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Copy pathapp_AGILE_LDP_Phenology.R
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app_AGILE_LDP_Phenology.R
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##############################################################################
########## AGILE LDP Phenology App ##########
##############################################################################
# Libraries
##############################################################################
# setwd("C:/Google Drive/gitfolder/AGILE_LDP_Phenology")
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 Phenology App"), hr(),
numericInput("Entry", "Entry", 1, 1, 324, 1),
tableOutput("EntryTable"),
radioButtons("Plot_Entry", "Plot Entry", c(T, F), T, inline = T),
checkboxGroupInput("MyClusters", "DTF Clusters", c("1","2","3","4","5","6","7","8"), c("1","2","3","4","5","6","7","8"), inline = T),
checkboxGroupInput("MyRegions", "Origins", 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("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"),
p(strong("Understanding photothermal interactions can help expand production range and increase genetic diversity of lentil (", em("Lens culinaris"), " Medik.)")),
p(em("Plants, People, Planet. (2020) 00: 1-11.")),
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("Tables", tabsetPanel(
tabPanel("LDP", h1("Lentil Diversity Panel"), DT::dataTableOutput("Table_LDP")),
tabPanel("Field Trials", h1("Field Trial Info"), DT::dataTableOutput("Table_FieldTrials")),
tabPanel("PCA", h1("PCA and Hierarchical Clustering of DTF"), DT::dataTableOutput("Table_PCA")),
tabPanel("Data", h1("Phenology Data"), DT::dataTableOutput("Table_Data")),
tabPanel("Coefs", h1("Photothermal Model Coeficients"), DT::dataTableOutput("Table_Coefs")),
tabPanel("DTF Predictions", h1("Field Trial Info"), DT::dataTableOutput("Table_Model"))
)), # Tables tab
tabPanel("Phenology", tabsetPanel(
tabPanel("Trait", tabsetPanel(
tabPanel("Trait Expts", plotlyOutput("Trait_Expts", height = 500)),
tabPanel("Trait Expt",
radioButtons("GroupBy", "Group by:", c("None","Cluster","Region"), "None", inline = T),
plotlyOutput("Trait_Expt", height = 500)),
tabPanel("Trait Map", leafletOutput("Trait_Map", height = 500))
)),
tabPanel("Traits", tabsetPanel(
tabPanel("ggridges", plotOutput("ggridges")),
tabPanel("Traits Expt", selectInput("Traits", "Traits", trts, c("DTF","DTS","DTM"), multiple = T),
plotlyOutput("Traits_Expt")),
tabPanel("Corr",
fluidRow(column(selectInput("Trait2", "Trait (x axis)", trts, "DTS"), width = 2),
column(selectInput("Expt2", "Expt (x axis)", names_Expt), width = 3),
column(radioButtons("AddTrendline", "Add Trendline", c(T,F), T, inline = T), width = 2)),
plotlyOutput("Corr", height = 500))
)),
tabPanel("EnvData", tabsetPanel(
tabPanel("EnvData Expts", plotOutput("EnvData_Expts")),
tabPanel("EnvData_Expt",
fluidRow(column(radioButtons("Plot_DTF", "Plot DTF Window", c(T,F), F, inline = T), width = 2),
column(radioButtons("Plot_DTS", "Plot DTS Window", c(T,F), F, inline = T), width = 2),
column(radioButtons("Plot_DTM", "Plot DTM Window", c(T,F), F, inline = T), width = 2)),
plotOutput("EnvData_Expt", height = 500))
))
)), # Phenology tab
tabPanel("Clusters", tabsetPanel(
tabPanel("PCs",
radioButtons("PC_Type","Plot", c("PC1 x PC2","PC1 x PC3","PC2 x PC3"), "PC1 x PC2", inline = T),
plotlyOutput("PCs", height = 500)),
