Choose columns of student records

Subset a data frame, selecting columns by matching a vector of character strings. A convenience function to reduce the dimensions of a MIDFIELD data table by selecting only those columns required by other midfieldr functions or that are required to form a composite key. Particularly useful in interactive sessions when viewing the data tables at various stages of an analysis.

Usage

select_records(dframe, type = NULL, ..., col_pattern = NULL)

Arguments

dframe

Data frame of student records from which columns are selected. Expected choices are student, term, course, degree or their equivalent.

type

Character identifying the record type. Possible values are "s", "t", "c", "d", or "a". See Details.

...

Not used for passing values; forces subsequent arguments to be referable only by name.

col_pattern

Character vector containing strings or regular expressions to be matched or partially matched to the column names of dframe.

Value

A data frame of the same type as dframe. The output has the following properties:

• Rows are not modified.

• Columns are a subset of the input, but appear in the same order.

• Groups are not necessarily preserved.

• Data frame attributes are preserved with the exception of grouped tibbles.

Details

Several midfieldr functions require input data frames containing specific variables (column names) such as mcid or cip6. In addition, the MIDFIELD data tables have specific variables that act as keys or composite keys to the information in that table. The type argument determines which columns are returned, if those columns exist in dframe:

• type = "s" (student table) returns columns mcid, race, sex

• type = "t" (term table) returns columns mcid, term, cip6, institution, level

• type = "c" (course table) returns columns mcid, term_course, abbrev, number

• type = "d" (degree table) returns columns mcid, term_degree, cip6

• type = "a" (default) returns all the above

Additional column names can be included by using the col_pattern argument. In all cases, unmatched search strings are silently ignored.

Examples

# Basic usage
select_records(toy_student[1:5])
#>              mcid   race    sex   institution
#>            <char> <char> <char>        <char>
#> 1: MCID3111158953  White Female Institution J
#> 2: MCID3111159270  White   Male Institution J
#> 3: MCID3111160513  White   Male Institution J
#> 4: MCID3111162677  White Female Institution J
#> 5: MCID3111164287  White   Male Institution J
select_records(toy_term[1:5])
#>              mcid   term   cip6   institution          level
#>            <char> <char> <char>        <char>         <char>
#> 1: MCID3111158953  19881 240102 Institution J  01 First-year
#> 2: MCID3111158953  19883 240102 Institution J  01 First-year
#> 3: MCID3111158953  19891 240102 Institution J 02 Second-year
#> 4: MCID3111158953  19893 240102 Institution J 02 Second-year
#> 5: MCID3111158953  19901 240102 Institution J  03 Third-year
select_records(toy_course[1:5])
#>              mcid term_course abbrev number   institution
#>            <char>      <char> <char> <char>        <char>
#> 1: MCID3111158953       19881   EDCI   2984 Institution J
#> 2: MCID3111158953       19881   MATH   1215 Institution J
#> 3: MCID3111158953       19881     MN   1004 Institution J
#> 4: MCID3111158953       19881   CHEM   1035 Institution J
#> 5: MCID3111158953       19881   CHEM   1045 Institution J
select_records(toy_degree[1:5])
#>              mcid term_degree   cip6   institution
#>            <char>      <char> <char>        <char>
#> 1: MCID3111159270       19913 141001 Institution J
#> 2: MCID3111162677       19913 140701 Institution J
#> 3: MCID3111164287       19913 141001 Institution J
#> 4: MCID3111171205       19913 270101 Institution B
#> 5: MCID3111172083       19913 520201 Institution B

