R/pguNormalizer.R
In SMLMS/pguIMP: 'pguIMP'
#' @title pgu.normalizer
#'
#' @description
#' Normalization of data. Part of pguIMP.
#'
#' @details
#' Performs a data normalization in order to achieve a standardized version of the dataframe.
#' This object is used by the shiny based gui and is not for use in individual R-scripts!
#'
#' @format [R6::R6Class] object.
#'
#' @importFrom dplyr filter group_by mutate pull select select_if summarise_all sym
#' @importFrom ggplot2 aes_string coord_flip element_blank element_rect geom_bar geom_boxplot
#' @importFrom ggplot2 geom_jitter ggplot layer_scales scale_x_continuous scale_y_continuous
#' @importFrom ggplot2 theme theme_linedraw xlab ylab
#' @importFrom gridExtra grid.arrange
#' @importFrom magrittr %>%
#' @importFrom R6 R6Class
#' @importFrom tibble is_tibble tibble
#' @importFrom tidyr gather gather_ pivot_longer spread
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
pgu.normalizer <- R6::R6Class("pgu.normalizer",
####################
# instance variables
####################
private = list(
.normAgentAlphabet = "character",
.normAgent = "factor",
.features = "character",
.normParameter = "tbl_df"
),
##################
# accessor methods
##################
active = list(
#' @field normAgentAlphabet
#' Returns the instance variable normAgentAlphabt.
normAgentAlphabet = function(){
return(private$.normAgentAlphabet)
},
#' @field normAgent
#' Returns the instance variable normAgent.
#' (character)
normAgent = function(){
return(as.character(private$.normAgent))
},
#' @field setNormAgent
#' Sets the instance variable normAgent.
#' (character)
setNormAgent = function(agent = "character") {
private$.normAgent <- factor(agent, levels = self$normAgentAlphabet)
},
#' @field features
#' Returns instance variable features.
#' (character)
features = function(){
return(private$.features)
},
#' @field normParameter
#' Returns the instance variable normParameter.
normParameter = function(){
return(private$.normParameter)
}
), #active
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.normalizer` object.
#' @param data_df
#' The data to be analyzed.
#' (tibble::tibble)
#' @return
#' A new `pgu.normalizer` object.
#' (pguIMP::pgu.normalizer)
initialize = function(data_df = "tbl_df"){
if(!tibble::is_tibble(data_df)){
data_df <- tibble::tibble(names <- "none",
values <- c(NA))
private$.normParameter <- tibble::tibble(feature = character(0),
max = numeric(0),
mean = numeric(0),
min = numeric(0),
sigma = numeric(0))
private$.features <- character(0)
} else{
self$detectNormParameter(data_df)
}
private$.normAgentAlphabet <-c("none", "min-max", "mean", "z-score")
self$setNormAgent <- "none"
}, #function
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.normalizer` is removed from heap.
finalize = function(){
print("Instance of pgu.normalizer removed from heap")
}, #function
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.normalizer` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.normalizer\n")
cat(rString)
cat("normAgentAlphabet:\n")
print(private$.normAgentAlphabet)
cat("\nnormAgent:\n")
print(private$.normAgent)
cat("\nnormParameter:\n")
print(private$.normParameter)
cat("\n\n")
invisible(self)
}, #function
#' @description
#' Resets instance variable `normParameter`
#' @param data_df
#' Dataframe to be analyzed.
#' (tibble::tibble)
detectNormParameter = function(data_df = "tbl_df"){
private$.normParameter <- data_df %>%
dplyr::select_if(is.numeric) %>%
tidyr::gather(key = "feature", value = "value") %>%
dplyr::group_by(feature) %>%
dplyr::summarise_all(c(min=min, max=max, mean=mean, sigma=sd), na.rm = TRUE) %>%
tidyr::pivot_longer(cols = -c("feature")) %>%
tidyr::spread(key = name, value = value)
private$.features <- self$normParameter %>%
dplyr::select(feature) %>%
dplyr::pull() %>%
unique()
}, #function
#' @description
#' Scales a tibble using the method defined by the instance variable normAgent
#' @param data_df
#' Dataframe to be scaled
#' (tible::tibble)
#' @return
#' A normalized version of the dataframe.