tabPanel("Ribbon", plotlyOutput("Ribbon", height = 500)),
tabPanel("Map", tabsetPanel(
tabPanel("Pies", radioButtons("PCA_Map_size", "Pie Size", c("Proportional", "Constant"), inline = T),
leafletOutput("Map_Pies", height = 500)),
tabPanel("Points", radioButtons("PCA_Map_Points", "Plot", c("GeoLocated", "All"), inline = T),
leafletOutput("Map_Points", height = 500))
) ),
tabPanel("Origins",
checkboxGroupInput("PCA_Countries", "Countries", unique(ldp$Origin), selected = c("Canada", "India"), inline = T),
tabsetPanel(
tabPanel("Expt", plotlyOutput("Origins_Expt")),
tabPanel("Expts", plotOutput("Origins_Expts"))
) ) ) ), # Clusters tab
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))
) ),
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$Origin))], selected = "USA", inline = T),
radioButtons("Select_Coef_Ad", "Select Coefficient", c("a","b","c"), "c", inline = T),
tabsetPanel(
tabPanel("TraitxCoef", plotlyOutput("Adapted1", height = 500)),
tabPanel("cxb", plotlyOutput("Adapted2", height = 500)),
tabPanel("Table",
downloadButton("Adapted_Table.csv", "Download Selected Table"),
DT::dataTableOutput("Adapted_Table"))
) )
) ) # PhotoThermal Model tab
) ) )
) #fluidpage
##############################################################################
# Server
##############################################################################
server <- function(input, output) {
###############################################################################
# - Sidebar
###############################################################################
output$EntryTable <- renderTable({
ldp %>% filter(Entry == input$Entry) %>% select(Name, Origin)
})
###############################################################################
# - Home
###############################################################################
###############################################################################
# - Tables
###############################################################################
output$Table_LDP <- DT::renderDataTable(ldp %>% select(-Lat2, -Lat3, -Lon2, -Lon3, -Cluster), options = list(pageLength = 324))
#
output$Table_FieldTrials <- DT::renderDataTable(ff %>% select(ExptShort, Expt, MacroEnv, Start, End, Days, T_mean, P_mean, Lat, Lon), options = list(pageLength = 18))
#
output$Table_Data <- DT::renderDataTable({
dd %>% select(Entry, Name, Expt, Origin, Region, Cluster, DTE, DTF, DTS, DTM, VEG, REP, Tb, Tf, Pc, Pf, PTT, DTF2) %>%
filter(Expt %in% input$Expts, Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
mutate_at(vars(DTE, DTF, DTS, DTM, VEG, REP, Tb, Tf, Pc, Pf, PTT, DTF2), funs(round(.,1)))
})
#
output$Table_PCA <- DT::renderDataTable(pca, options = list(pageLength = 324))
#
output$Table_Coefs <- DT::renderDataTable(m2, options = list(pageLength = 324))
#
output$Table_Model <- DT::renderDataTable(m1%>%filter(Expt%in% input$Expts), options = list(pageLength = 324))
#
###############################################################################
# - Phenology
###############################################################################
# input <- list(Expt = "Rosthern, Canada 2017", Trait = "DTF", Plot_Entry = T, Entry = 1, GroupByCluster = T, MyClusters = c("1","2","3","4"), MyRegions = c("Asia","Europe"))
output$Trait_Expt <- renderPlotly({
xx <- dd %>% select(Entry, Name, Expt, ExptShort, Origin, Region, SubRegion, Cluster, input$Trait) %>%
filter(Expt == input$Expt) %>%
