#' 'RCDMShinyViewer'

#'

#' RCDMShinyViewer function visualizes result of database analysis

#'

#'

#' @param Radiology_Occurrence_Table is a result of R-CDM : Radiology_Occurrence_Table<-radiologyOccurrenceTable(DICOMList)

#' @param Radiology_Image_Table is a result of R-CDM : Radiology_Image_Table<-radiologyImageTable(DICOMList)

#' @import dplyr

#' @import shiny

#' @import ggplot2

#' @import DT

#' @importFrom magrittr "%>%"

#'

#'

#' @return result of database analysis

#' @examples

#' Radiology_Occurrence_Table<-radiologyOccurrenceTable(DICOMList)

#' Radiology_Image_Table<-radiologyImageTable(DICOMList)

#' RCDMShinyViewer(Radiology_Occurrence_Table, Radiology_Image_Table)

#' @export

RCDMShinyViewer<-function(Radiology_Occurrence_Table, Radiology_Image_Table){

    ui <- fluidPage(

        titlePanel('Radiology-CDM'),

        sidebarLayout(

            sidebarPanel(

                helpText('You can see reactive result of your database analysis by selecting Occurrence date, and modality'),

                sliderInput(inputId = "dateTime",

                            "Occurrence date",

                            min = min(as.Date(Radiology_Occurrence_Table$studyDateTime)),

                            max = max(as.Date(Radiology_Occurrence_Table$studyDateTime)+1),

                            value = c(as.Date('2000-01-01'), as.Date('2015-01-01'))),

                selectInput('mod', 'modality', choices = unique(Radiology_Occurrence_Table$modality), multiple = T),

                helpText('After you select the occurrence date, and modality, you have to select protocol concept ID, and phase additionally. After that extract list of DICOM images files corresponding to the selected conditions by clicking extract button.'),

                selectInput('Pro', 'select Radiology Protocol Concept ID', choices = unique(Radiology_Occurrence_Table$radiologyProtocolConceptId), multiple = T),

                selectInput('Pha', 'select Radiology Phase Concept ID', choices = unique(Radiology_Image_Table$radiologyPhaseConceptId), multiple = T),

                downloadButton('downloadData', 'Extract'),

                width=3),

            mainPanel(

                fluidRow(

                    h2('Database Analysis'),

                    column(6,

                           h4('Count of occurrences'),

                           plotOutput(outputId = "modalityCount")),

                    column(6,

                           h4('Count of images'),

                           plotOutput(outputId = "imageCount")),

                    h2('Find images you want!'),

                    column(12,

                           h4('OMOP concept IDs in your database'),

                           DTOutput(outputId = "protocolConceptId"),

                           h3(textOutput('txt')))

                ),

                width=9)

        )

    )

    server <- function(input, output){

        output$modalityCount <- renderPlot({

            modality1<-Radiology_Occurrence_Table %>% select(modality)

            barplot(table(factor(modality1$modality, levels=unique(Radiology_Occurrence_Table$modality))), border="#69b3a2", col="white", ylim=c(0, max(table(modality1))))

            par(new=TRUE)

            modality2<-Radiology_Occurrence_Table %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime)) %>% select(modality)

            barplot(table(factor(modality2$modality, levels=unique(Radiology_Occurrence_Table$modality))), col=rgb(0.2,0.4,0.6,0.6), border="#69b3a2", ylim=c(0, max(table(modality1))))

            par(new=TRUE)

            modality3<-Radiology_Occurrence_Table %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime) & modality %in% c(input$mod)) %>% select(modality)

            barplot(table(factor(modality3$modality, levels=unique(Radiology_Occurrence_Table$modality))), col=7, border="#69b3a2", ylim=c(0, max(table(modality1))))

            par(new=TRUE)

        })

        output$imageCount <- renderPlot({

            image1<-Radiology_Occurrence_Table %>% select(modality)

            image2<-Radiology_Occurrence_Table %>% select(imageTotalCount)

            barplot(table(factor(rep(image1$modality, image2$imageTotalCount), levels=unique(Radiology_Occurrence_Table$modality))), border="#69b3a2", col="white", ylim=c(0, max(table(rep(image1$modality, image2$imageTotalCount)))))

            par(new=TRUE)

            image3<-Radiology_Occurrence_Table %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime)) %>% select(modality)

            image4<-Radiology_Occurrence_Table %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime)) %>% select(imageTotalCount)

            barplot(table(factor(rep(image3$modality, image4$imageTotalCount), levels=unique(Radiology_Occurrence_Table$modality))), col=rgb(0.2,0.4,0.6,0.6), border="#69b3a2", ylim=c(0, max(table(rep(image1$modality, image2$imageTotalCount)))))

            par(new=TRUE)

            image5<-Radiology_Occurrence_Table %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime) & modality %in% c(input$mod)) %>% select(modality)

            image6<-Radiology_Occurrence_Table %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime) & modality %in% c(input$mod)) %>% select(imageTotalCount)

            barplot(table(factor(rep(image5$modality, image6$imageTotalCount), levels=unique(Radiology_Occurrence_Table$modality))), col=7, border="#69b3a2", ylim=c(0, max(table(rep(image1$modality, image2$imageTotalCount)))))

