### Create UpSetR Plot from Shadow Data - R Source: https://github.com/njtierney/naniar/blob/main/NEWS.md Converts airquality data to shadow format suitable for UpSetR visualization. The as_shadow_upset() function prepares missing data structure for upset plot creation, enabling visual exploration of missing data patterns across multiple variables. ```r airquality %>% as_shadow_upset() %>% UpSetR::upset() ``` -------------------------------- ### miss_prop_summary with NULL input error Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/miss-prop-summary.md Demonstrates the error raised when miss_prop_summary receives a NULL value. The function includes explicit NULL checking to prevent processing of null inputs with a specific error message. ```r miss_prop_summary(NULL) ``` -------------------------------- ### shade() NULL Input Validation Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/shade.md Demonstrates the error raised when shade() receives a NULL value as input. The function validates that input is not NULL and throws an error with the input type information. ```r shade(NULL) ``` -------------------------------- ### miss_prop_summary with character input error Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/miss-prop-summary.md Demonstrates the error raised when miss_prop_summary receives a character string instead of a dataframe. The validation check ensures the input inherits from data.frame class. ```r miss_prop_summary("a") ``` -------------------------------- ### miss_prop_summary with numeric input error Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/miss-prop-summary.md Demonstrates the error raised when miss_prop_summary receives a numeric value instead of a dataframe. The function validates input type and returns a clear error message indicating the received class type. ```r miss_prop_summary(0) ``` -------------------------------- ### shade() Zero-Length List Validation Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/shade.md Illustrates the error raised when shade() receives an empty list. Similar to numeric vectors, lists must have length greater than 0 for the function to execute successfully. ```r shade(list()) ``` -------------------------------- ### miss_prop_summary with matrix input error Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/miss-prop-summary.md Demonstrates the error raised when miss_prop_summary receives a matrix object instead of a dataframe. Although matrices are array-like structures, they do not inherit from data.frame and trigger validation error. ```r miss_prop_summary(matrix(airquality)) ``` -------------------------------- ### shade() Nested List Column Processing Limitation Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/shade.md Demonstrates the constraint when shade() attempts to process list columns with nested structures and the broken parameter. The function does not support additional levels of missing data representation when shading list columns. ```r shade(list(3, list(1), c(2, 3), list()), broken = 3) ``` -------------------------------- ### nabular Error with Matrix Input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/nabular.md Illustrates the error handling when nabular receives a matrix or array object. The as_shadow() function identifies the matrix/array class and returns an appropriate error message. ```r nabular(matrix(0)) ``` -------------------------------- ### recode_shadow with Invalid Input - R Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/special-missing-values.md Demonstrates the error thrown by recode_shadow() when passed a regular dataframe without shadow columns. The function expects shadow columns to be present in the input data. Shadow columns track missing values and must be created using shade(), bind_shadow(), or related functions before calling recode_shadow(). ```r recode_shadow(df, temp = .where(wind == -99 ~ "bananas")) ``` -------------------------------- ### nabular Error with NULL Input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/nabular.md Demonstrates the error raised when nabular receives a NULL value. The validation explicitly checks for NULL inputs and returns a specific error message indicating the input is NULL. ```r nabular(NULL) ``` -------------------------------- ### shade() Zero-Length Numeric Vector Validation Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/shade.md Shows the error condition when shade() receives a numeric vector with length 0. The function requires input to have length greater than 0 to proceed with shading operations. ```r shade(numeric(0)) ``` -------------------------------- ### prop_complete_row() Error Handling for Invalid Inputs Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/prop_row.md Demonstrates error conditions when prop_complete_row() is called with invalid input types. The function requires a data frame input and will throw an error if passed a numeric vector or NULL value, providing clear error messages indicating the expected input type. ```r prop_complete_row(numeric(0)) # Error in `prop_complete_row()`: # ! Input must inherit from # We see class: ``` ```r prop_complete_row(NULL) # Error in `prop_complete_row()`: # ! Input must not be NULL # Input is ``` -------------------------------- ### impute_mean_all with NULL input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/impute-median.md Demonstrates error handling when impute_mean_all receives NULL instead of a data.frame. The function validates input class and raises an error indicating NULL was received. ```R impute_mean_all(NULL) ``` -------------------------------- ### prop_miss_row() Error Handling for Invalid Inputs Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/prop_row.md Demonstrates error conditions when prop_miss_row() is called with invalid input types. The function requires a data frame input and will throw an error if passed a numeric vector or NULL value, providing clear error messages indicating the expected input type. ```r prop_miss_row(numeric(0)) # Error in `prop_miss_row()`: # ! Input must inherit from # We see class: ``` ```r prop_miss_row(NULL) # Error in `prop_miss_row()`: # ! Input must not be NULL # Input is ``` -------------------------------- ### nabular Error with Numeric Input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/nabular.md Demonstrates the error raised when nabular receives a numeric value instead of a dataframe. The as_shadow() function validates input inheritance and returns an error message indicating the received class type. ```r nabular(0) ``` -------------------------------- ### n_complete_row() Error Handling for Invalid Inputs Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/prop_row.md Demonstrates error conditions when n_complete_row() is called with invalid input types. The function requires a data frame input and will throw an error if passed a numeric vector or NULL value, providing clear error messages indicating the expected input type. ```r n_complete_row(numeric(0)) # Error in `n_complete_row()`: # ! Input must inherit from # We see class: ``` ```r n_complete_row(NULL) # Error in `n_complete_row()`: # ! Input must not be NULL # Input is ``` -------------------------------- ### n_miss_row() Error Handling for Invalid Inputs Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/prop_row.md Demonstrates error conditions when n_miss_row() is called with invalid input types. The function requires a data frame input and will throw an error if passed a numeric vector or NULL value, providing clear error messages indicating the expected input type. ```r n_miss_row(numeric(0)) # Error in `n_miss_row()`: # ! Input must inherit from # We see class: ``` ```r n_miss_row(NULL) # Error in `n_miss_row()`: # ! Input must not be NULL # Input is ``` -------------------------------- ### impute_median_at with NULL input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/impute-median.md Demonstrates error handling when impute_median_at receives NULL instead of a data.frame. The function validates input class and raises an error indicating NULL was received. ```R impute_median_at(NULL) ``` -------------------------------- ### impute_median_all with invalid numeric input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/impute-median.md Demonstrates error handling when impute_median_all receives a numeric vector instead of a data.frame. The function validates input class and raises an error indicating the received class type. ```R impute_median_all(numeric(0)) ``` -------------------------------- ### impute_median_if with NULL input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/impute-median.md Demonstrates error handling when impute_median_if receives NULL instead of a data.frame. The function validates input class and raises an error indicating NULL was received. ```R impute_median_if(NULL) ``` -------------------------------- ### impute_median_at with invalid numeric input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/impute-median.md Demonstrates error handling when impute_median_at receives a numeric vector instead of a data.frame. The function validates input class and raises an error indicating the received class type. ```R impute_median_at(numeric(0)) ``` -------------------------------- ### nabular Error with Character Input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/nabular.md Shows the error condition when nabular is called with a character string. The validation function checks that input inherits from data.frame class and rejects character inputs with a descriptive error. ```r nabular("a") ``` -------------------------------- ### impute_median_if with invalid numeric input Source: https://github.com/njtierney/naniar/blob/main/tests/testthat/_snaps/impute-median.md Demonstrates error handling when impute_median_if receives a numeric vector instead of a data.frame. The function validates input class and raises an error indicating the received class type. ```R impute_median_if(numeric(0)) ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.