# Programs

In the US, instructional programs are encoded by 6-digit numbers curated by the US Department of Education. The US standard encoding format is a two-digit number followed by a period, followed by a four-digit number, for example, 14.0102. MIDFIELD uses the same numerals, but omits the period, i.e., 140102, and stores the variable as a character string.

This article in the MIDFIELD workflow.

1. Planning
2. Initial processing
   - Data sufficiency
   - Degree seeking
   - Identify programs
3. Blocs
4. Groupings
5. Metrics
6. Displays

## Definitions

program

  US academic field of study. Can be used to indicate a specialty within a field or a collection of fields within a Department, College, or University. Programs are denoted by the Classification of Instructional Programs (CIP), a taxonomy of academic programs curated by the US Department of Education (NCES 2010).

CIP

  Classification of Instructional Programs, a taxonomy of academic programs curated by the US Department of Education (NCES 2010). The 2010 codes are included with midfieldr in the data set `cip`.

`cip6`

  Character variable in the `term` and `degree` data tables of program observations. Values are 6-digit CIP codes.

## Method

We search the `cip` data set included with midfieldr using a variety of techniques to obtain the set of 6-digit CIP codes for the programs under study. We assign custom program names to codes or groups of codes.

## Taxonomy

Academic programs have three levels of codes and names:

- 6-digit code, a specific program
- 4-digit code, a group of 6-digit programs of comparable content
- 2-digit code, a grouping of 4-digit groups of related content

Specialties within a discipline are encoded at the 6-digit level, the discipline itself is represented by one or more 4-digit codes (roughly corresponding to an academic department), and a collection of disciplines are represented by one or more 2-digit codes (roughly corresponding to an academic college).

For example, Geotechnical Engineering (140802) is a specialty in Civil Engineering (1408) which is a department in the college of Engineering (14).

To illustrate the taxonomy in a little more detail, we show in the table the programs assigned to the 2-digit code 41, “Science Technologies, Technicians”. This 2-digit grouping is subdivided into 5 groups at the 4-digit level (codes 4100–4199) which are further subdivided into 9 programs at the 6-digit level (codes 410000–419999).

Table 1: CIP taxonomy
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
41 | Science Technologies, Technicians | 4100 | Science Technologies, Technicians, General | 410000 | Science Technologies, Technicians, General
41 | ↓ | 4101 | Biology Technician, Biotechnology Laboratory Technician | 410101 | Biology Technician, Biotechnology Laboratory Technician
41 | ↓ | 4102 | Nuclear and Industrial Radiologic Technologies, Technicians | 410204 | Industrial Radiologic Technology, Technician
41 | ↓ | 4102 | ↓ | 410205 | Nuclear, Nuclear Power Technology, Technician
41 | ↓ | 4102 | ↓ | 410299 | Nuclear and Industrial Radiologic Technologies, Technicians, Other
41 | ↓ | 4103 | Physical Science Technologies, Technicians | 410301 | Chemical Technology, Technician
41 | ↓ | 4103 | ↓ | 410303 | Chemical Process Technology
41 | ↓ | 4103 | ↓ | 410399 | Physical Science Technologies, Technicians, Other
41 | ↓ | 4199 | Science Technologies, Technicians, Other | 419999 | Science Technologies, Technicians, Other

A 2-digit program can include anywhere from four 4-digit programs (e.g., code 24 Liberal Arts and Sciences, General Studies and Humanities) to 238 4-digit programs (e.g., code 51 Health Professions and Related Clinical Sciences).

And 4-digit programs include anywhere from one 6-digit program (e.g., code 4100 above) to 37 6-digit programs (e.g., code 1313 Education).

Unfortunately, some disciplines can comprise more than one 4-digit code. For example, the programs that comprise the broad discipline of Industrial and Systems Engineering encompass four distinct 4-digit codes: 1427 Systems Engineering, 1435 Industrial Engineering, 1436 Manufacturing Engineering, and 1437 Operations Research. Hence the importance of being able to search all CIP data for programs of interest.

