app_AGILE_Predict_DTF.R

##############################################################################
##########           AGILE LDP Phenology App                        ##########
##############################################################################
# Libraries
##############################################################################
# setwd("C:/Google Drive/gitfolder/AGILE_LDP_Phenology/PredictDTF")
library(shiny); library(shinythemes); library(plotly)
library(leaflet); library(leaflet.minicharts)
library(tidyverse)  # data wrangling
library(scales)     # rescale()
library(rworldmap)  # mapBubbles()
library(ggpubr)     # ggarrange()
library(ggbeeswarm) # geom_quasirandom()
library(FactoMineR) # PCA() & HCPC()
library(plot3D)     # 3D plots
##############################################################################
# Pre-App R work
##############################################################################
# General color palettes
colors <- c("darkred",   "darkorange3", "darkgoldenrod2", "deeppink3",
            "steelblue", "darkorchid4", "cornsilk4",      "darkgreen")
# Expts color palette
colors_Expt <- c("darkolivegreen3", "darkolivegreen4", "palegreen2", "palegreen3",
                 "palegreen4", "darkgreen", "orangered2", "orangered4",  "coral2",
                 "coral4", "orange2", "orange4", "aquamarine3","aquamarine4",
                 "slateblue1", "slateblue4", "deepskyblue3", "deepskyblue4" )
# Locations
names_Location <- c("Rosthern, Canada", "Sutherland, Canada",  "Central Ferry, USA",
                    "Bhopal, India",    "Jessore, Bangladesh", "Bardiya, Nepal",
                    "Cordoba, Spain",   "Marchouch, Morocco",  "Metaponto, Italy" )
# Experiments
names_Expt <- c("Rosthern, Canada 2016",    "Rosthern, Canada 2017",
                "Sutherland, Canada 2016",  "Sutherland, Canada 2017",
                "Sutherland, Canada 2018",  "Central Ferry, USA 2018",
                "Bhopal, India 2016",       "Bhopal, India 2017",
                "Jessore, Bangladesh 2016", "Jessore, Bangladesh 2017",
                "Bardiya, Nepal 2016",      "Bardiya, Nepal 2017",
                "Cordoba, Spain 2016",      "Cordoba, Spain 2017",
                "Marchouch, Morocco 2016",  "Marchouch, Morocco 2017",
                "Metaponto, Italy 2016",    "Metaponto, Italy 2017" )
# Experiment short names
names_ExptShort <- c("Ro16", "Ro17", "Su16", "Su17", "Su18", "Us18",
                     "In16", "In17", "Ba16", "Ba17", "Ne16", "Ne17",
                     "Sp16", "Sp17", "Mo16", "Mo17", "It16", "It17" )
# Shape
mypch <- c(11,14,2,6,17,8, 0,5,15,18,7,9, 3,4,1,16,10,13)
#
trts <- c("DTE","DTF","DTS","DTM","VEG","REP","RDTF","DTF2","DTF2_scaled", "Tb", "Tf", "Pc", "Pf", "PTT")
# Scaling function
traitScale <- function(x, trait) {
  xout <- rep(NA, nrow(x))
  for(i in unique(x$Expt)) {
    mn <- x %>% filter(Expt == i) %>% pull(trait) %>% min(na.rm = T)
    mx <- x %>% filter(Expt == i) %>% pull(trait) %>% max(na.rm = T)
    rg <- mx - mn
    xout <- ifelse(x$Expt == i, rescale(x %>% pull(trait), c(1,5), c(mn,mx)), xout)
  }
  xout
}
# Country info
ct <- read.csv("data/data_countries.csv")
# Lentil Diversity Panel metadata
regions <- c("Africa", "Asia", "Europe", "Americas", "ICARDA", "USDA", "Unknown")
ldp <- read.csv("data/data_ldp.csv") %>%
  left_join(select(ct, Origin=Country, Region, SubRegion), by = "Origin") %>%
  mutate(Region = ifelse(Origin %in% c("ICARDA","USDA","Unknown"), as.character(Origin), as.character(Region)),
         SubRegion = ifelse(Origin %in% c("ICARDA","USDA","Unknown"), as.character(Origin), as.character(SubRegion)),
         Region = factor(Region, levels = regions),
         Lat2 = ifelse(duplicated(Lat), jitter(Lat, 1, 0.1), Lat),
         Lon2 = ifelse(duplicated(Lon), jitter(Lon, 1, 0.1), Lon),
         Lat3 = ifelse(is.na(Lat), ct$Lat[match(Origin, ct$Country)], Lat),
         Lat3 = ifelse(duplicated(Lat3), jitter(Lat3, 1, 0.1), Lat3),
         Lon3 = ifelse(is.na(Lon), ct$Lon[match(Origin, ct$Country)], Lon),
         Lon3 = ifelse(duplicated(Lon3), jitter(Lon3, 1, 0.1), Lon3) )
# Modeling
m1 <- read.csv("data/model_t+p_d.csv") %>%
  mutate(Expt = factor(Expt, levels = names_Expt))
m2 <- read.csv("data/model_t+p_coefs.csv")
# PCA results
pca <- read.csv("data/data_pca_results.csv") %>%
  mutate(Cluster = factor(Cluster)) %>% select(-Region) %>%
  select(Entry, Name, Origin, Cluster, everything()) %>%
  left_join(select(ldp, Entry, Region), by = "Entry")
ldp <- ldp %>% left_join(select(pca, Entry, Cluster), by = "Entry")
# Tf, Pf, PTT
ptt <- read.csv("data/data_tb_pc.csv") %>% select(Entry, Expt, Tb, Pc, Tf, Pf, PTT) %>%
  mutate(Expt = factor(Expt, levels = names_Expt))
# Prep raw data
# Note: DTF2 = non-flowering genotypes <- group_by(Expt) %>% max(DTF)
rr <- read.csv("data/data_raw.csv") %>%
  mutate(Rep = factor(Rep), Year = factor(Year), PlantingDate = as.Date(PlantingDate),
         Location    = factor(Location, levels = names_Location),
         Expt        = factor(Expt,     levels = names_Expt),
         ExptShort   = plyr::mapvalues(Expt, names_Expt, names_ExptShort),
         DTF2_scaled = traitScale(., "DTF2"),
         RDTF = round(1 / DTF2, 6),
         VEG  = DTF - DTE,
         REP  = DTM - DTF) %>%
