter names are also the default values for arguments in several midfieldr functions.

`post_bacc_terms()`

Identify rows of post-baccalaureate terms to exclude.

In most cases, we are not generally interested in academic terms beyond the first degree term, so we use the results of this function to exclude post-first-degree terms from the source data.

post_bacc_terms() identifies terms later than the first baccalaureate, if any.

term <- post_bacc_terms(term_source, midfield_table = degree)
degree <- post_bacc_terms(degree_source, midfield_table = degree)

post_bacc_terms() adds a column indicating the cluster a term belongs to with respect to the first degree term.

look_at(term)
#> Classes 'data.table' and 'data.frame':   639915 obs. of  15 variables:
#>  $ mcid               : chr  "MCID3111142225" "MCID3111142283" "MCID311114228"..
#>  $ term               : chr  "19881" "19881" "19883" "19885" ...
#>  $ cip6               : chr  "140901" "240102" "240102" "190601" ...
#>  $ institution        : chr  "Institution B" "Institution J" "Institution J" "..
#>  $ level              : chr  "01 First-year" "01 First-year" "01 First-year" "..
#>  $ standing           : chr  "Good Standing" "Academic Probation" "Academic P"..
#>  $ coop               : chr  "No" "No" "No" "No" ...
#>  $ hours_term         : num  7 6 12 6 6 6 6 18 15 14 ...
#>  $ hours_term_attempt : num  7 6 12 6 6 6 6 18 18 14 ...
#>  $ hours_cumul        : num  7 6 18 24 30 36 42 63 78 14 ...
#>  $ hours_cumul_attempt: num  7 6 18 24 30 36 42 63 81 14 ...
#>  $ gpa_term           : num  2.56 1.85 1.93 2.15 1.85 1.2 1.85 2.33 2.32 2.15 ..
#>  $ gpa_cumul          : num  2.56 1.85 1.9 1.96 1.94 1.82 1.82 1.98 2.04 2.15 ..
#>  $ first_degree_term  : chr  "19881" NA NA NA ...
#>  $ term_cluster       : chr  "first-degree" "pre-degree" "pre-degree" "pre-de"..

The possible term cluster values are given by,

term[, sort(unique(term_cluster), na.last = FALSE)]
#> [1] "first-degree"      "post-first-degree" "pre-degree"

We filter to exclude all terms labeled “post-first-degree” and drop the temporary columns.

term <- term[!"post-first-degree", on = "term_cluster"]
degree <- degree[!"post-first-degree", on = "term_cluster"]

term[, c("term_cluster", "first_degree_term") := NULL]
degree[, c("term_cluster", "first_degree_term") := NULL]

look_at(term)
#> Classes 'data.table' and 'data.frame':   632917 obs. of  13 variables:
#>  $ mcid               : chr  "MCID3111142225" "MCID3111142283" "MCID311114228"..
#>  $ term               : chr  "19881" "19881" "19883" "19885" ...
#>  $ cip6               : chr  "140901" "240102" "240102" "190601" ...
#>  $ institution        : chr  "Institution B" "Institution J" "Institution J" "..
#>  $ level              : chr  "01 First-year" "01 First-year" "01 First-year" "..
#>  $ standing           : chr  "Good Standing" "Academic Probation" "Academic P"..
#>  $ coop               : chr  "No" "No" "No" "No" ...
#>  $ hours_term         : num  7 6 12 6 6 6 6 18 15 14 ...
#>  $ hours_term_attempt : num  7 6 12 6 6 6 6 18 18 14 ...
#>  $ hours_cumul        : num  7 6 18 24 30 36 42 63 78 14 ...
#>  $ hours_cumul_attempt: num  7 6 18 24 30 36 42 63 81 14 ...
#>  $ gpa_term           : num  2.56 1.85 1.93 2.15 1.85 1.2 1.85 2.33 2.32 2.15 ..
#>  $ gpa_cumul          : num  2.56 1.85 1.9 1.96 1.94 1.82 1.82 1.98 2.04 2.15 ..

