key_value_calculator <- function(tisefka = NULL,key_value =NULL){
tisefka <- na.omit(tisefka)
my_value <- vector()
if("Sum"%in%key_value){
my_value["Sum"] <- sum(tisefka,na.rm = TRUE)
}
if("Average"%in%key_value){
my_value["Average"] <- colMeans(tisefka,na.rm = TRUE)
}
if("Maximum"%in%key_value){
my_value["Maximum"] <- max(tisefka,na.rm = TRUE)
}
if("Minimum"%in%key_value ){
my_value["Minimum"] <- min(tisefka,na.rm = TRUE)
}
if("First Value"%in%key_value){
my_value["First Value"] <- head(tisefka,1)
}
if( "Last Value"%in%key_value ){
my_value["Last Value"]<- tail(tisefka,1)
}
return(round(my_value,3))
}
#------------------------ multiple-select, multiple output
#' Saldae Dashboard Module UI (analytics)
#' @description Saldae Dashboard module UI : forecasting
#' @author Farid Azouaou
#' @param id server module ID
#' @param div_width dimension information about the framework(html object)
#' @param mod_title module title (default NULL)
#' @return UI module
#' @export
SA_tisefka_forecast_UI <- function(id,mod_title = NULL ,div_width = "col-xs-12 col-sm-6 col-md-8") {
ns <- NS(id)
fluidPage(
fluidRow(
column(width = 12,
uiOutput(ns("analytics_header_box"))
)
),
fluidRow(
column(width = 6,
uiOutput(ns("analytics_config_box"))
),
column(width = 6,
uiOutput(ns("analytics_advanced_box"))
)
),
uiOutput(ns("graphs_ui"))
)
}
#' Saldae Dashboard Module Server Analytics
#' @description Saldae Dashboard module SERVER : render and generate multiple output objects for analytics
#' @author Farid Azouaou
#' @param input input shinydashboard elements containing information to use for output generation
#' @param output output shinydashboard element
#' @param session shiny session
#' @param tisefka reactive object containing data
#' @param div_width dimension information about the framework(html object)
#' @return output objects to be displayed in corresponding UI module
#' @export
SA_tisefka_forecast_mod <- function(input, output, session,i18n ,tisefka,div_width = "col-xs-6 col-sm-12 col-md-6") {
tisefka_choices <- reactive({
req(tisefka())
tisefka()$numeric_variables
})
tisefka_tizegzawin <- reactive({
req(tisefka())
tisefka()$tisefka_tizegzawin
})
non_numeric_variables <- reactive({
req(tisefka())
tisefka()$non_numeric_variables
})
categoricals_unique_values <- reactive({
req(tisefka())
tisefka()$categoricals_unique_values
})
ts_time_units <- reactive({
tisefka()$ts_time_units
})
ns <- session$ns
output$analytics_header_box <- renderUI({
bs4Dash::box(title = i18n$t("Forecasting Board"),collapsible = TRUE,
status = "info",width = 12,solidHeader = TRUE,
#-----HEADER CONTENT
fluidRow(
column(width = 3,uiOutput(ns("select_element"))) ,
column(width = 2,uiOutput(ns("var_granularity"))),
column(width = 2,uiOutput(ns("aggregation_metric"))),
# column(width = 4,uiOutput(ns("SA_outliers"))),
column(width = 3,shiny::br(), uiOutput(ns("forecasting_submit")))
),
uiOutput(ns("non_numeric_variables_inputs")),
SaldaeReporting::add_to_report_ui(ns("add_forecasting"))
)
})
output$actuals_time_periode <- renderUI({
req(tisefka_tizegzawin())
actuals_time_periode <- tisefka_tizegzawin()%>%dplyr::pull(date)
actuals_time_periode <- c(min(actuals_time_periode),max(actuals_time_periode))
dateRangeInput(inputId = ns("actuals_time_periode"), label = i18n$t("Time periode"),
max = actuals_time_periode[2], min = actuals_time_periode[1], start =actuals_time_periode[1], end = actuals_time_periode[2])
})
output$forecast_horizon <- renderUI({
req(tisefka_iheggan())
