server.R

server <- function(input, output) {
  df <- reactiveVal("AN")
  output$id1 <- downloadHandler(
    filename = function() {
      paste0("My data ", Sys.Date(), ".xlsm")},
    content = function(My_data){
      file.copy("database.xlsm",My_data)}
  )
  observeEvent(input$id2,{
    print(input$id2)
    datae <- read_excel(input$id2$datapath,range = "B12:C212")
    data2 <- read_excel(input$id2$datapath,range = "XFA12:XFB212")
    print(datae)
    print(data2)
    print(cbind(datae,data2))
    df(cbind(datae,data2))
    print(names(df()))
    output$plot1 <- renderPlotly({
      plot1 <- df() %>%
        filter(!is.na(situation)) %>%
        group_by(situation) %>%
        summarise(count = n()) %>%
        ggplot(aes(x = situation, y = count, fill = situation)) +
        geom_bar(stat = "identity") +
        theme_classic() +
        theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5)) +
        ggtitle("Total count of transactions by status") +
        xlab("") +
        ylab("") +
        scale_fill_manual(values = c("#BFBFBF", "#417FA6"))
      # scale_fill_brewer(palette="Dark2")
      ggplotly(plot1,tooltip = c("y")
      )
    })
    output$plot2 <- renderPlotly({
      plot2 <- df() %>%
        filter(!is.na(situation)) %>%
        group_by(`type of process`,situation) %>%
        summarise(count = n()) %>%
        ggplot(aes(x = `type of process`, y = count, fill = situation)) +
        geom_bar(stat = "identity") +
        ggtitle("Count of each transaction type by status") +
        xlab("") +
        ylab("") +
        theme_classic() +
        theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5)) +
        scale_fill_manual(values = c("#BFBFBF", "#417FA6"))
      # scale_fill_brewer(palette="Dark2")
      ggplotly(plot2,tooltip = c("y","fill")
      )
    })
    output$plot3 <- renderPlotly({
      plot3 <- df() %>%
        filter(situation == "finished") %>%
        mutate(`last process` = as.Date(`last process`,format = "%y-%m-%d"),
               `first process` = as.Date(`first process`,format = "%y-%m-%d"),
               Average = `last process` - `first process`) %>%
        group_by(`type of process`) %>%
        summarise(Average = round(mean(Average))) %>%
        ggplot(aes(x = `type of process`, y = Average, fill = `type of process`)) +
        geom_bar(stat = "identity") +
        ggtitle("Average process time by transaction in days") +
        xlab("") +
        ylab("Days") +
        theme_classic() +
        theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5)) +
        scale_fill_manual(values = c("#A2A6F2","#BFBFBF", "#417FA6","#3F89A6","#0D0D0D"))
      ggplotly(plot3,tooltip = c("y")
      )
    })
    output$plot4 <- renderPlotly({
      plot4 <- df() %>%
        filter(situation == "finished") %>%
        mutate(`last process` = as.Date(`last process`,format = "%y-%m-%d")) %>%
        group_by(`last process`) %>%
        summarise(count = n()) %>%
        ggplot(aes(x = `last process`, y = count)) +
        geom_line(color = "#3F89A6") +
        geom_point() +
        ggtitle("Count of finished transactions by date") +
        xlab("End of transaction") +
        ylab("") +
        theme_classic() +
        theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1), plot.title = element_text(hjust = 0.5))
      ggplotly(plot4,tooltip = c("y")
      )
    })
    output$box1 <- renderValueBox({
      boxA = df() %>%
        filter(!is.na(`type of process`)) %>%
        summarise(count = n()
        )
      valueBox(boxA$count,"Number of transactions")
    })
    output$box2 <- renderValueBox({
      boxB = df() %>%
        filter(situation == "finished") %>%
        mutate(`last process` = as.Date(`last process`,format = "%y-%m-%d"),
               `first process` = as.Date(`first process`,format = "%y-%m-%d"),
               Average = `last process` - `first process`) %>%
        summarise(Average = round(mean(Average)))
      valueBox(boxB$Average,"Average process time in days")
    })
    output$box3 <- renderValueBox({
      boxC = df() %>%
        filter(situation == "finished") %>%
        group_by(`type of process`) %>%
        summarise(count = n())
      
      boxC <- boxC[boxC$count == max(boxC$count),"type of process"]
      
      valueBox(boxC$`type of process`,"Most finished transaction type")
    })
    
  })
}