  left_join(ptt, by = c("Entry","Expt")) %>%
  left_join(select(ldp, Entry, Cluster, Origin, Region, SubRegion), by = "Entry")
# Average raw data
dd <- rr %>%
  group_by(Entry, Name, Expt, Location, Year, Origin, Region, SubRegion, Cluster) %>%
  summarise_at(vars(DTE, DTF, DTS, DTM, VEG, REP, RDTF, DTF2, DTF2_scaled, Tb, Tf, Pc, Pf, PTT),
               funs(mean), na.rm = T) %>% ungroup() %>%
  mutate(Expt        = factor(Expt, levels = names_Expt),
         ExptShort   = plyr::mapvalues(Expt, names_Expt, names_ExptShort),
         Location    = factor(Location, levels = names_Location),
         DTF2_scaled = traitScale(., "DTF2"))
# Prep environmental data
ee <- read.csv("data/data_env.csv") %>%
  mutate(Date      = as.Date(Date),
         ExptShort = plyr::mapvalues(Expt, names_Expt, names_ExptShort),
         ExptShort = factor(ExptShort, levels = names_ExptShort),
         Expt      = factor(Expt,      levels = names_Expt),
         Location  = factor(Location,  levels = names_Location),
         DayLength_rescaled = rescale(DayLength, to = c(0, 40)) )
# Prep field trial info
xx <- dd %>%
  group_by(Expt) %>%
  summarise_at(vars(DTE, DTF, DTS, DTM), funs(min, mean, max), na.rm = T) %>%
  ungroup()
ff <- read.csv("data/data_info.csv") %>%
  mutate(Start = as.Date(Start), Expt = factor(Expt, levels = names_Expt)) %>%
  left_join(xx, by = "Expt")
for(i in unique(ee$Expt)) {
  ee <- ee %>%
    filter(Expt != i | (Expt == i & DaysAfterPlanting <= ff$DTM_max[ff$Expt == i]))
}
e2 <- ee
for(i in unique(ee$Expt)) {
  e2 <- e2 %>%
    filter(Expt != i | (Expt == i & DaysAfterPlanting <= ff$DTF_max[ff$Expt==i]))
}
tt <- e2 %>% group_by(Location, Year) %>%
  summarise(T_mean = mean(Temp_mean, na.rm = T), T_sd = sd(Temp_mean, na.rm = T),
            P_mean = mean(DayLength, na.rm = T), P_sd = sd(DayLength, na.rm = T) ) %>%
  ungroup() %>%
  mutate(Expt = paste(Location, Year)) %>%
  select(-Location, -Year)
me <- c("Temperate", "South Asia", "Mediterranean")
ff <- ff %>% left_join(tt, by = "Expt") %>%
  mutate(ExptShort = plyr::mapvalues(Expt, names_Expt, names_ExptShort),
         ExptShort = factor(ExptShort, levels = names_ExptShort),
         MacroEnv  = factor(MacroEnv,  levels = me),
         Expt      = factor(Expt,      levels = names_Expt),
         T_mean = round(T_mean, 1),
         P_mean = round(P_mean, 1)) %>% arrange(ExptShort)
# ggplot theme
theme_AGL <- theme_bw() + theme(strip.background = element_rect(fill = "White"))
#
# R^2 function
modelR2 <- function(x, y) {
  1 - ( sum((x - y)^2, na.rm = T) / sum((x - mean(x, na.rm = T))^2, na.rm = T))
}
# RMSE function
modelRMSE <- function(x, y) {
  sqrt(sum((x-y)^2) / length(x))
}
##############################################################################
# User interface
##############################################################################
ui <- fluidPage(theme = shinytheme("yeti"), br(),
  sidebarLayout(
    sidebarPanel(p("LDP DTF Prediction App"), hr(),
                 numericInput("Entry", "Entry", 1, 1, 324, 1),
                 tableOutput("EntryTable"),
                 radioButtons("Plot_Entry", "Plot Entry", c(T, F), T, inline = T),
                 checkboxGroupInput("MyClusters", "Clusters", c("1","2","3","4","5","6","7","8"), c("1","2","3","4","5","6","7","8"), inline = T),
                 checkboxGroupInput("MyRegions", "Regions", regions, regions, inline = T),
                 selectInput("Trait", "Trait", trts, "DTF"),
                 selectInput("Expt", "Expt", names_Expt, "Rosthern, Canada 2016"),
                 checkboxGroupInput("Expts", "Expts", names_Expt, selected = names_Expt),
                 width = 2),
    mainPanel(
      tabsetPanel(
        tabPanel("Home", br(),
                 p("App Author: Derek Michael Wright"),
                 p("Contact: derek.wright@usask.ca"), 
                 p("Last Updated: 10-08-2020"), 
                 hr(),
                 p(strong("Understanding photothermal interactions can help expand production range and increase genetic diversity of lentil (", em("Lens culinaris"), " Medik.)")),
                 p("Derek Wright, Sandesh Neupane, Taryn Heidecker, Teketel Haile, Crystal Chan, Clarice Coyne, Sripada Udupa, Fatima Henkrar, Eleonora Barilli, Diego Rubiales, Tania Gioia, Reena Mehra, Ashutosh Sarker, Rajeev Dhakal, Babul Anwar, Debashish Sarker, Albert Vandenberg, and Kirstin E. Bett"),
                 hr(), p(strong("Project Collaborators:")),
                 p("- Department of Plant Sciences and Crop Development Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada"),
                 p("- United States Department of Agriculture Western Region Plant Introduction Station, Pullman, Washington, USA"),
                 p("- International Center for Agriculture Research in the Dry Areas, Rabat, Morocco"),
                 p("- Institute for Sustainable Agriculture, Spanish National Research Council, Cordoba, Spain"),
                 p("- School of Agriculture, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy"),
                 p("- International Center for Agriculture Research in the Dry Areas, New Delhi, India"),
                 p("- Local Initiatives for Biodiversity, Research and Development, Pokhara, Nepal"),
