R/pguDelegate.R
In SMLMS/pguIMP: 'pguIMP'
#' @title pgu.delegate
#'
#' @description
#' Manages the communication between the shiny gui layer and the classes of the pguIMP package
#'
#' @details
#' Comprises all needed classes from the pguIMP package and manages the communication between the gui and the analysis.
#' This object is used by the shiny based gui and is not for use in individual R-scripts!
#'
#' @format [R6::R6Class] object.
#' @importFrom R6 R6Class
#' @importFrom magrittr %>%
#' @importFrom dplyr all_of any_vars everything filter filter_all mutate mutate_if pull rename right_join select select_if slice summarise_all
#' @importFrom DT datatable formatStyle renderDataTable styleEqual
#' @importFrom shiny includeHTML Progress renderPlot renderUI showNotification updateCheckboxInput updateNumericInput updateSelectInput
#' @importFrom tibble add_column as_tibble rownames_to_column tibble
#' @importFrom tidyr pivot_longer
#' @importFrom tools file_path_sans_ext
#'
#' @include pguStatus.R
#' @include pguFile.R
#' @include importDataSet.R
#' @include pguData.R
#' @include pguLimitsOfQuantification.R
#' @include pguFilter.R
#' @include pguExplorer.R
#' @include pguOptimizer.R
#' @include pguTransformator.R
#' @include pguModel.R
#' @include pguNormDist.R
#' @include pguNormalizer.R
#' @include pguMissings.R
#' @include pguMissingsCharacterizer.R
#' @include pguOutliers.R
#' @include pguImputation.R
#' @include pguValidator.R
#' @include pguCorrValidator.R
#' @include pguExporter.R
#' @include pguReporter.R
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
pgu.delegate <- R6::R6Class("pgu.delegate",
####################
# instance variables
####################
private = list(
.status = "pgu.status",
.fileName = "pgu.file",
.loqFileName = "pgu.file",
.rawData = "pgu.data",
.filterSet = "pgu.filter",
.filteredData = "pgu.data",
.loq = "pgu.limitsOfQuantification",
.loqMutatedData = "pgu.data",
.explorer= "pgu.explorer",
.optimizer = "pgu.optimizer",
.transformator = "pgu.transformator",
.model = "pgu.model",
.transformedData = "pgu.data",
.featureModel = "pgu.normDist",
.normalizer = "pgu.normalizer",
.normalizedData = "pgu.data",
.missings = "pgu.missings",
.missingsCharacterizer = "pgu.missingsCharacterizer",
.outliers = "pgu.outliers",
.imputer = "pgu.imputation",
.imputedData = "pgu.data",
.cleanedData = "pgu.data",
.validator = "pgu.validatior",
.corrValidator = "pgu.corrValidator",
.exporter = "pgu.exporter",
.reporter = "pgu.reporter",
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.delegate` is removed from heap.
finalize = function() {
print("Instance of pgu.delegate removed from heap")
} #function
),
##################
# accessor methods
##################
active = list(
#' @field status
#' Returns the instance variable status
#' (pguIMP::pgu.status)
status = function(){
return(private$.status)
},
#' @field fileName
#' Returns the instance variable fileName
#' (pguIMP::pgu.file)
fileName = function(){
return(private$.fileName)
},
#' @field loqFileName
#' Returns the instance variable loqFileName
#' (pguIMP::pgu.file)
loqFileName = function(){
return(private$.loqFileName)
},
#' @field rawData
#' Returns the instance variable rawData
#' (pguIMP::pgu.data)
rawData = function(){
return(private$.rawData)
},
#' @field filterSet
#' Returns the instance variable filterSet
#' (pguIMP::pgu.filter)
filterSet = function(){
return(private$.filterSet)
},
#' @field filteredData
#' Returns the instance variable filteredData
#' (pguIMP::pgu.data)
filteredData = function(){
return(private$.filteredData)
},
#' @field loq
#' Returns the instance variable loq
#' (pguIMP::pgu.limitsOfQuantification)
loq = function(){
return(private$.loq)
},
#' @field loqMutatedData
#' Returns the instance variable loqMutatedData
#' (pguIMP::pgu.data)
loqMutatedData = function(){
return(private$.loqMutatedData)
},
#' @field explorer
#' Returns the instance variable explorer
#' (pguIMP::pgu.explorer)
explorer = function(){
return(private$.explorer)
},
#' @field optimizer
#' Returns the instance variable optimizer
#' (pguIMP::pgu.optimizer)
optimizer = function(){
return(private$.optimizer)
},
#' @field transformator
#' Returns the instance variable transformator
#' (pguIMP::pgu.transformator)
transformator = function(){
return(private$.transformator)
},
#' @field model
#' Returns the instance variable model
#' (pguIMP::pgu.model)
model = function(){
return(private$.model)
},
#' @field transformedData
#' Returns the instance variable transformedData
#' (pguIMP::pgu.data)
transformedData = function(){
return(private$.transformedData)
},
#' @field featureModel
#' Returns the instance variable featureModel
#' (pguIMP::pgu.normDist)
featureModel = function(){
return(private$.featureModel)
},
#' @field normalizer
#' Returns the instance variable normalizer
#' (pguIMP::pgu.normalizer)
normalizer = function(){
return(private$.normalizer)
},
#' @field normalizedData
#' Returns the instance variable normalizedData
#' (pguIMP::pgu.data)
normalizedData = function(){
return(private$.normalizedData)
},
#' @field missings
#' Returns the instance variable missings
#' (pguIMP::pgu.missings)
missings = function(){
return(private$.missings)
},
#' @field missingsCharacterizer
#' Returns the instance variable missingsCharacterizer
#' (pguIMP::pgu.missingsCharacterizer)
missingsCharacterizer = function(){
return(private$.missingsCharacterizer)
},
#' @field outliers
#' Returns the instance variable outlierd
#' (pguIMP::pgu.outliers)
outliers = function(){
return(private$.outliers)
},
#' @field imputer
#' Returns the instance variable imputer
#' (pguIMP::pgu.imputation)
imputer = function(){
return(private$.imputer)
},
#' @field imputedData
#' Returns the instance variable imputedData
#' (pguIMP::pgu.data)
imputedData = function(){
return(private$.imputedData)
},
#' @field cleanedData
#' Returns the instance variable cleanedData
#' (pguIMP::pgu.data)
cleanedData = function(){
return(private$.cleanedData)
},
#' @field validator
#' Returns the instance variable validator
#' (pguIMP::pgu.validator)
validator = function(){
return(private$.validator)
},
#' @field corrValidator
#' Returns the instance variable corrValidator
#' (pguIMP::pgu.corrValidator)
corrValidator = function(){
return(private$.corrValidator)
},
#' @field exporter
#' Returns the instance variable exporter
#' (pguIMP::pgu.exporter)
exporter = function(){
return(private$.exporter)
},
#' @field reporter
#' Returns the instance variable reporter
#' (pguIMP::pgu.reporter)
reporter = function(){
return(private$.reporter)
}
),#acessors
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.delegate` object.
#' @param data
#' The data to be analyzed.
#' (tibble::tibble)
#' @return
#' A new `pgu.delegate` object.
#' (pguIMP::pgu.delegate)
initialize = function(data = "tbl_df") {
print("Instance of pgu.delegate allocated")
private$.status <- pgu.status$new()
private$.fileName <- pgu.file$new()
private$.loqFileName <- pgu.file$new()
private$.rawData <- pgu.data$new()
private$.loq <- pgu.limitsOfQuantification$new()
#private$.metadata <- pgu.data$new()
private$.filterSet <- pgu.filter$new()
private$.filteredData <- pgu.data$new()
#private$.filteredMetadata <- pgu.data$new()
private$.explorer <- pgu.explorer$new()
private$.loqMutatedData <- pgu.data$new()
private$.optimizer <- pgu.optimizer$new()
private$.transformator <- pgu.transformator$new()
private$.model <- pgu.model$new()
private$.transformedData <- pgu.data$new()
private$.featureModel <- pgu.normDist$new()
private$.normalizer <- pgu.normalizer$new()
private$.normalizedData <- pgu.data$new()
private$.missings <- pgu.missings$new()
private$.missingsCharacterizer <- pgu.missingsCharacterizer$new()
private$.outliers <- pgu.outliers$new()
private$.imputer <- pgu.imputation$new()
private$.imputedData <- pgu.data$new()
private$.cleanedData <- pgu.data$new()
private$.validator <- pgu.validator$new()
private$.corrValidator <- pgu.corrValidator$new()
private$.exporter <- pgu.exporter$new()
private$.reporter <- pgu.reporter$new()
}, #function
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.delegate` object.
#' @return
#' string
print = function()
{
sprintf("\npgu.delegate\n\n") %>%
cat()
print(self$status)
print(self$fileName)
print(self$loqFileName)
print(self$importer)
print(self$rawData)
print(self$loq)
print(self$metadata)
print(self$filterSet)
print(self$filteredData)
print(self$filteredMetadata)
print(self$explorer)
print(self$loqMutatedData)
print(self$optimizer)
print(self$transformator)
print(self$model)
print(self$transformedData)
print(self$featureModel)
print(self$normalizer)
print(self$normalizedData)
print(self$missings)
print(self$missingsCharacterizer)
print(self$outliers)
print(self$imputer)
print(self$imputedData)
print(self$cleanedData)
print(self$validator)
print(self$corrValidator)
print(self$exporter)
print(self$reporter)
invisible(self)
}, #end pguIMP::pgu.delegate$print()
####################
# import functions #
####################
#' @description
#' Updates the import gui
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_gui = function(input, output, session)
{
shiny::updateNumericInput(session = session,
inputId = "ni.importSheetIndex",
value = private$.fileName$sheetIndex)
shiny::updateSelectInput(session = session,
inputId = "si.importSeparator",
selected = private$.fileName$separator)
shiny::updateNumericInput(session = session,
inputId = "ni.importSkip",
value = private$.fileName$skipRows)
shiny::updateSelectInput(session = session,
inputId = "si.importHeader",
selected = private$.fileName$header)
shiny::updateSelectInput(session = session,
inputId = "si.importNaChar",
selected = private$.fileName$naChar)
}, #end pguIMP::pgu.delegate$updateImportGui()
#' @description
#' Manages the data upload to the R server.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
query_data = function(input, output, session)
{
if (length(input$fi.import$datapath) > 0){
private$.fileName$reset(uploadFileName = input$fi.import$datapath,
fileName = input$fi.import$name,
sheetIndex = input$ni.importSheetIndex,
separator = input$si.importSeparator,
skipRows = input$ni.importSkip,
columnName = input$si.importColnames,
naChar = input$si.importNaChar)
private$.fileName$fit()
private$.status$update(processName = "dataUploaded", value = TRUE)
} #if
else{
private$.status$update(processName = "dataUploaded", value = FALSE)
shiny::showNotification(paste("File name field must not left empty."),type = "error", duration = 10)
} #else
if((private$.fileName$suffix != "csv") &&
(private$.fileName$suffix != "txt") &&
(private$.fileName$suffix != "xls") &&
(private$.fileName$suffix != "xlsx")){
private$.status$update(processName = "dataUploaded", value = FALSE)
shiny::showNotification(paste("Invalid file type. Please select a valid file of type '.arff', '.csv', '.txt', '.xls' or '.xlsx'."),type = "error", duration = 10)
} #if
}, #end pguIMP::pgi.delegate$queryData()
#' @description
#' Imports uploaded data from the R server into the instance variable rawData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
import_data = function(input, output, session)
{
if (private$.status$query(processName = "dataUploaded")){
tryCatch({
# private$.rawData$setRawData <- private$.importer$importData(self$fileName)
private$.rawData$setRawData <- pguIMP::importDataSet(obj = self$fileName)
private$.rawData$fit()
private$.status$update(processName = "dataImported", value = TRUE)
},
error = function(e) {
private$.status$update(processName = "dataImported", value = FALSE)
shiny::showNotification(paste(e),type = "error", duration = 10)
}#error
)#tryCatch
}#if
else{
private$.status$update(processName = "dataUploaded", value = FALSE)
shiny::showNotification(paste("No file uploaded to import. Please upload a valid file first."),type = "error", duration = 10)
}#else
}, #end pguIMP::pgu.delegate$importData()
#' @description
#' Updates the tbl.importDataTypes table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_data_Types_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.importDataTypes <- DT::renderDataTable({self$rawData$classInformation %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawData_type") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
}#output
)#output
}#if
else{
output$tbl.importDataTypes <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_import_data_Types_tbl
#' @description
#' Updates the tbl.importDataStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_data_statistics_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.importDataStatistics <- DT::renderDataTable({self$rawData$statistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawData_statistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
}#output
)#output
}#if
else{
output$tbl.importDataStatistics <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_import_data_statistics_tbl
#' @description
#' Updates the tbl.importMissingsStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_missings_statistics_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.importMissingsStatistics <- DT::renderDataTable({self$rawData$missingsStatistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawData_missings") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
}#output
)#output
}#if
else{
output$tbl.importMissingsStatistics <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_import_missings_statistics_tbl
####################
# filter functions #
####################
#' @description
#' Updates the tbl.filter table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_select_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.filterSelect <- DT::renderDataTable({self$rawData$rawData %>%
DT::datatable(
extensions = "Buttons",
rownames = FALSE,
filter = "top",
selection = list("multiple", target = "column"),
options = list(
stateSave = TRUE,
scrollX = TRUE,
scrollY = '45vh',
paging = FALSE,
autoWidth = TRUE,
columnDefs = list(list(width = '75px', targets = "_all")),
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("filtered_data") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#optins
)#DT::datatable
})#output
}#if
else{
output$tbl.filterSelect <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_filter_select_tbl
#' @description
#' Queries the filter parameters selected by the user in the gui
#' and stores them in the instance variable filterSet.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
# if (length(input$tbl.filter_rows_all) < 1) {
# private$.filterSet$resetRowIdx(data = self$rawData$rawData)
# }#if
# else {
# private$.filterSet$setRowIdx <- input$tbl.filter_rows_all
# }#else
# if (length(input$tbl.filter_columns_selected) < 1) {
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# colSelection <- input$tbl.filter_columns_selected - ncol(self$metadata$rawData) + 1
# colSelection <- colSelection[colSelection > 0] + 1
# if(length(colSelection) < 1){
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# colSelection <- colSelection %>%
# append(1) %>%
# unique() %>%
# sort
# private$.filterSet$setColIdx <- colSelection
# }#else
# }#else
private$.filterSet$reset(data_df = self$rawData$rawData)
private$.filterSet$setRowIdx <- input$tbl.filterSelect_rows_all
if (!is.null(input$tbl.filterSelect_columns_selected))
{
private$.filterSet$setColIdx <- input$tbl.filterSelect_columns_selected + 1
}
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
}#else
}, #end pguIMP::pgu.delegate$update_filter
#' @description
#' Queries the filter parameters selected by the user in the gui
#' inverts them
#' and stores them in the instance variable filterSet.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_inverse = function(input, output, session){
if(private$.status$query(processName = "dataImported")){
# if (length(input$tbl.filter_rows_all) < 1) {
# private$.filterSet$resetRowIdx(data = self$rawData$rawData)
# }#if
# else {
# private$.filterSet$setRowIdx <- input$tbl.filter_rows_all
# }#else
# if (length(input$tbl.filter_columns_selected) < 1) {
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# colSelection <- input$tbl.filter_columns_selected - ncol(self$metadata$rawData) + 1
# colSelection <- colSelection[colSelection > 0] + 1
# idx <- seq(1,ncol(self$rawData$rawData), 1)
# iverseColSelection <- idx[-c(colSelection)]
# if(length(iverseColSelection) < 1){
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# iverseColSelection <- iverseColSelection %>%
# append(1) %>%
# unique() %>%
# sort()
# private$.filterSet$setColIdx <- iverseColSelection
# }#else
# }#else
private$.filterSet$reset(data_df = self$rawData$rawData)
private$.filterSet$setRowIdx <- input$tbl.filterSelect_rows_all
if (!is.null(input$tbl.filterSelect_columns_selected))
{
idx <- seq(1,ncol(self$rawData$rawData), 1)
iverseColSelection <- idx[-c(input$tbl.filterSelect_columns_selected + 1)]
private$.filterSet$setColIdx <- iverseColSelection
}
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
} #else
}, #function
#' @description
#' Generates a filter set that selects the whole data frame.
#' Stores them in the instance variable filterSet.
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
reset_filter = function(input, output, session){
if(private$.status$query(processName = "dataImported")){
private$.filterSet$reset(data_df = self$rawData$rawData)
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Filters the data corresponding to the user defined parameters stored in the instance variable filterSet.
#' Results are stored in the instance variables filteredData and filteredMetadata.
#' Updated the instance variable filterSet.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
filter_data = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
private$.filteredData$setRawData <- self$rawData$rawData %>%
self$filterSet$predict()
private$.filteredData$fit()
private$.status$update(processName = "dataFiltered", value = TRUE)
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the tbl.filterStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_statistics_tbl = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.filterStatistics <- DT::renderDataTable({self$filteredData$statistics %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '25vh',
paging = FALSE,
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all")),
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("filter_statistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})#output
}#if
else{
output$tbl.filterStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.filterMissings table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_missings_tbl = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.filterMissings <- DT::renderDataTable({self$filteredData$missingsStatistics %>%
format.data.frame(scientific = FALSE, digits = 2) %>%
DT::datatable(
options = list(
scrollX = TRUE,
scrollY = '25vh',
paging = FALSE,
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all")),
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("filter_missings") %>%
tools::file_path_sans_ext(),
text = "Download"
)) #buttons
)#options
)#DT::datatable
})#output
}#if
else{
output$tbl.filterMissings <- DT::renderDataTable(NULL)
}#else
}, #function
#####################
# explore functions #
#####################
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_gui = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
private$.explorer$reset(data_df = self$filteredData$rawData,
abs = "Sample Name",
ord = "Sample Name"
)
featureNames <- self$explorer$rawData %>%
colnames()
updateSelectInput(session, "si.exploreAbs", choices = featureNames)
updateSelectInput(session, "si.exploreAbs", selected = featureNames[1])
updateSelectInput(session, "si.exploreOrd", choices = featureNames)
updateSelectInput(session, "si.exploreOrd", selected = featureNames[1])
}#if
}, #function
#' @description
#' Transfers the information oabout the selected abscissa attribute to the explorer class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_abscissa = function(input, output, session){
if(private$.status$query(processName = "dataFiltered"))
{
private$.explorer$setAbscissa <- input$si.exploreAbs
private$.explorer$fit()
}
}, #function
#' @description
#' Transfers the information oabout the selected ordinate attribute to the explorer class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_ordinate = function(input, output, session){
if(private$.status$query(processName = "dataFiltered"))
{
private$.explorer$setOrdinate <- input$si.exploreOrd
private$.explorer$fit()
}
}, #function
#' @description
#' Updates the exploration abscissa vs. ordinate scatter plot
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_graphic = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$plt.exploreGraphic <- shiny::renderPlot(
self$explorer$scatterPlot(),
height = 400,
bg="transparent")
}#if
else{
output$plt.exploreGraphic <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the abscissa compound plot
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_abscissa_graphic = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$plt.exploreAbscissaGraphic <- shiny::renderPlot(
self$explorer$abscissaPlot(),
height = 400,
bg="transparent"
)
}#if
else{
output$plt.exploreAbscissaGraphic <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the ordinate compound plot
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_ordinate_graphic = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$plt.exploreOrdinateGraphic <- shiny::renderPlot(
self$explorer$ordinatePlot(),
height = 400,
bg="transparent"
)
}#if
else{
output$plt.exploreOrdinateGraphic <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numerical abscissa analysis table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_abscissa_table = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.exploreAbscissaStatistics <- DT::renderDataTable({
self$explorer$abscissaStatistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
# scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(sprintf("abs_%s_statistics", input$si.exploreAbs)) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.exploreAbscissaStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical ordinate analysis table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_ordinate_table = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.exploreOrdinateStatistics <- DT::renderDataTable({
self$explorer$ordinateStatistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(sprintf("ord_%s_statistics", input$si.exploreOrd)) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.exploreOrdinateStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
########################
# LOQ Upload functions #
########################
#' @description
#' Initializes the LOQ object after filtering.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
reset_loq_values = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
self$filteredData$numericalAttributeNames %>%
private$.loq$reset()
self$update_loq_upload_gui(input, output, session)
private$.status$update(processName = "loqImported", value = TRUE)
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_upload_gui = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
output$tbl.loqUploadValues <- DT::renderDataTable({
self$loq$loq %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("LOQ_values") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.loqDefineValues <- DT::renderDataTable(NULL)
}#else
},
#' @description
#' Manages the loq data upload to the R server.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
query_loq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered")){
if (length(input$fi.LoqImport$datapath) > 0){
private$.loqFileName$reset(uploadFileName = input$fi.LoqImport$datapath,
fileName = input$fi.LoqImport$name,
sheetIndex = input$ni.LoqSheetIndex,
separator = input$si.LoqSeparator,
skipRows = input$ni.LoqSkip,
columnName = input$si.LoqColnames,
naChar = input$si.LoqNaChar)
private$.loqFileName$fit()
} #if
else{
shiny::showNotification(paste("File name field must not left empty."),type = "error", duration = 10)
} #else
if((private$.fileName$suffix != "csv") &&
(private$.fileName$suffix != "txt") &&
(private$.fileName$suffix != "xls") &&
(private$.fileName$suffix != "xlsx")){
shiny::showNotification(paste("Invalid file type. Please select a valid file of type '.csv', '.txt', '.xls' or '.xlsx'."),type = "error", duration = 10)
} #if
}
}, #end pguIMP::pgi.delegate$query_loq()
#' @description
#' Imports the loq data upload to the R server.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
import_loq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
private$.loq$reset(attribute_names = self$filteredData$numericalAttributeNames,
data_df = pguIMP::importDataSet(obj = self$loqFileName))
self$update_loq_upload_gui(input, output, session)
private$.status$update(processName = "loqImported", value = TRUE)
}
},
########################
# LOQ Define functions #
########################
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_gui = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
self$update_loq_define_feature(input, output, session)
self$update_loq_define_lloq(input, output, session)
self$update_loq_define_uloq(input, output, session)
self$update_loq_define_table(input, output, session)
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_feature = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
shiny::updateSelectInput(session,
inputId = "si.LoqDefineFeature",
choices = self$loq$loq$attribute,
selected = self$loq$loq$attribute[1])
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_lloq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
shiny::updateNumericInput(session,
inputId = "ni.LoqDefineLLOQ",
value = self$loq$attribute_lloq(attribute = input$si.LoqDefineFeature))
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_uloq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
shiny::updateNumericInput(session,
inputId = "ni.LoqDefineULOQ",
value = self$loq$attribute_uloq(attribute = input$si.LoqDefineFeature))
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_table = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
output$tbl.loqDefineValues <- DT::renderDataTable({
self$loq$loq %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("LOQ_values") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.loqDefineValues <- DT::renderDataTable(NULL)
}#else
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_menu = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
self$update_loq_define_lloq(input, output, session)
self$update_loq_define_uloq(input, output, session)
}
},
#' @description
#' Updates loq class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
set_loq_define_values = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
private$.loq$set_attribute_lloq(attribute = input$si.LoqDefineFeature,
value = input$ni.LoqDefineLLOQ)
private$.loq$set_attribute_uloq(attribute = input$si.LoqDefineFeature,
value = input$ni.LoqDefineULOQ)
}
},
#' @description
#' Updates loq class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
set_loq_define_values_globally = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
for(attribute in self$loq$loq$attribute)
{
private$.loq$set_attribute_lloq(attribute = attribute,
value = input$ni.LoqDefineLLOQ)
private$.loq$set_attribute_uloq(attribute = attribute,
value = input$ni.LoqDefineULOQ)
}
}
},
########################
# LOQ Detect functions #
########################
#' #' @description
#' #' Imports uploaded data from the R server into the instance variable loqData.