## Load data

Start. If you are writing your own script to follow along, we use these packages in this article:

    library(midfieldr)
    library(data.table)

Loads with midfieldr. Prepared data, adapted from (NCES 2010). View data dictionary via `?cip`.

- `cip`

## Inspect the `cip` data

First glance.

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

All variables in `cip` are character strings, which protects the leading zeros of some CIP codes.

    # Names and class of the CIP variables
    cip[, lapply(.SD, class)]
    #>         cip2  cip2name      cip4  cip4name      cip6  cip6name
    #> 1: character character character character character character

The number of unique programs.

    # 2-digit level
    sort(unique(cip$cip2))
    #>  [1] "01" "03" "04" "05" "09" "10" "11" "12" "13" "14" "15" "16" "19" "22" "23"
    #> [16] "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37" "38"
    #> [31] "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "54"
    #> [46] "99"

    # 4-digit level
    length(unique(cip$cip4))
    #> [1] 394

    # 6-digit level
    length(unique(cip$cip6))
    #> [1] 1582

A sample of program names uses a random number generator, so your result will differ from that shown.

    # 2-digit name sample
    sample(cip[, cip2name], 10)
    #>  [1] "Education"
    #>  [2] "Foreign Languages, Literatures and Linguistics"
    #>  [3] "Business, Management, Marketing and Related Support Services"
    #>  [4] "Engineering"
    #>  [5] "Family and Consumer Sciences, Human Sciences"
    #>  [6] "Engineering Technology"
    #>  [7] "Health Professions and Related Clinical Sciences"
    #>  [8] "Business, Management, Marketing and Related Support Services"
    #>  [9] "Health Professions and Related Clinical Sciences"
    #> [10] "Physical Sciences"

    # 4-digit name sample
    sample(cip[, cip4name], 10)
    #>  [1] "Allied Health Diagnostic, Intervention Treatment Professions"
    #>  [2] "Applied Horticulture, Horticultural Business Services"
    #>  [3] "Ophthalmic and Optometric Support Services and Allied Professions"
    #>  [4] "Specialized Sales, Merchandising and Marketing Operations"
    #>  [5] "Engineering-Related Fields"
    #>  [6] "Teacher Education and Professional Development, Specific Subject Areas"
    #>  [7] "Allied Health Diagnostic, Intervention Treatment Professions"
    #>  [8] "Leatherworking and Upholstery"
    #>  [9] "Health, Medical Preparatory Programs"
    #> [10] "Research and Experimental Psychology"

    # 6-digit name sample
    sample(cip[, cip6name], 10)
    #>  [1] "Soil Sciences, Other"
    #>  [2] "Health, Medical Physics"
    #>  [3] "Adult Literacy Tutor, Instructor"
    #>  [4] "Environmental Design, Architecture"
    #>  [5] "Advanced, Graduate Dentistry and Oral Sciences, Other"
    #>  [6] "Dental Materials (MS, PhD)"
    #>  [7] "Drafting and Design Technology, Technician, General"
    #>  [8] "Chemical Engineering Technology, Technician"
    #>  [9] "Social Science Teacher Education"
    #> [10] "Sports and Exercise"

## `filter_cip()`

Subset the `cip` data frame, retaining rows that match or partially match a vector of character strings.

Arguments.

- `keep_text` Character vector of search text for retaining rows, not case-sensitive. Can be empty if `drop_text` is used.

- `drop_text` Character vector of search text for dropping rows, not case-sensitive, default NULL. Argument to be used by name.

- `cip` Data frame to be subset, default `cip`. Argument to be used by name.

- `select` Character vector of column names to search and return, default all columns. Argument to be used by name.

Equivalent usage. The following implementations yield identical results,

    # First argument named, CIP argument if used must be named
    x <- filter_cip(keep_text = c("engineering"), cip = cip)

    # First argument unnamed, use default CIP argument
    y <- filter_cip("engineering")

    # Demonstrate equivalence
    same_content(x, y)
    #> [1] TRUE

Output. Subset of `cip` with rows matching elements of `keep_text`. Additional subsetting if optional arguments specified. Examples follow.

## Using a keyword search

Filtering the CIP data for all programs containing the word “engineering” yields 119 observations.

