| Title: | Creates Assertion Tests |
| Version: | 0.7.0 |
| Description: | Offers a comprehensive set of assertion tests to help users validate the integrity of their data. These tests can be used to check for specific conditions or properties within a dataset and help ensure that data is accurate and reliable. The package is designed to make it easy to add quality control checks to data analysis workflows and to aid in identifying and correcting any errors or inconsistencies in data. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | checkmate, cli, dplyr, findR, janitor, kit, lubridate, magrittr, purrr, readr, stats, stringr, tibble, tidyr |
| Suggests: | devtools (≥ 2.4.5), knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-02-10 12:11:25 UTC; esr316 |
| Author: | Tomer Iwan [aut, cre, cph], Hajo Bons [aut, cph] |
| Maintainer: | Tomer Iwan <t.iwan@vu.nl> |
| Repository: | CRAN |
| Date/Publication: | 2025-02-10 12:30:05 UTC |
Assert Date Value in Column
Description
This function asserts that the values in a specified column of a data frame are of Date type.
It uses the checkmate::assert_date function to perform the assertion.
Usage
assert_date_named(column, df, prefix_column = NULL, ...)
Arguments
column |
A character vector or string with the column name to be tested. |
df |
The data frame that contains the column. |
prefix_column |
A character string that will be prepended to the column name in the assertion message. Default is NULL. |
... |
Additional parameters are passed to the |
Value
None
Check if the fieldnames of the dataset are the same
Description
Assert Field Consistency Between Data and Metadata
Usage
assert_field_consistency(new_data, field_info)
Arguments
new_data |
A data frame. The new dataset whose field names need to be checked. |
field_info |
A data frame. Metadata containing a column named |
Details
This function checks for consistency between the field names in new data and the field names specified in a metadata reference. It warns if there are missing fields in the new data or if new unexpected fields appear in the data that are not defined in the metadata.
Value
No return value. The function issues warnings if there are inconsistencies in field names.
Assert Field Uniqueness Consistency Between Data and Metadata
Description
This function checks whether the uniqueness of columns in a new dataset matches the expected uniqueness defined in a metadata reference. It warns if any columns do not conform to the expected uniqueness.
Usage
assert_field_distinctness(new_data, metadata)
Arguments
new_data |
A data frame. The dataset whose column uniqueness needs to be verified. |
metadata |
A data frame. Metadata containing a column named |
Value
No return value. The function issues warnings if any columns deviate from their expected uniqueness.
Assert Field Existence in New Data
Description
This function checks whether all fields that existed in a previous dataset are still present in a new dataset, based on a metadata reference. It warns if any fields from the previous dataset are missing in the new dataset.
Usage
assert_field_existence(new_data, previous_data, metadata)
Arguments
new_data |
A data frame. The new dataset whose field names need to be checked. |
previous_data |
A data frame. The previous dataset used as a reference for expected fields. |
metadata |
A data frame. Metadata containing a column named |
Value
No return value. The function issues warnings if any expected fields are missing in the new dataset.
Assert Logical Value in Column
Description
This function asserts that the values in a specified column of a data frame are logical.
It uses the checkmate::assert_logical function to perform the assertion.
Usage
assert_logical_named(column, df, prefix_column = NULL, ...)
Arguments
column |
A character vector or string with the column name to be tested. |
df |
The data frame that contains the column. |
prefix_column |
A character string that will be prepended to the column name in the assertion message. Default is NULL. |
... |
Additional parameters are passed to the |
Value
None
Examples
# Create a data frame
df <- data.frame(a = c(TRUE, FALSE, TRUE, FALSE), b = c(1, 2, 3, 4))
# Assert that the values in column "a" are logical
assert_logical_named("a", df)
Assert Consistency of Missing Values in Data
Description
This function checks whether the percentage of missing values in a dataset matches the documented percentage in a metadata reference. It warns if there are significant discrepancies.