#' #' Updates the instance class status.
#' #' @param input
#' #' Pointer to shiny input
#' #' @param output
#' #' Pointer to shiny output
#' #' @param session
#' #' Pointer to shiny session
#' importLoq = function(input, output, session){
#' if (private$.status$query(processName = "dataImported")){
#' tryCatch({
#' private$.loq$setLoq <- private$.importer$importLoq(self$fileName)
#' private$.status$update(processName = "loqImported", value = TRUE)
#' },
#' error = function(e) {
#' private$.status$update(processName = "loqImported", value = FALSE)
#' shiny::showNotification(paste(e),type = "error", duration = 10)
#' }#error
#' )#tryCatch
#' }#if
#' else{
#' private$.status$update(processName = "loqImported", value = FALSE)
#' shiny::showNotification(paste("No file uploaded to import. Please upload a valid file first."),type = "error", duration = 10)
#' }#else
#' }, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_gui = function(input, output, session){
if(private$.status$query(processName = "loqImported")){
self$update_loq_na_handling(input, output, session)
shiny::updateSelectInput(session,
"si.loqDetectFeature",
choices = self$filteredData$numericalAttributeNames,
selected = self$filteredData$numericAttributeNames[1])
}#if
else{
shiny::showNotification(paste("No filtered data set. Please filter data set first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the si.loqHandling shiny widget
#' corresponding to the respective user defined parameter.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_na_handling = function(input, output, session){
if(private$.status$query(processName = "loqImported"))
{
shiny::updateSelectInput(session,
"si.loqNaHandling",
choices = self$loq$naHandlingAlphabet,
selected = self$loq$naHandlingAgent)
}
}, #function
#' @description
#' Runs the outlier detection routine of the instance variable outliers.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
init_detect_loq = function(input, output, session){
if(private$.status$query(processName = "loqImported")){
private$.loq$setNaHandlingAgent <- self$loq$naHandlingAlphabet[1]
self$filteredData$numerical_data() %>%
private$.loq$fit()
private$.status$update(processName = "loqDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No filtered data set. Please filter data set first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Runs the outlier detection routine of the instance variable outliers.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
detect_loq = function(input, output, session){
if(private$.status$query(processName = "loqImported")){
private$.loq$setNaHandlingAgent <- input$si.loqNaHandling
self$filteredData$numerical_data() %>%
private$.loq$fit()
private$.status$update(processName = "loqDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No filtered data set. Please filter data set first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the numerical loq statistics analysis table
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_statistics_tbl = function(input, output, session){
if(private$.status$query(processName = "loqDetected")){
output$tbl.loqDetectStatistics <- DT::renderDataTable(
self$loq$loqStatistics %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("loqStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.loqDetectStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical loq table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_outlier_tbl = function(input, output, session){
if(private$.status$query(processName = "loqDetected"))
{
output$tbl.loqDetectOutlier <- DT::renderDataTable(
self$loq$outliers %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("loqOutliers") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqDetectOutlier <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "loqDetected")){
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$filteredData$rawData, by = "Sample Name")
# dfOutlier <- self$loq$outliers
# idx <- dfOutlier[["measurement"]][!duplicated(dfOutlier[["measurement"]])]
# t <- dfData %>%
# dplyr::slice(idx) %>%
# dplyr::mutate_if(is.numeric, round, 3) %>%
# DT::datatable(
# options = list(
# scrollX = TRUE,
# scrollY = '350px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("loqOutlier"),
# text = "Download"
# ))#buttons
# )#options
# )#DT::datatable
# for (featureName in self$filteredData$numericFeatureNames){
# featureOutlier <- dfOutlier %>%
# dplyr::filter(feature == featureName) %>%
# dplyr::mutate_if(is.numeric, round, 3)
# if (nrow(featureOutlier)>0){
# t <- DT::formatStyle(t,
# featureName,
# backgroundColor = styleEqual(dfData %>%
# dplyr::select(!!featureName) %>%
# dplyr::slice(featureOutlier[["measurement"]]) %>%
# unlist() %>%
# as.numeric() %>%
# round(digits = 3),
# featureOutlier[["color"]])
#
# )#t
# }#if
# }#for
# }#if
# output$tbl.loqDetectOutlier <- DT::renderDataTable(t)
}, #function
#' @description
#' Updates the loq statistics graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_statistics_graphic = function(input, output, session){
if(self$status$query(processName = "loqDetected")){
output$plt.loqDetectStatistics <- shiny::renderPlot(
self$loq$plot_loq_distribution(),
height = 400,
bg="transparent"
)
}#if
else{
output$plt.loqDetectStatistics <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the loq feature compound graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_attribute_graphic = function(input, output, session){
if(self$status$query(processName = "loqDetected")){
output$plt.loqDetectFeature <- shiny::renderPlot(
self$loq$attribute_plot(data_df = self$filteredData$rawData, attribute = input$si.loqDetectFeature),
height = 425,
bg="transparent"
)
}#if
else{
output$plt.loqDetectFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numerical loq feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_attribute_tbl = function(input, output, session){
if(private$.status$query(processName = "loqDetected"))
{
suffix <- sprintf("%s_outliers", input$si.loqDetectFeature)
output$tbl.loqDetectFeature <- DT::renderDataTable(
self$loq$attribute_outliers(attribute = input$si.loqDetectFeature) %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqDetectFeature <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(private$.status$query(processName = "loqDetected")){
# feature <- input$si.loqDetectFeature
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$filteredData$rawData %>%
# dplyr::select(c("Sample Name", !!feature)),
# by = "Sample Name")
#
# dfOutlier <- self$loq$featureOutlier(feature = feature)
#
# t <- dfData %>%
# DT::datatable(options = list(scrollX = TRUE,
# scrollY = '300px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("rawData_type"),
# text = "Download"
# ))
# ))#DT::datatable
# if (nrow(dfOutlier) > 0){
# t <- DT::formatStyle(
# t,
# feature,
# backgroundColor = DT::styleEqual(dfData %>%
# dplyr::select(!!feature) %>%
# dplyr::slice(dfOutlier[["measurement"]]) %>%
# unlist() %>%
# round(digits = 3),
# dfOutlier[["color"]]))
# }#if
# }#if
# output$tbl.loqDetectFeature <- DT::renderDataTable(t)
}, #function
########################
# LOQ mutate functions #
########################
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_gui = function(input, output, session)
{
if(private$.status$query(processName = "loqDetected"))
{
self$update_lloq_substitute(input, output, session)
self$update_uloq_substitute(input, output, session)
shiny::updateSelectInput(session,
"si.loqMutateFeature",
choices = self$filteredData$numericalAttributeNames,
selected = self$filteredData$numericalAttributeNames[1])
}#if
else{
shiny::showNotification(paste("No loq outliers detected. Please screen for loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the si.lloqSubstitute shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_lloq_substitute = function(input, output, session)
{
shiny::updateSelectInput(session,
"si.lloqSubstitute",
choices = self$loq$lloqSubstituteAlphabet,
selected = self$loq$lloqSubstituteAgent)
}, #function
#' @description
#' Updates the si.uloqSubstitute shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_uloq_substitute = function(input, output, session)
{
shiny::updateSelectInput(session,
"si.uloqSubstitute",
choices = self$loq$uloqSubstituteAlphabet,
selected = self$loq$uloqSubstituteAgent)
}, #function
#' @description
#' Calls the mutation routine of the instance variable loq on the instance variable filteredData.
#' The reult is stored in the instance variable loqMutatedData
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
init_mutate_loq = function(input, output, session)
{
if(private$.status$query(processName = "loqDetected"))
{
private$.loq$setLloqSubstituteAgent <- self$loq$lloqSubstituteAlphabet[1]
private$.loq$setUloqSubstituteAgent <- self$loq$uloqSubstituteAlphabet[1]
private$.loqMutatedData$setRawData <- self$filteredData$numerical_data() %>%
self$loq$predict() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.loqMutatedData$fit()
private$.status$update(processName = "loqMutated", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq outliers detected. Please screen for loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Calls the mutation routine of the instance variable loq on the instance variable filteredData.
#' The reult is stored in the instance variable loqMutatedData
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
mutate_loq = function(input, output, session)
{
if(private$.status$query(processName = "loqDetected"))
{
private$.loq$setLloqSubstituteAgent <- input$si.lloqSubstitute
private$.loq$setUloqSubstituteAgent <- input$si.uloqSubstitute
private$.loqMutatedData$setRawData <- self$filteredData$numerical_data() %>%
self$loq$predict() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.loqMutatedData$fit()
private$.status$update(processName = "loqMutated", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq outliers detected. Please screen for loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the numerical loq mutate outliers table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_data_tbl = function(input, output, session){
if(private$.status$query(processName = "loqMutated"))
{
output$tbl.loqMutateData <- DT::renderDataTable(
self$loqMutatedData$rawData %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("loq_mutated_data") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqMutateData <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "loqMutated")){
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$loqMutatedData$rawData, by = "Sample Name")
# dfOutlier <- self$loq$outliers
# idx <- dfOutlier[["measurement"]][!duplicated(dfOutlier[["measurement"]])]
# t <- dfData %>%
# dplyr::mutate_if(is.numeric, round, 3) %>%
# DT::datatable(
# options = list(
# scrollX = TRUE,
# scrollY = '350px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("loqData"),
# text = "Download"
# ))#buttons
# )#options
# )#DT::datatable
# for (featureName in self$loqMutatedData$numericFeatureNames){
# featureOutlier <- dfOutlier %>%
# dplyr::filter(feature == featureName) %>%
# dplyr::mutate_if(is.numeric, round, 3)
# if (nrow(featureOutlier)>0){
# t <- DT::formatStyle(t,
# featureName,
# backgroundColor = styleEqual(dfData %>%
# dplyr::select(!!featureName) %>%
# dplyr::slice(featureOutlier[["measurement"]]) %>%
# unlist() %>%
# as.numeric() %>%
# round(digits = 3),
# featureOutlier[["color"]])
#
# )#t
# }#if
# }#for
# }#if
# output$tbl.loqMutateData <- DT::renderDataTable(t)
}, #function
#' @description
#' Updates the loq mutate statistics graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_statistics_graphic = function(input, output, session)
{
if(self$status$query(processName = "loqMutated"))
{
output$plt.loqMutateStatistics <- shiny::renderPlot(
self$loq$plot_loq_distribution(),
height = 400,
bg="transparent"
)#output
}#if
else{
output$plt.loqMutateStatistics <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the loq mutate feature graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_attribute_graphic = function(input, output, session)
{
if(self$status$query(processName = "loqMutated"))
{
output$plt.loqMutateFeature <- shiny::renderPlot(
self$loq$attribute_plot(data_df = self$loqMutatedData$rawData, attribute = input$si.loqMutateFeature),
height = 425,
bg="transparent"
)#output
}#if
else{
output$plt.loqMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numeric loq mutate feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
init_loq_mutate_attribute_tbl = function(input, output, session){
if(private$.status$query(processName = "loqMutated"))
{
suffix <- sprintf("%s_outliers_mutated", self$loq$loq$attribute[1])
data_df <- self$loq$attribute_outliers(attribute = self$loq$loq$attribute[1])
idx <- dplyr::pull(data_df, instance)
data_df <- data_df %>%
dplyr::mutate(mutated = dplyr::pull(self$loqMutatedData$rawData, self$loq$loq$attribute[1])[idx])
output$tbl.loqMutateFeature <- DT::renderDataTable(
data_df %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqMutateFeature <- DT::renderDataTable(NULL)
}
},
#' @description
#' Updates the numeric loq mutate feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_attribute_tbl = function(input, output, session){
if(private$.status$query(processName = "loqMutated"))
{
suffix <- sprintf("%s_outliers_mutated", input$si.loqMutateFeature)
data_df <- self$loq$attribute_outliers(attribute = input$si.loqMutateFeature)
idx <- dplyr::pull(data_df, instance)
data_df <- data_df %>%
dplyr::mutate(mutated = dplyr::pull(self$loqMutatedData$rawData, input$si.loqMutateFeature)[idx])
output$tbl.loqMutateFeature <- DT::renderDataTable(
data_df %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqMutateFeature <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "loqMutated")){
# feature <- input$si.loqMutateFeature
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$loqMutatedData$rawData %>%
# dplyr::select(c("Sample Name", !!feature)),
# by = "Sample Name")
#
# dfOutlier <- self$loq$featureOutlier(feature = feature)
#
# t <- dfData %>%
# DT::datatable(options = list(scrollX = TRUE,
# scrollY = '300px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("rawData_type"),
# text = "Download"
# ))#buttons
# ))#DT::datatable
# if (nrow(dfOutlier) > 0){
# t <- DT::formatStyle(
# t,
# feature,
# backgroundColor = DT::styleEqual(dfData %>%
# dplyr::select(!!feature) %>%
# dplyr::slice(dfOutlier[["measurement"]]) %>%
# unlist() %>%
# round(digits = 3),
# dfOutlier[["color"]])
# )#t
# }#if
# }#if
# output$tbl.loqMutateFeature <- DT::renderDataTable(t)
}, #function
#############################################
# Trafo Detect functions (Parameter Wizard) #
#############################################
#' @description
#' Calls the optimize routine of the instance variable optimizer on the instance variable loqMutatedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
optimizeTrafoParameter = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
trafoAlphabet <- c("none")
if(input$cb.wizardLog){
trafoAlphabet <- c(trafoAlphabet, "log2", "logNorm", "log10")
}#if
if(input$cb.wizardRoot){
trafoAlphabet <- c(trafoAlphabet, "squareRoot", "cubeRoot")
}#if
if(input$cb.wizardArcsine){
trafoAlphabet <- c(trafoAlphabet, "arcsine")
}#if
if(input$cb.wizardInverse){
trafoAlphabet <- c(trafoAlphabet, "inverse")
}#if
if(input$cb.wizardTLOP){
trafoAlphabet <- c(trafoAlphabet, "tukeyLOP")
}#if
if(input$cb.wizardBoxCox){
trafoAlphabet <- c(trafoAlphabet, "boxCox")
}#if
# data in obj ändern
private$.optimizer$resetOptimizer(data = self$loqMutatedData$numerical_ata())
private$.optimizer$setTrafoAlphabet <- trafoAlphabet
private$.optimizer$setMirror <- input$cb.wizardMirror
progress <- shiny::Progress$new(session, min = 1, max = length(self$optimizer$trafoAlphabet)*2)
progress$set(message = "optimizing transformation parameters ...", value = 1)
private$.optimizer$optimize(data = self$loqMutatedData$numerical_data(), progress = progress)
on.exit(progress$close())
private$.status$update(processName = "modelOptimized", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq analysis perfomred. Please mutate loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the detected trafo types table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateDetectedTrafoTypes = function(input, output, session){
if(self$status$query(processName = "modelOptimized")){
output$tbl.trafoDetectTypes <- DT::renderDataTable(
self$optimizer$optTypes %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$bluntFileName("optTrafoTypes"),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoDetectTypes <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the detected trafo parameters table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateDetectedTrafoParameter = function(input, output, session){
if(self$status$query(processName = "modelOptimized")){
output$tbl.trafoDetectParameters <- DT::renderDataTable(
self$optimizer$optParameter %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$bluntFileName("optTrafoParameter"),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoDetectParameters <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoDetectGui = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
trafoAlphabet <- self$optimizer$trafoAlphabet
shiny::updateCheckboxInput(session,
"cb.wizardLog",
value = any(grepl(pattern = "log",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardRoot",
value = any(grepl(pattern = "Root",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardArcsine",
value = any(grepl(pattern = "arcsine",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardInverse",
value = any(grepl(pattern = "inverse",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardTLOP",
value = any(grepl(pattern = "tukeyLOP",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardBoxCox",
value = any(grepl(pattern = "boxCox",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardMirror",
value = self$optimizer$mirror)
}#if
else{
shiny::showNotification(paste("No loq analysis perfomred. Please mutate loq outliers first."),type = "error", duration = 10)
}#else
}, #function
##############################################
# trafo mutate functions (model by gaussian) #
##############################################
#' @description
#' Updates the si.trafoMutateFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeature = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateSelectInput(session,
inputId = "si.trafoMutateFeature",
choices = self$loqMutatedData$numericalAttributeNames,
selected = self$loqMutatedData$numericalAttributeNames[1])
}#if
}, #function
#' @description
#' Updates the si.trafoMutateType shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateType = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateSelectInput(session,
inputId = "si.trafoMutateType",
choices = self$transformator$trafoAlphabet,
selected = self$transformator$trafoType(feature = input$si.trafoMutateFeature)
)
}#if
}, #function
#' @description
#' Updates the ni.trafoMutateLambda shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateLambda = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateNumericInput(session,
inputId = "ni.trafoMutateLambdaLOP",
value = self$transformator$lambdaLOP(feature = input$si.trafoMutateFeature))
}#if
}, #function
#' @description
#' Updates the cb.trafoMutateMirror shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateMirror = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateCheckboxInput(session,
inputId = "cb.trafoMutateMirror",
value = self$transformator$mirrorLogic(feature = input$si.trafoMutateFeature)
)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetTrafoMutateGui = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
self$loqMutatedData$numerical_data() %>%
private$.transformator$resetTrafoParameter()
self$updateTrafoMutateFeature(input, output, session)
self$updateTrafoMutateType(input, output, session)
self$updateTrafoMutateLambda(input, output, session)
self$updateTrafoMutateMirror(input, output, session)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGui = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
self$updateTrafoMutateType(input, output, session)
self$updateTrafoMutateLambda(input, output, session)
self$updateTrafoMutateMirror(input, output, session)
}#if
}, #function
#' @description
#' Estimates the optimal transformation parameters.