    # Filter basics
    filter_cip("engineering")
    #>      cip2                                         cip2name cip4
    #>   1:   14                                      Engineering 1401
    #>   2:   14                                      Engineering 1401
    #>   3:   14                                      Engineering 1402
    #>  ---
    #> 117:   29                            Military Technologies 2903
    #> 118:   29                            Military Technologies 2903
    #> 119:   51 Health Professions and Related Clinical Sciences 5123
    #>                                                   cip4name   cip6
    #>   1:                                  Engineering, General 140101
    #>   2:                                  Engineering, General 140102
    #>   3: Aerospace, Aeronautical and Astronautical Engineering 140201
    #>  ---
    #> 117:                             Military Applied Sciences 290301
    #> 118:                             Military Applied Sciences 290303
    #> 119:            Rehabilitation and Therapeutic Professions 512312
    #>                                                              cip6name
    #>   1:                                             Engineering, General
    #>   2:                                                  Pre-Engineering
    #>   3:     Aerospace, Aeronautical and Astronautical, Space Engineering
    #>  ---
    #> 117:                                       Combat Systems Engineering
    #> 118:                                            Engineering Acoustics
    #> 119: Assistive, Augmentative Technology and Rehabiliation Engineering

The `drop_text` and `select` arguments have to be named explicitly. Columns in `select` are subset after filtering for `keep_text` and `drop_text`.

    # Optional arguments drop_text and select
    filter_cip("engineering",
      drop_text = c("related", "technology", "technologies"),
      select = c("cip6", "cip6name")
    )
    #>       cip6                                                     cip6name
    #>  1: 140101                                         Engineering, General
    #>  2: 140102                                              Pre-Engineering
    #>  3: 140201 Aerospace, Aeronautical and Astronautical, Space Engineering
    #> ---
    #> 52: 144401                                        Engineering Chemistry
    #> 53: 144501                           Biological, Biosystems Engineering
    #> 54: 149999                                           Engineering, Other

Suppose we want to find the CIP codes and names for all programs in Civil Engineering. The search is insensitive to case, so we start with the following code chunk.

    # Example 1 filter using keywords
    filter_cip("civil")

Table 2. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
05 | Area, Ethnic, Cultural and Gender and Group Studies | 0501 | Area Studies | 050102 | American, United States Studies, Civilization
05 | Area, Ethnic, Cultural and Gender and Group Studies | 0501 | Area Studies | 050103 | Asian Studies, Civilization
05 | Area, Ethnic, Cultural and Gender and Group Studies | 0501 | Area Studies | 050106 | European Studies, Civilization
14 | Engineering | 1408 | Civil Engineering | 140801 | Civil Engineering, General
14 | Engineering | 1408 | Civil Engineering | 140802 | Geotechnical Engineering
14 | Engineering | 1408 | Civil Engineering | 140803 | Structural Engineering
14 | Engineering | 1408 | Civil Engineering | 140804 | Transportation and Highway Engineering
14 | Engineering | 1408 | Civil Engineering | 140805 | Water Resources Engineering
14 | Engineering | 1408 | Civil Engineering | 140899 | Civil Engineering, Other
15 | Engineering Technology | 1502 | Civil Engineering Technologies, Technicians | 150201 | Civil Engineering Technology, Technician
15 | Engineering Technology | 1513 | Drafting, Design Engineering Technologies, Technicians | 151304 | Civil Drafting and Civil Engineering CAD, CADD
30 | Muti, Interdisciplinary Studies | 3022 | Classical and Ancient, Oriental Studies - Multi, Interdisciplinary Studies | 302201 | Multi, Interdisciplinary Studies - Ancient Studies, Civilization

The search returns some programs with Civilization in their names as well as Engineering Technology. If we wanted Civil Engineering only, we can use a sequence of function calls, where the outcome of the one operation is assigned to the first argument of the next operation.

The following code chunk could be read as, “Start with the default `cip` data frame, then keep any rows in which ‘civil’ is detected, then keep any rows in which ‘engineering’ is detected, then drop any rows in which ‘technology’ is detected.” The first pass operates on `cip`, but successive passes do not. If used, the `cip` argument must be named.

