Shiny_visual_app/appTest/app.R

In proto4426/ValUSunSSN: Val-U-Sun project functions

library(shiny)

#options(shiny.error = browser)

#load('/home/piss/PissoortRepo/ValUSunSSN/data/Filled/DataSSN.RData')
#x <- data.mat2[, "wnUC2"]


data("data.mat2.fin")
#data("SSN_filled_all")
#data("silsoSSN")

names_stations <- colnames(data.mat2.fin)
names_stations <- gsub("-", "", names_stations)


ui <- fluidPage(

  titlePanel("Val-U-Sun project! Compare 2 methods by the residuals"),

  # Sidebar with a slider input for the number of bins
  fluidRow(
    column(3,
           selectInput("stations",
                       "Which Station ? (1st row)",
                       names_stations),
           selectInput("stations2",
                       "Which second Station ? (2nd row)",
                       names_stations)
    ),
    width = "100px",
    #)
    # checkboxInput("na", "Show only filled values?"),
    column(3, offset = 1,
           selectInput("na", "Compare interpolsvd with ?",
                       c("Filled from Chris" = "chris",
                         "Silso" = "silso",
                         "Filled with splines" = "splines")),
           checkboxInput("points", "Show Points ?"),
           checkboxInput("percent", "Nomalize by Silso ?")
           # numericInput("size", "Size of points", value = 0.2, min = 0, max = 1)
    ),

    # Show a plot of the generated distribution
    #mainPanel(
    plotOutput("plot1", height = '650px', width = "900px")
    #)

  )
)

library(ValUSunSSN)
library(ggplot2)
library(plotly)
library(gridExtra)
library(grid)
library(RColorBrewer)
#options(shiny.error = browser)


data("data.mat")
data("data.mat2.fin")
data("SSN_filled_all")
data("silsoSSN")
data("ssn_splines")
## For comparisons with chris :
data("ssn_chris") # Data filled by Chris's method
data("z_final60") # Data filled by our method (from 1960)


# Handle the station's names for some datasets
colnames(data.mat2.fin) <- gsub("-", "",  colnames(data.mat2.fin))

ssn_chris <- ssn_chris[,3:61]
colnames(ssn_chris) <- substring(colnames(ssn_chris), 1, 7)
colnames(ssn_chris) <- gsub("-", "", colnames(ssn_chris))
colnames(ssn_chris) <- gsub("_", "", colnames(ssn_chris))
colnames(ssn_chris) <- gsub("[.]", "", colnames(ssn_chris))
colnames(ssn_chris)  <- gsub(".{0}d$", "", colnames(ssn_chris))
# Do manually some of the mismatches
colnames(ssn_chris['wnUC2ol']) <- 'wnUC2old'



#### For the differences

# 1) with Chris
colnames(z_final60) <- colnames(data.mat2.fin)
diff_chris <- as.data.frame(z_final60 - ssn_chris[,colnames(z_final60) %in% colnames(ssn_chris)])
# Keep only matched stations (problem with name encoding)
diff_chris <- cbind(diff_chris,
                    Date = data.mat[data.mat$decdate>1960 & data.mat$decdate<2015,]$Date)

# In percentages (normalized by the value obtained with our method)
mean_res <- apply(diff_chris[,-ncol(diff_chris)], 2, mean, na.rm = T)
#diff_chris_perc <- cbind( (diff_chris[,-ncol(diff_chris)] / z_final60) * 100, Date = diff_chris$Date)
chris_mat_perc <- sweep(diff_chris[,-ncol(diff_chris)], 2, 100-abs(mean_res), "/") * 1
#diff_chris_perc <- cbind( (diff_chris[,-ncol(diff_chris)] / mean_res) * 100, Date = diff_chris$Date)
diff_chris_perc <- cbind( chris_mat_perc, Date = diff_chris$Date)

# normalized by silso
weights_silso <- silsoSSN[silsoSSN$decdate>1960 & silsoSSN$decdate<2015,]$SSN + 1
diff_chris_silso <- sweep(diff_chris[,-ncol(diff_chris)], 1,
                          weights_silso, "/") * 1
diff_chris_perc <- cbind( diff_chris_silso, Date = diff_chris$Date)



# 2) With silso
#diff_silso <- as.data.frame(sweep(SSN_filled_all, 2, silsoSSN$SSN,  "-"))
#diff_silsoo <- apply(SSN_filled_all, 2, function(x) x - silsoSSN$SSN)
diff_silso <- as.data.frame(SSN_filled_all - silsoSSN$SSN)
colnames(diff_silso) <- colnames(data.mat2.fin)
diff_silso <- cbind(diff_silso, Date = data.mat$Date)

