update

Dockerfile

@@ -23,7 +23,8 @@ RUN R -e "remotes::install_local('/srv/shiny-server/radiant.quickgen',dependenci
 RUN R -e "remotes::install_local('/srv/shiny-server/radiant-master',dependencies=TRUE, type='source', upgrade='never',force=TRUE)"
 
 WORKDIR /data
-ENV HOST_API_BASE="https://ds.cixincloud.com/data-search-api"
+# ENV HOST_API_BASE="https://ds.cixincloud.com/data-search-api"
+ENV HOST_API_BASE="http://10.120.120.34:8300/data-search-api"
 CMD ["R", "-e", "radiant::radiant(host='0.0.0.0', port=3838)"]
 
 #sudo -i

notice.txt

-上传模块
-devtools::install_local("/home/wuzekai/radiant/radiant.data", force = TRUE)
-devtools::install_local("/home/wuzekai/radiant/radiant.basics", force = TRUE)
-devtools::install_local("/home/wuzekai/radiant/radiant.design", force = TRUE)
-devtools::install_local("/home/wuzekai/radiant/radiant.model", force = TRUE)
-devtools::install_local("/home/wuzekai/radiant/radiant.multivariate", force = TRUE)
-devtools::install_local("/home/wuzekai/radiant/radiant.quickgen", force = TRUE)
-devtools::install_local("/home/wuzekai/radiant/radiant-master", force = TRUE)
-
-清理原文件
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant.data
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant.basics
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant.design
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant.model
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant.multivariate
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant.quickgen
-rm -rf /root/miniconda3/envs/radiant/lib/R/library/radiant-master
-
-启动软件
-sudo -i
-conda activate radiant
-cd /home/wuzekai/radiant
-R
-options(browser = 'false'); radiant::radiant(host='0.0.0.0', port=8105)
-
-杀掉端口
-lsof -i :8105
-kill -9 <PID>
\ No newline at end of file

radiant-master/inst/translations/translation_zh.csv

@@ -89,7 +89,7 @@ Spearman,斯皮尔曼秩相关,correlation_ui.R
 Kendall,肯德尔秩相关,correlation_ui.R
 Calculate correlation,计算相关性,correlation_ui.R
 Basics > Tables,基础统计 > 表格,correlation_ui.R
-Correlation,相关性,"correlation_ui.R,init.R"
+Correlation,相关性分析,"correlation_ui.R,init.R"
 Adjust for {factor} variables,针对 {factor} 变量进行调整,"correlation_ui.R,full_factor_ui.R"
 Calculate adjusted p.values,计算调整后的 p 值,correlation_ui.R
 Correlation cutoff:,相关性阈值:,correlation_ui.R
@@ -541,8 +541,6 @@ Save factorial design:,保存实验设计：,doe_ui.R
 Save factors:,保存因素：,doe_ui.R
 Design factors:,实验因素,doe_ui.R
 Generated experimental design:,生成的实验设计：,doe.R
-Level 1:,水平 1：,doe_ui.R
-Level 2:,水平 2：,doe_ui.R
 Design > DOE,设计 > 实验设计,doe_ui.R
 Design of Experiments,实验设计,"doe_ui.R,init.R"
 Variables:,变量：,"randomizer_ui.R, sampling_ui.R,full_factor_ui.R"
@@ -1245,3 +1243,29 @@ Data Quality,数据质量,
 Select plot types,选择图表类型,
 Select one or more variables,选择一个或多个变量,
 Missing Value Analysis,缺失值分析,
+Select variables to analyze:,选择要分析的变量：,
+Missing Heatmap,缺失热图,
+Missing Barplot,缺失条形图,
+Time Series,时间序列,
+Time Series Analysis,时间序列分析,
+Moving Average,移动平均（MA）,
+Exp. Smoothing,指数平滑（EST）,
+Autocorrelation,自相关分析（ACF）,
+Interval:,时间间隔：,
+MA Window Size:,平均移动窗口大小：,
+Forecast Periods:,预测期：,
+Basics > Time Series,基础统计 > 时间序列,
+Aggregation Method:,聚合方法：,
+Please select at least one plot layer.,请至少选择一个图像。,
+Original Data,原始数据,
+Moving Average Line,移动平均线,
+Exp. Smoothing Line,指数平滑线,
+ACF Plot,自相关图,
+Day,日,
+Week,星期,
+Month,月,
+Quarter,季度,
+Year,年,
+Mean (Average),均值,
+Sum (Total),求和,
+Count (Rows),计数,