left_join(select(ff, Expt, MacroEnv), by = "Expt") %>%
gather(Trait, Value, input$Trait) %>%
mutate(Trait = factor(Trait, levels = c(input$Trait)) )
if(input$GroupBy == "None") {
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(x = "", y = Value))
}
if(input$GroupBy == "Region") {
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(x = Region, y = Value))
}
if(input$GroupBy == "Cluster") {
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(x = Cluster, y = Value))
}
mp <- mp +
geom_violin(fill = "grey", alpha = 0.5, color = NA) +
geom_quasirandom(aes(color = Cluster, key1 = Entry, key2 = Name, key3 = Origin)) +
scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
theme_AGL +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(title = input$Expt, y = input$Trait, 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(Expts = names_Expt, Trait = "DTF", Plot_Entry = "TRUE", Entry = 1, MyClusters = c("1","2","3","4"))
output$Trait_Expts <- renderPlotly({
xx <- dd %>% select(Entry, Name, Expt, ExptShort, Origin, Cluster, Region, input$Trait) %>%
filter(Expt %in% input$Expts) %>%
left_join(select(ff, Expt, MacroEnv), by = "Expt") %>%
gather(Trait, Value, input$Trait) %>%
mutate(Trait = factor(Trait, levels = c(input$Trait)) )
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(x = ExptShort, y = Value)) +
geom_violin(aes(fill = MacroEnv), alpha = 0.25, color = NA) +
geom_quasirandom(aes(key1 = Entry, key2 = Name, key3 = Origin, key4 = Cluster, key5 = Expt, color = Cluster), alpha = 0.5) +
theme_AGL +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
legend.position = "none") +
scale_fill_manual(values = c("darkgreen","darkred","darkblue")) +
scale_color_manual(values = colors[as.numeric(input$MyClusters)])
labs(x = NULL, y = input$Trait)
if(input$Plot_Entry == T) {
mp <- mp +
geom_line(data = xx %>% filter(Entry == input$Entry, !is.na(Value)), aes(group = Entry), color = "Red") +
geom_point(data = xx %>% filter(Entry == input$Entry),
aes(key1 = Entry, key2 = Name, key3 = Origin, key4 = Cluster, key5 = Expt),
color = "Black", fill = "Red", pch = 23)
}
mp
})
# input <- list(Traits = c("DTF","DTS","DTM"), Expt = "Rosthern, Canada 2017", Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"))
output$Traits_Expt <- renderPlotly({
xx <- dd %>% filter(Expt == input$Expt) %>%
gather(Trait, Value, DTE, DTF, DTF2, DTS, DTM, VEG, REP, RDTF, DTF2_scaled, Tb, Tf, Pc, Pf, PTT) %>%
filter(Trait %in% input$Traits) %>%
mutate(Trait = factor(Trait, levels = unique(Trait)))
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(x = "", y = Value)) +
geom_violin() +
geom_quasirandom(aes(color = Cluster, key1 = Entry, key2 = Name, key3 = Origin)) +
facet_wrap(.~Trait, scales = "free_y", nrow = 1) +
scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
theme_AGL +
labs(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(Expt = "Rosthern, Canada 2016", Plot_Entry = T, Entry = 1, Plot_DTF = T, Plot_DTS = F, Plot_DTM = F)
output$EnvData_Expt <- renderPlot({
xx <- ee %>% filter(Expt == input$Expt)
yy <- ff %>% filter(Expt == input$Expt)
y1 <- select(yy, Expt, Location, Year, min = DTF_min, max = DTF_max) %>%
mutate(Trait = "DTF")
y2 <- select(yy, Expt, Location, Year, min = DTS_min, max = DTS_max) %>%
mutate(Trait = "DTS")
y3 <- select(yy, Expt, Location, Year, min = DTM_min, max = DTM_max) %>%
mutate(Trait = "DTM")
yy <- bind_rows(y1, y2, y3)
xE <- dd %>% filter(Entry == input$Entry, Expt == input$Expt)