forecast_horizon <- ceiling(nrow(tisefka_iheggan())/3)
shiny::sliderInput(inputId = ns("forecast_horizon"), label = i18n$t("Forecast Horizon"), min = 5, max = nrow(tisefka_iheggan()), value = forecast_horizon)
})
output$analytics_config_box <- renderUI({
shinyWidgets::dropdown(
tags$h2("Advanced Configs"),
fluidRow(
column(colourpicker::colourInput(ns("obs_col"), i18n$t("Observations"), "#00AFBB", palette = "limited"),width = 3),
column(colourpicker::colourInput(ns("fcast_col"), i18n$t("Predictions"), "#E1AF00", palette = "limited"),width = 3)
),
fluidRow(
column(width = 6,uiOutput(ns("actuals_time_periode"))),
column(width = 6,uiOutput(ns("forecast_horizon")))
),
fluidRow(
column(width = 6,uiOutput(ns("SA_key_figure_select")))
),
style = "unite", icon = icon("gear"),
status = "primary", width = "450px",
animate = shinyWidgets::animateOptions(
enter = shinyWidgets::animations$fading_entrances$fadeInLeftBig,
exit = shinyWidgets::animations$fading_exits$fadeOutRightBig
)
)
})
# output$SA_outliers <- renderUI({
# shinyWidgets::prettySwitch(
# inputId = ns("SA_outliers"),
# label = i18n$t("Anomaly detection"),
# status = "info",
# fill = TRUE)
# })
output$forecasting_submit <- renderUI({
req(input$variable_picker)
bs4Dash::actionButton(inputId = ns("forecasting_submit"), label = i18n$t("Start"), icon = icon("play"), status = "info")
})
observeEvent(eventExpr=non_numeric_variables(),handlerExpr= {
non_numeric_variables()%>%purrr::imap( ~{
output_name_app <- paste0("non_numeric_variables_", .x)
output[[output_name_app]] <- renderUI({
ml_choices <- tisefka()$var_factors[[.x]]
shinyWidgets::pickerInput(
inputId = ns(output_name_app),
label = gsub("_"," ",.x),
choices = categoricals_unique_values()[[.x]],
options = list(
`actions-box` = TRUE,
size = 10,
`selected-text-format` = "count > 3"
),
multiple = TRUE
)
})
})
})
output$non_numeric_variables_inputs <- renderUI({
req(non_numeric_variables())
fluidRow(
purrr::map(non_numeric_variables(), ~{
column(width = 2, uiOutput(ns(paste0("non_numeric_variables_",.x))))
})
)
})
output$select_element <- renderUI({
req(tisefka_tizegzawin())
shinyWidgets::pickerInput(inputId = ns("variable_picker"),
label = i18n$t("Target variables"),
multiple = TRUE,
choices = tisefka_choices(),
selected = NULL
)
})
output$var_granularity <- renderUI({
req(non_numeric_variables())
shinyWidgets::pickerInput(inputId = ns("var_granularity"),
label = i18n$t("Granularity"),
multiple = TRUE,
choices = non_numeric_variables(),
selected = NULL
)
})
# aggregation metric
output$aggregation_metric <- renderUI({
req(non_numeric_variables())
aggregation_choices <- c("Average","Sum","Min","Max","Median")
names(aggregation_choices) <- i18n$t(aggregation_choices)
shinyWidgets::pickerInput(inputId = ns("aggregation_metric"),
label = i18n$t("Aggregation"),
multiple = FALSE,
selected = aggregation_choices[1],
choices = aggregation_choices
)
})
tisefka_iheggan <- reactive({
req(tisefka_tizegzawin())
req(input$variable_picker)
aggreg_fun <- SA_aggregation_funs(aggregation_metric = input$aggregation_metric )
tisefka_iheggan <- tisefka_tizegzawin()
if(length(non_numeric_variables())>0){
categ_input_filter <-non_numeric_variables()%>%purrr::map(~input[[paste0("non_numeric_variables_",.x)]])%>%
stats::setNames(non_numeric_variables())
categ_input_filter <- categ_input_filter[!unlist(lapply(categ_input_filter, is.null))]
for(cat_input in names(categ_input_filter)){
if("NA" %in% categ_input_filter[[cat_input]])categ_input_filter[[cat_input]] <- c( categ_input_filter[[cat_input]], NA)