radiant.basics/NAMESPACE

@@ -23,6 +23,7 @@ S3method(plot,prob_tdist)
 S3method(plot,prob_unif)
 S3method(plot,single_mean)
 S3method(plot,single_prop)
+S3method(plot,time_series)
 S3method(print,rcorr)
 S3method(summary,compare_means)
 S3method(summary,compare_props)
@@ -46,6 +47,7 @@ S3method(summary,prob_tdist)
 S3method(summary,prob_unif)
 S3method(summary,single_mean)
 S3method(summary,single_prop)
+S3method(summary,time_series)
 export(clt)
 export(compare_means)
 export(compare_props)
@@ -78,6 +80,7 @@ export(run_kw)
 export(run_norm_test)
 export(single_mean)
 export(single_prop)
+export(time_series)
 import(ggplot2)
 import(radiant.data)
 import(shiny)

radiant.basics/R/time_series.R

+#' Time Series Analysis (Smart Aggregation for Date & Numeric)
+#'
+#' @param dataset Dataset name
+#' @param var_num The numeric variable to analyze (Y)
+#' @param var_date The time variable (X). Can be Date or Numeric.
+#' @param agg_level Aggregation level ("day", "week", "month", "quarter", "year")
+#' @param agg_fun Aggregation function ("mean", "sum", "count")
+#' @param ma_n Window size for Moving Average
+#' @param fc_periods Number of periods to forecast
+#' @param methods Vector of methods: "ma", "ets", "acf"
+#' @param data_filter Filter expression
+#' @param envir Environment
+#'
+#' @return A list of results
+#' @export
+time_series <- function(dataset, var_num, var_date = NULL,
+                        agg_level = "month", agg_fun = "mean",
+                        ma_n = 3, fc_periods = 0, methods = c("ma"),
+                        data_filter = "", envir = parent.frame()) {
+  
+  # === 1. 数据准备 ===
+  vars <- c(var_num)
+  if (!is.null(var_date) && var_date != "") vars <- c(vars, var_date)
+  
+  df_raw <- get_data(dataset, vars, filt = data_filter, na.rm = TRUE, envir = envir)
+  
+  # === 2. 智能聚合逻辑 ===
+  # 检查 X 轴数据类型
+  is_date_col <- !is.null(var_date) && var_date != "" && inherits(df_raw[[var_date]], c("Date", "POSIXt"))
+  is_num_col  <- !is.null(var_date) && var_date != "" && is.numeric(df_raw[[var_date]])
+  
+  df <- df_raw # 默认回退
+  
+  if (is_date_col || is_num_col) {
+    requireNamespace("dplyr")
+    
+    if (is_date_col) {
+      # A. 如果是日期：使用 lubridate 对齐
+      requireNamespace("lubridate")
+      df_raw$ts_group_col <- lubridate::floor_date(df_raw[[var_date]], unit = agg_level)
+      step_num <- 1 # 仅用于后续判断
+      
+    } else if (is_num_col) {
+      # B. 如果是数字：执行分箱 (Binning)
+      # 将 "Month" 映射为 "30个单位", "Week" -> "7个单位"
+      step_num <- switch(agg_level, 
+                         "week" = 7, 
+                         "month" = 30, 
+                         "quarter" = 90, 
+                         "year" = 365, 
+                         1) # default day=1
+      
+      # 核心算法：向下取整分箱
+      df_raw$ts_group_col <- floor(df_raw[[var_date]] / step_num) * step_num
+    }
+    
+    # 执行聚合
+    df <- df_raw %>%
+      dplyr::group_by(ts_group_col) %>%
+      dplyr::summarise(
+        y_agg = if (agg_fun == "count") dplyr::n() 
+        else if (agg_fun == "sum") sum(.data[[var_num]], na.rm = TRUE)
+        else mean(.data[[var_num]], na.rm = TRUE)
+      ) %>%
+      dplyr::arrange(ts_group_col) %>%
+      dplyr::rename(!!var_date := ts_group_col, !!var_num := y_agg) %>%
+      as.data.frame()
+    
+  } else {
+    # 如果没选变量，生成 Index