#' Updates the GUI
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoMutateFit = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
progress <- shiny::Progress$new(session, min = 1, max = length(self$loqMutatedData$numericalAttributeNames))
progress$set(message = "Optimizing model parameter", value = 1)
self$loqMutatedData$numerical_data() %>%
private$.transformator$resetTrafoParameter()
on.exit(progress$close())
self$loqMutatedData$numerical_data() %>%
private$.transformator$fit()
private$.status$update(processName = "modelOptimized", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq analysis perfomred. Please mutate loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Calls the transformation routine of the instance variable transformator on the instance variable loqMutatedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoMutateGlobal = function(input, output, session){
if(self$status$query(processName = "modelOptimized")){
progress <- shiny::Progress$new(session, min = 1, max = length(self$loqMutatedData$numericalAttributeNames))
progress$set(message = "Perfomring transformation", value = 1)
on.exit(progress$close())
# self$loqMutatedData$numericData() %>%
# private$.transformator$resetTrafoParameter()
for (feature in self$loqMutatedData$numericalAttributeNames){
private$.transformator$setTrafoType(feature = feature,
type = input$si.trafoMutateType)
private$.transformator$setMirrorLogic(feature = feature,
logic = input$cb.trafoMutateMirror)
}#for
# self$loqMutatedData$numericData() %>%
# private$.transformator$estimateTrafoParameter()
self$loqMutatedData$numerical_data() %>%
private$.transformator$mutateData() %>%
private$.model$resetModel(progress)
private$.transformedData$setRawData <- self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
# self$model$scaleData() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.transformedData$fit()
private$.status$update(processName = "modelDefined", value = TRUE)
}#if
else{
shiny::showNotification(paste("Model parameters not optimized. Please optimize first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Calls the transformation routine of the instance variable transformator on a user defined attribute
#' of the instance variable loqMutatedData.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoMutateFeature = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
private$.transformator$setTrafoType(feature = input$si.trafoMutateFeature,
type = input$si.trafoMutateType)
private$.transformator$setLambdaLOP(feature = input$si.trafoMutateFeature,
lambda = input$ni.trafoMutateLambdaLOP)
private$.transformator$setMirrorLogic(feature = input$si.trafoMutateFeature,
logic = input$cb.trafoMutateMirror)
# self$loqMutatedData$numericData() %>%
# private$.transformator$estimateTrafoParameter()
self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
dplyr::select(input$si.trafoMutateFeature) %>%
private$.featureModel$resetNormDist()
tryCatch({
private$.featureModel$fit()
},
error = function(e) {
errorString <- sprintf("Error: could not optimize model parameters for %s transformation of feature %s. Trafo type is reset to 'none'",
input$si.trafoMutateType,
input$si.trafoMutateFeature)
shiny::showNotification(paste(errorString),type = "error", duration = 10)
shiny::updateSelectInput(session,
inputId = "si.trafoMutateType",
choices = self$transformator$trafoAlphabet,
selected = "none")
shiny::updateCheckboxInput(session,
inputId = "cb.trafoMutateMirror",
value = FALSE)
private$.transformator$setTrafoType(feature = input$si.trafoMutateFeature,
type = input$si.trafoMutateType)
private$.transformator$setMirrorLogic(feature = input$si.trafoMutateFeature,
logic = input$cb.trafoMutateMirror)
# self$loqMutatedData$numericData() %>%
# private$.transformator$estimateTrafoParameter()
private$.featureModel$resetNormDist(data = self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
dplyr::select(input$si.trafoMutateFeature)
)
private$.featureModel$fit()
})#tryCatch
private$.model$setNormDist(data = self$featureModel, feature = input$si.trafoMutateFeature)
private$.transformedData$setRawData <- self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
# self$model$scaleData() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.transformedData$fit()
}#if
else{
shiny::showNotification(paste("No global model defined. Please defina a global transformation model first."),type = "error", duration = 10)
}#else
}, #functions
#' @description
#' Updates the trafo mutate feature graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeatureGraphic = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$plt.trafoMutateFeature <- shiny::renderPlot(
self$model$plotModel(feature = input$si.trafoMutateFeature),
bg="transparent"
)#output
}#if
else{
output$plt.trafoMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the trafo mutate feature patameter table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeatureParameterTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateFeatureParameter <- DT::renderDataTable(
self$model$fitResultFeature(feature = input$si.trafoMutateFeature) %>%
pguIMP::transposeTibble() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("featureTrafoParameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateFeatureParameter <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the trafo mutate feature quality table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeatureQualityTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateFeatureQuality <- DT::renderDataTable(
self$model$testResultFeature(feature = input$si.trafoMutateFeature) %>%
pguIMP::transposeTibble() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("featureTrafoQuality") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatble
)#output
}#if
else{
output$tbl.trafoMutateFeatureQuality <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the trafo mutate global parameter table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalParameterTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalParameter <- DT::renderDataTable(
self$transformator$trafoParameter %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalTrafoParameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalParameter <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalModel table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalModelTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalModel <- DT::renderDataTable(
self$model$modelParameterData() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalModelParameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#ouput
}#if
else{
output$tbl.trafoMutateGlobalModel <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalQuality table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalQualityTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalQuality <- DT::renderDataTable(
self$model$modelQualityData() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalModelQuality") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalQuality <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalTests table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalTestsTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalTests <- DT::renderDataTable(
self$model$testResultData() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalModelQuality") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalTests <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalData table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalDataTbl = function(input, output, session){
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalData <- DT::renderDataTable(
self$transformedData$rawData %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
DT::datatable(
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("transformedData") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalData <- DT::renderDataTable(NULL)
}#else
}, #function
#############################
# trafo normalize functions #
#############################
#' @description
#' Updates the si.trafoNormFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormFeature = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
shiny::updateSelectInput(session,
"si.trafoNormFeature",
choices = self$transformedData$numericalAttributeNames,
selected = self$transformedData$numericalAttributeNames[1])
}#if
}, #function
#' @description
#' Updates the si.trafoNormMethod shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormMethod = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
shiny::updateSelectInput(session,
"si.trafoNormMethod",
choices = self$normalizer$normAgentAlphabet,
selected = self$normalizer$normAgent
)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormGui = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
self$updateTrafoNormMethod(input, output, session)
self$updateTrafoNormFeature(input, output, session)
}#if
}, #function
#' @description
#' Calls the scale routine of the instance variable normalizer on the instance variable transformedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoNormMutate = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
private$.normalizer$setNormAgent <- input$si.trafoNormMethod
self$transformedData$numerical_data() %>%
private$.normalizer$detectNormParameter()
private$.normalizedData$setRawData <- self$transformedData$numerical_data() %>%
self$normalizer$scale_data() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.normalizedData$fit()
private$.status$update(processName = "normalized", value = TRUE)
}#if
else{
shiny::showNotification(paste("No transformation model selected. Please transfrom data first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the impute norm feature compound graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormFeatureGraphic = function(input, output, session){
if(self$status$query(processName = "normalized")){
output$plt.trafoNormFeature <- shiny::renderPlot(
self$normalizer$featurePlot(data_df = self$normalizedData$rawData, feature = input$si.trafoNormFeature),
height = 425,
bg="transparent"
)#output
}#if
else{
output$plt.trafoNormFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetTrafoNormGui = function(input, output, session){
if(self$status$query(processName = "normalized")){
self$updateTrafoNormMethod(input, output, session)
}#if
}, #function
#' @description
#' Updates the numerical impute norm analysis table for a user defined feature.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormFeatureStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormFeatureStatistics <- DT::renderDataTable({
self$normalizedData$statistics %>%
dplyr::filter(Value == input$si.trafoNormFeature) %>%
dplyr::select_if(is.numeric) %>%
tidyr::pivot_longer(cols = dplyr::everything()) %>%
dplyr::rename(statistics = "name") %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormFeatureStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical impute norm analysis table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormStatistics <- DT::renderDataTable({
self$normalizedData$statistics %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("normalized_statistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the impute norm parameter table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormParameterTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormParameter <- DT::renderDataTable({
self$normalizer$normParameter %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("normalization_parameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormParameter <- DT::renderDataTable(NULL)
}#else
},#function
#' @description
#' Updates the impute norm scaled data table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormDataTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormData <- DT::renderDataTable({
self$normalizedData$rawData %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("normalized_data") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormData <- DT::renderDataTable(NULL)
}#else
},#function
#############################
# impute missings functions #
#############################
#' @description
#' Calls the missing detection routine of the instance variable imputer
#' on the instance variable normalizedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
imputeMissingsAnalyze = function(input, output, session){
if(self$status$query(processName = "normalized")){
progress <- shiny::Progress$new(session, min = 1, max = length(self$normalizedData$numericalAttributeNames))
progress$set(message = "Characterizing missings", value = 1)
on.exit(progress$close())
self$normalizedData$numerical_data() %>%
private$.missings$resetImputationParameter()
self$normalizedData$numerical_data() %>%
private$.missingsCharacterizer$analyze(progress)
private$.status$update(processName = "naDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No normalized data available. Please normalize data first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the plt.imputeMissingsSummary graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsGraphic = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$plt.imputeMissingsSummary <- shiny::renderPlot(
self$missings$imputationSiteHeatMap(),
bg="transparent"
)
}#if
else{
output$plt.imputeMissingsSummary <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the tbl.imputeMissingsStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "naDetected")){
output$tbl.imputeMissingsStatistics <- DT::renderDataTable(
self$missings$imputationParameter %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("missingsStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMissingsDistribution table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsDistributionTbl = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$tbl.imputeMissingsDistribution <- DT::renderDataTable(
self$normalizedData$numerical_data() %>%
self$missings$imputationSiteDistribution() %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("missingsDistribution") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsDistribution <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the plt.imputeMissingsPairs graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingCharacteristicsGraphic = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$plt.imputeMissingsPairs <- shiny::renderPlot(
self$normalizedData$numerical_data() %>%
self$missingsCharacterizer$plot_pair_dist(),
bg="transparent"
)
}#if
else{
output$plt.imputeMissingsPairs<- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #fnunction
#' @description
#' Updates the tbl.imputeMissingsCharacteristics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsCharacteristicsTbl = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$tbl.imputeMissingsCharacteristics <- DT::renderDataTable(
self$missingsCharacterizer$missingsCharacteristics_df %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("missingsMissings") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsCharacteristics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeDetectDetail table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsDetailTbl = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$tbl.imputeMissingsDetail <- DT::renderDataTable(
self$normalizedData$rawData %>%
dplyr::filter_all(dplyr::any_vars(is.na(.))) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationSiteDetectionDetail") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsDetail <- DT::renderDataTable(NULL)
}#else
}, #function
#'
#' #' @description
#' #' Updates the tbl.imputeDetectData table.
#' #' @param input
#' #' Pointer to shiny input
#' #' @param output
#' #' Pointer to shiny output
#' #' @param session
#' #' Pointer to shiny session
#' updateImputeMissingsDataTbl = function(input, output, session){
#' if(self$status$query(processName = "naDetected")){
#' output$tbl.imputeMissingsData <- DT::renderDataTable(
#' self$filteredMetadata$rawData %>%
#' dplyr::right_join(self$normalizedData$rawData, by = "Sample Name") %>%
#' format.data.frame(scientific = TRUE, digits = 4) %>%
#' DT::datatable(
#' extensions = "Buttons",
#' options = list(
#' scrollX = TRUE,
#' scrollY = '350px',
#' paging = FALSE,
#' dom = "Blfrtip",
#' buttons = list(list(
#' extend = 'csv',
#' filename = self$fileName$predict("imputationSiteDetectionData") %>%
#' tools::file_path_sans_ext(),
#' text = "Download"
#' ))#buttons
#' )#options
#' )#DT::datatable
#' )#output
#' }#if
#' else{
#' output$tbl.imputeMissingsData <- DT::renderDataTable(NULL)
#' }#else
#' }, #function
#############################
# impute outliers functions #
#############################
#' @description
#' Updates the si.imputeOutliersMethod shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersMethod = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateSelectInput(session,
"si.imputeOutliersMethod",
choices = self$outliers$outliersAgentAlphabet,
selected = self$outliers$outliersAgent
)
}#if
}, #function
#' @description
#' Updates the si.imputeOutliersFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersFeature = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateSelectInput(session,
"si.imputeOutliersFeature",
choices = self$normalizedData$numericalAttributeNames,
selected = self$normalizedData$numericalAttributeNames[1])
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersAlpha shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersAlpha = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersAlpha",
value = self$outliers$alpha)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersEpsilon shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersEpsilon = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersEpsilon",
value = self$outliers$epsilon)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersMinSamples shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersMinSamples = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersMinSamples",
value = self$outliers$minSamples)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersGamma shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersGamma = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersGamma",
value = self$outliers$gamma)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersNu shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersNu = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersNu",
value = self$outliers$nu)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersCutoff shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersCutoff = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersCutoff",
value = self$outliers$cutoff)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersK shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersK = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersK",
value = self$outliers$k)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersSeed shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersSeed = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersSeed",
value = self$outliers$seed)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersGui = function(input, output, session){
if(self$status$query(processName = "naDetected")){
self$updateImputeOutliersMethod(input, output, session)
self$updateImputeOutliersFeature(input, output, session)
self$updateImputeOutliersAlpha(input, output, session)
self$updateImputeOutliersEpsilon(input, output, session)
self$updateImputeOutliersMinSamples(input, output, session)
self$updateImputeOutliersGamma(input, output, session)
self$updateImputeOutliersNu(input, output, session)
self$updateImputeOutliersCutoff(input, output, session)
self$updateImputeOutliersK(input, output, session)
self$updateImputeOutliersSeed(input, output, session)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetImputeOutliersGui = function(input, output, session){
if(self$status$query(processName = "naDetected")){
self$updateImputeOutliersMethod(input, output, session)
self$updateImputeOutliersAlpha(input, output, session)
self$updateImputeOutliersEpsilon(input, output, session)
self$updateImputeOutliersMinSamples(input, output, session)
self$updateImputeOutliersGamma(input, output, session)
self$updateImputeOutliersNu(input, output, session)
self$updateImputeOutliersCutoff(input, output, session)
self$updateImputeOutliersK(input, output, session)
self$updateImputeOutliersSeed(input, output, session)
}#if
},#function
#' @description
#' Calls the detectOutliers routine of the instance variable outliers
#' on the instance variable normalizedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
imputeOutliersDetect = function(input, output, session){
if(self$status$query(processName = "naDetected")){
private$.outliers$setOutliersAgent <- input$si.imputeOutliersMethod
private$.outliers$setAlpha <- input$ni.imputeOutliersAlpha
private$.outliers$setEpsilon <- input$ni.imputeOutliersEpsilon
private$.outliers$setMinSamples <- input$ni.imputeOutliersMinSamples
private$.outliers$setGamma <- input$ni.imputeOutliersGamma
private$.outliers$setNu <- input$ni.imputeOutliersNu
private$.outliers$setCutoff <- input$ni.imputeOutliersCutoff
private$.outliers$setK <- input$ni.imputeOutliersK
private$.outliers$setSeed <- input$ni.imputeOutliersSeed
self$resetImputeOutliersGui(input, output, session)
progress <- shiny::Progress$new(session, min = 1, max = length(self$loqMutatedData$numericalAttributeNames))
progress$set(message = "Searching for anomalies", value = 1)
on.exit(progress$close())
self$normalizedData$numerical_data() %>%
private$.outliers$detectOutliers(progress)
private$.status$update(processName = "outliersDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No missings detected. Please detect missings first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the plt.outliersImputeSummary graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersGraphic = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
output$plt.outliersImputeSummary <- shiny::renderPlot(
self$outliers$plotOutliersDistribution(),
bg="transparent"
)
}#if
else{
output$plt.outliersImputeSummary <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the plt.outliersImputeFeature graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersFeatureGraphic = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
output$plt.outliersImputeFeature <- shiny::renderPlot(
self$outliers$featurePlot(data_df = self$normalizedData$numerical_data(),
feature = input$si.imputeOutliersFeature),
height = 475,
bg="transparent"
)
}#if
else{
output$plt.outliersImputeFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numeric outlier feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersFeatureTbl = function(input, output, session)
{
if(self$status$query(processName = "outliersDetected"))
{
suffix <- sprintf("%s_outliers", input$si.imputeOutliersFeature)
output$tbl.outliersImputeFeature <- DT::renderDataTable(
self$outliers$outliers %>%
dplyr::filter(feature == !!input$si.imputeOutliersFeature) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.outliersImputeFeature <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "outliersDetected")){
# feature <- input$si.imputeOutliersFeature
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$normalizedData$rawData %>%
# dplyr::select(c("Sample Name", !!feature)),
# by = "Sample Name")
#
# dfOutlier <- self$outliers$featureOutlier(feature = feature)
#
# t <- dfData %>%
# dplyr::mutate_if(is.numeric, round, 3) %>%
# DT::datatable(
# options = list(
# scrollX = TRUE,
# scrollY = '350px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$predict("OutliersData") %>%
# tools::file_path_sans_ext(),
# text = "Download"
# ))#buttons
# )#options
# )#DT::datatable
#
# featureOutlier <- dfOutlier %>%
# dplyr::mutate_if(is.numeric, round, 3)
# if (nrow(featureOutlier)>0){
# t <- DT::formatStyle(t,
# feature,
# backgroundColor = styleEqual(dfData %>%
# dplyr::select(!!feature) %>%
# dplyr::slice(featureOutlier[["measurement"]]) %>%
# unlist() %>%
# as.numeric() %>%
# round(digits = 3),
# featureOutlier[["color"]])
#
# )#t
# }#if
# }#if
# output$tbl.outliersImputeFeature <- DT::renderDataTable(t)
}, #function
#' @description
#' Updates the numerical loq statistics analysis table
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "outliersDetected")){
output$tbl.outliersImputeStatistics <- DT::renderDataTable(
self$outliers$outliersStatistics %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("outliersStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.outliersImputeStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical outlier table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersDetailTbl = function(input, output, session){
if(self$status$query(processName = "outliersDetected"))
{
output$tbl.outliersImputeDetail <- DT::renderDataTable(
self$outliers$outliers %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("outliers") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.outliersImputeDetail <- DT::renderDataTable(NULL)
}
}, #function
###########################
# impute mutate functions #
###########################
#' @description
#' Updates the si.imputeMutateFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateFeature = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateSelectInput(session,
"si.imputeMutateFeature",
choices = self$normalizedData$numericalAttributeNames,
selected = self$normalizedData$numericalAttributeNames[1]
)
}#if
}, #function
#' @description
#' Updates the si.imputeMutateMethod shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateMethod = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateSelectInput(session,
"si.imputeMutateMethod",
choices = self$imputer$imputationAgentAlphabet,
selected = self$imputer$imputationAgent
)
}#if
}, #function
#' @description
#' Updates the ni.imputeMutateNumberOfNeighbors shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateNNeighbors = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateNumberOfNeighbors",
value = self$imputer$nNeighbors)
}#if
},
#' @description
#' Updates the ni.imputeMutatePredFrac shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutatePredFrac = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutatePredFrac",
value = self$imputer$pred_frac)
}#if
},
#' @description
#' Updates the ni.imputeMutateOutfluxThr shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateOutfluxThr = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateOutfluxThr",
value = self$imputer$outflux_thr)
}#if
},
#' @description
#' Updates the ni.imputeMutateSeed shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateSeed = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateSeed",
value = self$imputer$seed)
}#if
}, #function
#' @description
#' Updates the ni.imputeMutateIterations shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateIterations = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateIterations",
value = self$imputer$iterations)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateGui = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
self$resetImputeMutateGui(input, output, session)
self$updateImputeMutateFeature(input, output, session)
}#if
}, #function
#' @description
#' Resets the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetImputeMutateGui = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
self$updateImputeMutateMethod(input, output, session)
self$updateImputeMutateNNeighbors(input, output, session)
self$updateImputeMutatePredFrac(input, output, session)
self$updateImputeMutateOutfluxThr(input, output, session)
self$updateImputeMutateSeed(input, output, session)
self$updateImputeMutateIterations(input, output, session)
}#if
}, #function
#' @description
#' Calls the mutate imputation site routine of the instance variable imputer on the instance variable transformedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