Usage
assert_missing_values(data, metadata)
Arguments
data |
A data frame. The dataset to check for missing values. |
metadata |
A data frame. Metadata containing expected missing value percentages and valid value counts.
It must include the columns |
Value
No return value. The function issues warnings if the actual missing value percentages deviate significantly from the documented values.
Assert No Duplicates in Group
Description
This function asserts that there are no duplicate rows in the specified columns of a data frame.
It groups the data frame by the specified columns, counts the number of unique values for each group, and checks if there are any groups with more than one row.
If there are, it prints an error message and stops the execution (unless assertion_fail is set to "warn").
Usage
assert_no_duplicates_in_group(df, group_vars, assertion_fail = "stop")
Arguments
df |
A data frame. |
group_vars |
A character vector of column names. |
assertion_fail |
A character string indicating the action to take if the assertion fails. Can be "stop" (default) or "warn". |
Value
The input data frame.
Assert Range Validation for Data Fields
Description
This function checks whether the values in a dataset fall within the expected minimum and maximum range as specified in the metadata. It warns if any values violate the expected range.
Usage
assert_range_validation(data, metadata)
Arguments
data |
A data frame. The dataset containing the fields to validate. |
metadata |
A data frame. Metadata containing expected minimum and maximum values for each field.
It must include the columns |
Value
No return value. The function issues warnings if any values fall outside the expected range.
Assert Type Consistency Between Data and Metadata
Description
This function checks whether the data types of fields in a dataset match the expected types specified in the metadata. It warns if any fields have a different type than expected.
Usage
assert_type_consistency(data, metadata)
Arguments
data |
A data frame. The dataset containing the fields to validate. |
metadata |
A data frame. Metadata specifying the expected data types for each field.
It must include the columns |
Value
No return value. The function issues warnings if any fields have an unexpected type.
Assert Message Based on Type
Description
This function asserts a message based on the type specified. It can either push the message to an AssertCollection, print a warning, or stop execution with an error message.
Usage
assertion_message(message, assertion_fail = "stop")
Arguments
message |
A character string representing the message to be asserted. |
assertion_fail |
A character string indicating the action to take if the assertion fails. Can be an AssertCollection, "warning", or "stop" (default). |
Value
None
Calculate the percentage of categories in a data vector
Description
This function calculates the percentage of each category in a given data vector and returns the top 10 categories along with their percentages. If the data vector is of Date class, it is converted to POSIXct. If the sum of the percentages is not 100%, an "Other" category is added to make up the difference, but only if the number of unique values exceeds 10. If the data vector is of POSIXct class and the smallest percentage is less than 1%, the function returns "Not enough occurrences."
Usage
calculate_category_percentages(data_vector)
Arguments
data_vector |
A vector of categorical data. |
Value
A character string detailing the top 10 categories and their percentages, or a special message indicating not enough occurrences or unsupported data type.
Examples
# Example with a character vector
data_vector <- c("cat", "dog", "bird", "cat", "dog", "cat", "other")
calculate_category_percentages(data_vector)
# Example with a Date vector
data_vector <- as.Date(c("2020-01-01", "2020-01-02", "2020-01-03"))
calculate_category_percentages(data_vector)
check double columns
Description
Check whether two dataframes have intersecting column names.
Usage
check_double_columns(x, y, connector = NULL)
Arguments
x |
Data frame x. |
y |
Data frame y. |
connector |
The connector columns as strings. Also possible as vector. |
Value
Message informing about overlap in columns between the dataframes.
See Also
Other tests:
check_no_duplicates_in_group(),
check_numeric_or_integer_type(),
check_posixct_type(),
duplicates_in_column(),
test_all_equal()
Examples
check_double_columns(mtcars, iris)
Check for Duplicate Rows in Selected Columns
Description
This function checks if there are any duplicate rows in the specified columns of a data frame. It prints the unique rows and returns a boolean indicating whether the number of rows in the original data frame is the same as the number of rows in the data frame with duplicate rows removed.
