| Type: | Package |
| Title: | Inspect and Clean Subject-Generated ID Codes and Related Data |
| Version: | 1.0.0 |
| Maintainer: | Annemarie Pläschke <anneplaeschke@gmail.com> |
| Description: | Makes data wrangling with ID-related aspects more comfortable. Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility. Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Depends: | R (≥ 2.10) |
| Imports: | dplyr, tibble, rlang |
| VignetteBuilder: | knitr |
| URL: | https://kuuuwe.github.io/trustmebro/, https://github.com/kuuuwe/trustmebro |
| Language: | en-US |
| BugReports: | https://github.com/kuuuwe/trustmebro/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-05-07 20:50:26 UTC; Anwender |
| Author: | Annemarie Pläschke
 [aut, cre,
cph],
Tobias Brändle
[aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-05-09 14:10:02 UTC |
Identify duplicate cases
Description
Identify duplicate cases in a data frame or tibble based on specific variables. A logical column 'has_dupes' is added, that indicates whether or not a row has duplicate values based on the provided variables.
Usage
find_dupes(data, ...)
Arguments
data |
A data frame or tibble |
... |
Variable names to check for duplicates |
Value
The original data frame or tibble with an additional logical column 'has_dupes' which is 'TRUE' for rows that have duplicates based on the specified variables and 'FALSE' otherwise.
Examples
# Example data
print(sailor_students)
# Find duplicate cases based on 'sgic', 'school' and 'class'
sailor_students_dupes <- find_dupes(sailor_students, sgic, school, class)
# Rows where 'has_dupes' is `TRUE` indicate duplicates based on the provided columns
print(sailor_students_dupes)
Inspect birthday-component of a string
Description
Check whether a given string contains exactly one two-digit number that represents a valid day of the month (between 01 and 31). The string is assumed to be a code (e.g., a SGIC), which may include letters and digits.
Usage
inspect_birthday(code)
Arguments
code |
A character string containing a SGIC or similar code that may include a numeric birthday-component. |
Value
A logical value: 'TRUE' if the string contains only one valid birthday-component (between 01 and 31), otherwise 'FALSE'.
Examples
inspect_birthday("DEF66") # FALSE - 66 is not a valid day
inspect_birthday("GHI02") # TRUE - 02 is a valid day
inspect_birthday("ABC12DEF34") # FALSE - Multiple numeric components
inspect_birthday("XYZ") # FALSE - No numeric component
inspect_birthday("JKL31") # TRUE - 31 is a valid day
Inspect birthday- and birthmonth-component of a string
Description
Checks whether a given string contains exactly one four-digit number representing a valid combination of a day (birthday) and a month (birth month). Numeric components can be interpreted in either "DDMM" (day-month) or "MMDD" (month-day) format, depending on the specified format. The string is assumed to be a code (e.g., a SGIC), which may include letters and digits.
Usage
inspect_birthdaymonth(code, format = "DDMM")
Arguments
code |
A character string containing a SGIC or similar code that may include a numeric component representing a birthday and birth month. |
format |
A string specifying the format of the date of birth components in code. Use "DDMM" for day-month format and "MMDD" for month-day format. Default is "DDMM". |
Value
A logical value: 'TRUE' if the string contains exactly one valid numeric component that forms a valid birthday (day and month), otherwise 'FALSE'.
Examples
inspect_birthdaymonth("DEF2802") # TRUE - 28th of February is a valid date
inspect_birthdaymonth("GHI3002") # FALSE - 30th of February is invalid
inspect_birthdaymonth("XYZ3112") # TRUE - 31st of December is valid
inspect_birthdaymonth("18DEF02") # FALSE - Multiple numeric components
inspect_birthdaymonth("XYZ") # FALSE - No numeric components
inspect_birthdaymonth("ABC1231", format = "MMDD") # TRUE - December 31st is valid
Inspect birthmonth-component of a string
Description
Check whether a given string contains exactly one two-digit number that represents a valid month of the year (between 01 and 12). The string is assumed to be a code (e.g., a SGIC), which may include letters and digits.
Usage
inspect_birthmonth(code)
Arguments
code |
A character string containing a SGIC or similar code that may include a numeric birth month-component. |
Value
A logical value: 'TRUE' if the string contains only one valid birth month-component (between 01 and 12), otherwise 'FALSE'.
Examples
inspect_birthday("DEF66") # FALSE - 66 is not a valid month
inspect_birthday("GHI02") # TRUE - 02 (February) is a valid month
inspect_birthday("ABC12DEF10") # FALSE - Multiple numeric components
inspect_birthday("XYZ") # FALSE - No numeric component
inspect_birthday("JKL11") # TRUE - 11 (November) is a valid day
Inspect if a string matches an expected pattern
Description
Check whether a given string matches a specified pattern using regular expressions (regex). The string is assumed to be a code (e.g., a SGIC), which should follow a predefined format.