+    if (is.null(var_date) || var_date == "") {
+      var_date <- "Index"
+      df$Index <- 1:nrow(df)
+      step_num <- 1
+    }
+  }
+  
+  y_val <- df[[var_num]]
+  res_list <- list()
+  metrics <- data.frame(Method = character(), RMSE = numeric(), MAE = numeric(), stringsAsFactors = FALSE)
+  
+  get_accuracy <- function(actual, fitted, name) {
+    err <- actual - fitted
+    c(name, round(sqrt(mean(err^2, na.rm=TRUE)), 3), round(mean(abs(err), na.rm=TRUE), 3))
+  }
+  
+  # === 3. 计算模块 ===
+  
+  # --- Moving Average ---
+  if ("ma" %in% methods) {
+    # 确保窗口不大于数据长度
+    safe_ma_n <- min(ma_n, length(y_val))
+    ma_weights <- rep(1/safe_ma_n, safe_ma_n)
+    df$ma_line <- as.numeric(stats::filter(y_val, ma_weights, sides = 1))
+    
+    valid <- !is.na(df$ma_line)
+    if (sum(valid) > 0) {
+      metrics[nrow(metrics)+1,] <- get_accuracy(y_val[valid], df$ma_line[valid], "Moving Average")
+    }
+  }
+  
+  # --- ETS (Holt-Winters) ---
+  if ("ets" %in% methods) {
+    # 尝试根据聚合粒度设定频率
+    freq <- 1
+    if (is_date_col) {
+      freq <- switch(agg_level, "month"=12, "quarter"=4, "week"=52, 7)
+    } else if (is_num_col) {
+      # 对于数值型，尝试推断周期，默认无季节性
+      freq <- 1 
+    }
+    
+    if (length(y_val) < 2 * freq) freq <- 1
+    ts_obj <- ts(y_val, frequency = freq)
+    
+    ets_model <- tryCatch(HoltWinters(ts_obj), 
+                          error = function(e) tryCatch(HoltWinters(ts_obj, gamma=FALSE), error = function(e) NULL))
+    
+    if (!is.null(ets_model)) {
+      fitted_raw <- as.numeric(ets_model$fitted[, 1])
+      pad <- rep(NA, length(y_val) - length(fitted_raw))
+      df$ets_line <- c(pad, fitted_raw)
+      
+      # 记录参数
+      res_list$ets_params <- list(alpha = ets_model$alpha, beta = ets_model$beta, gamma = ets_model$gamma)
+      
+      # 记录误差
+      metrics[nrow(metrics)+1,] <- get_accuracy(y_val[!is.na(df$ets_line)], df$ets_line[!is.na(df$ets_line)], "Exp. Smoothing")
+      
+      # === 预测 (Forecast) ===
+      if (fc_periods > 0) {
+        fc_out <- predict(ets_model, n.ahead = fc_periods, prediction.interval = TRUE, level = 0.95)
+        
+        last_time <- tail(df[[var_date]], 1)
+        
+        # 生成未来时间轴 (区分日期和数字)
+        if (inherits(last_time, "Date") || inherits(last_time, "POSIXt")) {
+          future_dates <- seq(from = last_time, by = agg_level, length.out = fc_periods + 1)[-1]
+        } else {
+          # 对于数字，按 step_num 递增
+          # 如果之前判定是数字列，使用之前的 step_num
+          step <- if(exists("step_num")) step_num else 1
+          future_dates <- seq(from = last_time + step, by = step, length.out = fc_periods)
+        }
+        
+        res_list$forecast <- data.frame(
+          date = future_dates,
+          fit = as.numeric(fc_out[, "fit"]),
+          upr = as.numeric(fc_out[, "upr"]),
+          lwr = as.numeric(fc_out[, "lwr"])
+        )
+      }
+    }
+  }
+  
+  # --- ACF ---
+  if ("acf" %in% methods) {
+    # 增加 tryCatch 防止数据太少报错
+    acf_out <- tryCatch(acf(y_val, plot = FALSE, na.action = na.pass), error = function(e) NULL)
+    if (!is.null(acf_out)) {
+      res_list$acf_df <- data.frame(lag = acf_out$lag, acf = acf_out$acf)
+    }
+  }
+  
+  # === 4. 返回 ===
+  res_list$dataset <- df
+  res_list$metrics <- metrics
+  res_list$var_num <- var_num
+  res_list$var_date <- var_date
+  res_list$agg_level <- agg_level
