pgu.missings: pgu.missings
In pguIMP: 'pguIMP'
Description
Format
Details
Active bindings
Methods
Author(s)
Description
Detects and substitutes missing values from data set.
Format
R6::R6Class object.
Details
Detects missing values in the transformed and normalized data set.
This object is used by the shiny based gui and is not for use in individual R-scripts!
Active bindings
imputationParameterReturns the instance variable outliersParameter.
(tibble::tibble)
imputationSitesReturns the instance variable imputationSites.
(tibble::tibble)
one_hot_dfReturns the positions of missings in one_hot encoding
(tibble::tibble)
amvReturns the instance variable amv.
(numeric)
Methods
Public methods
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Method new()
Creates and returns a new pgu.missings object.
Usage
pgu.missings$new(data_df = "tbl_df")
Arguments
data_dfThe data to be cleaned.
(tibble::tibble)
Returns
A new pgu.missings object.
(pguIMP::pgu.missings)
Method finalize()
Clears the heap and
indicates that instance of pgu.missings is removed from heap.
Usage
pgu.missings$finalize()
Method print()
Prints instance variables of a pgu.missings object.
Usage
pgu.missings$print()
Returns
string
Method resetImputationParameter()
Resets instance variables and
identifies missings in the normalized data set.
Usage
pgu.missings$resetImputationParameter(data_df = "tbl_df")
Arguments
data_dfDataframe to be analyzed.
(tibble::tibble)
Method featureIdx()
Returns the position of an attribute within a data frame.
Usage
pgu.missings$featureIdx(feature = "character")
Arguments
featureThe attribute's name.
(character)
Returns
The postion of the attribute.
(numeric)
Method filterFeatures()
Selects features cotaining missing values from a dataset.
Usage
pgu.missings$filterFeatures(data_df = "tbl_df")
Arguments
data_dfDataframe to be analyzed.
(tibble::tibble)
Returns
The filtered data frame.
(tibble::tibble)
Method gatherMeasurements()
Calculates the number of values of a vector.
Usage
pgu.missings$gatherMeasurements(value = "numeric")
Arguments
valueA vector comprising numeric data.
(numeric)
Returns
The lenght of the vector.
(numeric)
Method gatherMissings()
Calculates the number of missing values of a vector.
Usage
pgu.missings$gatherMissings(value = "numeric")
Arguments
valueA vector comprising numeric data.
(numeric)
Returns
The number of missing in the vector.
(numeric)
Method gatherExistings()
Calculates the number of existing values of a vector.
Usage
pgu.missings$gatherExistings(value = "numeric")
Arguments
valueA vector comprising numeric data.
(numeric)
Returns
The number of existing values in the vector.
(numeric)
Method gatherFractionOfMissings()
Calculates the fraction of missing values of a vector.
Usage
pgu.missings$gatherFractionOfMissings(value = "numeric")
Arguments
valueA vector comprising numeric data.
(numeric)
Returns
The fraction of missing values in the vector.
(numeric)
Method gatherImputationStatistics()
Gathers statistical information about missing values
that are provided by the classes public gather functions.
The information is stored within the classes instance variable imputationParameter
Usage
pgu.missings$gatherImputationStatistics(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Method one_hot()
Gathers statistical information about missing values
in one hot format.
The result is stored in the instance variable one_hot_df.
Usage
pgu.missings$one_hot(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Method detectImputationSites()
Detects missing values within the data frame and
writes the to the instance variable imputationsites.
Usage
pgu.missings$detectImputationSites(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Method imputationSiteDistribution()
Numeric representation of the distribution of missing values within the data frame.
Usage
pgu.missings$imputationSiteDistribution(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed.
(tibble::tibble)
Returns
A data frame
(tibble::tibble)
#' @description
#' Merges the numeric attributes of the pguIMP data with its metadata.
#' @param data_df
#' The data frame to be analyzed.
#' (tibble::tibble)
#' @param metadata_df
#' The corresponding metadata.
#' (tibble::tibble)
#' @return
#' A data frame
#' (tibble::tibble)
mergeImputationSiteData = function(data_df = "tbl_df", metadata_df = "tbl_df")
dfMerge <- data_df
if(nrow(data_df) == nrow(metadata_df))
dfMerge <- dplyr::bind_cols(metadata_df, data_df)
#if
dfMerge %>%
dplyr::filter_all(dplyr::any_vars(is.na(.))) %>%
return()
, #function
Method imputationSiteHeatMap()
Displays the distribution of missing values in form of a heatmap.
Usage
pgu.missings$imputationSiteHeatMap()
Returns
A heatmap plot.
(ggplot2::ggplot)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.missings$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
pguIMP documentation built on Sept. 30, 2021, 5:08 p.m.