Usage
check_duplicates(data, columns)
Arguments
data |
A data frame. |
columns |
A character vector of column names. |
Value
A logical value indicating whether the number of rows in the original data frame is the same as the number of rows in the data frame with duplicate rows removed.
Examples
# Create a data frame
df <- data.frame(a = c(1, 2, 3, 1), b = c(4, 5, 6, 4), c = c(7, 8, 9, 7))
# Check for duplicate rows in the first two columns
check_duplicates(df, c("a", "b"))
Check for columns with only NA values
Description
This function checks if there are any columns in the provided dataframe that contain only NA values. If such columns exist, their names are added to the provided collection.
Usage
check_na_columns(df, collection)
Arguments
df |
A dataframe. |
collection |
A list to store the names of the columns with only NA values. |
Value
The updated collection.
Examples
# Create a dataframe with some columns containing only NA values
df <- data.frame(a = c(1, NA, 3), b = c(NA, NA, NA), c = c(4, 5, 6))
collection <- checkmate::makeAssertCollection()
check_na_columns(df, collection)
Check for No Duplicate Rows
Description
This function checks if there are any duplicate rows in the provided dataframe. If there are duplicate rows, a message is added to the provided collection.
Usage
check_no_duplicate_rows(dataframe, collection, unique_columns = NULL)
Arguments
dataframe |
A dataframe. |
collection |
A list to store the message if there are duplicate rows. |
unique_columns |
Default is NULL. If provided, these are the columns to check for uniqueness. |
Value
The updated collection.
Examples
# Create a dataframe with some duplicate rows
dataframe <- data.frame(a = c(1, 1, 2), b = c(2, 2, 3))
collection <- checkmate::makeAssertCollection()
check_no_duplicate_rows(dataframe, collection, c("a", "b"))
Check for No Duplicates in Group
Description
This function checks if there is exactly one row per group in the provided dataframe. If there are multiple rows per group, the assertion fails.
Usage
check_no_duplicates_in_group(
dataframe,
group_variables = NULL,
assertion_fail = "stop"
)
Arguments
dataframe |
The dataframe to be checked. |
group_variables |
The group variables as a character vector. The default is NULL. |
assertion_fail |
How the function reacts to a failure. This can be a "warning", where only a warning is given on the failure, or a "stop", where the function execution is stopped and the message is displayed, or an "AssertCollection", where the failure message is added to an assertion collection. |
See Also
Other assertions:
check_numeric_or_integer_type(),
check_posixct_type()
Other tests:
check_double_columns(),
check_numeric_or_integer_type(),
check_posixct_type(),
duplicates_in_column(),
test_all_equal()
Examples
# Create a dataframe with some groups having more than one row
dataframe <- data.frame(a = c(1, 1, 2), b = c(2, 2, 3), c = c("x", "x", "y"))
# Check the uniqueness of rows per group
check_no_duplicates_in_group(dataframe)
Check for Non-Zero Rows
Description
This function checks if there are more than 0 rows in the provided dataframe. If there are 0 rows, a message is added to the provided collection.
Usage
check_non_zero_rows(dataframe, collection)
Arguments
dataframe |
A dataframe. |
collection |
A list to store the message if there are 0 rows. |
Value
The updated collection.
Examples
# Create an empty dataframe
dataframe <- data.frame()
collection <- checkmate::makeAssertCollection()
check_non_zero_rows(dataframe, collection)
Check for Numeric or Integer Type
Description
This function checks if the specified column in the provided dataframe has a numeric or integer type.
It uses the checkmate::assert_numeric or checkmate::assert_integer function to perform the assertion,
depending on the value of the field_type parameter.
Usage
check_numeric_or_integer_type(
column_name,
dataframe,
column_prefix = NULL,
field_type = "numeric",
...