+  res_list$agg_fun <- agg_fun
+  res_list$methods <- methods
+  res_list$df_name <- if (is_string(dataset)) dataset else deparse(substitute(dataset))
+  
+  as.list(res_list) %>% add_class("time_series")
+}
+
+#' Summary method
+#' @export
+summary.time_series <- function(object, ...) {
+  cat("Time Series Analysis\n")
+  cat("Data      :", object$df_name, "\n")
+  cat("Variable  :", object$var_num, "\n")
+  cat("Time Var  :", object$var_date, "\n")
+  cat("Aggregation:", object$agg_level, "(", object$agg_fun, ")\n\n")
+  
+  if (nrow(object$metrics) > 0) {
+    cat("Model Accuracy (Goodness of Fit):\n")
+    print(object$metrics, row.names = FALSE)
+    cat("---\n")
+  }
+  
+  if ("ets" %in% object$methods && !is.null(object$ets_params)) {
+    p <- object$ets_params
+    cat("Exp. Smoothing Parameters:\n")
+    cat(sprintf("Alpha: %.3f, Beta: %.3f, Gamma: %.3f\n", p$alpha, ifelse(is.null(p$beta), 0, p$beta), ifelse(is.null(p$gamma), 0, p$gamma)))
+  }
+  
+  if (!is.null(object$forecast)) {
+    cat("\nForecast (Next", nrow(object$forecast), "periods):\n")
+    print(head(object$forecast, 5))
+  }
+}
+
+#' Plot method
+#' @export
+plot.time_series <- function(x, plots = c("original", "ma", "ets"), shiny = FALSE, custom = FALSE, ...) {
+  
+  plot_list <- list()
+  
+  if (any(c("original", "ma", "ets") %in% plots)) {
+    
+    p <- ggplot(x$dataset, aes(x = .data[[x$var_date]], y = .data[[x$var_num]])) +
+      labs(title = paste("Time Series:", x$var_num), subtitle = paste("Aggregated by", x$agg_level)) +
+      theme_minimal()
+    
+    if ("original" %in% plots) {
+      p <- p + geom_line(color = "black", alpha = 0.3) + 
+        geom_point(color = "black", alpha = 0.3, size = 1.5)
+    }
+    
+    if ("ma" %in% plots && "ma_line" %in% names(x$dataset)) {
+      p <- p + geom_line(aes(y = ma_line, color = "Moving Avg"), linewidth = 0.8)
+    }
+    
+    if ("ets" %in% plots && "ets_line" %in% names(x$dataset)) {
+      p <- p + geom_line(aes(y = ets_line, color = "Exp Smoothing"), linewidth = 0.8)
+    }
+    
+    if ("ets" %in% plots && !is.null(x$forecast)) {
+      p <- p + 
+        geom_ribbon(data = x$forecast, aes(x = date, ymin = lwr, ymax = upr, y = fit), fill = "red", alpha = 0.15, inherit.aes = FALSE) +
+        geom_line(data = x$forecast, aes(x = date, y = fit, color = "Forecast"), linetype = "dashed", linewidth = 0.8, inherit.aes = FALSE)
+    }
+    
+    p <- p + scale_color_manual(name = "Legend", 
+                                values = c("Moving Avg" = "blue", "Exp Smoothing" = "red", "Forecast" = "red"))
+    
+    plot_list[[1]] <- p
+  }
+  
+  if ("acf" %in% plots && "acf" %in% x$methods && !is.null(x$acf_df)) {
+    ci <- 0.95
+    clim <- qnorm((1 + ci)/2) / sqrt(nrow(x$dataset))
+    p_acf <- ggplot(x$acf_df, aes(x = lag, y = acf)) +
+      geom_segment(aes(xend = lag, yend = 0), color = "darkblue", linewidth = 1) +
+      geom_hline(yintercept = c(clim, -clim), linetype = "dashed", color = "blue") +
+      geom_hline(yintercept = 0) +
+      labs(title = "Autocorrelation (ACF)", y = "ACF", x = "Lag") + theme_minimal()
+    plot_list[[length(plot_list) + 1]] <- p_acf
+  }
+  
+  if (length(plot_list) > 0) {
+    patchwork::wrap_plots(plot_list, ncol = 1) %>%
+      (function(obj) if (shiny) obj else print(obj))
+  }
+}
\ No newline at end of file