                 p("- Bangladesh Agricultural Research Institute, Jessore, Bangladesh"), hr(),
                 p(strong("Project Sponsors:")),
                 p("- Saskatchewan Pulse Growers Association"),
                 p("- Western Grains Research Foundation"),
                 p("- GenomePrairie"),
                 p("- GenomeCanada"),
                 p("- Saskatchewan Ministry of Agriculture")
        ), # Home tab
        tabPanel("Predict DTF", 
                 fluidRow(column(numericInput("Mean_T", "Mean Temperature",13,0,25,0.1),
                                 numericInput("Mean_P", "Mean Photoperiod",12,0,18,0.1), width = 2),
                          column(numericInput("DTF_Min", "Min DTF", 30,30,200,1),
                                 numericInput("DTF_Max", "Max DTF", 80,30,200,1), width = 2)),
                 tabsetPanel(
                   tabPanel("Clusters", plotlyOutput("Predict_DTF_Clusters", height = 500)),
                   tabPanel("DTFxCoef", 
                            radioButtons("Select_Coef_DTF", "Select Coefficient", c("a","b","c"), "c", inline = T),
                            plotlyOutput("Predict_DTF_DTFxCoef", height = 500)),
                   tabPanel("cxb", plotlyOutput("Predict_DTF_cxb", height = 500)),
                   tabPanel("Table", 
                            downloadButton("Adapted_DTF_Table.csv", "Download Selected Table"),
                            DT::dataTableOutput("Adapted_DTF_Table"))
                 ) ),
        tabPanel("\"Adaptated\"",
                 fluidRow(column(selectInput("Ad_Country", "Adaptation Country", unique(ldp$Origin)[order(unique(ldp$Origin))], selected = "Canada"), width = 3),
                          column(numericInput("sd_Mult","sd Multiplier",1,0.5,3,0.1), width = 2)),
                 checkboxGroupInput("Ad_Countries", "Countries", unique(ldp$Origin)[order(unique(ldp$Origin))], selected = "USA", inline = T),
                 radioButtons("Select_Coef_Ad", "Select Coefficient", c("a","b","c"), "c", inline = T),
                 tabsetPanel(
                   tabPanel("TraitxCoef",  plotlyOutput("Adapted1", height = 500)),
                   tabPanel("cxb", plotlyOutput("Adapted2", height = 500)),
                   tabPanel("Table", 
                            downloadButton("Adapted_Table.csv", "Download Selected Table"),
                            DT::dataTableOutput("Adapted_Table")) 
                 ) ),
        #
        tabPanel("PhotoThermal Model", tabsetPanel(
          tabPanel("PhotoThermal Plane", br(), plotOutput("PhotoThermal_Plane" , height = 500)),
          tabPanel("Obs vs Pre", tabsetPanel(
            tabPanel("All", plotlyOutput("OxP_All", height = 500)),
            tabPanel("Expt",  plotlyOutput("OxP_Expt", height = 500)),
            tabPanel("Expts", plotOutput("OxP_Expts", height = 500))
          )),
          tabPanel("Coefficients", tabsetPanel(
            tabPanel("abc",   plotlyOutput("abc", height = 500)),
            tabPanel("TraitxCoef",
                     radioButtons("Select_abc", "Select Coefficient", c("a","b","c"), "a", inline = T),
                     plotlyOutput("TraitxCoef", height = 500)),
            tabPanel("cxb",   plotlyOutput("cxb", height = 500))
            ) ),
          tabPanel("T+P Increase", 
                   fluidRow(column(numericInput("TempIncrease","Temperature Increase",1,-10,10,0.1), width = 2),
                            column(numericInput("PhotoIncrease","Photoperiod Increase",1,-10,10,0.1), width = 2)), tabsetPanel(
                              tabPanel("Expts",    plotlyOutput("T_P_Expts", height = 500)),
                              tabPanel("Clusters", plotOutput("T_P_Clusters")),
                              tabPanel("Expt", plotlyOutput("T_P_Cluster", height = 500)) 
                            ) )
        ) ) # PhotoThermal Model tab
    ) ) ) 
) #fluidpage
##############################################################################
# Server
##############################################################################
server <- function(input, output) {
  ###############################################################################
  # - Sidebar
  ###############################################################################
  output$EntryTable <- renderTable({
    ldp %>% filter(Entry == input$Entry) %>% select(Name, Origin)
  })
  ###############################################################################
  # - Home
  ###############################################################################
  ###############################################################################
  # -PhotoThermal Model
  ###############################################################################
  # input <- list(Entry = 3)
  output$PhotoThermal_Plane <- renderPlot({
    xx <- rr %>% filter(!is.na(RDTF)) %>%
      left_join(select(ff, Expt, T_mean, P_mean, MacroEnv), by = "Expt")
    #
    x1 <- xx %>% filter(Entry == input$Entry) %>% arrange(MacroEnv) %>% 
      mutate(myPal = as.character(plyr::mapvalues(MacroEnv, 
               c("Temperate", "South Asia", "Mediterranean"), c("darkgreen", "darkorange3", "darkblue"))))
    x <- x1$T_mean
    y <- x1$P_mean
    z <- x1$RDTF
    fit <- lm(z ~ x + y)
    # predict values on regular xy grid