# Normalized by Silso
weights_silso <- silsoSSN$SSN + 1
diff_silso_p <- sweep(diff_silso[,-ncol(diff_silso)], 1,
                      weights_silso, "/") * 1
diff_silso_perc <- cbind( diff_silso_p, Date = data.mat$Date)


# 3) with splines
diff_splines <- as.data.frame(SSN_filled_all - ssn_splines)
colnames(diff_splines) <- colnames(data.mat2.fin)
diff_splines <- cbind(diff_splines,  Date = data.mat$Date)

# Normalized by Silso
weights_silso <- silsoSSN$SSN + 1
diff_splines_silso <- sweep(diff_splines[,-ncol(diff_splines)], 1,
                            weights_silso, "/") * 1
diff_splines_perc <- cbind( diff_splines_silso, Date = diff_splines$Date)



# Define server logic required to draw a histogram
server <- function(input, output) {

  output$plot1 <- renderPlot({

    myPalette <- colorRampPalette(rev(brewer.pal(4, "Spectral")))


    ## 1) Data from Chris !

    if(input$na == "chris") {

      if(input$percent == F){

        # Have the same coordinates for the compared graphs
        y_borders <- c(min(diff_chris[,input$stations],
                           diff_chris[,input$stations2]),
                       max(diff_chris[,input$stations],
                           diff_chris[,input$stations2]))
        xylim <- coord_cartesian(ylim = y_borders)


        gchris1 <- ggplot(diff_chris, aes_string(x = "Date", y = input$stations,
                                                 col = input$stations)) +
          theme_piss() +  ggtitle('Residuals with Chris method for 1st station') +
          xylim
        gchris2 <- ggplot(diff_chris, aes_string(x = "Date", y = input$stations2,
                                                 col = input$stations2)) +
          theme_piss() +  ggtitle('Residuals with Chris method for 2nd station') +
          xylim
      }
      ## In percentage
      else{
        y_borders <- c(min(diff_chris_perc[,input$stations],
                           diff_chris_perc[,input$stations2]),
                       max(diff_chris_perc[,input$stations],
                           diff_chris_perc[,input$stations2]))
        xylim <- coord_cartesian(ylim = y_borders)


        gchris1 <- ggplot(diff_chris_perc, aes_string(x = "Date", y = input$stations,
                                                      col = input$stations)) +
          theme_piss() +  ggtitle('Residuals with Chris method for 1st station') +
          labs(y = paste(input$stations, "(in %)")) +  xylim
        gchris2 <- ggplot(diff_chris_perc, aes_string(x = "Date", y = input$stations2,
                                                      col = input$stations2)) +
          theme_piss() +  ggtitle('Residuals with Chris method for 2nd station') +
          labs(y = paste(input$stations2, "(in %)")) + xylim
      }



      if(input$points == T){
        gchris1 <- gchris1 + geom_point( size = 0.1)
        gchris2 <- gchris2 + geom_point(size=0.1)
      }
      else{
        gchris1 <- gchris1 + geom_line( )
        gchris2 <- gchris2 + geom_line()
      }

      gchris1 <- gchris1 + solar.cycle() +
        scale_colour_gradientn(colours = myPalette(10), limits  = y_borders)
      gchris2 <- gchris2 + solar.cycle() +
        scale_colour_gradientn(colours = myPalette(10), limits = y_borders)

      grid.arrange(gchris1, gchris2, nrow = 2,
                   top = textGrob(expression(" Comparison of SSN methods + solar cycle "),
                                  gp = gpar(fontsize = 25, font = 3, col ="red")))
    }


    # 2) Silso :

    if(input$na == "silso"){
      if(input$percent == F){

        # Have the same coordinates for the compared graphs
        y_borders <- c(min(diff_silso[,input$stations],
                           diff_silso[,input$stations2]),
                       max(diff_silso[,input$stations],
                           diff_silso[,input$stations2]))
        xylim <- coord_cartesian(ylim = y_borders)


        g_silso1 <- ggplot(diff_silso, aes_string(x = "Date", y = input$stations,
                                                  col = input$stations)) +
          theme_piss() +  #ggtitle(expression(atop('SSN coming from Silso ',
          #atop(italic("same series everywhere"), ""))))
          labs(title = 'Residuals compared with the SILSO for 1st station ',
               subtitle = "It is the same series for each stations") +
          theme(plot.subtitle=element_text(size=14, hjust=0.5, face="italic", colour="#33666C")) +
          xylim
        g_silso2 <- ggplot(diff_silso, aes_string(x = "Date", y = input$stations2,
                                                  col = input$stations2)) +
          theme_piss() +
          labs(title = 'Residuals compared with the SILSO for 2nd station',
               subtitle = "It is the same series for each stations") +
          theme(plot.subtitle=element_text(size=14, hjust=0.5, face="italic", colour="#33666C")) +
          xylim