mp <- ggplot(xx)
if(input$Plot_DTF == F) {
yy <- yy %>% filter(Trait != "DTF")
} else { if(input$Plot_Entry == T) { mp <- mp + geom_vline(xintercept = xE$DTF) } }
if(input$Plot_DTS == F) {
yy <- yy %>% filter(Trait != "DTS")
} else { if(input$Plot_Entry == T) { mp <- mp + geom_vline(xintercept = xE$DTS) } }
if(input$Plot_DTM == F) {
yy <- yy %>% filter(Trait != "DTM")
} else { if(input$Plot_Entry == T) { mp <- mp + geom_vline(xintercept = xE$DTM) } }
myrects <- as.logical(c(input$Plot_DTF, input$Plot_DTS, input$Plot_DTM))
yy <- yy %>% mutate(Trait = factor(Trait, levels = c("DTF","DTS","DTM")[myrects]))
if(as.logical(input$Plot_DTF) | as.logical(input$Plot_DTS) | as.logical(input$Plot_DTM) == TRUE) {
mp <- mp + geom_rect(data = yy, aes(xmin = min, xmax = max, fill = Trait),
ymin = -Inf, ymax = Inf, alpha = 0.4)
}
mp <- mp +
geom_line(aes(x = DaysAfterPlanting, y = DayLength_rescaled, color = "Blue")) +
geom_line(aes(x = DaysAfterPlanting, y = Temp_mean, color = "darkred") ) +
geom_ribbon(aes(x = DaysAfterPlanting, ymin = Temp_min, ymax = Temp_max),
fill = alpha("darkred", 0.25),
color = alpha("darkred", 0.25)) +
facet_grid(.~Expt, scales = "free_x", space = "free_x") +
scale_color_manual(name = NULL, values = c("Blue", "darkred"),
labels = c("Day length", "Temperature") ) +
scale_fill_manual(name = NULL,
values = c("darkgreen", "darkblue", "darkgoldenrod2")[myrects]) +
coord_cartesian(ylim=c(0,40)) +
theme_AGL +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
scale_y_continuous(sec.axis = sec_axis(~ (16.62 - 9.11) * . / (40 - 0) + 9.11,
breaks = c(10, 12, 14, 16), name = "Hours")) +
labs(y = expression(paste(degree, "Celcius")), x = "Days after Sowing") +
guides(colour = guide_legend(order = 1), fill = guide_legend(order = 2))
mp
})
# input <- list(Expts = c("Rosthern, Canada 2016", "Rosthern, Canada 2017")
observe({
output$EnvData_Expts <- renderPlot({
# Prep data
xx <- ee %>% filter(Expt %in% input$Expts)
yy <- ff %>% filter(Expt %in% input$Expts) %>%
mutate(DTF_min = Start + DTF_min, DTF_max = Start + DTF_max,
DTM_min = Start + DTM_min, DTM_max = Start + DTM_max)
y1 <- select(yy, Expt, Location, Year, min = DTF_min, max = DTF_max) %>%
mutate(Trait = "DTF")
y2 <- select(yy, Expt, Location, Year, min = DTM_min, max = DTM_max) %>%
mutate(Trait = "DTM")
yy <- bind_rows(y1, y2)
# Plot
ggplot(xx) +
geom_rect(data = yy, aes(xmin = min, xmax = max, fill = Trait),
ymin = 0, ymax = 40, alpha = 0.4) +
geom_line(aes(x = Date, y = DayLength_rescaled, color = "Blue")) +
geom_line(aes(x = Date, y = Temp_mean, color = "darkred") ) +
geom_ribbon(aes(x = Date, ymin = Temp_min, ymax = Temp_max),
fill = alpha("darkred", 0.25),
color = alpha("darkred", 0.25)) +
facet_grid(Expt~., scales = "free_x", space = "free_x") +
scale_color_manual(name = NULL, values = c("Blue", "darkred"),
labels = c("Day length", "Temperature") ) +
scale_fill_manual(name = NULL, values = c("darkgreen", "darkgoldenrod2")) +
coord_cartesian(ylim=c(0,40)) +
theme_AGL +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
scale_x_date(breaks = "1 month", labels = date_format("%B")) +
scale_y_continuous(sec.axis = sec_axis(~ (16.62 - 9.11) * . / (40 - 0) + 9.11,
breaks = c(10, 12, 14, 16), name = "Hours")) +
labs(y = expression(paste(degree, "Celcius"))) +
guides(colour = guide_legend(order = 1), fill = guide_legend(order = 2))
}, height = 150*length(input$Expts))
})