    # First search
    first_pass <- filter_cip("civil")

    # Refine the search
    second_pass <- filter_cip("engineering", cip = first_pass)

    # Refine further
    third_pass <- filter_cip(drop_text = "technology", cip = second_pass)

Table 3. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
14 | Engineering | 1408 | Civil Engineering | 140801 | Civil Engineering, General
14 | Engineering | 1408 | Civil Engineering | 140802 | Geotechnical Engineering
14 | Engineering | 1408 | Civil Engineering | 140803 | Structural Engineering
14 | Engineering | 1408 | Civil Engineering | 140804 | Transportation and Highway Engineering
14 | Engineering | 1408 | Civil Engineering | 140805 | Water Resources Engineering
14 | Engineering | 1408 | Civil Engineering | 140899 | Civil Engineering, Other

Equivalent usage. Seeing that all Civil Engineering programs have the same `cip4name`, we could have used `keep_text = c("civil engineering")` to narrow the search to rows that match the full phrase. The following implementations yield identical results,

    # Three passes
    x <- filter_cip("civil")
    x <- filter_cip("engineering", cip = x)
    x <- filter_cip(drop_text = "technology", cip = x)

    # Combined search
    y <- filter_cip("civil engineering", drop_text = "technology")

    # Demonstrate equivalence
    same_content(x, y)
    #> [1] TRUE

## Using a numerical code search

Suppose we want to study programs relating to German culture, language, and literature. Using “german” for the `keep_text` value yields

    # Search on text
    filter_cip("german")

Table 4. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
05 | Area, Ethnic, Cultural and Gender and Group Studies | 0501 | Area Studies | 050125 | German Studies
13 | Education | 1313 | Teacher Education and Professional Development, Specific Subject Areas | 131326 | German Language Teacher Education
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160500 | Germanic Languages, Literatures and Linguistics, General
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160501 | German Language and Literature
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160502 | Scandinavian Languages, Literatures and Linguistics
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160503 | Danish Language and Literature
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160504 | Dutch, Flemish Language and Literature
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160505 | Norwegian Language and Literature
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160506 | Swedish Language and Literature
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160599 | Germanic Languages, Literatures and Linguistics, Other

From the 6-digit program names we find only two that are of interest, German Studies (050125) and German Language and Literature (160501). We use a character vector to assign these two codes to the `keep_text` argument.

    # Search on codes
    filter_cip(c("050125", "160501"))

Table 5. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
05 | Area, Ethnic, Cultural and Gender and Group Studies | 0501 | Area Studies | 050125 | German Studies
16 | Foreign Languages, Literatures and Linguistics | 1605 | Germanic Languages, Literatures Linguistics | 160501 | German Language and Literature

If the 6-digit codes are entered as integers, they produce an error.

    # Search that produces an error
    filter_cip(c(050125, 160501))
    #> Error in filter_cip(c(50125, 160501)): Assertion on 'keep_text' failed. Must be of class 'string', not 'double'.

## Using a regular expression search

Specifying 4-digit codes yields a data frame all 6-digit programs containing the 4-digit string. We use the regular expression notation `^` to match the start of the strings.

    # example 3 filter using regular expressions
    filter_cip(c("^1410", "^1419"))

Table 6. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
14 | Engineering | 1410 | Electrical, Electronics and Communications Engineering | 141001 | Electrical, Electronics and Communications Engineering
14 | Engineering | 1410 | Electrical, Electronics and Communications Engineering | 141003 | Laser and Optical Engineering
14 | Engineering | 1410 | Electrical, Electronics and Communications Engineering | 141004 | Telecommunications Engineering
14 | Engineering | 1410 | Electrical, Electronics and Communications Engineering | 141099 | Electrical, Electronics and Communications Engineering, Other
14 | Engineering | 1419 | Mechanical Engineering | 141901 | Mechanical Engineering

The 2-digit series represent the most general groupings of related programs. Here, the result includes all History programs.