imputeMutateMutate = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
private$.imputer$setImputationAgent <- input$si.imputeMutateMethod
private$.imputer$setNNeighbors <-input$ni.imputeMutateNumberOfNeighbors
private$.imputer$setPred_frac <- input$ni.imputeMutatePredFrac
private$.imputer$setOutflux_thr <- input$ni.imputeMutateOutfluxThr
private$.imputer$setSeed <- input$ni.imputeMutateSeed
private$.imputer$setIterations <- input$ni.imputeMutateIterations
private$.imputer$gatherImputationSites(missings_df = private$.missings$imputationSites,
outliers_df = private$.outliers$outliers)
private$.imputer$analyzeImputationSites(data_df = self$normalizedData$numerical_data())
private$.imputer$detectPredictors(data_df = self$normalizedData$numerical_data())
progress <- shiny::Progress$new(session, min = 0, max = 1)
progress$set(message = "Mutate imputation sites", value = 0)
on.exit(progress$close())
private$.imputedData$setRawData <- self$normalizedData$numerical_data() %>%
self$imputer$handleImputationSites(progress) %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.imputedData$fit()
if(self$imputer$success){
private$.cleanedData$setRawData <- self$imputedData$numerical_data() %>%
self$normalizer$rescale_data() %>%
self$transformator$reverseMutateData() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.cleanedData$fit()
private$.status$update(processName = "imputed", value = TRUE)
}
else{
private$.status$update(processName = "imputed", value = FALSE)
shiny::showNotification(paste("Solution of imputation mutate procedure was not satisfactoy. Pleas refine your variables or method and try again. See the Help Webpage of pguIMP for help with the variabes."),type = "error", duration = 20)
}
# progress <- shiny::Progress$new(session, min = 1, max = length(self$normalizedData$numericFeatureNames))
# progress$set(message = "Impute Data", value = 0)
# on.exit(progress$close())
#
# name <- as.name("Sample Name")
# private$.imputedData$setRawData <- self$normalizedData$numericData() %>%
# self$imputer$handleImputationSites(progress = progress) %>%
# # self$model$rescaleData() %>%
# # self$transformator$reverseMutateData() %>%
# tibble::add_column(!! name := self$normalizedData$rawData %>%
# dplyr::select(!!name) %>%
# unlist() %>%
# as.character()) %>%
# dplyr::select(c(!!name, self$normalizedData$numericFeatureNames))
# private$.status$update(processName = "imputed", value = TRUE)
}#if
else{
shiny::showNotification(paste("No outliers detected. Please detect outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the plt.imputeMutateFlux graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeFluxGraphic = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$plt.imputeMutateFlux <- shiny::renderPlot(
self$imputer$fluxPlot(),
bg="transparent"
)
}#if
else{
output$plt.imputeMutateFlux <- shiny::renderPlot(NULL,bg="transparent")
}#else
}, #function
#' @description
#' Updates the plt.imputeMutateSummary graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateGraphic = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$plt.imputeMutateSummary <- shiny::renderPlot(
self$imputer$imputationSiteHeatMap(),
bg="transparent"
)
}#if
else{
output$plt.imputeMutateSummary <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "imputed")){
output$tbl.imputeMutateStatistics <- DT::renderDataTable(
self$imputer$imputationStatistics %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationSiteStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateDistribution table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateDistributionTbl = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$tbl.imputeMutateDistribution <- DT::renderDataTable(
self$imputer$imputationSiteDistribution %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationSiteDistribution") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateDistribution <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the plt.imputeMutateFeatureDetail graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateFeatureDetailGraphic = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$plt.imputeMutateFeatureDetail <- shiny::renderPlot(
self$imputer$featurePlot(data = self$imputedData$numerical_data(),
feature = input$si.imputeMutateFeature),
height = 475,
bg = "transparent"
)#output
}#if
else{
output$plt.imputeMutateFeatureDetail <- shiny::renderPlot(NULL, bg = "transparent")
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateFeatureDetail table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateFeatureDetailTbl = function(input, output, session){
if (self$status$query(processName = "imputed")){
output$tbl.imputeMutateFeatureDetail <- DT::renderDataTable(
self$imputedData$rawData %>%
dplyr::select(dplyr::all_of(c(self$imputedData$categoricalAttributeNames, input$si.imputeMutateFeature))) %>%
dplyr::slice(self$imputer$imputationSiteIdxByFeature(feature = input$si.imputeMutateFeature)) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationFeatureDetails") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateFeatureDetail <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateDetail table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateDetailTbl = function(input, output, session){
if(self$status$query(processName = "imputed")){
dfImputationSites <- self$imputer$imputationSites
idx <- dfImputationSites[["idx"]][!duplicated(dfImputationSites[["idx"]])]
output$tbl.imputeMutateDetail <- DT::renderDataTable(
self$imputedData$rawData %>%
dplyr::slice(idx) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationDetail") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateDetail <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateData table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateDataTbl = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$tbl.imputeMutateData <- DT::renderDataTable(
self$imputedData$rawData %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationData") %>%
tools::file_path_sans_ext(),
text = "Download"
))#button
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateData <- DT::renderDataTable(NULL)
}#else
}, #function
############################
# outlier detect functions #
############################
########################
# validation functions #
########################
#' @description
#' Calls the validate routine of the instance variable validator on the instance variables rawData and clenaedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
validate = function(input, output, session){
if(self$status$query(processName = "imputed")){
progress <- shiny::Progress$new(session, min = 0, max = 1.0)
progress$set(message = "Validate imputation", value = 0)
on.exit(progress$close())
private$.validator$validate(org = self$filteredData$numerical_data(),
imp = self$cleanedData$numerical_data() %>%
dplyr::select_if(function(x){!all(is.na(x))}),
progress = progress)
private$.status$update(processName = "validated", value = TRUE)
private$.corrValidator$fit(org_df = self$normalizedData$numerical_data(),
imp_df = self$imputedData$numerical_data())
}#if
else{
shiny::showNotification(paste("No imputation performed. Please perform imputation first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the si.analysisValidationFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateAnalysisValidationGui = function(input, output, session){
if(self$status$query(processName = "validated")){
shiny::updateSelectInput(session,
"si.analysisValidationFeature",
choices = self$validator$features,
selected = self$validator$features[1]
)
}#if
else{
shiny::updateSelectInput(session,
"si.analysisValidationFeature",
choices = list(),
selected = 1
)
} #else
}, #function
#' @description
#' Updates the plt.analysisValidationFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateAnalysisValidationGraphic = function(input, output, session){
if(self$status$query(processName = "validated")){
output$plt.analysisValidationFeature <- shiny::renderPlot(
self$validator$featurePlot(org_df = self$filteredData$numerical_data(),
imp_df = self$cleanedData$numerical_data(),
lloq = self$loq$attribute_lloq(attribute = input$si.analysisValidationFeature),
uloq = self$loq$attribute_uloq(attribute = input$si.analysisValidationFeature),
impIdx_df = self$imputer$imputationSites,
feature = input$si.analysisValidationFeature),
bg="transparent"
)#output
}#if
else{
output$plt.plt.analysisValidationFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updtates the tbl.analysisValidationTest table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateAnalysisValidationTestTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.analysisValidationTest <- DT::renderDataTable(
self$validator$testStatistics_df %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("validationTests") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.analysisValidationTest <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.centralMomentsOrg table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCentralMomentsOrgTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.centralMomentsOrg <- DT::renderDataTable(
self$validator$centralMoments_org %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawCentralMoments") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.centralMomentsOrg <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.centralMomentsImp table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCentralMomentsImpTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.centralMomentsImp <- DT::renderDataTable(
self$validator$centralMoments_imp %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("impCentralMoments") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.centralMomentsImp <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.centralMomentsDelta table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCentralMomentsDeltaTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.centralMomentsDelta <- DT::renderDataTable(
self$validator$centralMoments_delta %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("deltaCentralMoments") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.centralMomentsDelta <- DT::renderDataTable(NULL)
} #else
}, #function
####################################
# correlation validation functions #
####################################
#' @description
#' Updtates the plt.correlationValidationScatter graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationScatterGraphic = function(input, output, session){
if(self$status$query(processName = "validated")){
output$plt.correlationValidationScatter <- shiny::renderPlot(
self$corrValidator$correlationScatterPlot(),
bg="transparent"
)#output
}#if
else{
output$plt.correlationValidationScatter <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updtates the plt.correlationValidationBoxPlot graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationBoxPlotGraphic = function(input, output, session){
if(self$status$query(processName = "validated")){
output$plt.correlationValidationBoxPlot <- shiny::renderPlot(
self$corrValidator$correlationCompoundPlot(),
height = 475,
bg="transparent"
)#output
}#if
else{
output$plt.correlationValidationBoxPlot <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updtates the tbl.correlationValidationDeviation table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationDeviationTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.correlationValidationDeviation <- DT::renderDataTable(
self$corrValidator$summary_df %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
scrollY = '350px',
autowidth = FALSE,
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("correlationValidationDeviation") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.correlationValidationData <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.correlationValidationData table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationDataTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.correlationValidationData <- DT::renderDataTable(
self$corrValidator$corr_df %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("correlationValidationData") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.correlationValidationData <- DT::renderDataTable(NULL)
} #else
}, #function
#########################
# data export functions #
#########################
#' @description
#' Creates and returns an export filename.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
#' @return
#' export filename
#' (character)
exportFileName = function(input, output, session){
private$.fileName$predict() %>%
return()
}, #function
#' @description
#' Exports the pguIMP analysis results
#' @param file
#' export filename
#' (character)
#' @param input
#' Pointer to shiny input
exportData = function(input, file){
if(self$status$query(processName = "validated")){
private$.exporter$setFileName <- file
gui_parameter <- tibble::tibble(
loq_na_handling = c(self$loq$naHandlingAgent),
lloq_substitute = c(self$loq$lloqSubstituteAgent),
uloq_substitute = c(self$loq$uloqSubstituteAgent),
normalization_type = c(self$normalizer$normAgent),
anomalies_method = c(self$outliers$outliersAgent),
alpha = c(self$outliers$alpha),
epsilon= c(self$outliers$epsilon),
minSamples = c(self$outliers$minSamples),
gamma = c(self$outliers$gamma),
nu = c(self$outliers$nu),
cutoff = c(self$outliers$cutoff),
k = c(self$outliers$k),
anomalies_seed = c(self$outliers$seed),
imputation_method = c(self$imputer$imputationAgent),
number_of_neighbors = c(self$imputer$nNeighbors),
fraction_of_predictors = c(self$imputer$pred_frac),
outflux_threshold = c(self$imputer$outflux_thr),
imputation_seed = c(self$imputer$seed),
iterations = c(self$imputer$iterations)
)
gui_parameter <- tibble::as_tibble(cbind(parameter = names(gui_parameter), t(gui_parameter)))
colnames(gui_parameter) <- c("parameter", "value")
list(raw_data = self$cleanedData$rawData,
loq = self$loq$loq,
giu_parameter = gui_parameter,
filter_parameter = tibble::tibble(features = c(self$metadata$featureNames, self$rawData$featureNames[2:length(self$rawData$featureNames)]),
filter_parameter = as.vector(input$tbl.filter_search_columns)),
filtered = self$filteredData$rawData,
loq_statistics = self$loq$loqStatistics,
loq_mutated = self$loqMutatedData$rawData,
trafo_parameter = self$transformator$trafoParameter,
transfromed = self$transformedData$rawData,
model_parameter = self$model$modelParameterData(),
model_quality = self$model$modelQualityData(),
model_statistics = self$model$testResultData(),
normalization_parameter = self$normalizer$normParameter,
normalized = self$normalizedData$rawData,
missings = self$missings$imputationSites,
missings_statistics = self$missings$imputationParameter,
missings_characteristics = self$missingsCharacterizer$missingsCharacteristics_df,
outliers = self$outliers$outliers %>%
dplyr::select(-c("color")),
outliers_statistics = self$outliers$outliersStatistics,
predictors = self$imputer$pred_mat %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
tibble::as_tibble(),
imputation_sites = self$imputer$one_hot_df,
imputation_statistics = self$imputer$imputationStatistics,
imputed = self$imputedData$rawData,
validation = self$validator$testStatistics_df
) %>%
self$exporter$writeDataToExcel()
}#if
}, #function
#############################
# analysis report functions #
#############################
#' @description
#' Creates and returns a report filename.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
#' @return
#' export filename
#' (character)
reportFileName = function(input, output, session){
private$.fileName$setSuffix <- "pdf"
private$.fileName$updateTimeString()
# private$.fileName$mergeFileName()
private$.fileName$exportFileName() %>%
return()
}, #function
#' @description
#' Exports a report on the pguIMP analysis
#' in pdf format.
#' @param input
#' Pointer to shiny input
#' @param file
#' export filename
#' (character)
writeReport = function(input, file){
print("state")
print(str(input$tbl.filter_search_columns))
if(self$status$query(processName = "validated")){
private$.reporter$setFilename <- file
analysis_parameter <- tibble::tibble(
fileName = c(self$fileName$fileName),
numberOfInstances = c(nrow(self$rawData$rawData)),
numberOfFeatures = c(ncol(self$rawData$rawData)),
numberOfMetaFeatures = c(ncol(self$metadata$rawData)),
numberOfNumericFeatures = c(length(self$rawData$numericalAttributeNames)),
numberOfNonNumericFeatures = c(length(self$rawData$categoricalAttributeNames)),
totalNumberOfMissings = c(self$rawData$rawData %>%
dplyr::select(dplyr::everything()) %>% # replace to your needs
dplyr::summarise_all(~ sum(is.na(.x))) %>%
sum()),
loq_na_handling = c(self$loq$naHandlingAgent),
lloq_substitute = c(self$loq$lloqSubstituteAgent),
uloq_substitute = c(self$loq$uloqSubstituteAgent),
normalization_type = c(self$normalizer$normAgent),
anomalies_method = c(self$outliers$outliersAgent),
alpha = c(self$outliers$alpha),
epsilon= c(self$outliers$epsilon),
minSamples = c(self$outliers$minSamples),
gamma = c(self$outliers$gamma),
nu = c(self$outliers$nu),
cutoff = c(self$outliers$cutoff),
k = c(self$outliers$k),
anomalies_seed = c(self$outliers$seed),
imputation_method = c(self$imputer$imputationAgent),
number_of_neighbors = c(self$imputer$nNeighbors),
fraction_of_predictors = c(self$imputer$pred_frac),
outflux_threshold = c(self$imputer$outflux_thr),
imputation_seed = c(self$imputer$seed),
iterations = c(self$imputer$iterations)
)
list(filter_parameter = tibble::tibble(features = c(self$metadata$featureNames, self$rawData$featureNames[2:length(self$rawData$featureNames)]),
filter_parameter = as.vector(input$tbl.filter_search_columns)) %>%
dplyr::filter(filter_parameter != ""),
selected_features = self$filteredData$numericalAttributeNames,
loq_statistics = self$loq$loqStatistics,
trafo_parameter = self$transformator$trafoParameter,
model_parameter = self$model$modelParameterData(),
model_quality = self$model$modelQualityData(),
model_statistics = self$model$testResultData(),
normalization_parameter = self$normalizer$normParameter,
missings_statistics = self$missings$imputationParameter,
missings_distribution = self$missings$imputationSiteDistribution(self$filteredData$numerical_data()),
outliers_statistics = self$outliers$outliersStatistics,
imputation_statistics = self$imputer$imputationStatistics,
imputation_distribution = self$imputer$imputationSiteDistribution,
validation_test = self$validator$testStatistics_df,
validation_corrSum = self$corrValidator$summary(),
validation_correlation = self$corrValidator$corr_df,
analysis_parameter = analysis_parameter
) %>%
self$reporter$write_report()
}#if
},#function
###################################
# update graphical user interface #
###################################
#' @description
#' Updates the gui if analysis parameters change.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
hide_outdated_results = function(input, output, session){
if(!private$.status$query(processName = "dataImported")){
output$tbl.importDataTypes <- DT::renderDataTable(NULL)
output$tbl.importDataStatistics <- DT::renderDataTable(NULL)
output$tbl.importMissingsStatistics <- DT::renderDataTable(NULL)
output$tbl.filterSelect <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "dataFiltered")){
output$plt.exploreGraphic <- shiny::renderPlot(NULL, bg="transparent")
output$plt.exploreAbscissaGraphic <- shiny::renderPlot(NULL, bg="transparent")
output$plt.exploreOrdinateGraphic <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.exploreAbscissaStatistics <- DT::renderDataTable(NULL)
output$tbl.exploreOrdinateStatistics <- DT::renderDataTable(NULL)
output$tbl.filterStatistics <- DT::renderDataTable(NULL)
output$tbl.filterMissings <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "loqImported")){
output$tbl.loqDefineValues <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "loqDetected")){
output$tbl.loqDetectStatistics <- DT::renderDataTable(NULL)
output$tbl.loqDetectOutlier <- DT::renderDataTable(NULL)
output$tbl.loqDetectData <- DT::renderDataTable(NULL)
output$plt.loqDetectStatistics <- shiny::renderPlot(NULL, bg="transparent")
output$plt.loqDetectFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.loqDetectFeature <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "loqMutated")){
output$tbl.loqMutateStatistics <- DT::renderDataTable(NULL)
output$tbl.loqMutateOutlier <- DT::renderDataTable(NULL)
output$tbl.loqMutateData <- DT::renderDataTable(NULL)
output$plt.loqMutateStatistics <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.loqMutateFeature <- DT::renderDataTable(NULL)
output$plt.loqMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
}#if
if(!self$status$query(processName = "modelOptimized")){
output$tbl.trafoDetectTypes <- DT::renderDataTable(NULL)
output$tbl.trafoDetectParameters <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "modelDefined")){
output$plt.trafoMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.trafoMutateFeatureParameter <- DT::renderDataTable(NULL)
output$tbl.trafoMutateFeatureQuality <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalParameter <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalModel <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalQuality <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalTests <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "normalized")){
output$plt.trafoNormFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.trafoNormFeatureStatistics<- DT::renderDataTable(NULL)
output$tbl.trafoNormParameter<- DT::renderDataTable(NULL)
output$tbl.trafoNormStatistics<- DT::renderDataTable(NULL)
output$tbl.trafoNormData<- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "naDetected")){
output$plt.imputeMissingsSummary <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.imputeMissingsStatistics <- DT::renderDataTable(NULL)
output$tbl.imputeMissingsDistribution <- DT::renderDataTable(NULL)
output$plt.imputeMissingsPairs <- shiny::renderPlot(NULL, bg ="transparent")
output$tbl.imputeMissingsCharacteristics <- DT::renderDataTable(NULL)
output$tbl.imputeMissingsDetail <- DT::renderDataTable(NULL)
output$tbl.imputeMissingsData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "outliersDetected")){
output$plt.outliersImputeSummary <- shiny::renderPlot(NULL, bg="transparent")
output$plt.outliersImputeFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.outliersImputeFeature <- DT::renderDataTable(NULL)
output$tbl.outliersImputeStatistics <- DT::renderDataTable(NULL)
output$tbl.outliersImputeDetail <- DT::renderDataTable(NULL)
output$tbl.outliersImputeData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "imputed")){
output$plt.imputeMutateSummary <- shiny::renderPlot(NULL, bg="transparent")
output$plt.imputeMutateFeatureDetail <- shiny::renderPlot(NULL, bg="transparent")
output$plt.imputeMutateFlux <- shiny::renderPlot(NULL, bg = "transparent")
output$tbl.imputeMutateFeatureDetail<- DT::renderDataTable(NULL)
output$tbl.imputeMutateStatistics <- DT::renderDataTable(NULL)
output$tbl.imputeMutateDistribution <- DT::renderDataTable(NULL)
output$tbl.imputeMutateDetail <- DT::renderDataTable(NULL)
output$tbl.imputeMutateData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "validated")){
output$plt.analysisValidationFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.analysisValidationTest <- DT::renderDataTable(NULL)
output$tbl.centralMomentsOrg <- DT::renderDataTable(NULL)
output$tbl.centralMomentsImp <- DT::renderDataTable(NULL)
output$tbl.centralMomentsDelta <- DT::renderDataTable(NULL)
output$plt.correlationValidationScatter <- shiny::renderPlot(NULL, bg = "transparent")
output$plt.correlationValidationBoxPlot <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.correlationValidationDeviation <- DT::renderDataTable(NULL)
output$tbl.correlationValidationData <- DT::renderDataTable(NULL)
} #if
# if(!self$status$query(processName = "correlated")){
# output$tbl.correlationMatrixR <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixPPearson <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixTau <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixPKendall <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixRho <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixPSpearman <- DT::renderDataTable(NULL)
# }#if
# if(!self$status$query(processName = "regression")){
# output$plt.regressionFeature <- shiny::renderPlot(NULL, bg="transparent")
# output$tbl.regressionFeature <- DT::renderDataTable(NULL)
# output$tbl.regressionIntercept <- DT::renderDataTable(NULL)
# output$tbl.regressionPIntercept <- DT::renderDataTable(NULL)
# output$tbl.regressionSlope <- DT::renderDataTable(NULL)
# output$tbl.regressionPSlope <- DT::renderDataTable(NULL)
# }#if
}, #function
#########################
# update help interface #
#########################
#' @description
#' Updates the gui if analysis parameters change.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_help_html = function(input, output, session)
{
if(input$switch.help)
{
wd <- getwd()
help_folder <- file.path(wd, "www/_site")
output$html.uploadHelp <- shiny::renderUI({
help_folder %>%
file.path("upload.html") %>%
shiny::includeHTML()
})
output$html.filterHelp <- shiny::renderUI({
help_folder %>%
file.path("filter.html") %>%
shiny::includeHTML()
})
output$html.exploreHelp <- shiny::renderUI({
help_folder %>%
file.path("explore.html") %>%
shiny::includeHTML()
})
output$html.uploadLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("upload_loq.html") %>%
shiny::includeHTML()
})
output$html.defineLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("define_loq.html") %>%
shiny::includeHTML()
})
output$html.detectLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("detect_loq.html") %>%
shiny::includeHTML()
})
output$html.substituteLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("substitute_loq.html") %>%
shiny::includeHTML()
})
output$html.transformHelp <- shiny::renderUI({
help_folder %>%
file.path("transform.html") %>%
shiny::includeHTML()
})
output$html.normalizeHelp <- shiny::renderUI({
help_folder %>%
file.path("normalize.html") %>%
shiny::includeHTML()
})
output$html.missingsHelp <- shiny::renderUI({
help_folder %>%
file.path("missings.html") %>%
shiny::includeHTML()
})
output$html.anomaliesHelp <- shiny::renderUI({
help_folder %>%
file.path("anomalies.html") %>%
shiny::includeHTML()
})
output$html.substituteImpHelp <- shiny::renderUI({
help_folder %>%
file.path("substitute_imp.html") %>%
shiny::includeHTML()
})
output$html.validateHelp <- shiny::renderUI({
help_folder %>%
file.path("validate.html") %>%
shiny::includeHTML()
})
}
else
{
output$html.uploadHelp <- shiny::renderUI({NULL})
output$html.filterHelp <- shiny::renderUI({NULL})
output$html.exploreHelp <- shiny::renderUI({NULL})
output$html.uploadLOQHelp <- shiny::renderUI({NULL})
output$html.defineLOQHelp <- shiny::renderUI({NULL})
output$html.detectLOQHelp <- shiny::renderUI({NULL})
output$html.substituteLOQHelp <- shiny::renderUI({NULL})
output$html.transformHelp <- shiny::renderUI({NULL})
output$html.normalizeHelp <- shiny::renderUI({NULL})
output$html.missingsHelp <- shiny::renderUI({NULL})
output$html.anomaliesHelp <- shiny::renderUI({NULL})
output$html.substituteImpHelp <- shiny::renderUI({NULL})
output$html.validateHelp <- shiny::renderUI({NULL})
}
}#function
)#public
)#class
SMLMS/pguIMP documentation built on Oct. 1, 2021, 12:01 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @title pgu.delegate
#'
#' @description
#' Manages the communication between the shiny gui layer and the classes of the pguIMP package
#'
#' @details
#' Comprises all needed classes from the pguIMP package and manages the communication between the gui and the analysis.
#' This object is used by the shiny based gui and is not for use in individual R-scripts!
#'
#' @format [R6::R6Class] object.