#' (tibble::tibble)
scale_data = function(data_df = "tbl_df"){
switch(self$normAgent,
"none"=data_df,
"min-max"=self$scale_minMax(data_df),
"mean"=self$scale_mean(data_df),
"z-score"=self$scale_zScore(data_df),
stop("unkown normAgent")) %>%
return()
}, #function
#' @description
#' Scales a tibble using min-max normalization
#' @param data_df
#' Dataframe to be scaled
#' (tibble::tibble)
#' @return
#' A min-max normalized version of the dataframe
scale_minMax = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$scale_minMax_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Scales a numeric object using min-max normalization
#' @param values
#' Values to be scaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' A min-max normalized version of the numeric object
scale_minMax_numeric = function(values = "numeric", feature = "character"){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
scaled_values <- (values - min_val) / (max_val - min_val)
return(scaled_values)
}, #function
#' @description
#' Scales a tibble using mean normalization
#' @param data_df
#' Dataframe to be scaled.
#' (tibble::tibble)
#' @return
#' A mean normalized version of the dataframe
scale_mean = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$scale_mean_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Scales a numeric object using mean normalization
#' @param values
#' Values to be scaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' A mean normalized version of the numeric object
scale_mean_numeric = function(values = "numeric", feature = character){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
scaled_values <- (values - mean_val) / (max_val - min_val)
return(scaled_values)
}, #function
#' @description
#' Scales a tibble using z-score normalization
#' @param data_df
#' Dataframe to be scaled
#' (tibble::tibble)
#' @return
#' A z-score normalized version of the dataframe
scale_zScore = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$scale_zScore_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Scales a numeric object using z-score normalization
#' @param values
#' Values to be scaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' A z-score normalized version of the numeric object
scale_zScore_numeric = function(values = "numeric", feature = character){
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
sigma_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("sigma") %>%
dplyr::pull() %>%
as.numeric()
scaled_values <- (values - mean_val) / (sigma_val)
return(scaled_values)
}, #function
#' @description
#' Rescales a tibble using the method defined by the instance variable normAgent
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tible::tibble)
#' @return
#' A rescaled version of the normalized dataframe.
#' (tibble::tibble)
rescale_data = function(data_df = "tbl_df"){
switch(self$normAgent,
"none"=data_df,
"min-max"=self$rescale_minMax(data_df),
"mean"=self$rescale_mean(data_df),
"z-score"=self$rescale_zScore(data_df),
stop("unkown normAgent")) %>%
return()
}, #function
#' @description
#' Rescales a tibble using min-max normalization
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tibble::tibble)
#' @return
#' A rescaled version of a min-max normalized dataframe
rescale_minMax = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$rescale_minMax_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Rescales a numeric object using min-max normalization
#' @param values
#' Normalized values to be rescaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' Rescaled version of min-max normalized numeric object
rescale_minMax_numeric = function(values = "numeric", feature = character){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
rescaled_values <- (values * (max_val - min_val)) + min_val
return(rescaled_values)
}, #function
#' @description
#' Rescales a tibble using mean normalization
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tibble::tibble)
#' @return
#' A rescaled version of a mean normalized dataframe
rescale_mean = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$rescale_mean_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Rescales a numeric object using mean normalization
#' @param values
#' Normalized values to be rescaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' Rescaled version of mean normalized numeric object
rescale_mean_numeric = function(values = "numeric", feature = character){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
rescaled_values <- (values * (max_val - min_val)) + mean_val
return(rescaled_values)
}, #function
#' @description
#' Rescales a tibble using z-score normalization
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tibble::tibble)
#' @return
#' A rescaled version of a z-score normalized dataframe
rescale_zScore = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$rescale_zScore_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Rescales a numeric object using z-score normalization
#' @param values
#' Normalized values to be rescaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' Rescaled version of z-score normalized numeric object
rescale_zScore_numeric = function(values = "numeric", feature = character){
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
sigma_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("sigma") %>%
dplyr::pull() %>%
as.numeric()
rescaled_values <- (values * sigma_val) + mean_val
return(rescaled_values)
}, #function
##############################
# Graphical output functions #
##############################
#' @description
#' Displays the distribution of an attribute values as histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A histogram.
#' (ggplot2::ggplot)
featureBarPlot = function(data_df = "tbl_df", feature = "character"){
feature <- dplyr::sym(feature)
p <- data_df %>%
ggplot2::ggplot(mapping = ggplot2::aes_string(x=feature), na.rm=TRUE) +
ggplot2::geom_bar(stat = "bin", bins=30) +
ggplot2::ylab("counts") +
ggplot2::xlab("value") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
}, #function
#' @description
#' Displays the distribution of an attribute's values as box plot.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A box plot.