We redefine our source material to incorporate the exclusion of post-baccalaureate terms.

term_source <- copy(term)
degree_source <- copy(degree)

`timely_term()`

Determine the term by which degree completion would be considered timely.

In this section, we begin refining the population, so we start with a unique set of IDs from the term record obtained above.

DT <- term[, .(mcid)]
DT <- unique(DT)

DT
#>                  mcid
#>                <char>
#>     1: MCID3111142225
#>     2: MCID3111142283
#>     3: MCID3111142290
#>    ---               
#> 97534: MCID3112898894
#> 97535: MCID3112898895
#> 97536: MCID3112898940

timely_term() builds a data frame with one row per student, a column for the timely completion term, and columns of supporting information. This data frame contains the required inputs for both data_sufficiency() and timely_completion().

DT <- timely_term(DT, midfield_table = term)

DT
#>                  mcid term_i       level_i adj_span timely_term
#>                <char> <char>        <char>    <num>      <char>
#>     1: MCID3111142225  19881 01 First-year        6       19933
#>     2: MCID3111142283  19881 01 First-year        6       19933
#>     3: MCID3111142290  19881 01 First-year        6       19933
#>    ---                                                         
#> 97534: MCID3112898894  20181 01 First-year        6       20233
#> 97535: MCID3112898895  20181 01 First-year        6       20233
#> 97536: MCID3112898940  20181 01 First-year        6       20233

`data_sufficiency()`

Identify members of the population to exclude due to insufficient data.

Data sufficiency is an assessment whether a student record lies sufficiently within their institution’s data range to unambiguously assess their completion status and if so include them in the study population.

data_sufficiency() builds on the output from timely_term(), labels rows to be included or excluded based on the data sufficiency finding, and generates additional columns of supporting information.

DT <- data_sufficiency(DT, midfield_table = term)

DT
#>                  mcid term_i       level_i adj_span timely_term   institution
#>                <char> <char>        <char>    <num>      <char>        <char>
#>     1: MCID3111142225  19881 01 First-year        6       19933 Institution B
#>     2: MCID3111142283  19881 01 First-year        6       19933 Institution J
#>     3: MCID3111142290  19881 01 First-year        6       19933 Institution J
#>    ---                                                                       
#> 97534: MCID3112898894  20181 01 First-year        6       20233 Institution B
#> 97535: MCID3112898895  20181 01 First-year        6       20233 Institution B
#> 97536: MCID3112898940  20181 01 First-year        6       20233 Institution B
#>        lower_limit upper_limit data_sufficiency
#>             <char>      <char>           <char>
#>     1:       19881       20181    exclude-lower
#>     2:       19881       20096    exclude-lower
#>     3:       19881       20096    exclude-lower
#>    ---                                         
#> 97534:       19881       20181    exclude-upper
#> 97535:       19881       20181    exclude-upper
#> 97536:       19881       20181    exclude-upper

The possible values for data sufficiency are:

DT[, sort(unique(data_sufficiency), na.last = FALSE)]
#> [1] "exclude-lower" "exclude-upper" "include"

We filter to retain rows labeled “include”. The resulting IDs define our baseline population.

population <- DT["include", on = "data_sufficiency", .(mcid)]
population <- unique(population)

population
#>                  mcid
#>                <char>
#>     1: MCID3111142689
#>     2: MCID3111142782
#>     3: MCID3111142881
#>    ---               
#> 76863: MCID3112785480
#> 76864: MCID3112800920
#> 76865: MCID3112870009

We use this population to filter our source material one last time. We use an inner join to return records for this population only.