      }
      # In percentage
      else{
        y_borders <- c(min(diff_silso_perc[,input$stations],
                           diff_silso_perc[,input$stations2]),
                       max(diff_silso_perc[,input$stations],
                           diff_silso_perc[,input$stations2]))
        xylim <- coord_cartesian(ylim = y_borders)


        g_silso1 <- ggplot(diff_silso_perc, aes_string(x = "Date", y = input$stations,
                                                       col = input$stations)) +
          theme_piss() +  ggtitle('Residuals with SILSO method for 1st station') +
          labs(y = paste(input$stations, "(in %)"),
               subtitle = "It is the same series for each stations") +  xylim
        g_silso2 <- ggplot(diff_silso_perc, aes_string(x = "Date", y = input$stations2,
                                                       col = input$stations2)) +
          theme_piss() +  ggtitle('Residuals with SILSO method for 2nd station') +
          labs(y = paste(input$stations2, "(in %)"),
               subtitle = "It is the same series for each stations") + xylim
      }


      if(input$points == T){
        g_silso1 <- g_silso1 + geom_point( size=0.1)
        g_silso2 <- g_silso2 + geom_point(size=0.1)

      }
      else{
        g_silso1 <- g_silso1 + geom_line()
        g_silso2 <- g_silso2 + geom_line()

      }

      g_silso1 <- g_silso1 + solar.cycle() +
        scale_colour_gradientn(colours = myPalette(10), limits = y_borders)
      g_silso2 <- g_silso2 + solar.cycle() +
        scale_colour_gradientn(colours = myPalette(10), limits = y_borders)


      grid.arrange(g_silso1, g_silso2, ncol = 1,
                   top = textGrob(expression(" Comparison of SSN methods + solar cycle"),
                                  gp = gpar(fontsize = 25, font = 3, col ="red")))
    }


    # 3) Comparisons with Splines method

    if(input$na == "splines") {

      if(input$percent == F){

        # Have the same coordinates for the compared graphs
        y_borders <- c(min(diff_splines[,input$stations],
                           diff_splines[,input$stations2]),
                       max(diff_splines[,input$stations],
                           diff_splines[,input$stations2]))
        xylim <- coord_cartesian(ylim = y_borders)


        g_splines <- ggplot(diff_splines, aes_string(x = "Date", y = input$stations,
                                                     col = input$stations)) +
          theme_piss() +  ggtitle('Filled with splines for 1st station') +
          xylim #+ coord_cartesian(ylim = c(0,100))
        g_splines2 <- ggplot(diff_splines, aes_string(x = "Date", y = input$stations2,
                                                      col = input$stations2)) +
          theme_piss() +  ggtitle('Filled with splines for 2nd station') +
          xylim
      }

      # In percentage
      else{
        y_borders <- c(min(diff_splines_perc[,input$stations],
                           diff_splines_perc[,input$stations2]),
                       max(diff_splines_perc[,input$stations],
                           diff_splines_perc[,input$stations2]))
        xylim <- coord_cartesian(ylim = y_borders)


        g_splines <- ggplot(diff_splines_perc, aes_string(x = "Date", y = input$stations,
                                                          col = input$stations)) +
          theme_piss() +  ggtitle('Residuals with Chris method for 1st station') +
          labs(y = paste(input$stations, "(in %)")) +  xylim
        g_splines2 <- ggplot(diff_splines_perc, aes_string(x = "Date", y = input$stations2,
                                                           col = input$stations2)) +
          theme_piss() +  ggtitle('Residuals with Chris method for 2nd station') +
          labs(y = paste(input$stations2, "(in %)")) + xylim
      }

      if(input$points == T){
        g_splines2 <- g_splines2 + geom_point(, size = 0.1)
        g_splines <- g_splines + geom_point( size=0.1)
      }
      else{
        g_splines2 <- g_splines2 + geom_line()
        g_splines <- g_splines + geom_line( )
      }

      g_splines2 <- g_splines2 + solar.cycle() +
        scale_colour_gradientn(colours = myPalette(10), limits = y_borders)
      g_splines <- g_splines + solar.cycle() +
        scale_colour_gradientn(colours = myPalette(10), limits = y_borders)

      grid.arrange(g_splines, g_splines2, nrow = 2,
                   top = textGrob(expression(" Comparison of SSN methods + solar cycle "),
                                  gp = gpar(fontsize = 25, font = 3, col ="red")))
    }


  })

}
shinyApp(ui = ui, server = server)


#runApp('/home/piss/PissoortRepo/ValUSunSSN/Shiny_visual_app/appTest')