R Package Documentation
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Description Format Details Active bindings Methods Author(s)
Description
Detects and substitutes missing values from data set.
Format
R6::R6Class object.
Details
Detects missing values in the transformed and normalized data set. This object is used by the shiny based gui and is not for use in individual R-scripts!
Active bindings
imputationParameterReturns the instance variable outliersParameter. (tibble::tibble)
imputationSitesReturns the instance variable imputationSites. (tibble::tibble)
one_hot_dfReturns the positions of missings in one_hot encoding (tibble::tibble)
amvReturns the instance variable amv. (numeric)
Methods
Public methods
Method new()
Creates and returns a new pgu.missings object.
Usage
pgu.missings$new(data_df = "tbl_df")
Arguments
data_dfThe data to be cleaned. (tibble::tibble)
Returns
A new pgu.missings object.
(pguIMP::pgu.missings)
Method finalize()
Clears the heap and
indicates that instance of pgu.missings is removed from heap.
Usage
pgu.missings$finalize()
Method print()
Prints instance variables of a pgu.missings object.
Usage
pgu.missings$print()
Returns
string
Method resetImputationParameter()
Resets instance variables and identifies missings in the normalized data set.
Usage
pgu.missings$resetImputationParameter(data_df = "tbl_df")
Arguments
data_dfDataframe to be analyzed. (tibble::tibble)
Method featureIdx()
Returns the position of an attribute within a data frame.
Usage
pgu.missings$featureIdx(feature = "character")
Arguments
featureThe attribute's name. (character)
Returns
The postion of the attribute. (numeric)
Method filterFeatures()
Selects features cotaining missing values from a dataset.
Usage
pgu.missings$filterFeatures(data_df = "tbl_df")
Arguments
data_dfDataframe to be analyzed. (tibble::tibble)
Returns
The filtered data frame. (tibble::tibble)
Method gatherMeasurements()
Calculates the number of values of a vector.
Usage
pgu.missings$gatherMeasurements(value = "numeric")
Arguments
valueA vector comprising numeric data. (numeric)
Returns
The lenght of the vector. (numeric)
Method gatherMissings()
Calculates the number of missing values of a vector.
Usage
pgu.missings$gatherMissings(value = "numeric")
Arguments
valueA vector comprising numeric data. (numeric)
Returns
The number of missing in the vector. (numeric)
Method gatherExistings()
Calculates the number of existing values of a vector.
Usage
pgu.missings$gatherExistings(value = "numeric")
Arguments
valueA vector comprising numeric data. (numeric)
Returns
The number of existing values in the vector. (numeric)
Method gatherFractionOfMissings()
Calculates the fraction of missing values of a vector.
Usage
pgu.missings$gatherFractionOfMissings(value = "numeric")
Arguments
valueA vector comprising numeric data. (numeric)
Returns
The fraction of missing values in the vector. (numeric)
Method gatherImputationStatistics()
Gathers statistical information about missing values
that are provided by the classes public gather functions.
The information is stored within the classes instance variable imputationParameter
Usage
pgu.missings$gatherImputationStatistics(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Method one_hot()
Gathers statistical information about missing values in one hot format. The result is stored in the instance variable one_hot_df.
Usage
pgu.missings$one_hot(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Method detectImputationSites()
Detects missing values within the data frame and
writes the to the instance variable imputationsites.
Usage
pgu.missings$detectImputationSites(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Method imputationSiteDistribution()
Numeric representation of the distribution of missing values within the data frame.
Usage
pgu.missings$imputationSiteDistribution(data_df = "tbl_df")
Arguments
data_dfThe data frame to be analyzed. (tibble::tibble)
Returns
A data frame (tibble::tibble) #' @description #' Merges the numeric attributes of the pguIMP data with its metadata. #' @param data_df #' The data frame to be analyzed. #' (tibble::tibble) #' @param metadata_df #' The corresponding metadata. #' (tibble::tibble) #' @return #' A data frame #' (tibble::tibble) mergeImputationSiteData = function(data_df = "tbl_df", metadata_df = "tbl_df") dfMerge <- data_df if(nrow(data_df) == nrow(metadata_df)) dfMerge <- dplyr::bind_cols(metadata_df, data_df) #if dfMerge %>% dplyr::filter_all(dplyr::any_vars(is.na(.))) %>% return() , #function
Method imputationSiteHeatMap()
Displays the distribution of missing values in form of a heatmap.
Usage
pgu.missings$imputationSiteHeatMap()
Returns
A heatmap plot. (ggplot2::ggplot)
Method clone()
The objects of this class are cloneable with this method.
Usage
pgu.missings$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Author(s)
Sebastian Malkusch, malkusch@med.uni-frankfurt.de
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