#' (ggplot2::ggplot)
featureBoxPlotWithSubset = function(data_df = "tbl_df", feature = "character"){
p <- data_df %>%
dplyr::select(feature) %>%
tidyr::gather_(key="feature", value="measurement", feature) %>%
ggplot2::ggplot(mapping=ggplot2::aes_string(x="feature",y="measurement"), na.rm=TRUE)+
ggplot2::geom_boxplot(na.rm=TRUE, outlier.shape = NA)+
ggplot2::geom_jitter(color = "darkblue", na.rm=TRUE) +
ggplot2::ylab("value") +
ggplot2::xlab("feature") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
}, #function
#' @description
#' Displays the distribution of an attribute's values as a composition of a box plot and a histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A composite plot.
#' (ggplot2::ggplot)
featurePlot = function(data_df = "tbl_df", feature = "character"){
p1 <- self$featureBoxPlotWithSubset(data_df, feature) +
ggplot2::theme(legend.position = c(0.9, 0.9),
legend.key = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
p2 <- self$featureBarPlot(data_df, feature)
limits <- ggplot2::layer_scales(p2)$x$range$range
p1 <- p1 +
ggplot2::scale_y_continuous(limits=limits)
p2 <- p2 +
ggplot2::scale_x_continuous(position = "top") +
ggplot2::coord_flip()
p <- gridExtra::grid.arrange(p1,p2, layout_matrix = rbind(c(1,1,2),c(1,1,2)),
top = textGrob(label = sprintf("Distribution of %s", feature)))
return(p)
}#function
)#public
)#class
SMLMS/pguIMP documentation built on Oct. 1, 2021, 12:01 a.m.
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#' @title pgu.normalizer
#'
#' @description
#' Normalization of data. Part of pguIMP.
#'
#' @details
#' Performs a data normalization in order to achieve a standardized version of the dataframe.
#' This object is used by the shiny based gui and is not for use in individual R-scripts!
#'
#' @format [R6::R6Class] object.
#'
#' @importFrom dplyr filter group_by mutate pull select select_if summarise_all sym
#' @importFrom ggplot2 aes_string coord_flip element_blank element_rect geom_bar geom_boxplot
#' @importFrom ggplot2 geom_jitter ggplot layer_scales scale_x_continuous scale_y_continuous
#' @importFrom ggplot2 theme theme_linedraw xlab ylab
#' @importFrom gridExtra grid.arrange
#' @importFrom magrittr %>%
#' @importFrom R6 R6Class
#' @importFrom tibble is_tibble tibble
#' @importFrom tidyr gather gather_ pivot_longer spread
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
pgu.normalizer <- R6::R6Class("pgu.normalizer",
####################
# instance variables
####################
private = list(
.normAgentAlphabet = "character",
.normAgent = "factor",
.features = "character",
.normParameter = "tbl_df"
),
##################
# accessor methods
##################
active = list(
#' @field normAgentAlphabet
#' Returns the instance variable normAgentAlphabt.
normAgentAlphabet = function(){
return(private$.normAgentAlphabet)
},
#' @field normAgent
#' Returns the instance variable normAgent.
#' (character)
normAgent = function(){
return(as.character(private$.normAgent))
},
#' @field setNormAgent
#' Sets the instance variable normAgent.
#' (character)
setNormAgent = function(agent = "character") {
private$.normAgent <- factor(agent, levels = self$normAgentAlphabet)
},
#' @field features
#' Returns instance variable features.
#' (character)
features = function(){
return(private$.features)
},
#' @field normParameter
#' Returns the instance variable normParameter.
normParameter = function(){
return(private$.normParameter)
}
), #active
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.normalizer` object.
#' @param data_df
#' The data to be analyzed.
#' (tibble::tibble)
#' @return
#' A new `pgu.normalizer` object.
#' (pguIMP::pgu.normalizer)
initialize = function(data_df = "tbl_df"){
if(!tibble::is_tibble(data_df)){
data_df <- tibble::tibble(names <- "none",
values <- c(NA))
private$.normParameter <- tibble::tibble(feature = character(0),
max = numeric(0),
mean = numeric(0),
min = numeric(0),
sigma = numeric(0))
private$.features <- character(0)
} else{
self$detectNormParameter(data_df)
}
private$.normAgentAlphabet <-c("none", "min-max", "mean", "z-score")
self$setNormAgent <- "none"
}, #function
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.normalizer` is removed from heap.
finalize = function(){
print("Instance of pgu.normalizer removed from heap")
}, #function
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.normalizer` object.