student_source <- population[student_source, on = "mcid", nomatch = NULL]
term_source <- population[term_source, on = "mcid", nomatch = NULL]
degree_source <- population[degree_source, on = "mcid", nomatch = NULL]

look_at(term_source)
#> Classes 'data.table' and 'data.frame':   525446 obs. of  13 variables:
#>  $ mcid               : chr  "MCID3111142689" "MCID3111142782" "MCID311114278"..
#>  $ term               : chr  "19883" "19883" "19885" "19893" ...
#>  $ cip6               : chr  "090401" "260101" "260101" "260101" ...
#>  $ institution        : chr  "Institution B" "Institution J" "Institution J" "..
#>  $ level              : chr  "01 First-year" "01 First-year" "02 Second-year""..
#>  $ standing           : chr  "Good Standing" "Good Standing" "Good Standing" "..
#>  $ coop               : chr  "No" "No" "No" "No" ...
#>  $ hours_term         : num  9 16 4 13 4 4 10 9 18 6 ...
#>  $ hours_term_attempt : num  9 16 4 13 4 4 10 9 18 6 ...
#>  $ hours_cumul        : num  18 26 30 56 60 64 74 83 21 27 ...
#>  $ hours_cumul_attempt: num  18 26 30 56 60 64 74 83 21 27 ...
#>  $ gpa_term           : num  3.33 2.8 3 2.84 4 3.25 2.26 2.43 2.55 2.15 ...
#>  $ gpa_cumul          : num  3.05 2.57 2.63 2.53 2.63 2.67 2.61 2.59 2.76 2.62..

In subsequent analysis, any variables we need from the source data has already been filtered to satisfy the data sufficiency constraint and to exclude post-baccalaureate terms.

From this point forward, anytime we need a fresh copy of any of the data tables, we copy the “source” version. Anytime we need a starting population, we use population or unique IDs from student_source or term_source.

student <- copy(student_source)
term <- copy(term_source)
degree <- copy(degree_source)

all.equal(population$mcid, student_source$mcid)
#> [1] TRUE
all.equal(population$mcid, unique(term_source$mcid))
#> [1] TRUE

`select_basic_cols()`

Choose columns required by midfieldr functions.

select_basic_cols() operates on student records to reduce the number of columns to those required by other midfieldr functions plus the key or composite key variables of the four data tables. With a smaller number of columns, the printout of the data frame is more readable, a benefit when working with the data interactively.

student <- select_basic_cols(student)
term <- select_basic_cols(term)
degree <- select_basic_cols(degree)

student
#>                  mcid          race    sex
#>                <char>        <char> <char>
#>     1: MCID3111142689      Hispanic Female
#>     2: MCID3111142782      Hispanic Female
#>     3: MCID3111142881 International   Male
#>    ---                                    
#> 76863: MCID3112785480         White   Male
#> 76864: MCID3112800920         White Female
#> 76865: MCID3112870009         White   Male

term
#>                   mcid   term   cip6   institution          level
#>                 <char> <char> <char>        <char>         <char>
#>      1: MCID3111142689  19883 090401 Institution B  01 First-year
#>      2: MCID3111142782  19883 260101 Institution J  01 First-year
#>      3: MCID3111142782  19885 260101 Institution J 02 Second-year
#>     ---                                                          
#> 525444: MCID3112870009  19953 240102 Institution B  01 First-year
#> 525445: MCID3112870009  19954 240102 Institution B  01 First-year
#> 525446: MCID3112870009  19983 240102 Institution B 02 Second-year

degree
#>                  mcid term_degree   cip6
#>                <char>      <char> <char>
#>     1: MCID3111142689       19913 090401
#>     2: MCID3111142782       19903 260101
#>     3: MCID3111142881       19894 450601
#>    ---                                  
#> 43845: MCID3112694738       20143 230101
#> 43846: MCID3112698681       20181 110701
#> 43847: MCID3112730841       20164 040401

Any variables you might need that have been dropped can always be recovered from the source tables we saved earlier. For example, if we needed GPA in our working term table, we can use a left join, knowing that student ID and term are the composite keys in this case.