# input <- list(Expts = c("Rosthern, Canada 2016","Rosthern, Canada 2017")
observe({
output$ggridges <- renderPlot({
xx <- dd %>% select(Expt, Cluster, Region, DTF, DTS, DTM) %>%
filter(Expt %in% input$Expts, Cluster %in% input$MyClusters) %>%
gather(Trait, Value, DTF, DTS, DTM) %>%
mutate(Trait = factor(Trait, levels = c("DTF", "DTS", "DTM")))
# Plot
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(x = Value, y = Expt, fill = Trait)) +
ggridges::geom_density_ridges(alpha = 0.7) +
scale_fill_manual(values = c("darkgreen", "darkred", "darkgoldenrod2")) +
theme_AGL +
theme(legend.position = "top", legend.margin = unit(c(0,0,0,0), "cm")) +
labs(y = NULL, x = "Days After Sowing")
mp
}, height = 50*length(input$Expts))
})
# input <- list(Expt = "Rosthern, Canada 2017", Trait = "DTF", Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"))
output$Trait_Map <- renderLeaflet({
xx <- dd %>% filter(Expt == input$Expt) %>%
gather(Trait, Value, DTE, DTF, DTF2, DTS, DTM, VEG, REP, RDTF, DTF2_scaled, Tb, Tf, Pc, Pf, PTT) %>%
filter(Trait == input$Trait) %>%
left_join(select(ldp, Entry, Lat=Lat3, Lon=Lon3), by = "Entry") %>%
filter(!is.na(Lat), !is.na(Lon)) %>%
select(Entry, Name, Origin, Cluster, Region, Lat, Lon, Expt, Trait, Value)
xE <- xx %>% filter(Entry == input$Entry)
pal <- colorNumeric(c("darkgoldenrod2", "darkblue"), xx$Value)
xx <- xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions)
#
mp <- leaflet() %>% addProviderTiles(providers$CartoDB.Positron) %>%
addMinicharts(
lat = xx$Lat, lng = xx$Lon, width = 10, fillColor = pal(xx$Value),
popup = popupArgs(supValues = xx, showValues = F)
) %>%
addLegend("bottomleft", pal = pal, values = xx$Value,
title = input$Trait, opacity = 1 )
if(input$Plot_Entry == T) {
mp <- mp %>% addMinicharts(lat = xE$Lat, lng = xE$Lon, width = 10, fillColor = "Red")
}
mp
})
# input <- list(Expt = "Rosthern, Canada 2017", Trait = "DTF", Expt2 = "Rosthern, Canada 2017", Trait2 = "DTS", Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"))
output$Corr <- renderPlotly({
x1 <- dd %>% select(Entry, Name, Expt, Origin, Cluster, Region, input$Trait) %>%
filter(Expt %in% input$Expt)
x2 <- dd %>% filter(Expt %in% input$Expt2) %>% select(Entry, input$Trait2)
xx <- left_join(x1, x2, by = "Entry") %>% as.data.frame()
r2 <- round(cor(xx[,input$Trait2], xx[,input$Trait], method = "pearson", use = "complete"), 3)
rmse <- round(modelRMSE(xx[,input$Trait2], xx[,input$Trait]), 1)
coefs <- round(lm(xx[,input$Trait2] ~ xx[,input$Trait])$coefficients, 3)
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions),
aes(y = get(input$Trait2), x = get(input$Trait)))
if(input$AddTrendline == T) { mp <- mp + geom_smooth(method = "lm") }
mp <- mp +
geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin, color = Cluster)) +
scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
theme_AGL +
theme(legend.position = "none") +
labs(title = paste(input$Expt, "| RR = ", r2, " | RMSE = ", rmse, " | y = ", coefs[2],"x + ", coefs[1]),
x = paste(input$Expt2, input$Trait2, sep = " - "),
y = paste(input$Expt, input$Trait, sep = " - ") )
if(input$Plot_Entry == T) {
mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
aes(key1 = Entry, key2 = Name, key3 = Origin),
color = "Black", fill = "Red", size = 3, pch = 23)
}
mp
})
###############################################################################
# - Clusters
###############################################################################
