#### DATA ####
#data("trajs")
#trajs<-read.csv("G:/dgsd/Dopro/Projets/2018/E - Etude_RSA/E-RSA_Appli_Shiny_Trajectoires/GitHub/ViCaTraj-master/data/trajs.csv",row.names = 1)
# rev(wesanderson::wes_palette(name = "Darjeeling1", n = length(alphabet(trajs)), type = "discrete"))->cpal.seq
# cpal(seqdata = trajs)<-cpal.seq
# seqtab(trajs[ , ], idxs = 0, format = "STS")->unique.trajs
#### SERVER ####
server <- function(input, output, session) {
library(ViCaTraj)
library(shiny)
library(TraMineR)
library(ggplot2)
library(TraMineRextras)
library(RColorBrewer)
library(DT)
library(RColorBrewer)
library(wesanderson)
library(shinythemes)
library(shinycssloaders)
library(cluster)
library(fastcluster)
library(gridExtra)
library(ggalluvial)
library(tidyr)
library(dplyr)
library(shinyWidgets)
library(formattable)
library(WeightedCluster)
library(stringr)
library(shinyjs)
library(classInt)
library(shinyBS)
library(forcats)
library(ggthemes)
library(tibble)
callModule(module = module_data, id = "id1")->DATAs
#observe({print(head(DATAs()$DATA_COMP))})
#### STATISTIQUES DESCRIPTIVES ####
##### SELECT AND PLOT MODULE #####
callModule(module = module_select_and_plot2, data = DATAs, id = "id2")
##### SELECT AND PLOT ANCIEN #####
####### Type de graph ####
#mise a jour des input et création de nouveaux input selon le type de sous-population chois
#### MODULE TABLE ####
callModule(module = module_tabdes, data = DATAs, id = "id3")
#### MODULE CLASSIF ####
callModule(module = module_classification, data=DATAs, id = "id5")->DATAs.c#DATA.CLASSIF$DATAs.c
#observe({
callModule(module = module_select_and_plot2, data = DATAs.c, #reactive(DATA.CLASSIF$DATAs.c),
id = "id25")
callModule(module = module_tabdes, data = DATAs.c, id = "id35")
# observe({
# input$reactKlass
# isolate({
# callModule(module = module_select_and_plot, data = DATAs.c(), #reactive(DATA.CLASSIF$DATAs.c),
# id = "id25")
# })
# })
#})
# observe({
# input$reactKlass
# isolate({
# callModule(module = module_tabdes, data = DATAs.c(), id = "id35")
# })
# })
}
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