)
Arguments
column_name |
A character vector or string with the column name to be tested. |
dataframe |
The dataframe that contains the column. |
column_prefix |
Default is NULL. If provided, this text is prepended to the variable name in the assertion message. |
field_type |
Default is "numeric". Specify "integer" to check if the column has an integer type. This parameter must be either "integer" or "numeric". |
... |
The remaining parameters are passed to the function assert_numeric or assert_integer. |
See Also
Other assertions:
check_no_duplicates_in_group(),
check_posixct_type()
Other tests:
check_double_columns(),
check_no_duplicates_in_group(),
check_posixct_type(),
duplicates_in_column(),
test_all_equal()
Examples
# Create a dataframe with a numeric column
dataframe <- data.frame(a = c(1, 2, 3))
# Check the numeric type of the 'a' column
check_numeric_or_integer_type("a", dataframe)
Check for POSIXct Type
Description
This function checks if the specified column in the provided dataframe has a POSIXct type. It uses the checkmate::assert_posixct function to perform the assertion.
Usage
check_posixct_type(column_name, dataframe, column_prefix = NULL, ...)
Arguments
column_name |
A character vector or string with the column name to be tested. |
dataframe |
The dataframe that contains the column. |
column_prefix |
Default is NULL. If provided, this text is prepended to the variable name in the assertion message. |
... |
The remaining parameters are passed to the function assert_posixct. |
See Also
Other assertions:
check_no_duplicates_in_group(),
check_numeric_or_integer_type()
Other tests:
check_double_columns(),
check_no_duplicates_in_group(),
check_numeric_or_integer_type(),
duplicates_in_column(),
test_all_equal()
Examples
# Create a dataframe with a POSIXct column
dataframe <- data.frame(date = as.POSIXct("2023-10-04"))
# Check the POSIXct type of the 'date' column
check_posixct_type("date", dataframe)
Check rows
Description
This function prints the number of rows of a data frame. This function is used to check that rows are not deleted or doubled unless expected.
Usage
check_rows(df, name = NULL)
Arguments
df |
The data frame whose rows are to be counted |
name |
The name of the data file (this will be printed) |
Value
A message is printed to the console with the number of rows of the data
Examples
check_rows(mtcars)
Check for Columns with Only 0s
Description
This function checks if there are any columns in the provided dataframe that contain only 0 values. If such columns exist, their names are added to the provided collection.
Usage
check_zero_columns(dataframe, collection)
Arguments
dataframe |
A dataframe. |
collection |
A list to store the names of the columns with only 0 values. |
Value
The updated collection.
Examples
# Create a dataframe with some columns containing only 0 values
dataframe <- data.frame(a = c(0, 0, 0), b = c(1, 2, 3), c = c(0, 0, 0))
collection <- checkmate::makeAssertCollection()
check_zero_columns(dataframe, collection)
Count more than 1
Description
Function to count the number of values greater than 1 in a vector This function is used in the function Check_columns_for_double_rows to count duplicate values.
Usage
count_more_than_1(x)
Arguments
x |
The vector to test |
Value
Number of values greater than 1.
Examples
count_more_than_1(c(1, 1, 4))
Create categorical details csv
Description
This function returns a categorical details csv. Containing categorical information about the dataset
Usage
create_categorical_details(data, mapping)
Arguments
data |
A dataframe for which to create a categorical details csv. |
mapping |
A dataframe containing a mapping named vector, containing preferred fieldnames Example: column_names <- c( mpg = "mpg", cyl = "cyl", disp = "disp", hp = "hp", drat = "drat", wt = "wt", qsec = "qsec", vs = "vs", am = "am", gear = "gear", carb = "carb", spare_tire = "spare_tire" |
Value
Dataframe containing categorical details
Create data types tibble
Description
This function returns a data types tibble. Containing type information about the dataset.
