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+++ b/RadETL/R/shiny.R
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+#' '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)
+}
+