    # Search on 2-digit code
    filter_cip("^54")

Table 7. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
54 | History | 5401 | History | 540101 | History, General
54 | History | 5401 | History | 540102 | American History (United States)
54 | History | 5401 | History | 540103 | European History
54 | History | 5401 | History | 540104 | History and Philosophy of Science and Technology
54 | History | 5401 | History | 540105 | Public, Applied History and Archival Administration
54 | History | 5401 | History | 540106 | Asian History
54 | History | 5401 | History | 540107 | Canadian History
54 | History | 5401 | History | 540108 | Military History
54 | History | 5401 | History | 540199 | History, Other

The series argument can include any combination of 2, 4, and 6-digit codes. It can also be passed to the function as a character vector.

    # Search on vector of codes
    codes_we_want <- c("^24", "^4102", "^450202")
    filter_cip(codes_we_want)

Table 8. Search results.
cip2 | cip2name | cip4 | cip4name | cip6 | cip6name
--- | --- | --- | --- | --- | ---
24 | Liberal Arts and Sciences, General Studies and Humanities | 2401 | Liberal Arts and Sciences, General Studies Humanities | 240101 | Liberal Arts and Sciences, Liberal Studies
24 | Liberal Arts and Sciences, General Studies and Humanities | 2401 | Liberal Arts and Sciences, General Studies Humanities | 240102 | General Studies
24 | Liberal Arts and Sciences, General Studies and Humanities | 2401 | Liberal Arts and Sciences, General Studies Humanities | 240103 | Humanities, Humanistic Studies
24 | Liberal Arts and Sciences, General Studies and Humanities | 2401 | Liberal Arts and Sciences, General Studies Humanities | 240199 | Liberal Arts and Sciences, General Studies and Humanities, Other
41 | Science Technologies, Technicians | 4102 | Nuclear and Industrial Radiologic Technologies, Technicians | 410204 | Industrial Radiologic Technology, Technician
41 | Science Technologies, Technicians | 4102 | Nuclear and Industrial Radiologic Technologies, Technicians | 410205 | Nuclear, Nuclear Power Technology, Technician
41 | Science Technologies, Technicians | 4102 | Nuclear and Industrial Radiologic Technologies, Technicians | 410299 | Nuclear and Industrial Radiologic Technologies, Technicians, Other
45 | Social Sciences | 4502 | Anthropology | 450202 | Physical Anthropology

## When search terms cannot be found

If the `keep_text` argument includes terms that cannot be found in the CIP data frame, the unsuccessful terms are identified in a message and the successful terms produce the usual output.

For example, the following `keep_text` argument includes three search terms that are not present in the CIP data (“111111”, “^55”, and “Bogus”) and two that are (“050125” and “160501”).

    # Unsuccessful terms produce a message
    sub_cip <- filter_cip(c("050125", "111111", "160501", "Bogus", "^55"))
    #> Can't find these terms: 111111, Bogus, ^55

    # But the successful terms are returned
    sub_cip
    #>    cip2                                            cip2name cip4
    #> 1:   05 Area, Ethnic, Cultural and Gender and Group Studies 0501
    #> 2:   16      Foreign Languages, Literatures and Linguistics 1605
    #>                                       cip4name   cip6
    #> 1:                                Area Studies 050125
    #> 2: Germanic Languages, Literatures Linguistics 160501
    #>                          cip6name
    #> 1:                 German Studies
    #> 2: German Language and Literature

However, as seen earlier, if none of the search terms are found, an error occurs.

    # When none of the search terms are found
    filter_cip(c("111111", "Bogus", "^55"))
    #> Error: The search result is empty. Possible causes are:
    #>  * 'cip' contained no matches to terms in 'keep_text'.
    #>  * 'drop_text' eliminated all remaining rows.

## CIP data from another source

If you use a CIP data set from another source, it must have the same structure as `cip`: six character columns named as follows,

    # Name and class of variables (columns) in cip
    unlist(lapply(cip, FUN = class))
    #>        cip2    cip2name        cip4    cip4name        cip6    cip6name
    #> "character" "character" "character" "character" "character" "character"

## Assigning program names

Programs in MIDFIELD data sets are encoded by 6-digit CIP codes. As we’ve shown, multiple 6-digit codes can be considered specialties within a larger program with a 4-digit code or even a set of distinct 4-digit codes. Thus the program names in `cip` are generally inadequate for grouping and summarizing. User-defined program names are nearly always required.