        })

        output$protocolConceptId <-renderDT({

            RadiologyPlaybook<-unique(LoincRsnaRadiologyPlaybook[,c(2,3)])

            RadiologyPlaybook<-data.frame(RadiologyPlaybook, row.names = NULL)

            if(is.null(input$mod)==T){

                dataframe1<-RadiologyPlaybook %>% filter(radiologyProtocolConceptId %in% Radiology_Occurrence_Table$radiologyProtocolConceptId)

                dataframe5<-Radiology_Occurrence_Table %>% filter(radiologyProtocolConceptId %in% RadiologyPlaybook$radiologyProtocolConceptId & as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime)) %>% select(radiologyProtocolConceptId, imageTotalCount, radiologyOccurrenceId)

                dataframe2<-split(dataframe5, as.character(dataframe5$radiologyProtocolConceptId))

                dataframe2<-lapply(dataframe2, function(x){

                    return(data.frame(radiologyProtocolConceptId=unique(x$radiologyProtocolConceptId), Count_of_occurrences=nrow(x), Count_of_images=sum(x$imageTotalCount)))}

                )

                dataframe2<-do.call(rbind, dataframe2)

                dataframe2<-data.frame(dataframe2, row.names = NULL)

                answerDF<-merge(dataframe1, dataframe2, by='radiologyProtocolConceptId')

                dataframe4<-Radiology_Occurrence_Table %>% filter(radiologyOccurrenceId %in% as.character(dataframe5$radiologyOccurrenceId)) %>% select(radiologyOccurrenceId, radiologyProtocolConceptId)

                dataframe3<-Radiology_Image_Table %>% filter(radiologyOccurrenceId %in% as.character(dataframe4$radiologyOccurrenceId)) %>% select(radiologyOccurrenceId, radiologyPhaseConceptId) %>% group_by(radiologyOccurrenceId, radiologyPhaseConceptId) %>% count()

                answer<-merge(dataframe4, dataframe3, by='radiologyOccurrenceId')

                answer<-split(answer, as.character(answer$radiologyProtocolConceptId))

                answer<-lapply(answer, function(x){

                    answer<-split(x, as.character(x$radiologyPhaseConceptId))

                    answer<-sapply(answer, function(y){

                        sum(y$n)

                    })

                    ImageCount<-c()

                    for (i in 1:length(answer)){

                        ImageCount<-c(ImageCount, sprintf('%s : %d', names(answer)[i], answer[i]))

                    }

                    ImageCount<-paste(ImageCount, collapse = '  /  ')

                    return(data.frame(radiologyProtocolConceptId=unique(x$radiologyProtocolConceptId), counts=ImageCount))

                })

                answer<-do.call(rbind, answer)

                answer<-merge(answerDF, answer, by='radiologyProtocolConceptId')

                my_vals = answer$radiologyProtocolConceptId

                my_colors = ifelse(my_vals %in% input$Pro,'orange','white')

                datatable(answer) %>% formatStyle(

                    'radiologyProtocolConceptId',

                    target = 'row',

                    backgroundColor = styleEqual(my_vals, my_colors))

            } else if (is.null(input$mod)==F) {

                mods<-paste(input$mod, collapse ='|')

                dataframe1<-RadiologyPlaybook %>% filter(radiologyProtocolConceptId %in% Radiology_Occurrence_Table$radiologyProtocolConceptId & grepl(mods, LongCommonName)==T)

                dataframe5<-Radiology_Occurrence_Table %>% filter(radiologyProtocolConceptId %in% RadiologyPlaybook$radiologyProtocolConceptId & as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime)) %>% select(radiologyProtocolConceptId, imageTotalCount, radiologyOccurrenceId)

                dataframe2<-split(dataframe5, as.character(dataframe5$radiologyProtocolConceptId))

                dataframe2<-lapply(dataframe2, function(x){

                    return(data.frame(radiologyProtocolConceptId=unique(x$radiologyProtocolConceptId), Count_of_occurrences=nrow(x), Count_of_images=sum(x$imageTotalCount)))}

