Select basic columns of student-level records — select_record

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_record_cols(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 preserved.
Data frame attributes are preserved for classes data.frame, data.table, or tbl_df.

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 the set of columns searched for in dframe. Unmatched search strings are silently ignored.

type = "s" (student table) searches for columns mcid, race, sex
type = "t" (term table) searches for columns mcid, term, cip6, institution, level
type = "c" (course table) searches for columns mcid, term_course, abbrev, number
type = "d" (degree table) searches for columns mcid, term_degree, cip6
type = "a" (default) searches for all of the columns listed above

Additional column names can be included in the search by using the col_pattern argument.

Examples

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

# Return columns by record type
select_record_cols(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_record_cols(toy_term[1:5], "t")
#>              mcid   institution   term   cip6          level
#>            <char>        <char> <char> <char>         <char>
#> 1: MCID3111158953 Institution J  19881 240102  01 First-year
#> 2: MCID3111158953 Institution J  19883 240102  01 First-year
#> 3: MCID3111158953 Institution J  19891 240102 02 Second-year
#> 4: MCID3111158953 Institution J  19893 240102 02 Second-year
#> 5: MCID3111158953 Institution J  19901 240102  03 Third-year
select_record_cols(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_record_cols(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_record_cols(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_record_cols(DT, "t", col_pattern = c("^gpa"))
#>               mcid   institution   term   cip6          level gpa_term
#>             <char>        <char> <char> <char>         <char>    <num>
#>  1: MCID3112802165 Institution B  20161 500702  01 First-year     3.88
#>  2: MCID3112802165 Institution B  20163 500702 02 Second-year     3.68
#>  3: MCID3112802165 Institution B  20171 500601 02 Second-year     3.80
#>  4: MCID3112802165 Institution B  20173 500601  03 Third-year     3.57
#>  5: MCID3112802165 Institution B  20181 500702  03 Third-year     3.88
#>  6: MCID3112803670 Institution B  20161 240199  01 First-year     3.63
#>  7: MCID3112804805 Institution B  20161 240199  01 First-year     2.31
#>  8: MCID3112804805 Institution B  20171 240199 02 Second-year     3.70
#>  9: MCID3112804805 Institution B  20173 030103 02 Second-year     3.27
#> 10: MCID3112804805 Institution B  20181 030103 02 Second-year     3.00
#> 11: MCID3112839822 Institution B  20171 400501  01 First-year     2.75
#> 12: MCID3112845932 Institution B  20173 240199  01 First-year     3.12
#> 13: MCID3112845932 Institution B  20181 110701 02 Second-year     3.44
#> 14: MCID3112846308 Institution B  20171 260202  01 First-year     3.49
#> 15: MCID3112846308 Institution B  20173 510201  01 First-year     3.68
#> 16: MCID3112846308 Institution B  20181 510201 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