# input <- list(PC_Type = "PC1 x PC2", Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"), MyRegions = c("Asia","Africa"))
output$PCs <- renderPlotly({
xx <- pca %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions)
hulltype <- paste(input$PCx, input$PCy)
if(input$PC_Type == "PC1 x PC2") { myx <- "PC1"; myy <- "PC2" }
if(input$PC_Type == "PC1 x PC3") { myx <- "PC1"; myy <- "PC3" }
if(input$PC_Type == "PC2 x PC3") { myx <- "PC2"; myy <- "PC3" }
find_hull <- function(df) df[chull(df[,myx], df[,myy]), ]
polys <- plyr::ddply(xx, "Cluster", find_hull) %>% mutate(Cluster = factor(Cluster))
mp <- ggplot(xx, aes(x = get(myx), y = get(myy), color = Cluster)) +
geom_polygon(data = polys, alpha = 0.15, aes(fill = Cluster)) +
geom_point(aes(key1 = Entry, key2 = Name, key3 = Origin)) +
scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
scale_fill_manual(values = colors[as.numeric(input$MyClusters)]) +
theme_AGL +
labs(title = "PCA", x = myx, y = myy)
if(input$Plot_Entry == T) {
mp <- mp + geom_point(data = pca %>% filter(Entry == input$Entry),
color = "Black", fill = "Red", size = 3, pch = 23)
}
mp
})
# input <- list(Expts = names_Expt, Trait = "DTF", Plot_Entry = T, Entry = 1, MyClusters = c("1","2","3","4"), MyRegions = c("Asia","Africa"))
output$Ribbon <- renderPlotly({
xx <- dd %>%
filter(Expt %in% input$Expts, Cluster %in% input$MyClusters, Region %in% input$MyRegions) %>%
group_by(Expt, ExptShort, Cluster) %>%
summarise(mean = mean(DTF2_scaled, na.rm = T), sd = sd(DTF2_scaled, na.rm = T)) %>%
ungroup() %>%
mutate(ClusterNum = plyr::mapvalues(Cluster, as.character(1:8), summary(pca$Cluster)))
xE <- dd %>% filter(Entry %in% input$Entry, Expt %in% input$Expts)
mp <- ggplot(xx, aes(x = ExptShort)) +
geom_ribbon(aes(ymin = mean - sd, ymax = mean + sd, fill = Cluster, group = Cluster),
alpha = 0.1, color = NA) +
geom_point(aes(y = mean, color = Cluster)) +
geom_line(aes(y = mean, color = Cluster, group = Cluster), size = 1) +
scale_color_manual(values = colors[as.numeric(input$MyClusters)]) +
scale_fill_manual(values = colors[as.numeric(input$MyClusters)]) +
coord_cartesian(ylim = c(0.95,5.05), expand = F) +
theme_AGL +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
strip.placement = "outside",
axis.line = element_line(), axis.ticks = element_line()) +
labs(y = "DTF (scaled 1-5)", x = NULL)
if(input$Plot_Entry == T) {
mp <- mp +
geom_line(data = xE, aes(y = DTF2_scaled, group = Entry), color = "Red") +
geom_point(data = xE, aes(key1 = Entry, key2 = Name, key3 = Origin, key4 = Cluster, key5 = Expt, y = DTF2_scaled),
color = "Black", fill = "Red", size = 2, pch = 23)
}
mp
})
# input <- list(MyClusters = c("1","2","3","4"))
output$Map_Pies <- renderLeaflet({
pal <- colorFactor(colors, domain = 1:8)
xx <- ldp %>%
filter(Cluster %in% input$MyClusters) %>%
group_by(Origin, Cluster) %>%
summarise(Count = n()) %>% ungroup()
x1 <- xx %>% group_by(Origin) %>% summarise(Total = sum(Count))
xx <- xx %>%
spread(Cluster, Count) %>%
left_join(select(ct, Origin=Country, Lat, Lon), by = "Origin") %>%
left_join(x1, by = "Origin") %>%
filter(!is.na(Lat), !is.na(Lon))
xx[is.na(xx)] <- 0
if(input$PCA_Map_size == "Proportional") {
mp <- leaflet() %>% addProviderTiles(providers$CartoDB.Positron) %>%
addMinicharts(
lat = xx$Lat, lng = xx$Lon, width = xx$Total+10, type = "pie",
chartdata = select(xx, input$MyClusters),
colorPalette = pal(as.numeric(input$MyClusters)) )
}