x <- copy(term)
source_cols <- term_source[, .(mcid, term, gpa_term, gpa_cumul)]
x <- source_cols[x, on = c("mcid", "term")]
x
#>                   mcid   term gpa_term gpa_cumul   cip6   institution
#>                 <char> <char>    <num>     <num> <char>        <char>
#>      1: MCID3111142689  19883     3.33      3.05 090401 Institution B
#>      2: MCID3111142782  19883     2.80      2.57 260101 Institution J
#>      3: MCID3111142782  19885     3.00      2.63 260101 Institution J
#>     ---                                                              
#> 525444: MCID3112870009  19953     3.57      3.71 240102 Institution B
#> 525445: MCID3112870009  19954     4.00      3.72 240102 Institution B
#> 525446: MCID3112870009  19983     4.00      3.87 240102 Institution B
#>                  level
#>                 <char>
#>      1:  01 First-year
#>      2:  01 First-year
#>      3: 02 Second-year
#>     ---               
#> 525444:  01 First-year
#> 525445:  01 First-year
#> 525446: 02 Second-year

Keys and composite keys to the four data tables are described in the midfielddata Data structure article.

`completion_status()`

Determines if program completion is timely or late.

This section often pertains to constructing a bloc of graduates, starting with the baseline population we obtained above.

DT <- copy(population)

completion_status() builds on the output from timely_term(), labels rows to indicate whether a student completes a degree timely or late compared to their timely completion term (or NA for no completion), and includes columns for the timely term and degree term as supporting information.

DT <- timely_term(DT, midfield_table = term)
DT <- completion_status(DT, midfield_table = degree)

DT
#>                  mcid term_i       level_i adj_span timely_term term_degree
#>                <char> <char>        <char>    <num>      <char>      <char>
#>     1: MCID3111142689  19883 01 First-year        6       19941       19913
#>     2: MCID3111142782  19883 01 First-year        6       19941       19903
#>     3: MCID3111142881  19893 01 First-year        6       19951       19894
#>    ---                                                                     
#> 76863: MCID3112785480  20071 01 First-year        6       20123        <NA>
#> 76864: MCID3112800920  20101 01 First-year        6       20153        <NA>
#> 76865: MCID3112870009  19951 01 First-year        6       20003        <NA>
#>        completion_status
#>                   <char>
#>     1:            timely
#>     2:            timely
#>     3:            timely
#>    ---                  
#> 76863:              <NA>
#> 76864:              <NA>
#> 76865:              <NA>

The possible values for completion status are:

DT[, sort(unique(completion_status), na.last = FALSE)]
#> [1] NA       "late"   "timely"

If we were constructing a bloc of timely graduates, we would filter to retain rows labeled “timely”. The resulting IDs would define our graduates bloc.

graduates <- DT["timely", on = "completion_status", .(mcid)]
graduates[, bloc := "grad"]
graduates
#>                  mcid   bloc
#>                <char> <char>
#>     1: MCID3111142689   grad
#>     2: MCID3111142782   grad
#>     3: MCID3111142881   grad
#>    ---                      
#> 40428: MCID3112692944   grad
#> 40429: MCID3112694738   grad
#> 40430: MCID3112730841   grad

Other functions

prep_fye_mice() Conditions data for imputing the starting majors of First-Year Engineering (FYE) students. Used when blocs of starters in Engineering are needed and an institution has a required FYE program. For details see FYE proxies.

order_multiway() Conditions data for Cleveland multiway charts. The ordering of its rows and panels is crucial to the perception of effects. Used when data have a multiway structure. For details see Multiway data and charts.

Utilities

look_at() for data frames, wraps base str() with preset arguments.
sort_uniq() for vectors, wraps base sort(unique()) with preset arguments.
catch_error() wraps base tryCatch() for errors with preset arguments.
check_equiv_frames() re-exported from the wrapr package

References

NCES. 2010. IPEDS Classification of Instructional Programs (CIP). National Center for Education Statistics. https://nces.ed.gov/ipeds/cipcode/.