Usage
create_data_types(data, mapping)
Arguments
data |
A dataframe for which to create a data types csv. |
mapping |
A dataframe containing a mapping named vector, containing preferred fieldnames Example: column_names <- c( mpg = "mpg", cyl = "cyl", disp = "disp", hp = "hp", drat = "drat", wt = "wt", qsec = "qsec", vs = "vs", am = "am", gear = "gear", carb = "carb", spare_tire = "spare_tire" ) |
Value
Tibble containing data_types
Create dataset summary statistics table
Description
This function creates a summary statistics table for a dataframe, providing insights into the nature of the data contained within. It includes detailed statistics for each column, such as column types, missing value percentages, minimum and maximum values for numeric columns, patterns for character columns, uniqueness of identifiers, and distributions.
Usage
create_dataset_summary_table(df_input)
Arguments
df_input |
A dataframe for which to create a summary statistics table. |
Value
A tibble with comprehensive summary statistics for each column in the input dataframe.
Create field info
Description
This function returns a dataframe containing field info information about the dataset
Usage
create_field_info(
data,
raw_data_path = NULL,
broker = NULL,
product = NULL,
public_dataset = NULL
)
Arguments
data |
A dataframe for which to create a field info csv. |
raw_data_path |
A string containing the original location of the original raw file |
broker |
The name of of the organisation or person that distributes the dataset |
product |
The name of the product where this dataset is used in |
public_dataset |
Boolean containing whether the dataset is publicly available is_primary_key Is_primary_key is variable that can be manually set to TRUE if the dataset contains a primary key. |
Value
Dataframe containing subset info
Create numeric details csv
Description
This function returns a numeric details csv. Containing numeric information about the dataset
Usage
create_numeric_details(data, mapping)
Arguments
data |
A dataframe for which to create a numeric details csv. |
mapping |
A dataframe containing a mapping named vector, containing preferred fieldnames Example: column_names <- c( mpg = "mpg", cyl = "cyl", disp = "disp", hp = "hp", drat = "drat", wt = "wt", qsec = "qsec", vs = "vs", am = "am", gear = "gear", carb = "carb", spare_tire = "spare_tire" ) |
Value
Dataframe containing numeric details.
Create subset fields
Description
This function returns a subsetfields info df. Containing subsetfields information about the dataset
Usage
create_subset_fields(data, mapping)
Arguments
data |
A dataframe for which to create a subsetfields csv. |
mapping |
A dataframe containing a mapping named vector, containing preferred fieldnames Example: column_names <- c( mpg = "mpg", cyl = "cyl", disp = "disp", hp = "hp", drat = "drat", wt = "wt", qsec = "qsec", vs = "vs", am = "am", gear = "gear", carb = "carb", spare_tire = "spare_tire" ) |
Value
Dataframe containing subset info
Drop NA column names
Description
Deletes columns whose name is NA or whose name is empty
Usage
drop_na_column_names(x)
Arguments
x |
dataframe |
Value
dataframe without columns that are NA
Duplicates in column
Description
Searches for duplicates in a data frame column.
Usage
duplicates_in_column(df, col)
Arguments
df |
Data frame. |
col |
Column name. |
Value
Rows containing duplicated values.
See Also
Other tests:
check_double_columns(),
check_no_duplicates_in_group(),
check_numeric_or_integer_type(),
check_posixct_type(),
test_all_equal()
Examples
duplicates_in_column(mtcars, "mpg")
Find Common Columns Between Data Frames
Description
This function identifies common column names between multiple data frames. It takes a variable number of data frames as input and returns a character vector containing the common column names.
Usage
find_common_columns(...)
Arguments
... |
A variable length list of data frames. |
Value
A character vector of column names found in common between all data frames.