    grid.lines = 12
    x.pred <- seq(min(x), max(x), length.out = grid.lines)
    y.pred <- seq(min(y), max(y), length.out = grid.lines)
    xy <- expand.grid( x = x.pred, y = y.pred)
    z.pred <- matrix(predict(fit, newdata = xy),
                     nrow = grid.lines, ncol = grid.lines)
    pchs <- plyr::mapvalues(x1$Expt, names_Expt, c(rep(16,6),rep(15,6),rep(17,6))) %>%
      as.character() %>% as.numeric()
    # fitted points for droplines to surface
    fitpoints <- predict(fit)
    # scatter plot with regression plane
    par(mar=c(1.5, 2.5, 1.5, 0.5))
    scatter3D(x, y, z, pch = pchs, cex = 2, zlim = c(0.005,0.03), main = unique(x1$Name),
              col = alpha(x1$myPal,0.5), colvar = as.numeric(x1$MacroEnv), colkey = F,
              theta = 40, phi = 25, ticktype = "detailed", cex.lab = 1, cex.axis = 0.5,
              xlab = "Temperature", ylab = "Photoperiod", zlab = "1 / DTF", col.grid = "gray90",bty = "u",
              surf = list(x = x.pred, y = y.pred, z = z.pred, col = "black",
                          facets = NA, fit = fitpoints))
  })
  # input <- list(Expts = names_Expt, Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"))
  output$OxP_All <- renderPlotly({
    xx <- m1 %>% left_join(select(pca, Entry, Origin, Region, Cluster), by = "Entry") %>%
      filter(!is.na(DTF), Expt %in% input$Expts, Cluster %in% input$MyClusters)
    r2 <- modelR2(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% pull(DTF),
                  xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% pull(Predicted_DTF))
    r2 <- round(r2, 3)
    #
    mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions), 
                 aes(x = DTF, y = Predicted_DTF, color = Expt)) +
      geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin, key4 = Cluster), alpha = 0.8) +
      geom_abline() +
      scale_x_continuous(limits = c(30,160)) +
      scale_y_continuous(limits = c(30,160)) +
      scale_color_manual(name = NULL, values = colors_Expt[levels(xx$Expt) %in% input$Expts]) +
      theme_AGL +
      labs(title = paste("1/DTF = a + bT + Pc | RR =", r2), y = "Predicted", x = "Observed")
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            aes(key1 = Entry, key2 = Name, key3 = Origin, key4 = Cluster),
                            color = "black", fill = "Red", size = 2, pch = 23)
    }
    mp
  })
  # input <- list(Expt = "Rosthern, Canada 2016", Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"), MyRegions = c("Asia","Africa"))
  output$OxP_Expt <- renderPlotly({
    xx <- m1 %>% left_join(select(pca, Entry, Origin, Region, Cluster), by = "Entry") %>%
      filter(!is.na(DTF), Expt == input$Expt)
    r2 <- modelR2(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% pull(DTF),
                  xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% pull(Predicted_DTF))
    r2 <- round(r2, 3)
    mymin <- min(xx$DTF, na.rm = T)
    mymax <- max(xx$DTF, na.rm = T)
    #
    mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions), 
                 aes(x = DTF, y = Predicted_DTF, color = Cluster)) +
      geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin), alpha = 0.8) +
      geom_abline() +
      scale_x_continuous(limits = c(mymin,mymax)) +
      scale_y_continuous(limits = c(mymin,mymax)) +
      scale_color_manual(name = NULL, values = colors[as.numeric(input$MyClusters)]) +
      theme_AGL +
      labs(title = paste(input$Expt, "| RR =", r2), y = "Predicted", x = "Observed")
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            aes(key1 = Entry, key2 = Name, key3 = Origin),
                            color = "Black", fill = "Red", size = 2, pch = 23)
    }
    mp
  })
  # input <- list(Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"), MyRegions = c("Asia","Africa"))
  output$OxP_Expts <- renderPlot({
    xx <- m1 %>% left_join(select(pca, Entry, Origin, Region, Cluster), by = "Entry") %>%
      filter(!is.na(DTF))
    mymean <- mean(xx$DTF)
    r2  <- 1 - (sum((xx$DTF - xx$Predicted_DTF)^2) / (sum((xx$DTF - mymean)^2)))
    r2 <- round(r2, 3)
    x1 <- xx %>% group_by(Expt) %>%
      summarise(Mean = mean(DTF)) %>% ungroup()
    #
    for(i in 1:nrow(x1)) {
      xi <- xx %>% filter(Expt == x1$Expt[i], Cluster %in% input$MyClusters, Region %in% input$MyRegions)
      x1[i,"r2"] <- round(1 - (sum((xi$DTF - xi$Predicted_DTF)^2) /
                               sum((xi$DTF - x1$Mean[i])^2)), 2)
    }
    #
    mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions), 
                 aes(x = DTF, y = Predicted_DTF)) +
      geom_point(aes(color = Cluster), alpha = 0.5) + geom_abline() +
      geom_label(x = 26, y = 143, color = "black", hjust = 0, vjust = 0,
                aes(label = r2), size = 3, data = x1) +
      facet_wrap(Expt~., ncol = 6, labeller = label_wrap_gen(width = 17)) +
      scale_x_continuous(limits = c(30,160)) +
      scale_y_continuous(limits = c(30,160)) +
      scale_color_manual(name = NULL, values = colors[as.numeric(input$MyClusters)]) +
      theme_AGL +
      theme(legend.position = "none") +
      labs(title = paste("1/DTF = a + bT + Pc | RR =", r2), y = "Predicted", x = "Observed")
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            color = "Black", fill = "Red", size = 2, pch = 23)