#' @importFrom R6 R6Class
#' @importFrom magrittr %>%
#' @importFrom dplyr all_of any_vars everything filter filter_all mutate mutate_if pull rename right_join select select_if slice summarise_all
#' @importFrom DT datatable formatStyle renderDataTable styleEqual
#' @importFrom shiny includeHTML Progress renderPlot renderUI showNotification updateCheckboxInput updateNumericInput updateSelectInput
#' @importFrom tibble add_column as_tibble rownames_to_column tibble
#' @importFrom tidyr pivot_longer
#' @importFrom tools file_path_sans_ext
#'
#' @include pguStatus.R
#' @include pguFile.R
#' @include importDataSet.R
#' @include pguData.R
#' @include pguLimitsOfQuantification.R
#' @include pguFilter.R
#' @include pguExplorer.R
#' @include pguOptimizer.R
#' @include pguTransformator.R
#' @include pguModel.R
#' @include pguNormDist.R
#' @include pguNormalizer.R
#' @include pguMissings.R
#' @include pguMissingsCharacterizer.R
#' @include pguOutliers.R
#' @include pguImputation.R
#' @include pguValidator.R
#' @include pguCorrValidator.R
#' @include pguExporter.R
#' @include pguReporter.R
#'
#' @author Sebastian Malkusch, \email{malkusch@@med.uni-frankfurt.de}
#'
#' @export
#'
pgu.delegate <- R6::R6Class("pgu.delegate",
####################
# instance variables
####################
private = list(
.status = "pgu.status",
.fileName = "pgu.file",
.loqFileName = "pgu.file",
.rawData = "pgu.data",
.filterSet = "pgu.filter",
.filteredData = "pgu.data",
.loq = "pgu.limitsOfQuantification",
.loqMutatedData = "pgu.data",
.explorer= "pgu.explorer",
.optimizer = "pgu.optimizer",
.transformator = "pgu.transformator",
.model = "pgu.model",
.transformedData = "pgu.data",
.featureModel = "pgu.normDist",
.normalizer = "pgu.normalizer",
.normalizedData = "pgu.data",
.missings = "pgu.missings",
.missingsCharacterizer = "pgu.missingsCharacterizer",
.outliers = "pgu.outliers",
.imputer = "pgu.imputation",
.imputedData = "pgu.data",
.cleanedData = "pgu.data",
.validator = "pgu.validatior",
.corrValidator = "pgu.corrValidator",
.exporter = "pgu.exporter",
.reporter = "pgu.reporter",
#' @description
#' Clears the heap and
#' indicates that instance of `pgu.delegate` is removed from heap.
finalize = function() {
print("Instance of pgu.delegate removed from heap")
} #function
),
##################
# accessor methods
##################
active = list(
#' @field status
#' Returns the instance variable status
#' (pguIMP::pgu.status)
status = function(){
return(private$.status)
},
#' @field fileName
#' Returns the instance variable fileName
#' (pguIMP::pgu.file)
fileName = function(){
return(private$.fileName)
},
#' @field loqFileName
#' Returns the instance variable loqFileName
#' (pguIMP::pgu.file)
loqFileName = function(){
return(private$.loqFileName)
},
#' @field rawData
#' Returns the instance variable rawData
#' (pguIMP::pgu.data)
rawData = function(){
return(private$.rawData)
},
#' @field filterSet
#' Returns the instance variable filterSet
#' (pguIMP::pgu.filter)
filterSet = function(){
return(private$.filterSet)
},
#' @field filteredData
#' Returns the instance variable filteredData
#' (pguIMP::pgu.data)
filteredData = function(){
return(private$.filteredData)
},
#' @field loq
#' Returns the instance variable loq
#' (pguIMP::pgu.limitsOfQuantification)
loq = function(){
return(private$.loq)
},
#' @field loqMutatedData
#' Returns the instance variable loqMutatedData
#' (pguIMP::pgu.data)
loqMutatedData = function(){
return(private$.loqMutatedData)
},
#' @field explorer
#' Returns the instance variable explorer
#' (pguIMP::pgu.explorer)
explorer = function(){
return(private$.explorer)
},
#' @field optimizer
#' Returns the instance variable optimizer
#' (pguIMP::pgu.optimizer)
optimizer = function(){
return(private$.optimizer)
},
#' @field transformator
#' Returns the instance variable transformator
#' (pguIMP::pgu.transformator)
transformator = function(){
return(private$.transformator)
},
#' @field model
#' Returns the instance variable model
#' (pguIMP::pgu.model)
model = function(){
return(private$.model)
},
#' @field transformedData
#' Returns the instance variable transformedData
#' (pguIMP::pgu.data)
transformedData = function(){
return(private$.transformedData)
},
#' @field featureModel
#' Returns the instance variable featureModel
#' (pguIMP::pgu.normDist)
featureModel = function(){
return(private$.featureModel)
},
#' @field normalizer
#' Returns the instance variable normalizer
#' (pguIMP::pgu.normalizer)
normalizer = function(){
return(private$.normalizer)
},
#' @field normalizedData
#' Returns the instance variable normalizedData
#' (pguIMP::pgu.data)
normalizedData = function(){
return(private$.normalizedData)
},
#' @field missings
#' Returns the instance variable missings
#' (pguIMP::pgu.missings)
missings = function(){
return(private$.missings)
},
#' @field missingsCharacterizer
#' Returns the instance variable missingsCharacterizer
#' (pguIMP::pgu.missingsCharacterizer)
missingsCharacterizer = function(){
return(private$.missingsCharacterizer)
},
#' @field outliers
#' Returns the instance variable outlierd
#' (pguIMP::pgu.outliers)
outliers = function(){
return(private$.outliers)
},
#' @field imputer
#' Returns the instance variable imputer
#' (pguIMP::pgu.imputation)
imputer = function(){
return(private$.imputer)
},
#' @field imputedData
#' Returns the instance variable imputedData
#' (pguIMP::pgu.data)
imputedData = function(){
return(private$.imputedData)
},
#' @field cleanedData
#' Returns the instance variable cleanedData
#' (pguIMP::pgu.data)
cleanedData = function(){
return(private$.cleanedData)
},
#' @field validator
#' Returns the instance variable validator
#' (pguIMP::pgu.validator)
validator = function(){
return(private$.validator)
},
#' @field corrValidator
#' Returns the instance variable corrValidator
#' (pguIMP::pgu.corrValidator)
corrValidator = function(){
return(private$.corrValidator)
},
#' @field exporter
#' Returns the instance variable exporter
#' (pguIMP::pgu.exporter)
exporter = function(){
return(private$.exporter)
},
#' @field reporter
#' Returns the instance variable reporter
#' (pguIMP::pgu.reporter)
reporter = function(){
return(private$.reporter)
}
),#acessors
###################
# memory management
###################
public = list(
#' @description
#' Creates and returns a new `pgu.delegate` object.
#' @param data
#' The data to be analyzed.
#' (tibble::tibble)
#' @return
#' A new `pgu.delegate` object.
#' (pguIMP::pgu.delegate)
initialize = function(data = "tbl_df") {
print("Instance of pgu.delegate allocated")
private$.status <- pgu.status$new()
private$.fileName <- pgu.file$new()
private$.loqFileName <- pgu.file$new()
private$.rawData <- pgu.data$new()
private$.loq <- pgu.limitsOfQuantification$new()
#private$.metadata <- pgu.data$new()
private$.filterSet <- pgu.filter$new()
private$.filteredData <- pgu.data$new()
#private$.filteredMetadata <- pgu.data$new()
private$.explorer <- pgu.explorer$new()
private$.loqMutatedData <- pgu.data$new()
private$.optimizer <- pgu.optimizer$new()
private$.transformator <- pgu.transformator$new()
private$.model <- pgu.model$new()
private$.transformedData <- pgu.data$new()
private$.featureModel <- pgu.normDist$new()
private$.normalizer <- pgu.normalizer$new()
private$.normalizedData <- pgu.data$new()
private$.missings <- pgu.missings$new()
private$.missingsCharacterizer <- pgu.missingsCharacterizer$new()
private$.outliers <- pgu.outliers$new()
private$.imputer <- pgu.imputation$new()
private$.imputedData <- pgu.data$new()
private$.cleanedData <- pgu.data$new()
private$.validator <- pgu.validator$new()
private$.corrValidator <- pgu.corrValidator$new()
private$.exporter <- pgu.exporter$new()
private$.reporter <- pgu.reporter$new()
}, #function
##########################
# print instance variables
##########################
#' @description
#' Prints instance variables of a `pgu.delegate` object.
#' @return
#' string
print = function()
{
sprintf("\npgu.delegate\n\n") %>%
cat()
print(self$status)
print(self$fileName)
print(self$loqFileName)
print(self$importer)
print(self$rawData)
print(self$loq)
print(self$metadata)
print(self$filterSet)
print(self$filteredData)
print(self$filteredMetadata)
print(self$explorer)
print(self$loqMutatedData)
print(self$optimizer)
print(self$transformator)
print(self$model)
print(self$transformedData)
print(self$featureModel)
print(self$normalizer)
print(self$normalizedData)
print(self$missings)
print(self$missingsCharacterizer)
print(self$outliers)
print(self$imputer)
print(self$imputedData)
print(self$cleanedData)
print(self$validator)
print(self$corrValidator)
print(self$exporter)
print(self$reporter)
invisible(self)
}, #end pguIMP::pgu.delegate$print()
####################
# import functions #
####################
#' @description
#' Updates the import gui
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_gui = function(input, output, session)
{
shiny::updateNumericInput(session = session,
inputId = "ni.importSheetIndex",
value = private$.fileName$sheetIndex)
shiny::updateSelectInput(session = session,
inputId = "si.importSeparator",
selected = private$.fileName$separator)
shiny::updateNumericInput(session = session,
inputId = "ni.importSkip",
value = private$.fileName$skipRows)
shiny::updateSelectInput(session = session,
inputId = "si.importHeader",
selected = private$.fileName$header)
shiny::updateSelectInput(session = session,
inputId = "si.importNaChar",
selected = private$.fileName$naChar)
}, #end pguIMP::pgu.delegate$updateImportGui()
#' @description
#' Manages the data upload to the R server.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
query_data = function(input, output, session)
{
if (length(input$fi.import$datapath) > 0){
private$.fileName$reset(uploadFileName = input$fi.import$datapath,
fileName = input$fi.import$name,
sheetIndex = input$ni.importSheetIndex,
separator = input$si.importSeparator,
skipRows = input$ni.importSkip,
columnName = input$si.importColnames,
naChar = input$si.importNaChar)
private$.fileName$fit()
private$.status$update(processName = "dataUploaded", value = TRUE)
} #if
else{
private$.status$update(processName = "dataUploaded", value = FALSE)
shiny::showNotification(paste("File name field must not left empty."),type = "error", duration = 10)
} #else
if((private$.fileName$suffix != "csv") &&
(private$.fileName$suffix != "txt") &&
(private$.fileName$suffix != "xls") &&
(private$.fileName$suffix != "xlsx")){
private$.status$update(processName = "dataUploaded", value = FALSE)
shiny::showNotification(paste("Invalid file type. Please select a valid file of type '.arff', '.csv', '.txt', '.xls' or '.xlsx'."),type = "error", duration = 10)
} #if
}, #end pguIMP::pgi.delegate$queryData()
#' @description
#' Imports uploaded data from the R server into the instance variable rawData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
import_data = function(input, output, session)
{
if (private$.status$query(processName = "dataUploaded")){
tryCatch({
# private$.rawData$setRawData <- private$.importer$importData(self$fileName)
private$.rawData$setRawData <- pguIMP::importDataSet(obj = self$fileName)
private$.rawData$fit()
private$.status$update(processName = "dataImported", value = TRUE)
},
error = function(e) {
private$.status$update(processName = "dataImported", value = FALSE)
shiny::showNotification(paste(e),type = "error", duration = 10)
}#error
)#tryCatch
}#if
else{
private$.status$update(processName = "dataUploaded", value = FALSE)
shiny::showNotification(paste("No file uploaded to import. Please upload a valid file first."),type = "error", duration = 10)
}#else
}, #end pguIMP::pgu.delegate$importData()
#' @description
#' Updates the tbl.importDataTypes table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_data_Types_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.importDataTypes <- DT::renderDataTable({self$rawData$classInformation %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawData_type") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
}#output
)#output
}#if
else{
output$tbl.importDataTypes <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_import_data_Types_tbl
#' @description
#' Updates the tbl.importDataStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_data_statistics_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.importDataStatistics <- DT::renderDataTable({self$rawData$statistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawData_statistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
}#output
)#output
}#if
else{
output$tbl.importDataStatistics <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_import_data_statistics_tbl
#' @description
#' Updates the tbl.importMissingsStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_import_missings_statistics_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.importMissingsStatistics <- DT::renderDataTable({self$rawData$missingsStatistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawData_missings") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
}#output
)#output
}#if
else{
output$tbl.importMissingsStatistics <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_import_missings_statistics_tbl
####################
# filter functions #
####################
#' @description
#' Updates the tbl.filter table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_select_tbl = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
output$tbl.filterSelect <- DT::renderDataTable({self$rawData$rawData %>%
DT::datatable(
extensions = "Buttons",
rownames = FALSE,
filter = "top",
selection = list("multiple", target = "column"),
options = list(
stateSave = TRUE,
scrollX = TRUE,
scrollY = '45vh',
paging = FALSE,
autoWidth = TRUE,
columnDefs = list(list(width = '75px', targets = "_all")),
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("filtered_data") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#optins
)#DT::datatable
})#output
}#if
else{
output$tbl.filterSelect <- DT::renderDataTable(NULL)
}#else
}, #end pguIMP::pgu.delegate$update_filter_select_tbl
#' @description
#' Queries the filter parameters selected by the user in the gui
#' and stores them in the instance variable filterSet.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
# if (length(input$tbl.filter_rows_all) < 1) {
# private$.filterSet$resetRowIdx(data = self$rawData$rawData)
# }#if
# else {
# private$.filterSet$setRowIdx <- input$tbl.filter_rows_all
# }#else
# if (length(input$tbl.filter_columns_selected) < 1) {
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# colSelection <- input$tbl.filter_columns_selected - ncol(self$metadata$rawData) + 1
# colSelection <- colSelection[colSelection > 0] + 1
# if(length(colSelection) < 1){
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# colSelection <- colSelection %>%
# append(1) %>%
# unique() %>%
# sort
# private$.filterSet$setColIdx <- colSelection
# }#else
# }#else
private$.filterSet$reset(data_df = self$rawData$rawData)
private$.filterSet$setRowIdx <- input$tbl.filterSelect_rows_all
if (!is.null(input$tbl.filterSelect_columns_selected))
{
private$.filterSet$setColIdx <- input$tbl.filterSelect_columns_selected + 1
}
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
}#else
}, #end pguIMP::pgu.delegate$update_filter
#' @description
#' Queries the filter parameters selected by the user in the gui
#' inverts them
#' and stores them in the instance variable filterSet.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_inverse = function(input, output, session){
if(private$.status$query(processName = "dataImported")){
# if (length(input$tbl.filter_rows_all) < 1) {
# private$.filterSet$resetRowIdx(data = self$rawData$rawData)
# }#if
# else {
# private$.filterSet$setRowIdx <- input$tbl.filter_rows_all
# }#else
# if (length(input$tbl.filter_columns_selected) < 1) {
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# colSelection <- input$tbl.filter_columns_selected - ncol(self$metadata$rawData) + 1
# colSelection <- colSelection[colSelection > 0] + 1
# idx <- seq(1,ncol(self$rawData$rawData), 1)
# iverseColSelection <- idx[-c(colSelection)]
# if(length(iverseColSelection) < 1){
# private$.filterSet$resetColIdx(data = self$rawData$rawData)
# }#if
# else{
# iverseColSelection <- iverseColSelection %>%
# append(1) %>%
# unique() %>%
# sort()
# private$.filterSet$setColIdx <- iverseColSelection
# }#else
# }#else
private$.filterSet$reset(data_df = self$rawData$rawData)
private$.filterSet$setRowIdx <- input$tbl.filterSelect_rows_all
if (!is.null(input$tbl.filterSelect_columns_selected))
{
idx <- seq(1,ncol(self$rawData$rawData), 1)
iverseColSelection <- idx[-c(input$tbl.filterSelect_columns_selected + 1)]
private$.filterSet$setColIdx <- iverseColSelection
}
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
} #else
}, #function
#' @description
#' Generates a filter set that selects the whole data frame.
#' Stores them in the instance variable filterSet.
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
reset_filter = function(input, output, session){
if(private$.status$query(processName = "dataImported")){
private$.filterSet$reset(data_df = self$rawData$rawData)
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Filters the data corresponding to the user defined parameters stored in the instance variable filterSet.
#' Results are stored in the instance variables filteredData and filteredMetadata.
#' Updated the instance variable filterSet.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
filter_data = function(input, output, session)
{
if(private$.status$query(processName = "dataImported")){
private$.filteredData$setRawData <- self$rawData$rawData %>%
self$filterSet$predict()
private$.filteredData$fit()
private$.status$update(processName = "dataFiltered", value = TRUE)
}#if
else{
shiny::showNotification(paste("No file imported. Please import a valid file first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the tbl.filterStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_statistics_tbl = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.filterStatistics <- DT::renderDataTable({self$filteredData$statistics %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '25vh',
paging = FALSE,
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all")),
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("filter_statistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})#output
}#if
else{
output$tbl.filterStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.filterMissings table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_filter_missings_tbl = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.filterMissings <- DT::renderDataTable({self$filteredData$missingsStatistics %>%
format.data.frame(scientific = FALSE, digits = 2) %>%
DT::datatable(
options = list(
scrollX = TRUE,
scrollY = '25vh',
paging = FALSE,
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all")),
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("filter_missings") %>%
tools::file_path_sans_ext(),
text = "Download"
)) #buttons
)#options
)#DT::datatable
})#output
}#if
else{
output$tbl.filterMissings <- DT::renderDataTable(NULL)
}#else
}, #function
#####################
# explore functions #
#####################
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_gui = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
private$.explorer$reset(data_df = self$filteredData$rawData,
abs = "Sample Name",
ord = "Sample Name"
)
featureNames <- self$explorer$rawData %>%
colnames()
updateSelectInput(session, "si.exploreAbs", choices = featureNames)
updateSelectInput(session, "si.exploreAbs", selected = featureNames[1])
updateSelectInput(session, "si.exploreOrd", choices = featureNames)
updateSelectInput(session, "si.exploreOrd", selected = featureNames[1])
}#if
}, #function
#' @description
#' Transfers the information oabout the selected abscissa attribute to the explorer class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_abscissa = function(input, output, session){
if(private$.status$query(processName = "dataFiltered"))
{
private$.explorer$setAbscissa <- input$si.exploreAbs
private$.explorer$fit()
}
}, #function
#' @description
#' Transfers the information oabout the selected ordinate attribute to the explorer class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_ordinate = function(input, output, session){
if(private$.status$query(processName = "dataFiltered"))
{
private$.explorer$setOrdinate <- input$si.exploreOrd
private$.explorer$fit()
}
}, #function
#' @description
#' Updates the exploration abscissa vs. ordinate scatter plot
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_graphic = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$plt.exploreGraphic <- shiny::renderPlot(
self$explorer$scatterPlot(),
height = 400,
bg="transparent")
}#if
else{
output$plt.exploreGraphic <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the abscissa compound plot
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_abscissa_graphic = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$plt.exploreAbscissaGraphic <- shiny::renderPlot(
self$explorer$abscissaPlot(),
height = 400,
bg="transparent"
)
}#if
else{
output$plt.exploreAbscissaGraphic <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the ordinate compound plot
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_ordinate_graphic = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$plt.exploreOrdinateGraphic <- shiny::renderPlot(
self$explorer$ordinatePlot(),
height = 400,
bg="transparent"
)
}#if
else{
output$plt.exploreOrdinateGraphic <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numerical abscissa analysis table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_abscissa_table = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.exploreAbscissaStatistics <- DT::renderDataTable({
self$explorer$abscissaStatistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
# scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(sprintf("abs_%s_statistics", input$si.exploreAbs)) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.exploreAbscissaStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical ordinate analysis table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_exploration_ordinate_table = function(input, output, session){
if(private$.status$query(processName = "dataFiltered")){
output$tbl.exploreOrdinateStatistics <- DT::renderDataTable({
self$explorer$ordinateStatistics %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(sprintf("ord_%s_statistics", input$si.exploreOrd)) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.exploreOrdinateStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
########################
# LOQ Upload functions #
########################
#' @description
#' Initializes the LOQ object after filtering.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
reset_loq_values = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
self$filteredData$numericalAttributeNames %>%
private$.loq$reset()
self$update_loq_upload_gui(input, output, session)
private$.status$update(processName = "loqImported", value = TRUE)
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_upload_gui = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
output$tbl.loqUploadValues <- DT::renderDataTable({
self$loq$loq %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("LOQ_values") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.loqDefineValues <- DT::renderDataTable(NULL)
}#else
},
#' @description
#' Manages the loq data upload to the R server.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
query_loq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered")){
if (length(input$fi.LoqImport$datapath) > 0){
private$.loqFileName$reset(uploadFileName = input$fi.LoqImport$datapath,
fileName = input$fi.LoqImport$name,
sheetIndex = input$ni.LoqSheetIndex,
separator = input$si.LoqSeparator,
skipRows = input$ni.LoqSkip,
columnName = input$si.LoqColnames,
naChar = input$si.LoqNaChar)
private$.loqFileName$fit()
} #if
else{
shiny::showNotification(paste("File name field must not left empty."),type = "error", duration = 10)
} #else
if((private$.fileName$suffix != "csv") &&
(private$.fileName$suffix != "txt") &&
(private$.fileName$suffix != "xls") &&
(private$.fileName$suffix != "xlsx")){
shiny::showNotification(paste("Invalid file type. Please select a valid file of type '.csv', '.txt', '.xls' or '.xlsx'."),type = "error", duration = 10)
} #if
}
}, #end pguIMP::pgi.delegate$query_loq()
#' @description
#' Imports the loq data upload to the R server.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
import_loq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
private$.loq$reset(attribute_names = self$filteredData$numericalAttributeNames,
data_df = pguIMP::importDataSet(obj = self$loqFileName))
self$update_loq_upload_gui(input, output, session)
private$.status$update(processName = "loqImported", value = TRUE)
}
},
########################
# LOQ Define functions #
########################
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_gui = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
self$update_loq_define_feature(input, output, session)
self$update_loq_define_lloq(input, output, session)
self$update_loq_define_uloq(input, output, session)
self$update_loq_define_table(input, output, session)
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_feature = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
shiny::updateSelectInput(session,
inputId = "si.LoqDefineFeature",
choices = self$loq$loq$attribute,
selected = self$loq$loq$attribute[1])
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_lloq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
shiny::updateNumericInput(session,
inputId = "ni.LoqDefineLLOQ",
value = self$loq$attribute_lloq(attribute = input$si.LoqDefineFeature))
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_uloq = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
shiny::updateNumericInput(session,
inputId = "ni.LoqDefineULOQ",
value = self$loq$attribute_uloq(attribute = input$si.LoqDefineFeature))
}
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_table = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
output$tbl.loqDefineValues <- DT::renderDataTable({
self$loq$loq %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '75vh',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("LOQ_values") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.loqDefineValues <- DT::renderDataTable(NULL)
}#else
},
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_define_menu = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
self$update_loq_define_lloq(input, output, session)
self$update_loq_define_uloq(input, output, session)
}
},
#' @description
#' Updates loq class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
set_loq_define_values = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
private$.loq$set_attribute_lloq(attribute = input$si.LoqDefineFeature,
value = input$ni.LoqDefineLLOQ)
private$.loq$set_attribute_uloq(attribute = input$si.LoqDefineFeature,
value = input$ni.LoqDefineULOQ)
}
},
#' @description
#' Updates loq class.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
set_loq_define_values_globally = function(input, output, session)
{
if(private$.status$query(processName = "dataFiltered"))
{
for(attribute in self$loq$loq$attribute)
{
private$.loq$set_attribute_lloq(attribute = attribute,
value = input$ni.LoqDefineLLOQ)
private$.loq$set_attribute_uloq(attribute = attribute,
value = input$ni.LoqDefineULOQ)
}
}
},
########################
# LOQ Detect functions #
########################
#' #' @description
#' #' Imports uploaded data from the R server into the instance variable loqData.