Examples
df1 <- data.frame(a = c(1, 2, 3), b = c(4, 5, 6))
df2 <- data.frame(a = c(7, 8, 9), b = c(10, 11, 12), c = c(13, 14, 15))
common_columns <- find_common_columns(df1, df2)
print(common_columns)
Find the maximum numeric value in a vector, ignoring non-numeric values
Description
Find the maximum numeric value in a vector, ignoring non-numeric values
Usage
find_maximum_value(numeric_vector)
Arguments
numeric_vector |
A vector from which to find the maximum numeric value. |
Value
The maximum numeric value in the input vector, or NA if none exist.
Examples
# Find the maximum of a numeric vector
find_maximum_value(c(3, 1, 4, 1, 5, 9)) # Returns 9
# Find the maximum of a mixed vector with non-numeric values
find_maximum_value(c(3, 1, 4, "two", 5, 9)) # Returns 9
# Attempt to find the maximum of a vector with only non-numeric values
find_maximum_value(c("one", "two", "three")) # Returns NA
Find the minimum numeric value in a vector, ignoring non-numeric values
Description
Find the minimum numeric value in a vector, ignoring non-numeric values
Usage
find_minimum_value(numeric_vector)
Arguments
numeric_vector |
A vector from which to find the minimum numeric value. |
Value
The minimum numeric value in the input vector, or NA if none exist.
Examples
# Find the minimum of a numeric vector
find_minimum_value(c(3, 1, 4, 1, 5, 9)) # Returns 1
# Find the minimum of a mixed vector with non-numeric values
find_minimum_value(c(3, 1, 4, "two", 5, 9)) # Returns 1
# Attempt to find the minimum of a vector with only non-numeric values
find_minimum_value(c("one", "two", "three")) # Returns NA
Find pattern in R scripts
Description
Function to search for a pattern in R scripts.
Usage
find_pattern_r(pattern, path = ".", case.sensitive = TRUE, comments = FALSE)
Arguments
pattern |
Pattern to search |
path |
Directory to search in |
case.sensitive |
Whether pattern is case sensitive or not |
comments |
whether to search in commented lines |
Value
Dataframe containing R script paths
Compute distribution statistics for a numeric vector
Description
This function computes summary statistics such as quartiles, mean, and standard deviation for a numeric vector.
Usage
get_distribution_statistics(data_vector)
Arguments
data_vector |
A numeric vector for which to compute summary statistics. |
Value
A character string describing the summary statistics of the input vector.
Examples
# Compute summary statistics for a numeric vector
data_vector <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
get_distribution_statistics(data_vector)
Retrieve the class of the first element of a vector
Description
Retrieve the class of the first element of a vector
Usage
get_first_element_class(input_vector)
Arguments
input_vector |
A vector whose first element's class is to be retrieved. |
Value
The class of the first element of the input vector.
Examples
# Get the class of the first element in a numeric vector
get_first_element_class(c(1, 2, 3)) # Returns "numeric"
# Get the class of the first element in a character vector
get_first_element_class(c("apple", "banana", "cherry")) # Returns "character"
Get values of column
Description
A function to determine what kind of values are present in columns.
Usage
get_values(df, column)
Arguments
df |
The dataframe |
column |
Column to get values from. |
Value
The class of the column values
Examples
get_values(mtcars, "mpg")
Identify Possible Join Pairs Between Data Frames
Description
This function identifies potential join pairs between two data frames based on the overlap between the distinct values in their columns. It returns a data frame showing the possible join pairs.
Usage
identify_join_pairs(..., similarity_cutoff = 0.2)
Arguments
... |
A list of two data frames. |
similarity_cutoff |
The minimal percentage of overlap between the distinct values in the columns. |
Value
A data frame showing candidate join pairs.
Examples
identify_join_pairs(iris, iris3)
Identify Outliers in a Data Frame Column
Description
This function identifies outliers in a specified column of a data frame. It returns a tibble containing the unique values, tally, and whether it is an outlier or not.
Usage
identify_outliers(df, var)
Arguments
df |
The data frame. |
var |
The column to check for outliers. |
Value
A tibble containing the unique values, tally, and whether each value is an outlier or not.
