Manipulate and interpret the metadata of a REDCap project

A collection of functions that assists handling REDCap project metadata.

Usage

regex_named_captures(pattern, text, perl = TRUE)

checkbox_choices(select_choices)

Arguments

pattern

The regular expression pattern. Required.

text

The text to apply the regex against. Required.

perl

Indicates if perl-compatible regexps should be used. Default is TRUE. Optional.

select_choices

The text containing the choices that should be parsed to determine the id and label values. Required.

Value

Currently, a tibble::tibble() is returned a row for each match, and a column for each named group within a match. For the retrieve_checkbox_choices() function, the columns will be.

• id: The numeric value assigned to each choice (in the data dictionary).

• label: The label assigned to each choice (in the data dictionary).

Details

The regex_named_captures() function is general, and not specific to REDCap; it accepts any arbitrary regular expression. It returns a tibble::tibble() with as many columns as named matches.

The checkbox_choices() function is specialized, and accommodates the "select choices" for a single REDCap checkbox group (where multiple boxes can be selected). It returns a tibble::tibble() with two columns, one for the numeric id and one for the text label.

The parse will probably fail if a label contains a pipe (i.e., |), since that the delimiter REDCap uses to separate choices presented to the user.

References

See the official documentation for permissible characters in a checkbox label. I'm bluffing here, because I don't know where this is located. If you know, please tell me.

Author

Will Beasley

Examples

# The weird ranges are to avoid the pipe character;
#   PCRE doesn't support character negation.
pattern_boxes <- "(?<=\\A| \\| )(?<id>\\d{1,}), (?<label>[\x20-\x7B\x7D-\x7E]{1,})(?= \\| |\\Z)"

choices_1 <- paste0(
  "1, American Indian/Alaska Native | ",
  "2, Asian | ",
  "3, Native Hawaiian or Other Pacific Islander | ",
  "4, Black or African American | ",
  "5, White | ",
  "6, Unknown / Not Reported"
)

# This calls the general function, and requires the correct regex pattern.
REDCapR::regex_named_captures(pattern=pattern_boxes, text=choices_1)
#> # A tibble: 6 × 2
#>   id    label                                    
#>   <chr> <chr>                                    
#> 1 1     American Indian/Alaska Native            
#> 2 2     Asian                                    
#> 3 3     Native Hawaiian or Other Pacific Islander
#> 4 4     Black or African American                
#> 5 5     White                                    
#> 6 6     Unknown / Not Reported                   

# This function is designed specifically for the checkbox values.
REDCapR::checkbox_choices(select_choices=choices_1)
#> # A tibble: 6 × 2
#>   id    label                                    
#>   <chr> <chr>                                    
#> 1 1     American Indian/Alaska Native            
#> 2 2     Asian                                    
#> 3 3     Native Hawaiian or Other Pacific Islander
#> 4 4     Black or African American                
#> 5 5     White                                    
#> 6 6     Unknown / Not Reported                   

# \dontrun{
uri         <- "https://redcap-dev-2.ouhsc.edu/redcap/api/"
token       <- "9A068C425B1341D69E83064A2D273A70"

ds_metadata <- redcap_metadata_read(uri, token)$data
#> The data dictionary describing 17 fields was read from REDCap in 0.2 seconds.  The http status code was 200.
choices_2   <- ds_metadata[ds_metadata$field_name == "race", ]$select_choices_or_calculations

REDCapR::regex_named_captures(pattern = pattern_boxes, text = choices_2)
#> # A tibble: 1 × 2
#>   id    label
#>   <chr> <chr>
#> 1 ""    ""   
# }

path_3     <- system.file(package = "REDCapR", "test-data/projects/simple/metadata.csv")
ds_metadata_3  <- read.csv(path_3)
choices_3  <- ds_metadata_3[ds_metadata_3$field_name=="race", "select_choices_or_calculations"]
REDCapR::regex_named_captures(pattern = pattern_boxes, text = choices_3)
#> # A tibble: 6 × 2
#>   id    label                                    
#>   <chr> <chr>                                    
#> 1 1     American Indian/Alaska Native            
#> 2 2     Asian                                    
#> 3 3     Native Hawaiian or Other Pacific Islander
#> 4 4     Black or African American                
#> 5 5     White                                    
#> 6 6     Unknown / Not Reported