#' #' Updates the instance class status.
#' #' @param input
#' #' Pointer to shiny input
#' #' @param output
#' #' Pointer to shiny output
#' #' @param session
#' #' Pointer to shiny session
#' importLoq = function(input, output, session){
#' if (private$.status$query(processName = "dataImported")){
#' tryCatch({
#' private$.loq$setLoq <- private$.importer$importLoq(self$fileName)
#' private$.status$update(processName = "loqImported", value = TRUE)
#' },
#' error = function(e) {
#' private$.status$update(processName = "loqImported", value = FALSE)
#' shiny::showNotification(paste(e),type = "error", duration = 10)
#' }#error
#' )#tryCatch
#' }#if
#' else{
#' private$.status$update(processName = "loqImported", value = FALSE)
#' shiny::showNotification(paste("No file uploaded to import. Please upload a valid file first."),type = "error", duration = 10)
#' }#else
#' }, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_gui = function(input, output, session){
if(private$.status$query(processName = "loqImported")){
self$update_loq_na_handling(input, output, session)
shiny::updateSelectInput(session,
"si.loqDetectFeature",
choices = self$filteredData$numericalAttributeNames,
selected = self$filteredData$numericAttributeNames[1])
}#if
else{
shiny::showNotification(paste("No filtered data set. Please filter data set first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the si.loqHandling shiny widget
#' corresponding to the respective user defined parameter.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_na_handling = function(input, output, session){
if(private$.status$query(processName = "loqImported"))
{
shiny::updateSelectInput(session,
"si.loqNaHandling",
choices = self$loq$naHandlingAlphabet,
selected = self$loq$naHandlingAgent)
}
}, #function
#' @description
#' Runs the outlier detection routine of the instance variable outliers.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
init_detect_loq = function(input, output, session){
if(private$.status$query(processName = "loqImported")){
private$.loq$setNaHandlingAgent <- self$loq$naHandlingAlphabet[1]
self$filteredData$numerical_data() %>%
private$.loq$fit()
private$.status$update(processName = "loqDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No filtered data set. Please filter data set first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Runs the outlier detection routine of the instance variable outliers.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
detect_loq = function(input, output, session){
if(private$.status$query(processName = "loqImported")){
private$.loq$setNaHandlingAgent <- input$si.loqNaHandling
self$filteredData$numerical_data() %>%
private$.loq$fit()
private$.status$update(processName = "loqDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No filtered data set. Please filter data set first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the numerical loq statistics analysis table
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_statistics_tbl = function(input, output, session){
if(private$.status$query(processName = "loqDetected")){
output$tbl.loqDetectStatistics <- DT::renderDataTable(
self$loq$loqStatistics %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("loqStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.loqDetectStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical loq table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_outlier_tbl = function(input, output, session){
if(private$.status$query(processName = "loqDetected"))
{
output$tbl.loqDetectOutlier <- DT::renderDataTable(
self$loq$outliers %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("loqOutliers") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqDetectOutlier <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "loqDetected")){
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$filteredData$rawData, by = "Sample Name")
# dfOutlier <- self$loq$outliers
# idx <- dfOutlier[["measurement"]][!duplicated(dfOutlier[["measurement"]])]
# t <- dfData %>%
# dplyr::slice(idx) %>%
# dplyr::mutate_if(is.numeric, round, 3) %>%
# DT::datatable(
# options = list(
# scrollX = TRUE,
# scrollY = '350px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("loqOutlier"),
# text = "Download"
# ))#buttons
# )#options
# )#DT::datatable
# for (featureName in self$filteredData$numericFeatureNames){
# featureOutlier <- dfOutlier %>%
# dplyr::filter(feature == featureName) %>%
# dplyr::mutate_if(is.numeric, round, 3)
# if (nrow(featureOutlier)>0){
# t <- DT::formatStyle(t,
# featureName,
# backgroundColor = styleEqual(dfData %>%
# dplyr::select(!!featureName) %>%
# dplyr::slice(featureOutlier[["measurement"]]) %>%
# unlist() %>%
# as.numeric() %>%
# round(digits = 3),
# featureOutlier[["color"]])
#
# )#t
# }#if
# }#for
# }#if
# output$tbl.loqDetectOutlier <- DT::renderDataTable(t)
}, #function
#' @description
#' Updates the loq statistics graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_statistics_graphic = function(input, output, session){
if(self$status$query(processName = "loqDetected")){
output$plt.loqDetectStatistics <- shiny::renderPlot(
self$loq$plot_loq_distribution(),
height = 400,
bg="transparent"
)
}#if
else{
output$plt.loqDetectStatistics <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the loq feature compound graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_attribute_graphic = function(input, output, session){
if(self$status$query(processName = "loqDetected")){
output$plt.loqDetectFeature <- shiny::renderPlot(
self$loq$attribute_plot(data_df = self$filteredData$rawData, attribute = input$si.loqDetectFeature),
height = 425,
bg="transparent"
)
}#if
else{
output$plt.loqDetectFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numerical loq feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_detect_attribute_tbl = function(input, output, session){
if(private$.status$query(processName = "loqDetected"))
{
suffix <- sprintf("%s_outliers", input$si.loqDetectFeature)
output$tbl.loqDetectFeature <- DT::renderDataTable(
self$loq$attribute_outliers(attribute = input$si.loqDetectFeature) %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqDetectFeature <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(private$.status$query(processName = "loqDetected")){
# feature <- input$si.loqDetectFeature
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$filteredData$rawData %>%
# dplyr::select(c("Sample Name", !!feature)),
# by = "Sample Name")
#
# dfOutlier <- self$loq$featureOutlier(feature = feature)
#
# t <- dfData %>%
# DT::datatable(options = list(scrollX = TRUE,
# scrollY = '300px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("rawData_type"),
# text = "Download"
# ))
# ))#DT::datatable
# if (nrow(dfOutlier) > 0){
# t <- DT::formatStyle(
# t,
# feature,
# backgroundColor = DT::styleEqual(dfData %>%
# dplyr::select(!!feature) %>%
# dplyr::slice(dfOutlier[["measurement"]]) %>%
# unlist() %>%
# round(digits = 3),
# dfOutlier[["color"]]))
# }#if
# }#if
# output$tbl.loqDetectFeature <- DT::renderDataTable(t)
}, #function
########################
# LOQ mutate functions #
########################
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_gui = function(input, output, session)
{
if(private$.status$query(processName = "loqDetected"))
{
self$update_lloq_substitute(input, output, session)
self$update_uloq_substitute(input, output, session)
shiny::updateSelectInput(session,
"si.loqMutateFeature",
choices = self$filteredData$numericalAttributeNames,
selected = self$filteredData$numericalAttributeNames[1])
}#if
else{
shiny::showNotification(paste("No loq outliers detected. Please screen for loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the si.lloqSubstitute shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_lloq_substitute = function(input, output, session)
{
shiny::updateSelectInput(session,
"si.lloqSubstitute",
choices = self$loq$lloqSubstituteAlphabet,
selected = self$loq$lloqSubstituteAgent)
}, #function
#' @description
#' Updates the si.uloqSubstitute shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_uloq_substitute = function(input, output, session)
{
shiny::updateSelectInput(session,
"si.uloqSubstitute",
choices = self$loq$uloqSubstituteAlphabet,
selected = self$loq$uloqSubstituteAgent)
}, #function
#' @description
#' Calls the mutation routine of the instance variable loq on the instance variable filteredData.
#' The reult is stored in the instance variable loqMutatedData
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
init_mutate_loq = function(input, output, session)
{
if(private$.status$query(processName = "loqDetected"))
{
private$.loq$setLloqSubstituteAgent <- self$loq$lloqSubstituteAlphabet[1]
private$.loq$setUloqSubstituteAgent <- self$loq$uloqSubstituteAlphabet[1]
private$.loqMutatedData$setRawData <- self$filteredData$numerical_data() %>%
self$loq$predict() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.loqMutatedData$fit()
private$.status$update(processName = "loqMutated", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq outliers detected. Please screen for loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Calls the mutation routine of the instance variable loq on the instance variable filteredData.
#' The reult is stored in the instance variable loqMutatedData
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
mutate_loq = function(input, output, session)
{
if(private$.status$query(processName = "loqDetected"))
{
private$.loq$setLloqSubstituteAgent <- input$si.lloqSubstitute
private$.loq$setUloqSubstituteAgent <- input$si.uloqSubstitute
private$.loqMutatedData$setRawData <- self$filteredData$numerical_data() %>%
self$loq$predict() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.loqMutatedData$fit()
private$.status$update(processName = "loqMutated", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq outliers detected. Please screen for loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the numerical loq mutate outliers table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_data_tbl = function(input, output, session){
if(private$.status$query(processName = "loqMutated"))
{
output$tbl.loqMutateData <- DT::renderDataTable(
self$loqMutatedData$rawData %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("loq_mutated_data") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqMutateData <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "loqMutated")){
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$loqMutatedData$rawData, by = "Sample Name")
# dfOutlier <- self$loq$outliers
# idx <- dfOutlier[["measurement"]][!duplicated(dfOutlier[["measurement"]])]
# t <- dfData %>%
# dplyr::mutate_if(is.numeric, round, 3) %>%
# DT::datatable(
# options = list(
# scrollX = TRUE,
# scrollY = '350px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("loqData"),
# text = "Download"
# ))#buttons
# )#options
# )#DT::datatable
# for (featureName in self$loqMutatedData$numericFeatureNames){
# featureOutlier <- dfOutlier %>%
# dplyr::filter(feature == featureName) %>%
# dplyr::mutate_if(is.numeric, round, 3)
# if (nrow(featureOutlier)>0){
# t <- DT::formatStyle(t,
# featureName,
# backgroundColor = styleEqual(dfData %>%
# dplyr::select(!!featureName) %>%
# dplyr::slice(featureOutlier[["measurement"]]) %>%
# unlist() %>%
# as.numeric() %>%
# round(digits = 3),
# featureOutlier[["color"]])
#
# )#t
# }#if
# }#for
# }#if
# output$tbl.loqMutateData <- DT::renderDataTable(t)
}, #function
#' @description
#' Updates the loq mutate statistics graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_statistics_graphic = function(input, output, session)
{
if(self$status$query(processName = "loqMutated"))
{
output$plt.loqMutateStatistics <- shiny::renderPlot(
self$loq$plot_loq_distribution(),
height = 400,
bg="transparent"
)#output
}#if
else{
output$plt.loqMutateStatistics <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the loq mutate feature graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_attribute_graphic = function(input, output, session)
{
if(self$status$query(processName = "loqMutated"))
{
output$plt.loqMutateFeature <- shiny::renderPlot(
self$loq$attribute_plot(data_df = self$loqMutatedData$rawData, attribute = input$si.loqMutateFeature),
height = 425,
bg="transparent"
)#output
}#if
else{
output$plt.loqMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numeric loq mutate feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
init_loq_mutate_attribute_tbl = function(input, output, session){
if(private$.status$query(processName = "loqMutated"))
{
suffix <- sprintf("%s_outliers_mutated", self$loq$loq$attribute[1])
data_df <- self$loq$attribute_outliers(attribute = self$loq$loq$attribute[1])
idx <- dplyr::pull(data_df, instance)
data_df <- data_df %>%
dplyr::mutate(mutated = dplyr::pull(self$loqMutatedData$rawData, self$loq$loq$attribute[1])[idx])
output$tbl.loqMutateFeature <- DT::renderDataTable(
data_df %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqMutateFeature <- DT::renderDataTable(NULL)
}
},
#' @description
#' Updates the numeric loq mutate feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_loq_mutate_attribute_tbl = function(input, output, session){
if(private$.status$query(processName = "loqMutated"))
{
suffix <- sprintf("%s_outliers_mutated", input$si.loqMutateFeature)
data_df <- self$loq$attribute_outliers(attribute = input$si.loqMutateFeature)
idx <- dplyr::pull(data_df, instance)
data_df <- data_df %>%
dplyr::mutate(mutated = dplyr::pull(self$loqMutatedData$rawData, input$si.loqMutateFeature)[idx])
output$tbl.loqMutateFeature <- DT::renderDataTable(
data_df %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.loqMutateFeature <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "loqMutated")){
# feature <- input$si.loqMutateFeature
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$loqMutatedData$rawData %>%
# dplyr::select(c("Sample Name", !!feature)),
# by = "Sample Name")
#
# dfOutlier <- self$loq$featureOutlier(feature = feature)
#
# t <- dfData %>%
# DT::datatable(options = list(scrollX = TRUE,
# scrollY = '300px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$bluntFileName("rawData_type"),
# text = "Download"
# ))#buttons
# ))#DT::datatable
# if (nrow(dfOutlier) > 0){
# t <- DT::formatStyle(
# t,
# feature,
# backgroundColor = DT::styleEqual(dfData %>%
# dplyr::select(!!feature) %>%
# dplyr::slice(dfOutlier[["measurement"]]) %>%
# unlist() %>%
# round(digits = 3),
# dfOutlier[["color"]])
# )#t
# }#if
# }#if
# output$tbl.loqMutateFeature <- DT::renderDataTable(t)
}, #function
#############################################
# Trafo Detect functions (Parameter Wizard) #
#############################################
#' @description
#' Calls the optimize routine of the instance variable optimizer on the instance variable loqMutatedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
optimizeTrafoParameter = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
trafoAlphabet <- c("none")
if(input$cb.wizardLog){
trafoAlphabet <- c(trafoAlphabet, "log2", "logNorm", "log10")
}#if
if(input$cb.wizardRoot){
trafoAlphabet <- c(trafoAlphabet, "squareRoot", "cubeRoot")
}#if
if(input$cb.wizardArcsine){
trafoAlphabet <- c(trafoAlphabet, "arcsine")
}#if
if(input$cb.wizardInverse){
trafoAlphabet <- c(trafoAlphabet, "inverse")
}#if
if(input$cb.wizardTLOP){
trafoAlphabet <- c(trafoAlphabet, "tukeyLOP")
}#if
if(input$cb.wizardBoxCox){
trafoAlphabet <- c(trafoAlphabet, "boxCox")
}#if
# data in obj ändern
private$.optimizer$resetOptimizer(data = self$loqMutatedData$numerical_ata())
private$.optimizer$setTrafoAlphabet <- trafoAlphabet
private$.optimizer$setMirror <- input$cb.wizardMirror
progress <- shiny::Progress$new(session, min = 1, max = length(self$optimizer$trafoAlphabet)*2)
progress$set(message = "optimizing transformation parameters ...", value = 1)
private$.optimizer$optimize(data = self$loqMutatedData$numerical_data(), progress = progress)
on.exit(progress$close())
private$.status$update(processName = "modelOptimized", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq analysis perfomred. Please mutate loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the detected trafo types table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateDetectedTrafoTypes = function(input, output, session){
if(self$status$query(processName = "modelOptimized")){
output$tbl.trafoDetectTypes <- DT::renderDataTable(
self$optimizer$optTypes %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$bluntFileName("optTrafoTypes"),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoDetectTypes <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the detected trafo parameters table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateDetectedTrafoParameter = function(input, output, session){
if(self$status$query(processName = "modelOptimized")){
output$tbl.trafoDetectParameters <- DT::renderDataTable(
self$optimizer$optParameter %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$bluntFileName("optTrafoParameter"),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoDetectParameters <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoDetectGui = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
trafoAlphabet <- self$optimizer$trafoAlphabet
shiny::updateCheckboxInput(session,
"cb.wizardLog",
value = any(grepl(pattern = "log",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardRoot",
value = any(grepl(pattern = "Root",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardArcsine",
value = any(grepl(pattern = "arcsine",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardInverse",
value = any(grepl(pattern = "inverse",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardTLOP",
value = any(grepl(pattern = "tukeyLOP",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardBoxCox",
value = any(grepl(pattern = "boxCox",
x = trafoAlphabet)))
shiny::updateCheckboxInput(session,
"cb.wizardMirror",
value = self$optimizer$mirror)
}#if
else{
shiny::showNotification(paste("No loq analysis perfomred. Please mutate loq outliers first."),type = "error", duration = 10)
}#else
}, #function
##############################################
# trafo mutate functions (model by gaussian) #
##############################################
#' @description
#' Updates the si.trafoMutateFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeature = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateSelectInput(session,
inputId = "si.trafoMutateFeature",
choices = self$loqMutatedData$numericalAttributeNames,
selected = self$loqMutatedData$numericalAttributeNames[1])
}#if
}, #function
#' @description
#' Updates the si.trafoMutateType shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateType = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateSelectInput(session,
inputId = "si.trafoMutateType",
choices = self$transformator$trafoAlphabet,
selected = self$transformator$trafoType(feature = input$si.trafoMutateFeature)
)
}#if
}, #function
#' @description
#' Updates the ni.trafoMutateLambda shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateLambda = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateNumericInput(session,
inputId = "ni.trafoMutateLambdaLOP",
value = self$transformator$lambdaLOP(feature = input$si.trafoMutateFeature))
}#if
}, #function
#' @description
#' Updates the cb.trafoMutateMirror shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateMirror = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
shiny::updateCheckboxInput(session,
inputId = "cb.trafoMutateMirror",
value = self$transformator$mirrorLogic(feature = input$si.trafoMutateFeature)
)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetTrafoMutateGui = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
self$loqMutatedData$numerical_data() %>%
private$.transformator$resetTrafoParameter()
self$updateTrafoMutateFeature(input, output, session)
self$updateTrafoMutateType(input, output, session)
self$updateTrafoMutateLambda(input, output, session)
self$updateTrafoMutateMirror(input, output, session)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGui = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
self$updateTrafoMutateType(input, output, session)
self$updateTrafoMutateLambda(input, output, session)
self$updateTrafoMutateMirror(input, output, session)
}#if
}, #function
#' @description
#' Estimates the optimal transformation parameters.