    }
    mp
  })
  # input <- list(Plot_Entry = T, Entry = 1, MyClusters = c("a","2","3","4"))
  output$abc <- renderPlotly({
    xx <- m2 %>%
      left_join(select(pca, Entry, Origin, Region, Cluster), by = "Entry") %>%
      select(Entry, Name, Origin, Cluster, Region, a, b, c) %>%
      gather(Trait, Value, a, b, c) %>%
      filter(Cluster %in% input$MyClusters)
    xE <- xx %>% filter(Entry == input$Entry)
    mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions), 
                 aes(x = Cluster, y = Value * 10000) ) +
      geom_violin(aes(fill = Cluster), color = NA, alpha = 0.7) +
      geom_quasirandom(size = 0.5, aes(key1 = Entry, key2 = Name, key3 = Origin)) +
      facet_wrap(Trait~., nrow = 1, scales = "free") +
      theme_AGL +
      theme(legend.position = "none", strip.text = element_text(face = "italic")) +
      scale_fill_manual(name = NULL, values = colors[as.numeric(input$MyClusters)]) +
      labs(y = "x 10000", x = "Cluster")
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xE, color = "Black", fill = "Red", size = 3, pch = 23,
                            aes(key1 = Entry, key2 = Name, key3 = Origin))
    }
    mp
  })
  # input <- list(Expt = "Rosthern, Canada 2016", Trait = "DTF", Select_abc = "c", Plot_Entry = T, Entry = 1)
  output$TraitxCoef <- renderPlotly({
    x1 <- dd %>% filter(Expt == input$Expt) %>% 
      select(Entry, Trait=input$Trait)
    xx <- m2 %>% rename(Coef=input$Select_abc) %>%
      left_join(select(pca, Entry, Origin, Region, Cluster), by = "Entry") %>%
      left_join(x1, by = "Entry")
    x1 <- xx %>%
      group_by(Origin) %>%
      summarise_at(vars(Trait, Coef), funs(mean, sd)) %>%
      rename(Coef=Coef_mean, Trait=Trait_mean)
    x2 <- x1 %>% mutate(CO = 1) %>%
      filter(Origin %in% c("Syria", "Jordan", "Turkey", "Lebanon", "Israel")) %>%
      select(Trait, Coef, everything())
    # Plot
    find_hull <- function(df) df[chull(df[,2], df[,1]), ]
    polys <- plyr::ddply(x2, "CO", find_hull)
    mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions), 
                 aes(y = Coef * 10000, x = Trait)) +
      geom_polygon(data = polys, fill = NA, color = "black") +
      geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster)) +
      scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
      theme_AGL +
      labs(x = paste(input$Expt, input$Trait), y = paste(input$Select_abc, "* 10000"))
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster),
                            color = "Black", fill = "Red", size = 3, pch = 23)
    }
    mp
  })
  # input <- list(Plot_Entry = T, Entry = 1)
  output$cxb <- renderPlotly({
    xx <- m2 %>% left_join(select(pca, Entry, Origin, Region, Cluster), by = "Entry")
    x1 <- xx %>%
      group_by(Origin) %>%
      summarise_at(vars(a, b, c), funs(mean, sd)) %>%
      rename(c=c_mean, b=b_mean)
    x2 <- x1 %>% mutate(CO = 1) %>%
      filter(Origin %in% c("Syria", "Jordan", "Turkey", "Lebanon", "Israel"))
    # Plot
    find_hull <- function(df) df[chull(df[,"c"], df[,"b"]), ]
    polys <- plyr::ddply(x2, "CO", find_hull)
    mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions), 
                 aes(x = c * 10000, y = b * 10000)) +
      geom_polygon(data = polys, fill = NA, color = "black") +
      geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster)) +
      scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
      theme_AGL
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster),
                            color = "Black", fill = "Red", size = 3, pch = 23)
    }
    mp
  })
  # input <- list(TempIncrease = 1, PhotoIncrease = 1, Expts = names_Expt, Plot_Entry = T, Entry = 1, MyClusters = c("a","2","3","4"))
  output$T_P_Expts <- renderPlotly({
    xx <- dd %>%
      select(Entry, Name, Expt, ExptShort, Origin, Cluster, Region, DTF) %>%
      left_join(select(m2, Entry, a, b, c), by = "Entry") %>%
      left_join(select(ff, Expt, MacroEnv, T_mean, P_mean), by = "Expt") %>%
      mutate(T_mean2 = T_mean + input$TempIncrease,
             P_mean2 = P_mean + input$PhotoIncrease,
             DTF_GW = 1 / (a + b * T_mean2 + c * P_mean2),
             DTF_P  = 1 / (a + b * T_mean + c * P_mean),
             Diff = DTF_P - DTF_GW,
             Expt = factor(Expt, levels = names_Expt)) %>%
      filter(Expt %in% input$Expts, Cluster %in% input$MyClusters, Region %in% input$MyRegions)
    # Plot
    mp <- ggplot(xx, aes(x = ExptShort, y = Diff)) +
      geom_violin(alpha = 0.3, color = NA) +
      geom_quasirandom(size = 0.7, aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster)) +
      facet_grid(.~MacroEnv, scales = "free_x") + #
      theme_AGL +
      theme(legend.position = "none",
            axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
            panel.grid.major.y = element_line(colour = "grey70", size = 0.5)) +
      scale_y_continuous(minor_breaks = seq(1,30,1),
                         breaks = seq(0,30,5)) +
      scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
      labs(title = paste("Temperature Increase of", input$TempIncrease),
           y = "Decrease in days to flower", x = NULL)
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            color = "Black", fill = "Red", size = 2, pch = 23)