Examples
df <- data.frame(a = c(1, 2, 3, 100, 101), b = c(4, 5, 6, 7, 8), c = c(7, 8, 9, 100, 101))
outliers <- identify_outliers(df, "a")
print(outliers)
Check if a column in a dataframe has unique values
Description
Check if a column in a dataframe has unique values
Usage
is_unique_column(column_name, data_frame)
Arguments
column_name |
The name of the column to check for uniqueness. |
data_frame |
A dataframe containing the column to check. |
Value
TRUE if the column has unique values, FALSE otherwise.
Examples
# Create a dataframe with a unique ID column
data_frame <- tibble::tibble(
id = c(1, 2, 3, 4, 5),
value = c("a", "b", "c", "d", "e")
)
is_unique_column("id", data_frame) # Returns TRUE
# Create a dataframe with duplicate values in the ID column
data_frame <- tibble::tibble(
id = c(1, 2, 3, 4, 5, 1),
value = c("a", "b", "c", "d", "e", "a")
)
is_unique_column("id", data_frame) # Returns FALSE
MD complete cases
Description
Print the complete cases of the data.
Usage
md_complete_cases(data, digits = 1)
Arguments
data |
The data frame. |
digits |
Default: 1. number of digits for rounding. |
Value
Message with the number of rows, number of rows with missing values and the percentage of complete rows.
Examples
# example code
md_complete_cases(iris)
iris$Sepal.Length[5] <- NA_character_
md_complete_cases(iris)
Construct Regex for Matching Function Parameter Content
Description
This function constructs a regex pattern for matching the content of a parameter in a function.
It uses the base::paste0 function to construct the regex pattern.
Usage
regex_content_parameter(parameter)
Arguments
parameter |
The parameter whose value is to be searched in a function. |
Value
A regex pattern as a character string.
Examples
# Create a parameter name
parameter <- "my_parameter"
# Construct a regex pattern for matching the content of the parameter
pattern <- regex_content_parameter(parameter)
Generate regular expression of a time.
Description
This function generates a regular expression for time based on the input format.
Usage
regex_time(format = "hh:mm")
Arguments
format |
The format of the time. Possible values are:
|
Value
A regular expression.
Examples
regex_time("hh:mm")
regex_time("h:m")
regex_time("hh:mm:ss")
regex_time("h:m:s")
regex_time("hh:mm:ss AM/PM")
regex_time("h:m:s AM/PM")
Generate regular expression of a year date.
Description
This function generates a regular expression for year date based on the input format.
Usage
regex_year_date(format = "yyyy")
Arguments
format |
The format of the year date. Possible values are:
|
Value
A regular expression.
Examples
regex_year_date("yyyy")
regex_year_date("yyyy-MM-dd")
regex_year_date("yyyy/MM/dd")
regex_year_date("yyyy.MM.dd")
regex_year_date("yyyy-M-d")
regex_year_date("yyyy/M/d")
regex_year_date("yyyy.M.d")
regex_year_date("yyyy-MM-dd HH:mm:ss")
regex_year_date("yyyy/MM/dd HH:mm:ss")
regex_year_date("yyyy-MM-dd HH:mm")
regex_year_date("yyyy/MM/dd HH:mm")
Remove Duplicates and NA Values from Input
Description
This function removes duplicate values and NA values from the input.
It first removes NA values from the input using the na.omit function from the stats package.
Then it removes duplicate values from the result using the unique function.
Usage
remove_duplicates_and_na(input)
Arguments
input |
A vector or data frame. |
Value
A vector or data frame with duplicate values and NA values removed.
Examples
# Create a vector with duplicate values and NA values
input <- c(1, 2, NA, 2, NA, 3, 4, 4, NA, 5)
# Remove duplicate values and NA values
output <- remove_duplicates_and_na(input)
print(output)
retrieve_function_calls
Description
retrieve_function_calls
Usage
retrieve_function_calls(script_name)
Arguments
script_name |
The script to search functions in |
Value
dataframe
Retrieve functions and packages
Description
Retrieves functions and their corresponding packages used in a given script.