#' @return
#' string
print = function(){
rString <- sprintf("\npgu.normalizer\n")
cat(rString)
cat("normAgentAlphabet:\n")
print(private$.normAgentAlphabet)
cat("\nnormAgent:\n")
print(private$.normAgent)
cat("\nnormParameter:\n")
print(private$.normParameter)
cat("\n\n")
invisible(self)
}, #function
#' @description
#' Resets instance variable `normParameter`
#' @param data_df
#' Dataframe to be analyzed.
#' (tibble::tibble)
detectNormParameter = function(data_df = "tbl_df"){
private$.normParameter <- data_df %>%
dplyr::select_if(is.numeric) %>%
tidyr::gather(key = "feature", value = "value") %>%
dplyr::group_by(feature) %>%
dplyr::summarise_all(c(min=min, max=max, mean=mean, sigma=sd), na.rm = TRUE) %>%
tidyr::pivot_longer(cols = -c("feature")) %>%
tidyr::spread(key = name, value = value)
private$.features <- self$normParameter %>%
dplyr::select(feature) %>%
dplyr::pull() %>%
unique()
}, #function
#' @description
#' Scales a tibble using the method defined by the instance variable normAgent
#' @param data_df
#' Dataframe to be scaled
#' (tible::tibble)
#' @return
#' A normalized version of the dataframe.
#' (tibble::tibble)
scale_data = function(data_df = "tbl_df"){
switch(self$normAgent,
"none"=data_df,
"min-max"=self$scale_minMax(data_df),
"mean"=self$scale_mean(data_df),
"z-score"=self$scale_zScore(data_df),
stop("unkown normAgent")) %>%
return()
}, #function
#' @description
#' Scales a tibble using min-max normalization
#' @param data_df
#' Dataframe to be scaled
#' (tibble::tibble)
#' @return
#' A min-max normalized version of the dataframe
scale_minMax = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$scale_minMax_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Scales a numeric object using min-max normalization
#' @param values
#' Values to be scaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' A min-max normalized version of the numeric object
scale_minMax_numeric = function(values = "numeric", feature = "character"){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
scaled_values <- (values - min_val) / (max_val - min_val)
return(scaled_values)
}, #function
#' @description
#' Scales a tibble using mean normalization
#' @param data_df
#' Dataframe to be scaled.
#' (tibble::tibble)
#' @return
#' A mean normalized version of the dataframe
scale_mean = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$scale_mean_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Scales a numeric object using mean normalization
#' @param values
#' Values to be scaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' A mean normalized version of the numeric object
scale_mean_numeric = function(values = "numeric", feature = character){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
scaled_values <- (values - mean_val) / (max_val - min_val)
return(scaled_values)
}, #function
#' @description
#' Scales a tibble using z-score normalization
#' @param data_df
#' Dataframe to be scaled
#' (tibble::tibble)
#' @return
#' A z-score normalized version of the dataframe
scale_zScore = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$scale_zScore_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Scales a numeric object using z-score normalization
#' @param values
#' Values to be scaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' A z-score normalized version of the numeric object
scale_zScore_numeric = function(values = "numeric", feature = character){
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
sigma_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("sigma") %>%
dplyr::pull() %>%
as.numeric()
scaled_values <- (values - mean_val) / (sigma_val)
return(scaled_values)
}, #function
#' @description
#' Rescales a tibble using the method defined by the instance variable normAgent
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tible::tibble)
#' @return
#' A rescaled version of the normalized dataframe.