                )

                dataframe2<-do.call(rbind, dataframe2)

                dataframe2<-data.frame(dataframe2, row.names = NULL)

                answerDF<-merge(dataframe1, dataframe2, by='radiologyProtocolConceptId')

                dataframe4<-Radiology_Occurrence_Table %>% filter(radiologyOccurrenceId %in% as.character(dataframe5$radiologyOccurrenceId)) %>% select(radiologyOccurrenceId, radiologyProtocolConceptId)

                dataframe3<-Radiology_Image_Table %>% filter(radiologyOccurrenceId %in% as.character(dataframe4$radiologyOccurrenceId)) %>% select(radiologyOccurrenceId, radiologyPhaseConceptId) %>% group_by(radiologyOccurrenceId, radiologyPhaseConceptId) %>% count()

                answer<-merge(dataframe4, dataframe3, by='radiologyOccurrenceId')

                answer<-split(answer, as.character(answer$radiologyProtocolConceptId))

                answer<-lapply(answer, function(x){

                    answer<-split(x, as.character(x$radiologyPhaseConceptId))

                    answer<-sapply(answer, function(y){

                        sum(y$n)

                    })

                    ImageCount<-c()

                    for (i in 1:length(answer)){

                        ImageCount<-c(ImageCount, sprintf('%s : %d', names(answer)[i], answer[i]))

                    }

                    ImageCount<-paste(ImageCount, collapse = '  /  ')

                    return(data.frame(radiologyProtocolConceptId=unique(x$radiologyProtocolConceptId), Count_of_Images_of_each_Phase=ImageCount))

                })

                answer<-do.call(rbind, answer)

                answer<-merge(answerDF, answer, by='radiologyProtocolConceptId')

                my_vals = answer$radiologyProtocolConceptId

                my_colors = ifelse(my_vals %in% input$Pro,'orange','white')

                datatable(answer) %>% formatStyle(

                    'radiologyProtocolConceptId',

                    target = 'row',

                    backgroundColor = styleEqual(my_vals, my_colors)

                )

            }

        })

        output$txt <-renderText({

            RadiologyPlaybook<-unique(LoincRsnaRadiologyPlaybook[,c(2,3)])

            RadiologyPlaybook<-data.frame(RadiologyPlaybook, row.names = NULL)

            answer1<-Radiology_Image_Table[, c('radiologyOccurrenceId', 'radiologyPhaseConceptId', 'dicomPath')]

            answer1<-answer1 %>% filter(radiologyOccurrenceId %in% Radiology_Occurrence_Table$radiologyOccurrenceId)

            answer2<-RadiologyPlaybook %>% filter(radiologyProtocolConceptId %in% Radiology_Occurrence_Table$radiologyProtocolConceptId)

            answer2<-merge(Radiology_Occurrence_Table[, c('radiologyOccurrenceId', 'studyDateTime', 'modality', 'radiologyProtocolConceptId')], answer2, by='radiologyProtocolConceptId')

            answer<-merge(answer1, answer2, by='radiologyOccurrenceId')

            answer<-answer %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime) & grepl(paste(input$mod, collapse ='|'), LongCommonName)==T & radiologyProtocolConceptId %in% input$Pro & radiologyPhaseConceptId %in% input$Pha)

            Counts<-nrow(answer)

            sprintf("You have selected %d images! Now extract list of DICOM files by clicking Export button.", Counts)

        })

        output$downloadData <- downloadHandler(

            filename = function() {

                paste('List_of_DICOM_files.csv')

            },

            content = function(file) {

                RadiologyPlaybook<-unique(LoincRsnaRadiologyPlaybook[,c(2,3)])

                RadiologyPlaybook<-data.frame(RadiologyPlaybook, row.names = NULL)

                answer1<-Radiology_Image_Table[, c('radiologyOccurrenceId', 'radiologyPhaseConceptId', 'dicomPath')]

                answer1<-answer1 %>% filter(radiologyOccurrenceId %in% Radiology_Occurrence_Table$radiologyOccurrenceId)

                answer2<-RadiologyPlaybook %>% filter(radiologyProtocolConceptId %in% Radiology_Occurrence_Table$radiologyProtocolConceptId)

                answer2<-merge(Radiology_Occurrence_Table[, c('radiologyOccurrenceId', 'studyDateTime', 'modality', 'radiologyProtocolConceptId')], answer2, by='radiologyProtocolConceptId')

                answer<-merge(answer1, answer2, by='radiologyOccurrenceId')

                answer<-answer %>% filter(as.Date(studyDateTime) >= min(input$dateTime) & as.Date(studyDateTime) <= max(input$dateTime) & grepl(paste(input$mod, collapse ='|'), LongCommonName)==T & radiologyProtocolConceptId %in% input$Pro & radiologyPhaseConceptId %in% input$Pha)

                write.csv(data.frame(answer$dicomPath, row.names = NULL), file)

            })

    }

    shinyApp(ui=ui,server=server)

}