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.
- Planning
-
Initial
processing
- Data sufficiency
- Degree seeking
- Identify programs
- Data sufficiency
- Blocs
- Groupings
- Metrics
- 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
termanddegreedata 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).
| 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:
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, UnspecifiedAll 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 characterThe 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] 1582A 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_textCharacter vector of search text for retaining rows, not case-sensitive. Can be empty ifdrop_textis used.drop_textCharacter vector of search text for dropping rows, not case-sensitive, default NULL. Argument to be used by name.cipData frame to be subset, defaultcip. Argument to be used by name.selectCharacter 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] TRUEOutput. 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 EngineeringThe 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, OtherSuppose 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")| 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)| 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] TRUEUsing 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")| 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"))| 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"))| 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")| 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)| 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 LiteratureHowever, 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,
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 ResearchTo 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 ISEVerify 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] TRUEReusable 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.