#' Updates the GUI
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoMutateFit = function(input, output, session){
if(self$status$query(processName = "loqMutated")){
progress <- shiny::Progress$new(session, min = 1, max = length(self$loqMutatedData$numericalAttributeNames))
progress$set(message = "Optimizing model parameter", value = 1)
self$loqMutatedData$numerical_data() %>%
private$.transformator$resetTrafoParameter()
on.exit(progress$close())
self$loqMutatedData$numerical_data() %>%
private$.transformator$fit()
private$.status$update(processName = "modelOptimized", value = TRUE)
}#if
else{
shiny::showNotification(paste("No loq analysis perfomred. Please mutate loq outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Calls the transformation routine of the instance variable transformator on the instance variable loqMutatedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoMutateGlobal = function(input, output, session){
if(self$status$query(processName = "modelOptimized")){
progress <- shiny::Progress$new(session, min = 1, max = length(self$loqMutatedData$numericalAttributeNames))
progress$set(message = "Perfomring transformation", value = 1)
on.exit(progress$close())
# self$loqMutatedData$numericData() %>%
# private$.transformator$resetTrafoParameter()
for (feature in self$loqMutatedData$numericalAttributeNames){
private$.transformator$setTrafoType(feature = feature,
type = input$si.trafoMutateType)
private$.transformator$setMirrorLogic(feature = feature,
logic = input$cb.trafoMutateMirror)
}#for
# self$loqMutatedData$numericData() %>%
# private$.transformator$estimateTrafoParameter()
self$loqMutatedData$numerical_data() %>%
private$.transformator$mutateData() %>%
private$.model$resetModel(progress)
private$.transformedData$setRawData <- self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
# self$model$scaleData() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.transformedData$fit()
private$.status$update(processName = "modelDefined", value = TRUE)
}#if
else{
shiny::showNotification(paste("Model parameters not optimized. Please optimize first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Calls the transformation routine of the instance variable transformator on a user defined attribute
#' of the instance variable loqMutatedData.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoMutateFeature = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
private$.transformator$setTrafoType(feature = input$si.trafoMutateFeature,
type = input$si.trafoMutateType)
private$.transformator$setLambdaLOP(feature = input$si.trafoMutateFeature,
lambda = input$ni.trafoMutateLambdaLOP)
private$.transformator$setMirrorLogic(feature = input$si.trafoMutateFeature,
logic = input$cb.trafoMutateMirror)
# self$loqMutatedData$numericData() %>%
# private$.transformator$estimateTrafoParameter()
self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
dplyr::select(input$si.trafoMutateFeature) %>%
private$.featureModel$resetNormDist()
tryCatch({
private$.featureModel$fit()
},
error = function(e) {
errorString <- sprintf("Error: could not optimize model parameters for %s transformation of feature %s. Trafo type is reset to 'none'",
input$si.trafoMutateType,
input$si.trafoMutateFeature)
shiny::showNotification(paste(errorString),type = "error", duration = 10)
shiny::updateSelectInput(session,
inputId = "si.trafoMutateType",
choices = self$transformator$trafoAlphabet,
selected = "none")
shiny::updateCheckboxInput(session,
inputId = "cb.trafoMutateMirror",
value = FALSE)
private$.transformator$setTrafoType(feature = input$si.trafoMutateFeature,
type = input$si.trafoMutateType)
private$.transformator$setMirrorLogic(feature = input$si.trafoMutateFeature,
logic = input$cb.trafoMutateMirror)
# self$loqMutatedData$numericData() %>%
# private$.transformator$estimateTrafoParameter()
private$.featureModel$resetNormDist(data = self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
dplyr::select(input$si.trafoMutateFeature)
)
private$.featureModel$fit()
})#tryCatch
private$.model$setNormDist(data = self$featureModel, feature = input$si.trafoMutateFeature)
private$.transformedData$setRawData <- self$loqMutatedData$numerical_data() %>%
self$transformator$mutateData() %>%
# self$model$scaleData() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.transformedData$fit()
}#if
else{
shiny::showNotification(paste("No global model defined. Please defina a global transformation model first."),type = "error", duration = 10)
}#else
}, #functions
#' @description
#' Updates the trafo mutate feature graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeatureGraphic = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$plt.trafoMutateFeature <- shiny::renderPlot(
self$model$plotModel(feature = input$si.trafoMutateFeature),
bg="transparent"
)#output
}#if
else{
output$plt.trafoMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the trafo mutate feature patameter table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeatureParameterTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateFeatureParameter <- DT::renderDataTable(
self$model$fitResultFeature(feature = input$si.trafoMutateFeature) %>%
pguIMP::transposeTibble() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("featureTrafoParameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateFeatureParameter <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the trafo mutate feature quality table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateFeatureQualityTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateFeatureQuality <- DT::renderDataTable(
self$model$testResultFeature(feature = input$si.trafoMutateFeature) %>%
pguIMP::transposeTibble() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("featureTrafoQuality") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatble
)#output
}#if
else{
output$tbl.trafoMutateFeatureQuality <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the trafo mutate global parameter table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalParameterTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalParameter <- DT::renderDataTable(
self$transformator$trafoParameter %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalTrafoParameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalParameter <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalModel table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalModelTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalModel <- DT::renderDataTable(
self$model$modelParameterData() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalModelParameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#ouput
}#if
else{
output$tbl.trafoMutateGlobalModel <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalQuality table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalQualityTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalQuality <- DT::renderDataTable(
self$model$modelQualityData() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalModelQuality") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalQuality <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalTests table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalTestsTbl = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalTests <- DT::renderDataTable(
self$model$testResultData() %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("globalModelQuality") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalTests <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.trafoMutateGlobalData table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoMutateGlobalDataTbl = function(input, output, session){
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
if(self$status$query(processName = "modelDefined")){
output$tbl.trafoMutateGlobalData <- DT::renderDataTable(
self$transformedData$rawData %>%
dplyr::mutate_if(is.numeric, round, 3) %>%
DT::datatable(
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("transformedData") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.trafoMutateGlobalData <- DT::renderDataTable(NULL)
}#else
}, #function
#############################
# trafo normalize functions #
#############################
#' @description
#' Updates the si.trafoNormFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormFeature = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
shiny::updateSelectInput(session,
"si.trafoNormFeature",
choices = self$transformedData$numericalAttributeNames,
selected = self$transformedData$numericalAttributeNames[1])
}#if
}, #function
#' @description
#' Updates the si.trafoNormMethod shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormMethod = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
shiny::updateSelectInput(session,
"si.trafoNormMethod",
choices = self$normalizer$normAgentAlphabet,
selected = self$normalizer$normAgent
)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormGui = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
self$updateTrafoNormMethod(input, output, session)
self$updateTrafoNormFeature(input, output, session)
}#if
}, #function
#' @description
#' Calls the scale routine of the instance variable normalizer on the instance variable transformedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
trafoNormMutate = function(input, output, session){
if(self$status$query(processName = "modelDefined")){
private$.normalizer$setNormAgent <- input$si.trafoNormMethod
self$transformedData$numerical_data() %>%
private$.normalizer$detectNormParameter()
private$.normalizedData$setRawData <- self$transformedData$numerical_data() %>%
self$normalizer$scale_data() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.normalizedData$fit()
private$.status$update(processName = "normalized", value = TRUE)
}#if
else{
shiny::showNotification(paste("No transformation model selected. Please transfrom data first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the impute norm feature compound graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormFeatureGraphic = function(input, output, session){
if(self$status$query(processName = "normalized")){
output$plt.trafoNormFeature <- shiny::renderPlot(
self$normalizer$featurePlot(data_df = self$normalizedData$rawData, feature = input$si.trafoNormFeature),
height = 425,
bg="transparent"
)#output
}#if
else{
output$plt.trafoNormFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetTrafoNormGui = function(input, output, session){
if(self$status$query(processName = "normalized")){
self$updateTrafoNormMethod(input, output, session)
}#if
}, #function
#' @description
#' Updates the numerical impute norm analysis table for a user defined feature.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormFeatureStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormFeatureStatistics <- DT::renderDataTable({
self$normalizedData$statistics %>%
dplyr::filter(Value == input$si.trafoNormFeature) %>%
dplyr::select_if(is.numeric) %>%
tidyr::pivot_longer(cols = dplyr::everything()) %>%
dplyr::rename(statistics = "name") %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormFeatureStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical impute norm analysis table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormStatistics <- DT::renderDataTable({
self$normalizedData$statistics %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("normalized_statistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the impute norm parameter table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormParameterTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormParameter <- DT::renderDataTable({
self$normalizer$normParameter %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("normalization_parameter") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormParameter <- DT::renderDataTable(NULL)
}#else
},#function
#' @description
#' Updates the impute norm scaled data table.
#' corresponding to the respective user defined attributes.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateTrafoNormDataTbl = function(input, output, session){
if(private$.status$query(processName = "normalized")){
output$tbl.trafoNormData <- DT::renderDataTable({
self$normalizedData$rawData %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
autoWidth = TRUE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("normalized_data") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
})
}#if
else{
output$tbl.trafoNormData <- DT::renderDataTable(NULL)
}#else
},#function
#############################
# impute missings functions #
#############################
#' @description
#' Calls the missing detection routine of the instance variable imputer
#' on the instance variable normalizedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
imputeMissingsAnalyze = function(input, output, session){
if(self$status$query(processName = "normalized")){
progress <- shiny::Progress$new(session, min = 1, max = length(self$normalizedData$numericalAttributeNames))
progress$set(message = "Characterizing missings", value = 1)
on.exit(progress$close())
self$normalizedData$numerical_data() %>%
private$.missings$resetImputationParameter()
self$normalizedData$numerical_data() %>%
private$.missingsCharacterizer$analyze(progress)
private$.status$update(processName = "naDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No normalized data available. Please normalize data first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the plt.imputeMissingsSummary graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsGraphic = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$plt.imputeMissingsSummary <- shiny::renderPlot(
self$missings$imputationSiteHeatMap(),
bg="transparent"
)
}#if
else{
output$plt.imputeMissingsSummary <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the tbl.imputeMissingsStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "naDetected")){
output$tbl.imputeMissingsStatistics <- DT::renderDataTable(
self$missings$imputationParameter %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("missingsStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMissingsDistribution table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsDistributionTbl = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$tbl.imputeMissingsDistribution <- DT::renderDataTable(
self$normalizedData$numerical_data() %>%
self$missings$imputationSiteDistribution() %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("missingsDistribution") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsDistribution <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the plt.imputeMissingsPairs graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingCharacteristicsGraphic = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$plt.imputeMissingsPairs <- shiny::renderPlot(
self$normalizedData$numerical_data() %>%
self$missingsCharacterizer$plot_pair_dist(),
bg="transparent"
)
}#if
else{
output$plt.imputeMissingsPairs<- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #fnunction
#' @description
#' Updates the tbl.imputeMissingsCharacteristics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsCharacteristicsTbl = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$tbl.imputeMissingsCharacteristics <- DT::renderDataTable(
self$missingsCharacterizer$missingsCharacteristics_df %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("missingsMissings") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsCharacteristics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeDetectDetail table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMissingsDetailTbl = function(input, output, session){
if(self$status$query(processName = "naDetected")){
output$tbl.imputeMissingsDetail <- DT::renderDataTable(
self$normalizedData$rawData %>%
dplyr::filter_all(dplyr::any_vars(is.na(.))) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationSiteDetectionDetail") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMissingsDetail <- DT::renderDataTable(NULL)
}#else
}, #function
#'
#' #' @description
#' #' Updates the tbl.imputeDetectData table.
#' #' @param input
#' #' Pointer to shiny input
#' #' @param output
#' #' Pointer to shiny output
#' #' @param session
#' #' Pointer to shiny session
#' updateImputeMissingsDataTbl = function(input, output, session){
#' if(self$status$query(processName = "naDetected")){
#' output$tbl.imputeMissingsData <- DT::renderDataTable(
#' self$filteredMetadata$rawData %>%
#' dplyr::right_join(self$normalizedData$rawData, by = "Sample Name") %>%
#' format.data.frame(scientific = TRUE, digits = 4) %>%
#' DT::datatable(
#' extensions = "Buttons",
#' options = list(
#' scrollX = TRUE,
#' scrollY = '350px',
#' paging = FALSE,
#' dom = "Blfrtip",
#' buttons = list(list(
#' extend = 'csv',
#' filename = self$fileName$predict("imputationSiteDetectionData") %>%
#' tools::file_path_sans_ext(),
#' text = "Download"
#' ))#buttons
#' )#options
#' )#DT::datatable
#' )#output
#' }#if
#' else{
#' output$tbl.imputeMissingsData <- DT::renderDataTable(NULL)
#' }#else
#' }, #function
#############################
# impute outliers functions #
#############################
#' @description
#' Updates the si.imputeOutliersMethod shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersMethod = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateSelectInput(session,
"si.imputeOutliersMethod",
choices = self$outliers$outliersAgentAlphabet,
selected = self$outliers$outliersAgent
)
}#if
}, #function
#' @description
#' Updates the si.imputeOutliersFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersFeature = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateSelectInput(session,
"si.imputeOutliersFeature",
choices = self$normalizedData$numericalAttributeNames,
selected = self$normalizedData$numericalAttributeNames[1])
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersAlpha shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersAlpha = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersAlpha",
value = self$outliers$alpha)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersEpsilon shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersEpsilon = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersEpsilon",
value = self$outliers$epsilon)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersMinSamples shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersMinSamples = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersMinSamples",
value = self$outliers$minSamples)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersGamma shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersGamma = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersGamma",
value = self$outliers$gamma)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersNu shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersNu = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersNu",
value = self$outliers$nu)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersCutoff shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersCutoff = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersCutoff",
value = self$outliers$cutoff)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersK shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersK = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersK",
value = self$outliers$k)
}#if
}, #function
#' @description
#' Updates the ni.imputeOutliersSeed shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersSeed = function(input, output, session){
if(self$status$query(processName = "naDetected")){
shiny::updateNumericInput(session,
"ni.imputeOutliersSeed",
value = self$outliers$seed)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersGui = function(input, output, session){
if(self$status$query(processName = "naDetected")){
self$updateImputeOutliersMethod(input, output, session)
self$updateImputeOutliersFeature(input, output, session)
self$updateImputeOutliersAlpha(input, output, session)
self$updateImputeOutliersEpsilon(input, output, session)
self$updateImputeOutliersMinSamples(input, output, session)
self$updateImputeOutliersGamma(input, output, session)
self$updateImputeOutliersNu(input, output, session)
self$updateImputeOutliersCutoff(input, output, session)
self$updateImputeOutliersK(input, output, session)
self$updateImputeOutliersSeed(input, output, session)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetImputeOutliersGui = function(input, output, session){
if(self$status$query(processName = "naDetected")){
self$updateImputeOutliersMethod(input, output, session)
self$updateImputeOutliersAlpha(input, output, session)
self$updateImputeOutliersEpsilon(input, output, session)
self$updateImputeOutliersMinSamples(input, output, session)
self$updateImputeOutliersGamma(input, output, session)
self$updateImputeOutliersNu(input, output, session)
self$updateImputeOutliersCutoff(input, output, session)
self$updateImputeOutliersK(input, output, session)
self$updateImputeOutliersSeed(input, output, session)
}#if
},#function
#' @description
#' Calls the detectOutliers routine of the instance variable outliers
#' on the instance variable normalizedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
imputeOutliersDetect = function(input, output, session){
if(self$status$query(processName = "naDetected")){
private$.outliers$setOutliersAgent <- input$si.imputeOutliersMethod
private$.outliers$setAlpha <- input$ni.imputeOutliersAlpha
private$.outliers$setEpsilon <- input$ni.imputeOutliersEpsilon
private$.outliers$setMinSamples <- input$ni.imputeOutliersMinSamples
private$.outliers$setGamma <- input$ni.imputeOutliersGamma
private$.outliers$setNu <- input$ni.imputeOutliersNu
private$.outliers$setCutoff <- input$ni.imputeOutliersCutoff
private$.outliers$setK <- input$ni.imputeOutliersK
private$.outliers$setSeed <- input$ni.imputeOutliersSeed
self$resetImputeOutliersGui(input, output, session)
progress <- shiny::Progress$new(session, min = 1, max = length(self$loqMutatedData$numericalAttributeNames))
progress$set(message = "Searching for anomalies", value = 1)
on.exit(progress$close())
self$normalizedData$numerical_data() %>%
private$.outliers$detectOutliers(progress)
private$.status$update(processName = "outliersDetected", value = TRUE)
}#if
else{
shiny::showNotification(paste("No missings detected. Please detect missings first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the plt.outliersImputeSummary graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersGraphic = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
output$plt.outliersImputeSummary <- shiny::renderPlot(
self$outliers$plotOutliersDistribution(),
bg="transparent"
)
}#if
else{
output$plt.outliersImputeSummary <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the plt.outliersImputeFeature graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersFeatureGraphic = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
output$plt.outliersImputeFeature <- shiny::renderPlot(
self$outliers$featurePlot(data_df = self$normalizedData$numerical_data(),
feature = input$si.imputeOutliersFeature),
height = 475,
bg="transparent"
)
}#if
else{
output$plt.outliersImputeFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the numeric outlier feature table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersFeatureTbl = function(input, output, session)
{
if(self$status$query(processName = "outliersDetected"))
{
suffix <- sprintf("%s_outliers", input$si.imputeOutliersFeature)
output$tbl.outliersImputeFeature <- DT::renderDataTable(
self$outliers$outliers %>%
dplyr::filter(feature == !!input$si.imputeOutliersFeature) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict(suffix) %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.outliersImputeFeature <- DT::renderDataTable(NULL)
}
# options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
# t <- NULL
# if(self$status$query(processName = "outliersDetected")){
# feature <- input$si.imputeOutliersFeature
# dfData <- self$filteredMetadata$rawData %>%
# dplyr::right_join(self$normalizedData$rawData %>%
# dplyr::select(c("Sample Name", !!feature)),
# by = "Sample Name")
#
# dfOutlier <- self$outliers$featureOutlier(feature = feature)
#
# t <- dfData %>%
# dplyr::mutate_if(is.numeric, round, 3) %>%
# DT::datatable(
# options = list(
# scrollX = TRUE,
# scrollY = '350px',
# paging = FALSE,
# dom = "Blfrtip",
# buttons = list(list(
# extend = 'csv',
# filename = self$fileName$predict("OutliersData") %>%
# tools::file_path_sans_ext(),
# text = "Download"
# ))#buttons
# )#options
# )#DT::datatable
#
# featureOutlier <- dfOutlier %>%
# dplyr::mutate_if(is.numeric, round, 3)
# if (nrow(featureOutlier)>0){
# t <- DT::formatStyle(t,
# feature,
# backgroundColor = styleEqual(dfData %>%
# dplyr::select(!!feature) %>%
# dplyr::slice(featureOutlier[["measurement"]]) %>%
# unlist() %>%
# as.numeric() %>%
# round(digits = 3),
# featureOutlier[["color"]])
#
# )#t
# }#if
# }#if
# output$tbl.outliersImputeFeature <- DT::renderDataTable(t)
}, #function
#' @description
#' Updates the numerical loq statistics analysis table
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "outliersDetected")){
output$tbl.outliersImputeStatistics <- DT::renderDataTable(
self$outliers$outliersStatistics %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("outliersStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.outliersImputeStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the numerical outlier table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeOutliersDetailTbl = function(input, output, session){
if(self$status$query(processName = "outliersDetected"))
{
output$tbl.outliersImputeDetail <- DT::renderDataTable(
self$outliers$outliers %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("outliers") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}else{
output$tbl.outliersImputeDetail <- DT::renderDataTable(NULL)
}
}, #function
###########################
# impute mutate functions #
###########################
#' @description
#' Updates the si.imputeMutateFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateFeature = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateSelectInput(session,
"si.imputeMutateFeature",
choices = self$normalizedData$numericalAttributeNames,
selected = self$normalizedData$numericalAttributeNames[1]
)
}#if
}, #function
#' @description
#' Updates the si.imputeMutateMethod shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateMethod = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateSelectInput(session,
"si.imputeMutateMethod",
choices = self$imputer$imputationAgentAlphabet,
selected = self$imputer$imputationAgent
)
}#if
}, #function
#' @description
#' Updates the ni.imputeMutateNumberOfNeighbors shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateNNeighbors = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateNumberOfNeighbors",
value = self$imputer$nNeighbors)
}#if
},
#' @description
#' Updates the ni.imputeMutatePredFrac shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutatePredFrac = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutatePredFrac",
value = self$imputer$pred_frac)
}#if
},
#' @description
#' Updates the ni.imputeMutateOutfluxThr shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateOutfluxThr = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateOutfluxThr",
value = self$imputer$outflux_thr)
}#if
},
#' @description
#' Updates the ni.imputeMutateSeed shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateSeed = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateSeed",
value = self$imputer$seed)
}#if
}, #function
#' @description
#' Updates the ni.imputeMutateIterations shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateIterations = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
shiny::updateNumericInput(session,
"ni.imputeMutateIterations",
value = self$imputer$iterations)
}#if
}, #function
#' @description
#' Updates the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateGui = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
self$resetImputeMutateGui(input, output, session)
self$updateImputeMutateFeature(input, output, session)
}#if
}, #function
#' @description
#' Resets the gui.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
resetImputeMutateGui = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
self$updateImputeMutateMethod(input, output, session)
self$updateImputeMutateNNeighbors(input, output, session)
self$updateImputeMutatePredFrac(input, output, session)
self$updateImputeMutateOutfluxThr(input, output, session)
self$updateImputeMutateSeed(input, output, session)
self$updateImputeMutateIterations(input, output, session)
}#if
}, #function
#' @description
#' Calls the mutate imputation site routine of the instance variable imputer on the instance variable transformedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
imputeMutateMutate = function(input, output, session){
if(self$status$query(processName = "outliersDetected")){
private$.imputer$setImputationAgent <- input$si.imputeMutateMethod
private$.imputer$setNNeighbors <-input$ni.imputeMutateNumberOfNeighbors
private$.imputer$setPred_frac <- input$ni.imputeMutatePredFrac
private$.imputer$setOutflux_thr <- input$ni.imputeMutateOutfluxThr
private$.imputer$setSeed <- input$ni.imputeMutateSeed
private$.imputer$setIterations <- input$ni.imputeMutateIterations
private$.imputer$gatherImputationSites(missings_df = private$.missings$imputationSites,
outliers_df = private$.outliers$outliers)
private$.imputer$analyzeImputationSites(data_df = self$normalizedData$numerical_data())
private$.imputer$detectPredictors(data_df = self$normalizedData$numerical_data())
progress <- shiny::Progress$new(session, min = 0, max = 1)
progress$set(message = "Mutate imputation sites", value = 0)
on.exit(progress$close())
private$.imputedData$setRawData <- self$normalizedData$numerical_data() %>%
self$imputer$handleImputationSites(progress) %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.imputedData$fit()
if(self$imputer$success){
private$.cleanedData$setRawData <- self$imputedData$numerical_data() %>%
self$normalizer$rescale_data() %>%
self$transformator$reverseMutateData() %>%
tibble::add_column(self$filteredData$categorical_data()) %>%
dplyr::select(dplyr::all_of(self$filteredData$attributeNames))
private$.cleanedData$fit()
private$.status$update(processName = "imputed", value = TRUE)
}
else{
private$.status$update(processName = "imputed", value = FALSE)
shiny::showNotification(paste("Solution of imputation mutate procedure was not satisfactoy. Pleas refine your variables or method and try again. See the Help Webpage of pguIMP for help with the variabes."),type = "error", duration = 20)
}
# progress <- shiny::Progress$new(session, min = 1, max = length(self$normalizedData$numericFeatureNames))
# progress$set(message = "Impute Data", value = 0)
# on.exit(progress$close())
#
# name <- as.name("Sample Name")
# private$.imputedData$setRawData <- self$normalizedData$numericData() %>%
# self$imputer$handleImputationSites(progress = progress) %>%
# # self$model$rescaleData() %>%
# # self$transformator$reverseMutateData() %>%
# tibble::add_column(!! name := self$normalizedData$rawData %>%
# dplyr::select(!!name) %>%
# unlist() %>%
# as.character()) %>%
# dplyr::select(c(!!name, self$normalizedData$numericFeatureNames))
# private$.status$update(processName = "imputed", value = TRUE)
}#if
else{
shiny::showNotification(paste("No outliers detected. Please detect outliers first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the plt.imputeMutateFlux graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeFluxGraphic = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$plt.imputeMutateFlux <- shiny::renderPlot(
self$imputer$fluxPlot(),
bg="transparent"
)
}#if
else{
output$plt.imputeMutateFlux <- shiny::renderPlot(NULL,bg="transparent")
}#else
}, #function
#' @description
#' Updates the plt.imputeMutateSummary graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateGraphic = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$plt.imputeMutateSummary <- shiny::renderPlot(
self$imputer$imputationSiteHeatMap(),
bg="transparent"
)
}#if
else{
output$plt.imputeMutateSummary <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateStatistics table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateStatisticsTbl = function(input, output, session){
if(private$.status$query(processName = "imputed")){
output$tbl.imputeMutateStatistics <- DT::renderDataTable(
self$imputer$imputationStatistics %>%
format.data.frame(scientific = FALSE, digits = 3) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationSiteStatistics") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateStatistics <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateDistribution table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateDistributionTbl = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$tbl.imputeMutateDistribution <- DT::renderDataTable(
self$imputer$imputationSiteDistribution %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationSiteDistribution") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateDistribution <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the plt.imputeMutateFeatureDetail graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateFeatureDetailGraphic = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$plt.imputeMutateFeatureDetail <- shiny::renderPlot(
self$imputer$featurePlot(data = self$imputedData$numerical_data(),
feature = input$si.imputeMutateFeature),
height = 475,
bg = "transparent"
)#output
}#if
else{
output$plt.imputeMutateFeatureDetail <- shiny::renderPlot(NULL, bg = "transparent")
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateFeatureDetail table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateFeatureDetailTbl = function(input, output, session){
if (self$status$query(processName = "imputed")){
output$tbl.imputeMutateFeatureDetail <- DT::renderDataTable(
self$imputedData$rawData %>%
dplyr::select(dplyr::all_of(c(self$imputedData$categoricalAttributeNames, input$si.imputeMutateFeature))) %>%
dplyr::slice(self$imputer$imputationSiteIdxByFeature(feature = input$si.imputeMutateFeature)) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationFeatureDetails") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateFeatureDetail <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateDetail table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateDetailTbl = function(input, output, session){
if(self$status$query(processName = "imputed")){
dfImputationSites <- self$imputer$imputationSites
idx <- dfImputationSites[["idx"]][!duplicated(dfImputationSites[["idx"]])]
output$tbl.imputeMutateDetail <- DT::renderDataTable(
self$imputedData$rawData %>%
dplyr::slice(idx) %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationDetail") %>%
tools::file_path_sans_ext(),
text = "Download"
))#buttons
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateDetail <- DT::renderDataTable(NULL)
}#else
}, #function
#' @description
#' Updates the tbl.imputeMutateData table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateImputeMutateDataTbl = function(input, output, session){
if(self$status$query(processName = "imputed")){
output$tbl.imputeMutateData <- DT::renderDataTable(
self$imputedData$rawData %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("imputationData") %>%
tools::file_path_sans_ext(),
text = "Download"
))#button
)#options
)#DT::datatable
)#output
}#if
else{
output$tbl.imputeMutateData <- DT::renderDataTable(NULL)
}#else
}, #function
############################
# outlier detect functions #
############################
########################
# validation functions #
########################
#' @description
#' Calls the validate routine of the instance variable validator on the instance variables rawData and clenaedData.