Usage
inspect_characterid(code, pattern)
Arguments
code |
A character string containing a SGIC or similar code that should follow a predefined format. |
pattern |
A character string specifying the expected pattern using regular expressions (regex). The pattern defines the format 'code' should match. |
Value
A logical value: 'TRUE' if the code matches the expected pattern, otherwise 'FALSE'
Examples
inspect_characterid("ABC1234", "^[A-Za-z]{3}[0-9]{4}$") #TRUE - Matches the pattern
inspect_characterid("12DBG45FG", "^[A-Za-z]{3}[0-9]{4}$") #FALSE - Does not match the pattern
Inspect if a number has the expected length
Description
Check whether a given numeric value has the expected number of digits.
Usage
inspect_numberid(number, expected_length)
Arguments
number |
A numeric value. |
expected_length |
An integer specifying the expected number of digits. |
Value
A logical value: 'TRUE' if 'number' has the expected length and consists only of digits, otherwise 'FALSE'.
Examples
inspect_numberid(12345, 5) # TRUE - 5 digits
inspect_numberid(1234, 5) # FALSE - 4 digits
Inspect if a value is in a recode map
Description
Check whether a given value is present as a key in a specified recode map. Inputs can be validated against a set of predefined categories or labels.
Usage
inspect_valinvec(value, recode_map)
Arguments
value |
A single value to inspect, which is checked against the keys of a recode map. |
recode_map |
A named vector where the names represent the keys to check against. The values of the vector are ignored. |
Value
A logical value: 'TRUE' if the 'value' is a key in the 'recode_map', otherwise 'FALSE'.
Examples
recode_map <- c(male = "M", female = "F")
inspect_valinvec("female", recode_map) # TRUE - "female" is a key in the recode map
inspect_valinvec("other", recode_map) # FALSE - "other" is not a key in the recode map
Purge strings in a data frame
Description
Clean specified character columns in a data frame or tibble by removing non-alphanumeric characters, replacing them with a specified character (default is "#"). Also replaces NA values and allows for additional characters to keep in the cleaned strings. The resulting strings are converted to uppercase.
Usage
purge_string(data, ..., replacement = "#", keep = "")
Arguments
data |
A data frame or tibble containing columns to be cleaned. |
... |
Variables to clean. If none are provided, all character columns will be processed. |
replacement |
A character string used to replace unwanted characters and empty strings. Default is "#". |
keep |
A character string containing any additional characters that should be retained in the cleaned strings. |
Value
A data frame or tibble with the specified character columns cleaned and modified as per the given parameters.
Examples
# Example data
print(sailor_students)
# Clean all character columns, replacing unwanted characters with "#", retaining "-"
sailor_students_cleaned <-
purge_string(sailor_students, sgic, school, class, gender, keep = "-")
# Tibble with cleaned 'sgic', 'school', 'class' and 'gender' columns
print(sailor_students_cleaned)
Recode a variable
Description
Recode a specified variable in a data frame or tibble based on a provided recode map. If the recode map is empty, the original variable is retained under a new name.
Usage
recode_valinvec(data, var, recode_map, new_var)
Arguments
data |
A data frame or tibble. |
var |
A variable to be recoded. |
recode_map |
A named vector specifying the recode map. |
new_var |
Name of the new variable holding the recoded values. |
Value
A data frame or tibble with the new variable added.
Examples
# Example data
print(sailor_students)
# Define a recode map for gender
recode_map_gender <- c("Female" = "F", "Male" = "M", "Other" = "X")
# Recode gender
sailor_students_recoded <-
recode_valinvec(sailor_students, gender, recode_map_gender, recode_gender)
# A tibble with a recoded gender variable
print(sailor_students_recoded)
key data on students from the sailor moon universe
Description
A fictional key data set.
Usage
sailor_keys
Format
'sailor_keys' A tibble with 12 rows and 6 columns:
- schoolyear
schoolyear
- guid
hexadecimal ID number
- name, birthday, sex
student information
- school, schoolnumber, class, grade_level
school information
- sgic1, sgic2, sgic3
subject generated ID
assessment data on students from the sailor moon universe
Description
A fictional assessment data set.
Usage
sailor_students
Format
'sailor_students' A tibble with 12 rows and 6 columns:
- sgic
Subject generated ID
- school
schoolnumber
- class
class designation
- gender
gender
- testscore_language, testscore_calculus
testscores