Usage
retrieve_functions_and_packages(path)
Arguments
path |
The complete path of the script. |
Value
Used_functions
Retrieve packages that are loaded in a script
Description
Retrieve packages that are loaded in a script
Usage
retrieve_package_usage(script_name)
Arguments
script_name |
The path to the R script |
Value
dataframe
retrieve_sourced_scripts
Description
retrieve_sourced_scripts
Usage
retrieve_sourced_scripts(script_name)
Arguments
script_name |
The main script to search |
Value
dataframe
retrieve_string_assignments
Description
retrieve_string_assignments
Usage
retrieve_string_assignments(script_name)
Arguments
script_name |
The script to search objects in |
Value
dataframe
Return Assertion Messages
Description
This function returns a message indicating whether an assertion test has passed or failed. An "assertion collection" from the checkmate package must be provided. The message can be returned as an error or a warning. For some assertions, only warnings are allowed, as an error would stop the script from running. This is done for the following assertions: percentage missing values, duplicates, subset, and set_equal.
Usage
return_assertions_message(
collection,
collection_name,
fail = "stop",
silent = FALSE,
output_map = NULL
)
Arguments
collection |
An object with the class "AssertCollection". |
collection_name |
The name of the collection. This name is mentioned in the messages. |
fail |
"stop" or "warning". If the assertions fail, an error is returned and the script output is stopped. If "warning", only a warning is returned. |
silent |
If FALSE (default), the success message is printed in the console. If TRUE, it is not shown. |
output_map |
A map, like 1. Read data, where the file is stored. |
Value
The message indicating whether the assertion test has passed or failed.
Read and return the mtcars testfile
Description
Gets the modified rds dataset for testing assertions.
Usage
return_mtcars_testfile()
Value
returns mtcars_test dataframe
Run All Data Validation Assertions
Description
This function performs multiple validation checks on a dataset using various assertion functions. It loads metadata from specified CSV files, validates the dataset against expected field properties, and stops execution if any warnings are encountered.
Usage
run_all_assertions(new_data, output_dir)
Arguments
new_data |
A data frame. The dataset to validate. |
output_dir |
A character string. The directory containing metadata CSV files ( |
Value
No return value. The function stops execution and displays warnings if any validation checks fail.
Detect string in file
Description
Detect string in file
Usage
str_detect_in_file(file, pattern, only_comments = FALSE, collapse = FALSE)
Arguments
file |
Path to file. |
pattern |
Pattern to match. |
only_comments |
default FALSE. Whether to only search in commented lines. |
collapse |
default: FALSE: search file line by line. If true, then pattern is search in the entire file at once after collapsing. (only_comments does not work when collapse is set to TRUE) |
Value
Boolean whether pattern exists in file.
Test all equal
Description
Test whether all values in a vector are equal.
Usage
test_all_equal(x, na.rm = FALSE)
Arguments
x |
Vector to test. |
na.rm |
default: FALSE. exclude NAs from the test. |
Value
Boolean result of the test
See Also
Other tests:
check_double_columns(),
check_no_duplicates_in_group(),
check_numeric_or_integer_type(),
check_posixct_type(),
duplicates_in_column()
Examples
test_all_equal(c(5, 5, 5))
test_all_equal(c(5, 6, 3))
unique id
Description
Check if parsed variable is a unique identifier. This function was adapted from: Source: https://edwinth.github.io/blog/unique_id/
Usage
unique_id(x, ...)
Arguments
x |
vector or dataframe. |
... |
optional variables, e.g. name of column or a vector of names. |
Value
Boolean whether variable is a unique identifier.
Examples
unique_id(iris, Species)
mtcars$name <- rownames(mtcars)
unique_id(mtcars, name)