tisefka_iheggan <- tisefka_iheggan%>%dplyr::filter(!!rlang::sym(cat_input)%in%categ_input_filter[[cat_input]])
}
}
if(is.null(input$var_granularity)){
if(is.null(aggreg_fun)) aggreg_fun <- sum
tisefka_iheggan<- tisefka_iheggan%>%dplyr::select(date,!!input$variable_picker)%>%
dplyr::group_by(date)%>%dplyr::summarise_all(aggreg_fun)
}else{
list_val_fn <- input$variable_picker%>%purrr::map(~aggreg_fun)%>%stats::setNames(input$variable_picker)
tisefka_iheggan<- tisefka_iheggan %>%
tidyr::pivot_wider(
id_cols = date,
names_from = input$var_granularity,
values_from = input$variable_picker,
values_fn = list_val_fn)
}
tisefka_iheggan <- tisefka_iheggan%>%dplyr::arrange(date)%>%
dplyr::group_by(date)%>%dplyr::summarise_all(aggreg_fun,na.rm = TRUE)
max_variables <- min(ncol(tisefka_iheggan),13)
tisefka_iheggan <- tisefka_iheggan[1:max_variables]
return(tisefka_iheggan)
})
target_variables <- reactive({
req(tisefka_iheggan())
target_variables <- colnames(tisefka_iheggan())
target_variables <- target_variables[target_variables!="date"]
return(target_variables)
})
#----------------
tisefka_forecast_aqerru <- eventReactive(input$forecasting_submit,{
req(tisefka_iheggan())
req(target_variables())
tisefka_forecast_aqerru <- SaldaeForecasting::Saldae_Forecaster(tisefka = tisefka_iheggan(),actuals_time_periode =input$actuals_time_periode ,target_variables = target_variables(), anomaly_detection = TRUE, Saldae_model = "saldae_prophet")
})
tisefka_forecast <- reactive({
req(tisefka_forecast_aqerru())
purrr::map(.x= tisefka_forecast_aqerru(), ~SaldaeForecasting::sbed_forecast_aqerru(.x , asurif_arzdat = input$forecast_horizon))
})
tisefka_plots <- reactive({
plot_settings <- list()
plot_settings[["colors_inu"]] <- c(input$obs_col,"darkgreen",input$fcast_col,"#EBCC2A")
purrr::map(.x =names(tisefka_forecast()),~ SaldaeForecasting::sekned_forecast_aqeru(fcast_df = tisefka_forecast()[[.x]],target_variable = .x ,plot_settings = plot_settings))%>%
stats::setNames(names(tisefka_forecast()))
})
tisefka_tables <- reactive({
req(tisefka_forecast())
return(purrr::map(.x = tisefka_forecast(),~DT::datatable(.x,extensions = c('Scroller','Buttons'), options = list(dom = 'Bfrtip',buttons = c('copy', 'csv', 'excel', 'pdf', 'print'), deferRender = TRUE, scrollY = 200,scrollX = TRUE, scroller = TRUE)) )%>%
stats::setNames(names(tisefka_forecast())))
})
output$SA_key_figure_select <- renderUI({
req(tisefka_forecast())
key_figures_choices <- c("Average","Sum","Maximum","Minimum","First Value","Last Value")
names(key_figures_choices) <- i18n$t(key_figures_choices)
shinyWidgets::pickerInput(
inputId = ns("SA_key_figure_select"),
label = i18n$t("Key Numbers"),
choices = key_figures_choices,
multiple = FALSE
)
})
#---------------------
observeEvent(eventExpr=tisefka_tables(),handlerExpr= {
purrr::map(names(tisefka_plots()), ~{
output_name_plot <- paste0("tisefka_plot_", .x)
output_name_table <- paste0("tisefka_table_", .x)
output_name_figures <- paste0("tisefka_key_figures_", .x)
output_name_performances <- paste0("forecast_performances_", .x)
output[[output_name_table]] <- DT::renderDataTable(tisefka_tables()[[.x]])
output[[output_name_plot]] <- plotly::renderPlotly(tisefka_plots()[[.x]])
output[[output_name_performances]] <- DT::renderDataTable({
forecast_performances()[[.x]]
})
output[[output_name_figures]] <- bs4Dash::renderInfoBox({
my_title <- paste(.x,":",input$SA_key_figure_select)
bs4Dash::infoBox(title = my_title,