# Return columns by record type
select_records(toy_student[1:5], type = "s")
#>              mcid   race    sex
#>            <char> <char> <char>
#> 1: MCID3111158953  White Female
#> 2: MCID3111159270  White   Male
#> 3: MCID3111160513  White   Male
#> 4: MCID3111162677  White Female
#> 5: MCID3111164287  White   Male
select_records(toy_term[1:5], "t")
#>              mcid   term   cip6   institution          level
#>            <char> <char> <char>        <char>         <char>
#> 1: MCID3111158953  19881 240102 Institution J  01 First-year
#> 2: MCID3111158953  19883 240102 Institution J  01 First-year
#> 3: MCID3111158953  19891 240102 Institution J 02 Second-year
#> 4: MCID3111158953  19893 240102 Institution J 02 Second-year
#> 5: MCID3111158953  19901 240102 Institution J  03 Third-year
select_records(toy_course[1:5], "c")
#>              mcid term_course abbrev number
#>            <char>      <char> <char> <char>
#> 1: MCID3111158953       19881   EDCI   2984
#> 2: MCID3111158953       19881   MATH   1215
#> 3: MCID3111158953       19881     MN   1004
#> 4: MCID3111158953       19881   CHEM   1035
#> 5: MCID3111158953       19881   CHEM   1045
select_records(toy_degree[1:5], "d")
#>              mcid term_degree   cip6
#>            <char>      <char> <char>
#> 1: MCID3111159270       19913 141001
#> 2: MCID3111162677       19913 140701
#> 3: MCID3111164287       19913 141001
#> 4: MCID3111171205       19913 270101
#> 5: MCID3111172083       19913 520201

# With col_patterns for additional columns
DT <- toy_student[141:146]
select_records(DT, "t", col_pattern = c("transfer", "hours_tranfer"))
#>              mcid   institution              transfer hours_transfer
#>            <char>        <char>                <char>          <num>
#> 1: MCID3112802165 Institution B First-Time in College             NA
#> 2: MCID3112803670 Institution B First-Time in College             NA
#> 3: MCID3112804805 Institution B First-Time in College             NA
#> 4: MCID3112839822 Institution B   First-Time Transfer             NA
#> 5: MCID3112845932 Institution B First-Time in College             NA
#> 6: MCID3112846308 Institution B First-Time in College             NA

# Using regular expressions
these_IDs <- DT$mcid
DT <- toy_term[mcid %chin% these_IDs]
select_records(DT, "t", col_pattern = c("^gpa"))
#>               mcid   term   cip6   institution          level gpa_term
#>             <char> <char> <char>        <char>         <char>    <num>
#>  1: MCID3112802165  20161 500702 Institution B  01 First-year     3.88
#>  2: MCID3112802165  20163 500702 Institution B 02 Second-year     3.68
#>  3: MCID3112802165  20171 500601 Institution B 02 Second-year     3.80
#>  4: MCID3112802165  20173 500601 Institution B  03 Third-year     3.57
#>  5: MCID3112802165  20181 500702 Institution B  03 Third-year     3.88
#>  6: MCID3112803670  20161 240199 Institution B  01 First-year     3.63
#>  7: MCID3112804805  20161 240199 Institution B  01 First-year     2.31
#>  8: MCID3112804805  20171 240199 Institution B 02 Second-year     3.70
#>  9: MCID3112804805  20173 030103 Institution B 02 Second-year     3.27
#> 10: MCID3112804805  20181 030103 Institution B 02 Second-year     3.00
#> 11: MCID3112839822  20171 400501 Institution B  01 First-year     2.75
#> 12: MCID3112845932  20173 240199 Institution B  01 First-year     3.12
#> 13: MCID3112845932  20181 110701 Institution B 02 Second-year     3.44
#> 14: MCID3112846308  20171 260202 Institution B  01 First-year     3.49
#> 15: MCID3112846308  20173 510201 Institution B  01 First-year     3.68
#> 16: MCID3112846308  20181 510201 Institution B 02 Second-year     3.81
#>     gpa_cumul
#>         <num>
#>  1:      3.88
#>  2:      3.78
#>  3:      3.79
#>  4:      3.73
#>  5:      3.76
#>  6:      3.63
#>  7:      2.31
#>  8:      2.89
#>  9:      3.01
#> 10:      3.01
#> 11:      2.75
#> 12:      2.89
#> 13:      3.08
#> 14:      3.49
#> 15:      3.58
#> 16:      3.67