radiant.basics/inst/app/init.R

@@ -49,7 +49,9 @@ options(
         "----", i18n$t("Tables"),
         tabPanel(i18n$t("Goodness of fit"), uiOutput("goodness")),
         tabPanel(i18n$t("Cross-tabs(Chi-square test, etc)"), uiOutput("cross_tabs")),
-        tabPanel(i18n$t("Correlation"), uiOutput("correlation"))
+        tabPanel(i18n$t("Correlation"), uiOutput("correlation")),
+        "----", i18n$t("Time Series"),
+        tabPanel(i18n$t("Time Series Analysis"), uiOutput("time_series")),
       )
     )
 )

radiant.basics/inst/app/tools/analysis/time_series_ui.R

+###############################
+## Time Series Analysis - ui
+## file: time_series_ui.R
+###############################
+
+# --- 1. 定义下拉菜单和选项常量 ---
+ts_methods <- c("ma", "ets", "acf")
+names(ts_methods) <- c(
+  i18n$t("Moving Average"),
+  i18n$t("Exp. Smoothing"),
+  i18n$t("Autocorrelation")
+)
+
+# 绘图图层选项（统一用方法1）
+ts_plot_opts <- c("original", "ma", "ets", "acf")
+names(ts_plot_opts) <- c(
+  i18n$t("Original Data"),
+  i18n$t("Moving Average Line"),
+  i18n$t("Exp. Smoothing Line"),
+  i18n$t("ACF Plot")
+)
+
+# 聚合粒度选项（统一用方法1）
+ts_agg_levels <- c("day", "week", "month", "quarter", "year")
+names(ts_agg_levels) <- c(
+  i18n$t("Day"), 
+  i18n$t("Week"), 
+  i18n$t("Month"), 
+  i18n$t("Quarter"), 
+  i18n$t("Year")
+)
+
+# 聚合函数选项（统一用方法1）
+ts_agg_funs <- c("mean", "sum", "count")
+names(ts_agg_funs) <- c(
+  i18n$t("Mean (Average)"), 
+  i18n$t("Sum (Total)"), 
+  i18n$t("Count (Rows)")
+)
+
+# --- 2. 初始化参数 ---
+# 读取后端 time_series 函数的默认参数
+ts_args <- as.list(formals(time_series))
+
+# --- 3. 打包用户输入 (ts_inputs) ---
+ts_inputs <- reactive({
+  # 处理数据过滤
+  ts_args$data_filter <- if (input$show_filter) input$data_filter else ""
+  ts_args$dataset <- input$dataset
+  ts_args$envir <- r_data
+  
+  # 自动循环匹配 input$ts_xxx 到参数 xxx
+  for (i in r_drop(names(ts_args))) {
+    ts_args[[i]] <- input[[paste0("ts_", i)]]
+  }
+  ts_args
+})
+
+# --- 4. 动态 UI 组件定义 ---
+
+# Y轴变量 (数值型)
+output$ui_ts_var_num <- renderUI({
+  isNum <- .get_class() %in% c("integer", "numeric", "ts")
+  vars <- c("None" = "", varnames()[isNum])
+  selectInput(
+    inputId = "ts_var_num", label = i18n$t("Numeric variable:"),
+    choices = vars, selected = state_single("ts_var_num", vars), multiple = FALSE
+  )
+})
+
+# X轴变量 (日期/时间型/数值型)
+output$ui_ts_var_date <- renderUI({
+  all_vars <- varnames()
+  # 自动检测日期类型的变量优先显示
+  isDate <- .get_class() %in% c("Date", "POSIXt", "POSIXct")
+  # 如果有日期变量，默认选中第一个
+  def_sel <- if (any(isDate)) all_vars[isDate][1] else ""
+  
+  vars <- c("None" = "", all_vars)
+  selectInput(
+    inputId = "ts_var_date", label = i18n$t("Time variable:"),
+    choices = vars, selected = state_single("ts_var_date", vars, def_sel), multiple = FALSE
+  )
+})
+
+# --- 5. 侧边栏主面板 (UI Sidebar) ---
+output$ui_time_series <- renderUI({
+  req(input$dataset)
+  tagList(
+    wellPanel(
+      # === 基础数据选择 ===
+      uiOutput("ui_ts_var_num"),
+      uiOutput("ui_ts_var_date"),
+      
+      # 改为垂直排列，确保下拉菜单可以正常弹出并被选择