> Most studies require deliberate assignment of user-defined program names to CIP codes or groups of CIP codes.

Here we demonstrate the creation of a data frame with all 6-digit CIP codes in a study plus their user-defined names.

By searching `cip`, we can find that the 4-digit codes for the four engineering programs are: Civil (1408), Electrical (1410), Mechanical (1419), and Industrial/Systems (1427, 1435, 1436, and 1437).

We obtain their 6-digit CIP codes. The 4-digit names are appropriate here. Our task is to create a variable with custom program names.

    # Changing the number of rows to print
    options(datatable.print.nrows = 15)

    # Four engineering programs
    four_programs <- filter_cip(c("^1408", "^1410", "^1419", "^1427", "^1435", "^1436", "^1437"))

    # Retain the needed columns
    four_programs <- four_programs[, .(cip6, cip4name)]
    four_programs
    #>       cip6                                               cip4name
    #>  1: 140801                                      Civil Engineering
    #>  2: 140802                                      Civil Engineering
    #>  3: 140803                                      Civil Engineering
    #>  4: 140804                                      Civil Engineering
    #>  5: 140805                                      Civil Engineering
    #>  6: 140899                                      Civil Engineering
    #>  7: 141001 Electrical, Electronics and Communications Engineering
    #>  8: 141003 Electrical, Electronics and Communications Engineering
    #>  9: 141004 Electrical, Electronics and Communications Engineering
    #> 10: 141099 Electrical, Electronics and Communications Engineering
    #> 11: 141901                                 Mechanical Engineering
    #> 12: 142701                                    Systems Engineering
    #> 13: 143501                                 Industrial Engineering
    #> 14: 143601                              Manufacturing Engineering
    #> 15: 143701                                    Operations Research

To make the assignments clear, our approach here will be to assign a new `program` column with NA values, then edit the new column values.

    # Assign a new column
    four_programs[, program := NA_character_]
    four_programs
    #>       cip6                                               cip4name program
    #>  1: 140801                                      Civil Engineering    <NA>
    #>  2: 140802                                      Civil Engineering    <NA>
    #>  3: 140803                                      Civil Engineering    <NA>
    #>  4: 140804                                      Civil Engineering    <NA>
    #>  5: 140805                                      Civil Engineering    <NA>
    #>  6: 140899                                      Civil Engineering    <NA>
    #>  7: 141001 Electrical, Electronics and Communications Engineering    <NA>
    #>  8: 141003 Electrical, Electronics and Communications Engineering    <NA>
    #>  9: 141004 Electrical, Electronics and Communications Engineering    <NA>
    #> 10: 141099 Electrical, Electronics and Communications Engineering    <NA>
    #> 11: 141901                                 Mechanical Engineering    <NA>
    #> 12: 142701                                    Systems Engineering    <NA>
    #> 13: 143501                                 Industrial Engineering    <NA>
    #> 14: 143601                              Manufacturing Engineering    <NA>
    #> 15: 143701                                    Operations Research    <NA>

### 1. Use `cip4name %ilike%` to recode one value

The `%like%` function is essentially a wrapper function around the base R `grepl()` function. The `%ilike%` version is case-insensitive. You can view the help page by running (the back-ticks facilitate a help search for terms starting with a symbol):

    # Run in Console
    ? `%like%`

In this approach, we search for one distinctive term only. We’re using abbreviations for compact output.