#' (tibble::tibble)
rescale_data = function(data_df = "tbl_df"){
switch(self$normAgent,
"none"=data_df,
"min-max"=self$rescale_minMax(data_df),
"mean"=self$rescale_mean(data_df),
"z-score"=self$rescale_zScore(data_df),
stop("unkown normAgent")) %>%
return()
}, #function
#' @description
#' Rescales a tibble using min-max normalization
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tibble::tibble)
#' @return
#' A rescaled version of a min-max normalized dataframe
rescale_minMax = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$rescale_minMax_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Rescales a numeric object using min-max normalization
#' @param values
#' Normalized values to be rescaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' Rescaled version of min-max normalized numeric object
rescale_minMax_numeric = function(values = "numeric", feature = character){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
rescaled_values <- (values * (max_val - min_val)) + min_val
return(rescaled_values)
}, #function
#' @description
#' Rescales a tibble using mean normalization
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tibble::tibble)
#' @return
#' A rescaled version of a mean normalized dataframe
rescale_mean = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$rescale_mean_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Rescales a numeric object using mean normalization
#' @param values
#' Normalized values to be rescaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' Rescaled version of mean normalized numeric object
rescale_mean_numeric = function(values = "numeric", feature = character){
min_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("min") %>%
dplyr::pull() %>%
as.numeric()
max_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("max") %>%
dplyr::pull() %>%
as.numeric()
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
rescaled_values <- (values * (max_val - min_val)) + mean_val
return(rescaled_values)
}, #function
#' @description
#' Rescales a tibble using z-score normalization
#' @param data_df
#' Normalized dataframe to be rescaled
#' (tibble::tibble)
#' @return
#' A rescaled version of a z-score normalized dataframe
rescale_zScore = function(data_df = "tbl_df"){
for (feature in self$features){
if(is.numeric(data_df[[feature]])){
data_df <- data_df %>%
dplyr::mutate(!!feature := self$rescale_zScore_numeric(data_df[[feature]], feature))
} #if
} #for
return(data_df)
}, #function
#' @description
#' Rescales a numeric object using z-score normalization
#' @param values
#' Normalized values to be rescaled. Either a number or a vector
#' (numeric)
#' @param feature
#' Character to idtentify the proper normalization parameters.
#' (character)
#' @return
#' Rescaled version of z-score normalized numeric object
rescale_zScore_numeric = function(values = "numeric", feature = character){
mean_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("mean") %>%
dplyr::pull() %>%
as.numeric()
sigma_val <- self$normParameter %>%
dplyr::filter(feature == !!feature) %>%
dplyr::select("sigma") %>%
dplyr::pull() %>%
as.numeric()
rescaled_values <- (values * sigma_val) + mean_val
return(rescaled_values)
}, #function
##############################
# Graphical output functions #
##############################
#' @description
#' Displays the distribution of an attribute values as histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A histogram.
#' (ggplot2::ggplot)
featureBarPlot = function(data_df = "tbl_df", feature = "character"){
feature <- dplyr::sym(feature)
p <- data_df %>%
ggplot2::ggplot(mapping = ggplot2::aes_string(x=feature), na.rm=TRUE) +
ggplot2::geom_bar(stat = "bin", bins=30) +
ggplot2::ylab("counts") +
ggplot2::xlab("value") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
}, #function
#' @description
#' Displays the distribution of an attribute's values as box plot.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A box plot.
#' (ggplot2::ggplot)
featureBoxPlotWithSubset = function(data_df = "tbl_df", feature = "character"){
p <- data_df %>%
dplyr::select(feature) %>%
tidyr::gather_(key="feature", value="measurement", feature) %>%
ggplot2::ggplot(mapping=ggplot2::aes_string(x="feature",y="measurement"), na.rm=TRUE)+
ggplot2::geom_boxplot(na.rm=TRUE, outlier.shape = NA)+
ggplot2::geom_jitter(color = "darkblue", na.rm=TRUE) +
ggplot2::ylab("value") +
ggplot2::xlab("feature") +
ggplot2::theme_linedraw() +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "transparent"), # bg of the panel
plot.background = ggplot2::element_rect(fill = "transparent", color = NA), # bg of the plot
legend.background = ggplot2::element_rect(fill = "transparent"),
legend.key = ggplot2::element_rect(fill = "transparent")
)
return(p)
}, #function
#' @description
#' Displays the distribution of an attribute's values as a composition of a box plot and a histogram.
#' @param data_df
#' dataframe to be analyzed.
#' (tibble::tibble)
#' @param feature
#' attribute to be shown.
#' (character)
#' @return
#' A composite plot.
#' (ggplot2::ggplot)
featurePlot = function(data_df = "tbl_df", feature = "character"){
p1 <- self$featureBoxPlotWithSubset(data_df, feature) +
ggplot2::theme(legend.position = c(0.9, 0.9),
legend.key = ggplot2::element_blank(),
legend.background = ggplot2::element_blank())
p2 <- self$featureBarPlot(data_df, feature)
limits <- ggplot2::layer_scales(p2)$x$range$range
p1 <- p1 +
ggplot2::scale_y_continuous(limits=limits)
p2 <- p2 +
ggplot2::scale_x_continuous(position = "top") +
ggplot2::coord_flip()
p <- gridExtra::grid.arrange(p1,p2, layout_matrix = rbind(c(1,1,2),c(1,1,2)),
top = textGrob(label = sprintf("Distribution of %s", feature)))
return(p)
}#function
)#public
)#class
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