+      conditionalPanel(
+        condition = "input.ts_var_date != ''",
+        selectInput(
+          inputId = "ts_agg_level", label = i18n$t("Interval:"), 
+          choices = ts_agg_levels, 
+          selected = state_single("ts_agg_level", ts_agg_levels, "month"),
+          multiple = FALSE
+        ),
+        
+        selectInput(
+          inputId = "ts_agg_fun", label = i18n$t("Aggregation Method:"), 
+          choices = ts_agg_funs, 
+          selected = state_single("ts_agg_fun", ts_agg_funs, "mean"),
+          multiple = FALSE
+        )
+      ),
+      
+      conditionalPanel(
+        condition = "input.tabs_time_series == i18n$t('Summary')",
+        h6("计算方法:"),
+        
+        # 选择计算方法
+        checkboxGroupInput(
+          inputId = "ts_methods", label = NULL,
+          choices = ts_methods,
+          selected = state_multiple("ts_methods", ts_methods, c("ma", "ets"))
+        ),
+        
+        # MA 参数设置
+        conditionalPanel(
+          condition = "input.ts_methods.includes('ma')",
+          numericInput("ts_ma_n", i18n$t("MA Window Size:"), 
+                       value = state_init("ts_ma_n", 3), min = 2, step = 1)
+        ),
+        
+        # 预测参数设置
+        numericInput(
+          inputId = "ts_fc_periods", label = i18n$t("Forecast Periods:"),
+          value = state_init("ts_fc_periods", 0), min = 0, step = 1
+        ),
+      ),
+      
+      # === 绘图图层配置 ===
+      conditionalPanel(
+        condition = "input.tabs_time_series == i18n$t('Plot')",
+        h6("图像:"),
+        selectizeInput(
+          inputId = "ts_plots", label = NULL,
+          choices = ts_plot_opts,
+          selected = state_multiple("ts_plots", ts_plot_opts, c("original", "ma", "ets")),
+          multiple = TRUE,
+          options = list(placeholder = "Select layers", plugins = list("remove_button", "drag_drop"))
+        )
+      )
+    ),
+    
+    # 帮助按钮和报告生成按钮
+    help_and_report(
+      modal_title = "Time Series Analysis",
+      fun_name = "time_series",
+      help_file = inclMD(file.path(getOption("radiant.path.basics"), "app/tools/help/time_series.md"))
+    )
+  )
+})
+
+# --- 6. 定义图形高度 ---
+ts_plot_height <- function() {
+  # 如果包含 ACF 图，增加高度
+  if ("acf" %in% input$ts_plots) 600 else 400
+}
+
+# --- 7. 注册主界面输出 (Main Panel) ---
+output$time_series <- renderUI({
+  # 注册打印和绘图输出
+  register_print_output("summary_time_series", ".summary_time_series")
+  register_plot_output("plot_time_series", ".plot_time_series", height_fun = "ts_plot_height")
+  
+  # 定义标签页
+  ts_output_panels <- tabsetPanel(
+    id = "tabs_time_series",
+    tabPanel(i18n$t("Summary"), verbatimTextOutput("summary_time_series")),
+    tabPanel(
+      i18n$t("Plot"),
+      download_link("dlp_time_series"),
+      plotOutput("plot_time_series", height = "100%")
+    )
+  )
+  
+  # 构建 Radiant 标准 Tab 面板
+  stat_tab_panel(
+    menu = i18n$t("Basics > Time Series"),
+    tool = i18n$t("Time Series Analysis"),
+    tool_ui = "ui_time_series",