    # Recode program using the 4-digit name
    four_programs[cip4name %ilike% "electrical", program := "EE"]
    four_programs
    #>       cip6                                               cip4name program
    #>  1: 140801                                      Civil Engineering    <NA>
    #>  2: 140802                                      Civil Engineering    <NA>
    #>  3: 140803                                      Civil Engineering    <NA>
    #>  4: 140804                                      Civil Engineering    <NA>
    #>  5: 140805                                      Civil Engineering    <NA>
    #>  6: 140899                                      Civil Engineering    <NA>
    #>  7: 141001 Electrical, Electronics and Communications Engineering      EE
    #>  8: 141003 Electrical, Electronics and Communications Engineering      EE
    #>  9: 141004 Electrical, Electronics and Communications Engineering      EE
    #> 10: 141099 Electrical, Electronics and Communications Engineering      EE
    #> 11: 141901                                 Mechanical Engineering    <NA>
    #> 12: 142701                                    Systems Engineering    <NA>
    #> 13: 143501                                 Industrial Engineering    <NA>
    #> 14: 143601                              Manufacturing Engineering    <NA>
    #> 15: 143701                                    Operations Research    <NA>

### 2. Use `cip6 %like%` to recode one value

In our second approach, we use the `%like%` function again, but apply it to a CIP code. Here we use the regular expression `^1408` meaning “starts with 1408.”

    # Recode program using the 4-digit code
    four_programs[cip6 %like% "^1408", program := "CE"]
    four_programs
    #>       cip6                                               cip4name program
    #>  1: 140801                                      Civil Engineering      CE
    #>  2: 140802                                      Civil Engineering      CE
    #>  3: 140803                                      Civil Engineering      CE
    #>  4: 140804                                      Civil Engineering      CE
    #>  5: 140805                                      Civil Engineering      CE
    #>  6: 140899                                      Civil Engineering      CE
    #>  7: 141001 Electrical, Electronics and Communications Engineering      EE
    #>  8: 141003 Electrical, Electronics and Communications Engineering      EE
    #>  9: 141004 Electrical, Electronics and Communications Engineering      EE
    #> 10: 141099 Electrical, Electronics and Communications Engineering      EE
    #> 11: 141901                                 Mechanical Engineering    <NA>
    #> 12: 142701                                    Systems Engineering    <NA>
    #> 13: 143501                                 Industrial Engineering    <NA>
    #> 14: 143601                              Manufacturing Engineering    <NA>
    #> 15: 143701                                    Operations Research    <NA>

### 3. Use `program := fcase()` to edit all values

In this approach, we use the data.table function `fcase()`, an implementation of the SQL CASE WHEN statement. The data.table function `%chin%` is like `%in%`, but for character vectors.

    # Recode all program values
    four_programs[, program := fcase(
      cip6 %like% "^1408", "CE",
      cip6 %like% "^1410", "EE",
      cip6 %like% "^1419", "ME",
      cip6 %chin% c("142701", "143501", "143601", "143701"), "ISE"
    )]
    four_programs <- four_programs[, .(cip6, program)]
    four_programs
    #>       cip6 program
    #>  1: 140801      CE
    #>  2: 140802      CE
    #>  3: 140803      CE
    #>  4: 140804      CE
    #>  5: 140805      CE
    #>  6: 140899      CE
    #>  7: 141001      EE
    #>  8: 141003      EE
    #>  9: 141004      EE
    #> 10: 141099      EE
    #> 11: 141901      ME
    #> 12: 142701     ISE
    #> 13: 143501     ISE
    #> 14: 143601     ISE
    #> 15: 143701     ISE

Verify prepared data. `study_programs`, included with midfieldr, contains the case study information developed above. Here we verify that the two data frames have the same content.

    # Demonstrate equivalence
    same_content(four_programs, study_programs)
    #> [1] TRUE

## Reusable code

Preparation. To provide a working example, we select the four engineering programs of the case study used throughout the articles (Civil, Electrical, Industrial/Systems, and Mechanical Engineering). We assume a prior search of `cip` yielded the relevant codes used here. Requires editing before reuse with different programs.

    # Edit as required for different programs
    selected_programs <- filter_cip(c("^1408", "^1410", "^1419", "^1427", "^1435", "^1436", "^1437"))

Programs. A summary code chunk for ready reference. Requires editing before reuse with different programs.

    # Recode program labels. Edit as required.
    selected_programs[, program := fcase(
      cip6 %like% "^1408", "CE",
      cip6 %like% "^1410", "EE",
      cip6 %like% "^1419", "ME",
      cip6 %chin% c("142701", "143501", "143601", "143701"), "ISE"
    )]
    selected_programs <- selected_programs[, .(cip6, program)]

## References

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