if(input$PCA_Map_size == "Constant") {
mp <- leaflet() %>% addProviderTiles(providers$CartoDB.Positron) %>%
addMinicharts(
lat = xx$Lat, lng = xx$Lon, width = 20, type = "pie",
chartdata = select(xx, input$MyClusters),
colorPalette = pal(as.numeric(input$MyClusters)) )
}
mp
})
# input <- list(Plot_Entry = T, Entry = 1, PCA_Map_Points = "All", MyClusters = c("a","2","3","4"))
output$Map_Points <- renderLeaflet({
pal <- colorFactor(colors, domain = 1:8)
xx <- ldp %>% select(-Lat, -Lon)
if(input$PCA_Map_Points == "All") {
xx <- xx %>% rename(Lat=Lat3, Lon=Lon3) %>% select(-Lat2,-Lon2)
} else { xx <- xx %>% rename(Lat=Lat2, Lon=Lon2) %>% select(-Lat3,-Lon3) }
xx <- xx %>% filter(!is.na(Lat), !is.na(Lon))
xE <- xx %>% filter(Entry == input$Entry)
xx <- xx %>% filter(Cluster %in% input$MyClusters, Region %in% input$MyRegions)
#
mp <- leaflet() %>% addProviderTiles(providers$CartoDB.Positron) %>%
addMinicharts(lng = xx$Lon, lat = xx$Lat, width = 10,
fillColor = pal(xx$Cluster),
popup = popupArgs(supValues = xx, showValues = F) ) %>%
addLegend("bottomleft", pal = pal, values = xx$Cluster,
title = "Cluster", opacity = 1 )
if(input$Plot_Entry == T) {
mp <- mp %>% addMinicharts(lng = xE$Lon, lat = xE$Lat, width = 10, fillColor = "Red" )
}
mp
})
# input <- list(Expt = "Rosthern, Canada 2017", Trait = "DTF", Plot_Entry = T, Entry = 1, PCA_Countries = c("Canada","India"))
output$Origins_Expt <- renderPlotly({
yy <- input$PCA_Countries
xx <- dd %>%
filter(Expt == input$Expt) %>%
mutate(Origin = factor(Origin, levels = unique(Origin)[rev(order(unique(Origin)))])) %>%
filter(Origin %in% yy) %>%
mutate(Origin = factor(Origin, levels = yy))
cols <- colors[as.character(1:8) %in% as.character(unique(xx$Cluster)) &
as.character(1:8) %in% unique(input$MyClusters)]
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters),
aes(y = get(input$Trait), x = Origin)) +
geom_quasirandom(aes(key1 = Entry, key2 = Name, color = Cluster)) +
facet_grid(.~Expt, scales = "free_y") +
scale_color_manual(values = cols) +
theme_AGL +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = input$Trait, x = NULL)
if(input$Plot_Entry == T) {
mp <- mp + geom_point(data = xx %>% filter(Entry == input$Entry),
aes(key1 = Entry, key2 = Name, color = Cluster),
color = "Black", fill = "Red", size = 3, pch = 23)
}
mp
})
# input <- list(Expts = c("Rosthern, Canada 2017", "Bhopal, India 2017"), Trait = "DTF", Plot_Entry = T, Entry = 1, PCA_Countries = c("Canada","India"), MyClusters = as.character(c(1:5,7:8)))
observe({
output$Origins_Expts <- renderPlot({
yy <- input$PCA_Countries
xx <- dd %>%
filter(Expt %in% input$Expts) %>%
mutate(Origin = factor(Origin, levels = unique(Origin)[rev(order(unique(Origin)))])) %>%
filter(Origin %in% yy) %>%
mutate(Origin = factor(Origin, levels = yy))
cols <- colors[as.character(1:8) %in% as.character(unique(xx$Cluster)) &
as.character(1:8) %in% unique(input$MyClusters)]
mp <- ggplot(xx %>% filter(Cluster %in% input$MyClusters),
aes(y = get(input$Trait), x = Origin)) +
geom_quasirandom(aes(color = Cluster), size = 3) +
facet_grid(Expt~., scales = "free_y") +
scale_color_manual(values = cols) +
theme_AGL +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(title = input$Trait, y = NULL, x = NULL)
if(input$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) )
})
###############################################################################
# -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) %>%