value = my_analytics_key_values()[[.x]],color = "success",
width = 6,
shiny::icon("fas fa-chart-bar")
)
})
#
})
})
# dynamic size of the panel
SA_div_width <- reactive({
req(target_variables())
if(length(target_variables())==1){
div_width <- c(12,12)
}else if(length(target_variables())==2){
div_width <- c(6,12)
}else{
div_width <- c(4,12)
}
return(div_width)
})
# display the panels
output$graphs_ui <- renderUI({
req(tisefka_plots())
plots_list <- purrr::imap(tisefka_plots(), ~{
bs4Dash::tabBox(width = SA_div_width()[1], title = .y,
tabPanel(icon("fas fa-chart-bar"),
plotly::plotlyOutput(ns(paste0("tisefka_plot_",.y)), height = "300px")
),tabPanel(icon("table"),
DT::dataTableOutput(ns(paste0("tisefka_table_",.y)))
),tabPanel(icon("align-left"),
shiny::textAreaInput(inputId = ns(paste0("tisefka_awal_",.y)),label = "Comments",value = "insert your comments here",width = "100%",height = "50%")
),tabPanel(icon("fa-scale-unbalanced"),
DT::dataTableOutput(ns(paste0("forecast_performances_",.y)))
),tabPanel(icon("percentage"),
fluidRow(
bs4Dash::infoBoxOutput(ns(paste0("tisefka_key_figures_",.y)), width = 8)
)# fluidRow
)# tabPanle: percentage
)
})
fluidRow(plots_list)
})
my_analytics_key_values <- reactive({
SA_key_figure_select<- input$SA_key_figure_select
if(is.null(SA_key_figure_select))SA_key_figure_select <- "Sum"
my_value <- purrr::map(names(tisefka_forecast()),~key_value_calculator(tisefka = tisefka_forecast()[[.x]][,"forecast"],key_value = SA_key_figure_select))%>%
stats::setNames(names(tisefka_forecast()))
})
forecast_performances <- reactive({
forecast_performances <- tisefka_forecast()%>%purrr::map(~SaldaeForecasting::calculate_forecast_performances(fcast_df = .x))%>%
stats::setNames(names(tisefka_forecast()))
})
# add to report
report_dir <- "./thaink2_report/"
report_details <- reactive({
list(
report_dir = report_dir,
report_id = "0001"
)
})
item_elements <- reactive({
req(tisefka_forecast())
req(tisefka_plots())
output_data <- prepare_data_to_report(data_object = tisefka_forecast(), output_type = "forecast")
data_result <- list(output_data = output_data,
output_graph = tisefka_plots(),
output_comment = "output_comment")
graph_type <- "lines"; categoricals <- NULL; output_type <- c("forecasting");time_frequency <- "hours"
list(
data_result = data_result, # result to include into the report, can be table or graph or both
granularity = input$var_granularity, # NULL or a list of categoricals
aggregation_metric = input$aggregation_metric, # raw, max, min, mean,
time_frequency = time_frequency, # hours, days, weeks, months, quarters, years
graph_type = graph_type, # lines , markers, aread
categoricals = categoricals,
output_type = output_type #c("exploration","forecast","growth_rate")
)
})
SaldaeReporting::add_to_report_server("add_forecasting", report_details = report_details(),item_elements = reactive({ item_elements() }))
#
analytics_output <- reactive({
output <- list()
output$analytics_plots <- tisefka_plots()
output$analytics_tisefka <- tisefka_forecast()
output$analytics_awal <- purrr::map(names(tisefka_plots()),~ input[[paste0("tisefka_awal_",.x)]])%>%stats::setNames(names(tisefka_plots()))
output$analytics_key_figures <- list(key_metric = input$SA_key_figure_select,
key_figures = my_analytics_key_values())
output$tisefka <- tisefka_iheggan()
output$forecast_performances <- forecast_performances()
output$analytics_settings <- "ulac"
return(output)
})
}
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