    }
    mp
  })
  # input <- list(TempIncrease = 1, PhotoIncrease = 1, Expt = names_Expt, Plot_Entry = T, Entry = 1, MyClusters = c("a","2","3","4"))
  observe({
    output$T_P_Clusters <- renderPlot({
      xx <- dd %>%
        select(Entry, Name, Expt, ExptShort, Cluster, Region, DTF) %>%
        left_join(select(m2, Entry, a, b, c), by = "Entry") %>%
        left_join(select(ff, Expt, MacroEnv, T_mean, P_mean), by = "Expt") %>%
        mutate(T_mean2 = T_mean + input$TempIncrease,
               P_mean2 = P_mean + input$PhotoIncrease,
               DTF_GW = 1 / (a + b * T_mean2 + c * P_mean2),
               DTF_P  = 1 / (a + b * T_mean + c * P_mean),
               Diff = DTF_P - DTF_GW,
               Expt = factor(Expt, levels = names_Expt)) %>%
        filter(Expt %in% input$Expts, Cluster %in% input$MyClusters, Region %in% input$MyRegions)
      mp <- ggplot(xx, aes(x = Cluster, y = Diff, fill = Cluster)) +
        geom_violin(color = NA, alpha = 0.5) +
        geom_quasirandom() +
        facet_grid(Expt~., scales = "free") +
        theme_AGL +
        theme(legend.position = "none") +
        scale_fill_manual(name = NULL, values = colors[as.numeric(input$MyClusters)]) +
        labs(y = "Decrease in days to flower", x = NULL)
      if(input$Plot_Entry == T) {
        mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                              color = "Black", fill = "Red", size = 3, pch = 23)
      }
      mp
    }, height = 250*length(input$Expts) )
  })
  # input <- list(TempIncrease = 1, Expt = "Rosthern, Canada 2017", Plot_Entry = T, Entry = 1, MyClusters = c("a","2","3","4"))
  output$T_P_Cluster <- renderPlotly({
    xx <- dd %>%
      select(Entry, Name, Expt, ExptShort, Origin, Region, Cluster, DTF) %>%
      left_join(select(m2, Entry, a, b, c), by = "Entry") %>%
      left_join(select(ff, Expt, MacroEnv, T_mean, P_mean), by = "Expt") %>%
      mutate(T_mean2 = T_mean + input$TempIncrease,
             P_mean2 = P_mean + input$PhotoIncrease,
             DTF_GW = 1 / (a + b * T_mean2 + c * P_mean2),
             DTF_P  = 1 / (a + b * T_mean + c * P_mean),
             Diff = DTF_P - DTF_GW,
             Expt = factor(Expt, levels = names_Expt)) %>%
      filter(Expt == input$Expt, Cluster %in% input$MyClusters, Region %in% input$MyRegions)
    mp <- ggplot(xx, aes(x = Cluster, y = Diff)) +
      geom_violin(aes(fill = Cluster), color = NA, alpha = 0.5) +
      geom_quasirandom(aes(key1 = Entry, key2 = Name, key3 = Origin)) +
      theme_AGL +
      theme(legend.position = "none") +
      scale_fill_manual(name = NULL, values = colors[as.numeric(input$MyClusters)]) +
      labs(title = paste(input$Expt, "| Temperature Increase of", input$TempIncrease),
           y = "Decrease in days to flower", x = NULL)
    if(input$Plot_Entry == T) {
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
                            aes(key1 = Entry, key2 = Name, key3 = Origin),
                            color = "Black", fill = "Red", size = 3, pch = 23)
    }
    mp
  })
  # input <- list(Mean_T = 13, Mean_P = 12, DTF_Max = 80, DTF_Min = 30, Select_Coef_DTF = "c", Plot_Entry = T, Entry = 1)
  output$Predict_DTF_Clusters <- renderPlotly({
    xx <- m2 %>% left_join(select(ldp, Entry, Origin, Region, Cluster), by = "Entry") %>%
      filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
      mutate(DTF_Prediction = round(1 / ( a + b * input$Mean_T  + c * input$Mean_P ), 1),
             Adapted = ifelse(DTF_Prediction > input$DTF_Min & DTF_Prediction < input$DTF_Max, "Adapted", "Not Adapted") )
    mp <- ggplot(xx, aes(x = Cluster, y = DTF_Prediction)) +
      geom_rect(xmin = 0, xmax = 9, ymin = input$DTF_Min, ymax = input$DTF_Max, 
                fill = "grey", color = NA, alpha = 0.3) +
      geom_violin(aes(fill = Cluster), color = NA, alpha = 0.7) +
      geom_quasirandom(aes(key1 = Entry, key2 = Name, key3 = Origin, color = Adapted)) +
      scale_fill_manual(values = colors[as.numeric(input$MyClusters)]) +
      scale_color_manual(values = c("black", "grey")) +
      theme_AGL +
      theme(legend.position = "none") +
      labs(title = paste("Mean Temperature =", input$Mean_T, "| Mean Photoperiod =", input$Mean_P),
           y = "DTF Prediction")
    if(input$Plot_Entry == T) { 
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry), color = "Black", fill = "Red", size = 3, pch = 23)
    }
    mp
  })
  # input <- list(Mean_T = 13, Mean_P = 12, DTF_Max = 80, DTF_Min = 30, Select_Coef_DTF = "c", Plot_Entry = T, Entry = 1)
  output$Predict_DTF_DTFxCoef <- renderPlotly({
    xx <- m2 %>% left_join(select(ldp, Entry, Origin, Region, Cluster), by = "Entry")  %>%
      filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
      mutate(DTF_Prediction = round(1 / ( a + b * input$Mean_T  + c * input$Mean_P ), 1),
             Adapted = ifelse(DTF_Prediction > input$DTF_Min & DTF_Prediction < input$DTF_Max, "Adapted", "Not Adapted") )
    mp <- ggplot(xx, aes(x = DTF_Prediction, y = get(input$Select_Coef_DTF) * 10000)) +
      geom_rect(ymin = -Inf, ymax = Inf, xmin = input$DTF_Min, xmax = input$DTF_Max, 
                fill = "grey", color = NA, alpha = 0.3) +
      geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster, pch = Adapted)) +
      scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
      scale_shape_manual(values = c(16,1)) +
      theme_AGL +
      labs(title = paste("Mean Temperature =", input$Mean_T, "| Mean Photoperiod =", input$Mean_P),
           y = paste(input$Select_Coef_DTF," * 10000"), x = "Predicted DTF")
    if(input$Plot_Entry == T) { 
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry), color = "Black", fill = "Red", size = 3, pch = 23)
    }
    mp
  })
  # input <- list(Mean_T = 13, Mean_P = 12, DTF_Max = 80, DTF_Min = 30, Plot_Entry = T, Entry = 1)
  output$Predict_DTF_cxb <- renderPlotly({
    xx <- m2 %>% left_join(select(ldp, Entry, Origin, Region, Cluster), by = "Entry") %>%
      filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
      mutate(DTF_Prediction = round(1 / ( a + b * input$Mean_T  + c * input$Mean_P ), 1),
             Adapted = ifelse(DTF_Prediction > input$DTF_Min & DTF_Prediction < input$DTF_Max, "Adapted", "Not Adapted") )
    mp <- ggplot(xx, aes(x = c * 10000, y = b * 10000)) +
      geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster, pch = Adapted)) +
      scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
      scale_shape_manual(values = c(16,1)) +
      theme_AGL +
      labs(title = paste("Mean Temperature =", input$Mean_T, "| Mean Photoperiod =", input$Mean_P))
    if(input$Plot_Entry == T) { 
      mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry), color = "Black", fill = "Red", size = 3, pch = 23)
    }
    mp
  })
  # input <- list(Mean_T = 13, Mean_P = 12, DTF_Max = 80, DTF_Min = 30)
  output$Adapted_DTF_Table <- DT::renderDataTable({
    xx <- m2 %>% left_join(select(ldp, Entry, Origin, Region, Cluster), by = "Entry") %>%
      filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
      mutate(DTF_Prediction = round(1 / ( a + b * input$Mean_T  + c * input$Mean_P ), 1)) %>%
      filter(DTF_Prediction > input$DTF_Min, DTF_Prediction < input$DTF_Max) %>%
      select(Entry, Name, Origin, Cluster, DTF_Prediction)
  }, options = list(pageLength = 324))
  # input <- list(Mean_T = 13, Mean_P = 12, DTF_Max = 80, DTF_Min = 30)
  output$Adapted_DTF_Table.csv <- downloadHandler(
    filename = function() {"Adapted_DTF_Table.csv"},
    content = function(file) {
      xx <- m2 %>% left_join(select(ldp, Entry, Origin, Region, Cluster), by = "Entry") %>%
        filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
        mutate(DTF_Prediction = round(1 / ( a + b * input$Mean_T  + c * input$Mean_P ), 1)) %>%
        filter(DTF_Prediction > input$DTF_Min, DTF_Prediction < input$DTF_Max) %>%
        select(Entry, Name, Origin, Cluster, DTF_Prediction)
      write.csv(xx, file, row.names = F)
      }
  )
  # input <- list(Ad_Country = "Canada", Ad_Countries = c("India"), sd_Mult = 1, Expt = "Rosthern, Canada 2016", Trait = "DTF", Select_Coef_Ad = "c", Plot_Entry = T, Entry = 1)
  output$Adapted1 <- renderPlotly({
    myOrigins <- unique(c(input$Ad_Country, input$Ad_Countries))
    x1 <- dd %>% filter(Expt == input$Expt, Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% 
      select(Entry, Trait=input$Trait)
    xx <- m2 %>% rename(Coef=input$Select_Coef_Ad) %>%
      left_join(select(ldp, Entry, Cluster, Origin), by = "Entry") %>%
      left_join(x1, by = "Entry") %>%
      mutate(Origin = as.character(Origin),
             Origin2 = ifelse(Origin %in% myOrigins, Origin, "Other"),
             Trait = ifelse(is.nan(Trait), max(.$Trait,na.rm = T), Trait))
    yy <- xx %>%
      group_by(Origin) %>%
      summarise_at(vars(Trait, Coef), funs(mean, sd), na.rm = T) %>%
      rename(Coef=Coef_mean, Trait=Trait_mean) %>%
      filter(Origin == input$Ad_Country)
    #
    myYmin <- yy$Coef -  input$sd_Mult * yy$Coef_sd
    myYmax <- yy$Coef +  input$sd_Mult * yy$Coef_sd
    myXmin <- yy$Trait - input$sd_Mult * yy$Trait_sd
    myXmax <- yy$Trait + input$sd_Mult * yy$Trait_sd
    xx <- xx %>%
      mutate(Origin2 = ifelse(Trait > myXmin & Trait < myXmax &
                                !Origin %in% myOrigins, "\"Adapted\"", Origin2),
             Origin2 = factor(Origin2, levels = c("Other", "\"Adapted\"", myOrigins)))
    #
    mp <- ggplot(yy, aes(y = Coef * 10000, x = Trait)) +
      geom_point(data = xx, alpha = 0.7,
                 aes(key1 = Entry, key2 = Name, key3 = Cluster, key4 = Origin, color = Origin2)) +
      geom_point(pch = 18, size = 3) +
      geom_errorbar(aes(ymin = myYmin * 10000, ymax = myYmax * 10000)) +
      geom_errorbarh(aes(xmin = myXmin, xmax = myXmax)) +
      theme_AGL +
      scale_color_manual(values = colors[c(7,8,1,3,5,6,2)]) +
      labs(x = paste(input$Expt, input$Trait), y = paste(input$Select_Coef_Ad, "* 10000"))
    if(input$Plot_Entry == T) {
      mp <- mp +
        geom_point(data = xx %>% filter(Entry == input$Entry),
                   color = "Black", fill = "Red", size = 3, pch = 23,
                   aes(key1 = Entry, key2 = Name, key3 = Cluster, key4 = Origin))
    }
    mp
  })
  # input <- list(Ad_Country="Canada", Ad_Countries="India", sd_Mult = 2, Trait = "DTF", Select_Coef_Ad = "c", Expt = "Sutherland, Canada 2018", Plot_Entry = T, Entry = 1)
  output$Adapted2 <- renderPlotly({
    myOrigins <- unique(c(input$Ad_Country, input$Ad_Countries))