#' Updates the instance class status.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
validate = function(input, output, session){
if(self$status$query(processName = "imputed")){
progress <- shiny::Progress$new(session, min = 0, max = 1.0)
progress$set(message = "Validate imputation", value = 0)
on.exit(progress$close())
private$.validator$validate(org = self$filteredData$numerical_data(),
imp = self$cleanedData$numerical_data() %>%
dplyr::select_if(function(x){!all(is.na(x))}),
progress = progress)
private$.status$update(processName = "validated", value = TRUE)
private$.corrValidator$fit(org_df = self$normalizedData$numerical_data(),
imp_df = self$imputedData$numerical_data())
}#if
else{
shiny::showNotification(paste("No imputation performed. Please perform imputation first."),type = "error", duration = 10)
}#else
}, #function
#' @description
#' Updates the si.analysisValidationFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateAnalysisValidationGui = function(input, output, session){
if(self$status$query(processName = "validated")){
shiny::updateSelectInput(session,
"si.analysisValidationFeature",
choices = self$validator$features,
selected = self$validator$features[1]
)
}#if
else{
shiny::updateSelectInput(session,
"si.analysisValidationFeature",
choices = list(),
selected = 1
)
} #else
}, #function
#' @description
#' Updates the plt.analysisValidationFeature shiny widget.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateAnalysisValidationGraphic = function(input, output, session){
if(self$status$query(processName = "validated")){
output$plt.analysisValidationFeature <- shiny::renderPlot(
self$validator$featurePlot(org_df = self$filteredData$numerical_data(),
imp_df = self$cleanedData$numerical_data(),
lloq = self$loq$attribute_lloq(attribute = input$si.analysisValidationFeature),
uloq = self$loq$attribute_uloq(attribute = input$si.analysisValidationFeature),
impIdx_df = self$imputer$imputationSites,
feature = input$si.analysisValidationFeature),
bg="transparent"
)#output
}#if
else{
output$plt.plt.analysisValidationFeature <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updtates the tbl.analysisValidationTest table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateAnalysisValidationTestTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.analysisValidationTest <- DT::renderDataTable(
self$validator$testStatistics_df %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("validationTests") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.analysisValidationTest <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.centralMomentsOrg table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCentralMomentsOrgTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.centralMomentsOrg <- DT::renderDataTable(
self$validator$centralMoments_org %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("rawCentralMoments") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.centralMomentsOrg <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.centralMomentsImp table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCentralMomentsImpTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.centralMomentsImp <- DT::renderDataTable(
self$validator$centralMoments_imp %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("impCentralMoments") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.centralMomentsImp <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.centralMomentsDelta table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCentralMomentsDeltaTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.centralMomentsDelta <- DT::renderDataTable(
self$validator$centralMoments_delta %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
autowidth = FALSE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("deltaCentralMoments") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.centralMomentsDelta <- DT::renderDataTable(NULL)
} #else
}, #function
####################################
# correlation validation functions #
####################################
#' @description
#' Updtates the plt.correlationValidationScatter graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationScatterGraphic = function(input, output, session){
if(self$status$query(processName = "validated")){
output$plt.correlationValidationScatter <- shiny::renderPlot(
self$corrValidator$correlationScatterPlot(),
bg="transparent"
)#output
}#if
else{
output$plt.correlationValidationScatter <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updtates the plt.correlationValidationBoxPlot graphic.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationBoxPlotGraphic = function(input, output, session){
if(self$status$query(processName = "validated")){
output$plt.correlationValidationBoxPlot <- shiny::renderPlot(
self$corrValidator$correlationCompoundPlot(),
height = 475,
bg="transparent"
)#output
}#if
else{
output$plt.correlationValidationBoxPlot <- shiny::renderPlot(NULL, bg="transparent")
}#else
}, #function
#' @description
#' Updtates the tbl.correlationValidationDeviation table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationDeviationTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.correlationValidationDeviation <- DT::renderDataTable(
self$corrValidator$summary_df %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
scrollY = '350px',
autowidth = FALSE,
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("correlationValidationDeviation") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.correlationValidationData <- DT::renderDataTable(NULL)
} #else
}, #function
#' @description
#' Updtates the tbl.correlationValidationData table.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
updateCorrelationValidationDataTbl = function(input, output, session){
if(self$status$query(processName = "validated")){
output$tbl.correlationValidationData <- DT::renderDataTable(
self$corrValidator$corr_df %>%
format.data.frame(scientific = TRUE, digits = 4) %>%
DT::datatable(
extensions = "Buttons",
options = list(
# scrollX = TRUE,
scrollY = '350px',
paging = FALSE,
dom = "Blfrtip",
buttons = list(list(
extend = 'csv',
filename = self$fileName$predict("correlationValidationData") %>%
tools::file_path_sans_ext(),
text = "Download"
)), #buttons
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all"))
)#options
)#DT::datatable
)#output
} #if
else{
output$tbl.correlationValidationData <- DT::renderDataTable(NULL)
} #else
}, #function
#########################
# data export functions #
#########################
#' @description
#' Creates and returns an export filename.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
#' @return
#' export filename
#' (character)
exportFileName = function(input, output, session){
private$.fileName$predict() %>%
return()
}, #function
#' @description
#' Exports the pguIMP analysis results
#' @param file
#' export filename
#' (character)
#' @param input
#' Pointer to shiny input
exportData = function(input, file){
if(self$status$query(processName = "validated")){
private$.exporter$setFileName <- file
gui_parameter <- tibble::tibble(
loq_na_handling = c(self$loq$naHandlingAgent),
lloq_substitute = c(self$loq$lloqSubstituteAgent),
uloq_substitute = c(self$loq$uloqSubstituteAgent),
normalization_type = c(self$normalizer$normAgent),
anomalies_method = c(self$outliers$outliersAgent),
alpha = c(self$outliers$alpha),
epsilon= c(self$outliers$epsilon),
minSamples = c(self$outliers$minSamples),
gamma = c(self$outliers$gamma),
nu = c(self$outliers$nu),
cutoff = c(self$outliers$cutoff),
k = c(self$outliers$k),
anomalies_seed = c(self$outliers$seed),
imputation_method = c(self$imputer$imputationAgent),
number_of_neighbors = c(self$imputer$nNeighbors),
fraction_of_predictors = c(self$imputer$pred_frac),
outflux_threshold = c(self$imputer$outflux_thr),
imputation_seed = c(self$imputer$seed),
iterations = c(self$imputer$iterations)
)
gui_parameter <- tibble::as_tibble(cbind(parameter = names(gui_parameter), t(gui_parameter)))
colnames(gui_parameter) <- c("parameter", "value")
list(raw_data = self$cleanedData$rawData,
loq = self$loq$loq,
giu_parameter = gui_parameter,
filter_parameter = tibble::tibble(features = c(self$metadata$featureNames, self$rawData$featureNames[2:length(self$rawData$featureNames)]),
filter_parameter = as.vector(input$tbl.filter_search_columns)),
filtered = self$filteredData$rawData,
loq_statistics = self$loq$loqStatistics,
loq_mutated = self$loqMutatedData$rawData,
trafo_parameter = self$transformator$trafoParameter,
transfromed = self$transformedData$rawData,
model_parameter = self$model$modelParameterData(),
model_quality = self$model$modelQualityData(),
model_statistics = self$model$testResultData(),
normalization_parameter = self$normalizer$normParameter,
normalized = self$normalizedData$rawData,
missings = self$missings$imputationSites,
missings_statistics = self$missings$imputationParameter,
missings_characteristics = self$missingsCharacterizer$missingsCharacteristics_df,
outliers = self$outliers$outliers %>%
dplyr::select(-c("color")),
outliers_statistics = self$outliers$outliersStatistics,
predictors = self$imputer$pred_mat %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
tibble::as_tibble(),
imputation_sites = self$imputer$one_hot_df,
imputation_statistics = self$imputer$imputationStatistics,
imputed = self$imputedData$rawData,
validation = self$validator$testStatistics_df
) %>%
self$exporter$writeDataToExcel()
}#if
}, #function
#############################
# analysis report functions #
#############################
#' @description
#' Creates and returns a report filename.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
#' @return
#' export filename
#' (character)
reportFileName = function(input, output, session){
private$.fileName$setSuffix <- "pdf"
private$.fileName$updateTimeString()
# private$.fileName$mergeFileName()
private$.fileName$exportFileName() %>%
return()
}, #function
#' @description
#' Exports a report on the pguIMP analysis
#' in pdf format.
#' @param input
#' Pointer to shiny input
#' @param file
#' export filename
#' (character)
writeReport = function(input, file){
print("state")
print(str(input$tbl.filter_search_columns))
if(self$status$query(processName = "validated")){
private$.reporter$setFilename <- file
analysis_parameter <- tibble::tibble(
fileName = c(self$fileName$fileName),
numberOfInstances = c(nrow(self$rawData$rawData)),
numberOfFeatures = c(ncol(self$rawData$rawData)),
numberOfMetaFeatures = c(ncol(self$metadata$rawData)),
numberOfNumericFeatures = c(length(self$rawData$numericalAttributeNames)),
numberOfNonNumericFeatures = c(length(self$rawData$categoricalAttributeNames)),
totalNumberOfMissings = c(self$rawData$rawData %>%
dplyr::select(dplyr::everything()) %>% # replace to your needs
dplyr::summarise_all(~ sum(is.na(.x))) %>%
sum()),
loq_na_handling = c(self$loq$naHandlingAgent),
lloq_substitute = c(self$loq$lloqSubstituteAgent),
uloq_substitute = c(self$loq$uloqSubstituteAgent),
normalization_type = c(self$normalizer$normAgent),
anomalies_method = c(self$outliers$outliersAgent),
alpha = c(self$outliers$alpha),
epsilon= c(self$outliers$epsilon),
minSamples = c(self$outliers$minSamples),
gamma = c(self$outliers$gamma),
nu = c(self$outliers$nu),
cutoff = c(self$outliers$cutoff),
k = c(self$outliers$k),
anomalies_seed = c(self$outliers$seed),
imputation_method = c(self$imputer$imputationAgent),
number_of_neighbors = c(self$imputer$nNeighbors),
fraction_of_predictors = c(self$imputer$pred_frac),
outflux_threshold = c(self$imputer$outflux_thr),
imputation_seed = c(self$imputer$seed),
iterations = c(self$imputer$iterations)
)
list(filter_parameter = tibble::tibble(features = c(self$metadata$featureNames, self$rawData$featureNames[2:length(self$rawData$featureNames)]),
filter_parameter = as.vector(input$tbl.filter_search_columns)) %>%
dplyr::filter(filter_parameter != ""),
selected_features = self$filteredData$numericalAttributeNames,
loq_statistics = self$loq$loqStatistics,
trafo_parameter = self$transformator$trafoParameter,
model_parameter = self$model$modelParameterData(),
model_quality = self$model$modelQualityData(),
model_statistics = self$model$testResultData(),
normalization_parameter = self$normalizer$normParameter,
missings_statistics = self$missings$imputationParameter,
missings_distribution = self$missings$imputationSiteDistribution(self$filteredData$numerical_data()),
outliers_statistics = self$outliers$outliersStatistics,
imputation_statistics = self$imputer$imputationStatistics,
imputation_distribution = self$imputer$imputationSiteDistribution,
validation_test = self$validator$testStatistics_df,
validation_corrSum = self$corrValidator$summary(),
validation_correlation = self$corrValidator$corr_df,
analysis_parameter = analysis_parameter
) %>%
self$reporter$write_report()
}#if
},#function
###################################
# update graphical user interface #
###################################
#' @description
#' Updates the gui if analysis parameters change.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
hide_outdated_results = function(input, output, session){
if(!private$.status$query(processName = "dataImported")){
output$tbl.importDataTypes <- DT::renderDataTable(NULL)
output$tbl.importDataStatistics <- DT::renderDataTable(NULL)
output$tbl.importMissingsStatistics <- DT::renderDataTable(NULL)
output$tbl.filterSelect <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "dataFiltered")){
output$plt.exploreGraphic <- shiny::renderPlot(NULL, bg="transparent")
output$plt.exploreAbscissaGraphic <- shiny::renderPlot(NULL, bg="transparent")
output$plt.exploreOrdinateGraphic <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.exploreAbscissaStatistics <- DT::renderDataTable(NULL)
output$tbl.exploreOrdinateStatistics <- DT::renderDataTable(NULL)
output$tbl.filterStatistics <- DT::renderDataTable(NULL)
output$tbl.filterMissings <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "loqImported")){
output$tbl.loqDefineValues <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "loqDetected")){
output$tbl.loqDetectStatistics <- DT::renderDataTable(NULL)
output$tbl.loqDetectOutlier <- DT::renderDataTable(NULL)
output$tbl.loqDetectData <- DT::renderDataTable(NULL)
output$plt.loqDetectStatistics <- shiny::renderPlot(NULL, bg="transparent")
output$plt.loqDetectFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.loqDetectFeature <- DT::renderDataTable(NULL)
}#if
if(!private$.status$query(processName = "loqMutated")){
output$tbl.loqMutateStatistics <- DT::renderDataTable(NULL)
output$tbl.loqMutateOutlier <- DT::renderDataTable(NULL)
output$tbl.loqMutateData <- DT::renderDataTable(NULL)
output$plt.loqMutateStatistics <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.loqMutateFeature <- DT::renderDataTable(NULL)
output$plt.loqMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
}#if
if(!self$status$query(processName = "modelOptimized")){
output$tbl.trafoDetectTypes <- DT::renderDataTable(NULL)
output$tbl.trafoDetectParameters <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "modelDefined")){
output$plt.trafoMutateFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.trafoMutateFeatureParameter <- DT::renderDataTable(NULL)
output$tbl.trafoMutateFeatureQuality <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalParameter <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalModel <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalQuality <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalTests <- DT::renderDataTable(NULL)
output$tbl.trafoMutateGlobalData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "normalized")){
output$plt.trafoNormFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.trafoNormFeatureStatistics<- DT::renderDataTable(NULL)
output$tbl.trafoNormParameter<- DT::renderDataTable(NULL)
output$tbl.trafoNormStatistics<- DT::renderDataTable(NULL)
output$tbl.trafoNormData<- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "naDetected")){
output$plt.imputeMissingsSummary <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.imputeMissingsStatistics <- DT::renderDataTable(NULL)
output$tbl.imputeMissingsDistribution <- DT::renderDataTable(NULL)
output$plt.imputeMissingsPairs <- shiny::renderPlot(NULL, bg ="transparent")
output$tbl.imputeMissingsCharacteristics <- DT::renderDataTable(NULL)
output$tbl.imputeMissingsDetail <- DT::renderDataTable(NULL)
output$tbl.imputeMissingsData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "outliersDetected")){
output$plt.outliersImputeSummary <- shiny::renderPlot(NULL, bg="transparent")
output$plt.outliersImputeFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.outliersImputeFeature <- DT::renderDataTable(NULL)
output$tbl.outliersImputeStatistics <- DT::renderDataTable(NULL)
output$tbl.outliersImputeDetail <- DT::renderDataTable(NULL)
output$tbl.outliersImputeData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "imputed")){
output$plt.imputeMutateSummary <- shiny::renderPlot(NULL, bg="transparent")
output$plt.imputeMutateFeatureDetail <- shiny::renderPlot(NULL, bg="transparent")
output$plt.imputeMutateFlux <- shiny::renderPlot(NULL, bg = "transparent")
output$tbl.imputeMutateFeatureDetail<- DT::renderDataTable(NULL)
output$tbl.imputeMutateStatistics <- DT::renderDataTable(NULL)
output$tbl.imputeMutateDistribution <- DT::renderDataTable(NULL)
output$tbl.imputeMutateDetail <- DT::renderDataTable(NULL)
output$tbl.imputeMutateData <- DT::renderDataTable(NULL)
}#if
if(!self$status$query(processName = "validated")){
output$plt.analysisValidationFeature <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.analysisValidationTest <- DT::renderDataTable(NULL)
output$tbl.centralMomentsOrg <- DT::renderDataTable(NULL)
output$tbl.centralMomentsImp <- DT::renderDataTable(NULL)
output$tbl.centralMomentsDelta <- DT::renderDataTable(NULL)
output$plt.correlationValidationScatter <- shiny::renderPlot(NULL, bg = "transparent")
output$plt.correlationValidationBoxPlot <- shiny::renderPlot(NULL, bg="transparent")
output$tbl.correlationValidationDeviation <- DT::renderDataTable(NULL)
output$tbl.correlationValidationData <- DT::renderDataTable(NULL)
} #if
# if(!self$status$query(processName = "correlated")){
# output$tbl.correlationMatrixR <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixPPearson <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixTau <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixPKendall <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixRho <- DT::renderDataTable(NULL)
# output$tbl.correlationMatrixPSpearman <- DT::renderDataTable(NULL)
# }#if
# if(!self$status$query(processName = "regression")){
# output$plt.regressionFeature <- shiny::renderPlot(NULL, bg="transparent")
# output$tbl.regressionFeature <- DT::renderDataTable(NULL)
# output$tbl.regressionIntercept <- DT::renderDataTable(NULL)
# output$tbl.regressionPIntercept <- DT::renderDataTable(NULL)
# output$tbl.regressionSlope <- DT::renderDataTable(NULL)
# output$tbl.regressionPSlope <- DT::renderDataTable(NULL)
# }#if
}, #function
#########################
# update help interface #
#########################
#' @description
#' Updates the gui if analysis parameters change.
#' @param input
#' Pointer to shiny input
#' @param output
#' Pointer to shiny output
#' @param session
#' Pointer to shiny session
update_help_html = function(input, output, session)
{
if(input$switch.help)
{
wd <- getwd()
help_folder <- file.path(wd, "www/_site")
output$html.uploadHelp <- shiny::renderUI({
help_folder %>%
file.path("upload.html") %>%
shiny::includeHTML()
})
output$html.filterHelp <- shiny::renderUI({
help_folder %>%
file.path("filter.html") %>%
shiny::includeHTML()
})
output$html.exploreHelp <- shiny::renderUI({
help_folder %>%
file.path("explore.html") %>%
shiny::includeHTML()
})
output$html.uploadLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("upload_loq.html") %>%
shiny::includeHTML()
})
output$html.defineLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("define_loq.html") %>%
shiny::includeHTML()
})
output$html.detectLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("detect_loq.html") %>%
shiny::includeHTML()
})
output$html.substituteLOQHelp <- shiny::renderUI({
help_folder %>%
file.path("substitute_loq.html") %>%
shiny::includeHTML()
})
output$html.transformHelp <- shiny::renderUI({
help_folder %>%
file.path("transform.html") %>%
shiny::includeHTML()
})
output$html.normalizeHelp <- shiny::renderUI({
help_folder %>%
file.path("normalize.html") %>%
shiny::includeHTML()
})
output$html.missingsHelp <- shiny::renderUI({
help_folder %>%
file.path("missings.html") %>%
shiny::includeHTML()
})
output$html.anomaliesHelp <- shiny::renderUI({
help_folder %>%
file.path("anomalies.html") %>%
shiny::includeHTML()
})
output$html.substituteImpHelp <- shiny::renderUI({
help_folder %>%
file.path("substitute_imp.html") %>%
shiny::includeHTML()
})
output$html.validateHelp <- shiny::renderUI({
help_folder %>%
file.path("validate.html") %>%
shiny::includeHTML()
})
}
else
{
output$html.uploadHelp <- shiny::renderUI({NULL})
output$html.filterHelp <- shiny::renderUI({NULL})
output$html.exploreHelp <- shiny::renderUI({NULL})
output$html.uploadLOQHelp <- shiny::renderUI({NULL})
output$html.defineLOQHelp <- shiny::renderUI({NULL})
output$html.detectLOQHelp <- shiny::renderUI({NULL})
output$html.substituteLOQHelp <- shiny::renderUI({NULL})
output$html.transformHelp <- shiny::renderUI({NULL})
output$html.normalizeHelp <- shiny::renderUI({NULL})
output$html.missingsHelp <- shiny::renderUI({NULL})
output$html.anomaliesHelp <- shiny::renderUI({NULL})
output$html.substituteImpHelp <- shiny::renderUI({NULL})
output$html.validateHelp <- shiny::renderUI({NULL})
}
}#function
)#public
)#class
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