    x1 <- dd %>% filter(Expt == input$Expt, Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% 
      select(Entry, Trait=input$Trait)
    xx <- m2 %>% 
      left_join(select(ldp, Entry, Cluster, Origin), by = "Entry") %>%
      left_join(x1, by = "Entry") %>%
      mutate(Origin = as.character(Origin),
             Origin2 = ifelse(Origin %in% myOrigins, Origin, "Other"))
    yy <- xx %>%
      group_by(Origin) %>%
      summarise_at(vars(c,b, Trait), funs(mean, sd), na.rm = T) %>%
      rename(b=b_mean, c=c_mean, Trait=Trait_mean) %>%
      filter(Origin == input$Ad_Country)
    #
    myYmin <- (yy$b - input$sd_Mult * yy$b_sd)
    myYmax <- (yy$b + input$sd_Mult * yy$b_sd)
    myXmin <- (yy$c - input$sd_Mult * yy$c_sd)
    myXmax <- (yy$c + input$sd_Mult * yy$c_sd)
    myDTFmin <- yy$Trait - input$sd_Mult * yy$Trait_sd
    myDTFmax <- yy$Trait + input$sd_Mult * yy$Trait_sd
    xx <- xx %>%
      mutate(Origin2 = ifelse(Trait > myDTFmin & Trait < myDTFmax &
                                !Origin2 %in% myOrigins, "\"Adapted\"", Origin2),
             Origin2 = factor(Origin2, levels = c("Other", "\"Adapted\"", myOrigins)))
    #
    mp <- ggplot(yy, aes(y = b * 10000, x = c * 10000)) +
      geom_point(data = xx, aes(key1 = Entry, key2 = Name, key3 = Cluster, key4 = Origin, color = Origin2), alpha = 0.7) +
      geom_point(aes(pch = Origin), size = 3) +
      geom_errorbar(aes(ymin = myYmin * 10000, ymax = myYmax * 10000)) +
      geom_errorbarh(aes(xmin = myXmin * 10000, xmax = myXmax * 10000)) +
      theme_AGL +
      scale_shape_manual(name = "Average", values = 18) +
      scale_color_manual(values = colors[c(7,8,1,3,5,6,2)]) +
      labs(title = input$Expt, x = "c * 10000", y = "b * 10000")
    if(input$Plot_Entry == T) {
      mp <- mp +
        geom_point(data = xx %>% filter(Entry == input$Entry),
                   color = "Black", fill = "Red", size = 3, pch = 23,
                   aes(key1 = Entry, key2 = Name, key3 = Cluster, key4 = Origin))
    }
    mp
  })
  # input <- list(Ad_Country="Canada", Ad_Countries="India", sd_Mult = 2, Expt = "Sutherland, Canada 2018", Trait = "DTF", Select_Coef_Ad = "c")
  output$Adapted_Table.csv <- downloadHandler(
    filename = function() {"Adapted_Table.csv"},
    content = function(file) {
      myOrigins <- unique(c(input$Ad_Country, input$Ad_Countries))
      x1 <- dd %>% filter(Expt == input$Expt, Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% 
        select(Entry, Trait=input$Trait)
      xx <- m2 %>% rename(Coef=input$Select_Coef_Ad) %>%
        left_join(select(ldp, Entry, Cluster, Origin), by = "Entry") %>%
        left_join(x1, by = "Entry") %>%
        mutate(Origin = as.character(Origin),
               Origin2 = ifelse(Origin %in% myOrigins, Origin, "Other"),
               Trait = round(ifelse(is.nan(Trait), max(.$Trait,na.rm = T), Trait),1))
      yy <- xx %>%
        group_by(Origin) %>%
        summarise_at(vars(Trait, Coef), funs(mean, sd), na.rm = T) %>%
        rename(Coef=Coef_mean, Trait=Trait_mean) %>%
        filter(Origin == input$Ad_Country)
      #
      myXmin <- yy$Trait - input$sd_Mult * yy$Trait_sd
      myXmax <- yy$Trait + input$sd_Mult * yy$Trait_sd
      xx <- xx %>%
        mutate(Origin2 = ifelse(Trait > myXmin & Trait < myXmax &
                                  !Origin %in% myOrigins, "\"Adapted\"", Origin2)) %>%
        filter(Origin2 == "\"Adapted\"") %>% 
        select(Entry, Name, Origin, Cluster, Coef, Trait) %>%
        mutate(Coef = round(Coef * 10000, 1))
      #colnames(xx)[colnames(xx)=="Coef"] <- input$Select_Coef_Ad
      #colnames(xx)[colnames(xx)=="Trait"] <- input$Trait
      write.csv(xx, file, row.names = F)
    }
  )
  # input <- list(Ad_Country="Canada", Ad_Countries="India", sd_Mult = 2, Expt = "Sutherland, Canada 2018", Trait = "DTF", Select_Coef_Ad = "c")
  output$Adapted_Table <- DT::renderDataTable({
    myOrigins <- unique(c(input$Ad_Country, input$Ad_Countries))
    x1 <- dd %>% filter(Expt == input$Expt, Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>% 
      select(Entry, Trait=input$Trait)
    xx <- m2 %>% rename(Coef=input$Select_Coef_Ad) %>%
      left_join(select(ldp, Entry, Cluster, Origin), by = "Entry") %>%
      left_join(x1, by = "Entry") %>%
      mutate(Origin = as.character(Origin),
             Origin2 = ifelse(Origin %in% myOrigins, Origin, "Other"),
             Trait = round(ifelse(is.nan(Trait), max(.$Trait,na.rm = T), Trait),1))
    yy <- xx %>%
      group_by(Origin) %>%
      summarise_at(vars(Trait, Coef), funs(mean, sd), na.rm = T) %>%
      rename(Coef=Coef_mean, Trait=Trait_mean) %>%
      filter(Origin == input$Ad_Country)
    #
    myXmin <- yy$Trait - input$sd_Mult * yy$Trait_sd
    myXmax <- yy$Trait + input$sd_Mult * yy$Trait_sd
    xx <- xx %>%
      mutate(Origin2 = ifelse(Trait > myXmin & Trait < myXmax &
                                !Origin %in% myOrigins, "\"Adapted\"", Origin2)) %>%
      filter(Origin2 == "\"Adapted\"") %>% 
      select(Entry, Name, Origin, Cluster, Coef, Trait) %>%
      mutate(Coef = round(Coef * 10000, 1))
    #colnames(xx)[colnames(xx)=="Coef"] <- input$Select_Coef_Ad
    #colnames(xx)[colnames(xx)=="Trait"] <- input$Trait
  }, options = list(pageLength = 324))
}
# Run the application
shinyApp(ui = ui, server = server)