+    output_panels = ts_output_panels
+  )
+})
+
+# --- 8. 后端计算逻辑连接 ---
+
+# 执行计算
+.time_series <- reactive({
+  tsi <- ts_inputs()
+  tsi$envir <- r_data # 确保环境传递正确
+  req(tsi$var_num)    # 必须选择了变量才执行
+  do.call(time_series, tsi)
+})
+
+# 生成 Summary
+.summary_time_series <- reactive({
+  summary(.time_series())
+})
+
+# 生成 Plot
+.plot_time_series <- reactive({
+  validate(
+    need(input$ts_plots, i18n$t("Please select at least one plot layer."))
+  )
+  withProgress(message = "Generating Plots...", value = 1, {
+    plot(.time_series(), plots = input$ts_plots, shiny = TRUE)
+  })
+})
+
+# --- 9. 报告生成 (Rmarkdown) ---
+time_series_report <- function() {
+  if (is.empty(input$ts_var_num)) return(invisible())
+  
+  # 判断是否生成图片代码
+  if (length(input$ts_plots) == 0) {
+    figs <- FALSE
+    outputs <- c("summary")
+    inp_out <- list("", "")
+  } else {
+    figs <- TRUE
+    outputs <- c("summary", "plot")
+    inp_out <- list("", list(plots = input$ts_plots, custom = FALSE))
+  }
+  
+  update_report(
+    inp_main = clean_args(ts_inputs(), ts_args),
+    fun_name = "time_series",
+    outputs = outputs,
+    figs = figs,
+    fig.width = 650,
+    fig.height = ts_plot_height()
+  )
+}
+
+# --- 10. 下载处理器 ---
+download_handler(
+  id = "dlp_time_series",
+  fun = download_handler_plot,
+  fn = function() paste0(input$dataset, "_time_series"),
+  type = "png",
+  caption = "Save time series plot",
+  plot = .plot_time_series,
+  width = 650,
+  height = ts_plot_height
+)
+
+# --- 11. 事件监听 ---
+observeEvent(input$time_series_report, {
+  r_info[["latest_screenshot"]] <- NULL
+  time_series_report()
+})
+
+observeEvent(input$time_series_screenshot, {
+  r_info[["latest_screenshot"]] <- NULL
+  radiant_screenshot_modal("modal_time_series_screenshot")
+})
+
+observeEvent(input$modal_time_series_screenshot, {
+  time_series_report()
+  removeModal()
+})
\ No newline at end of file

radiant.basics/inst/app/tools/help/time_series.md

+xxxxx
\ No newline at end of file

radiant.basics/man/plot.time_series.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/time_series.R
+\name{plot.time_series}
+\alias{plot.time_series}
+\title{Plot method}
+\usage{
+\method{plot}{time_series}(x, plots = c("original", "ma", "ets"), shiny = FALSE, custom = FALSE, ...)
+}
+\description{
+Plot method
+}

radiant.basics/man/summary.time_series.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/time_series.R
+\name{summary.time_series}
+\alias{summary.time_series}
+\title{Summary method}
+\usage{
+\method{summary}{time_series}(object, ...)
+}
+\description{
+Summary method
+}

radiant.basics/man/time_series.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/time_series.R
+\name{time_series}
+\alias{time_series}
+\title{Time Series Analysis (Smart Aggregation for Date & Numeric)}
+\usage{
+time_series(
+  dataset,
+  var_num,
+  var_date = NULL,
+  agg_level = "month",
+  agg_fun = "mean",
+  ma_n = 3,
+  fc_periods = 0,
+  methods = c("ma"),
+  data_filter = "",
+  envir = parent.frame()
+)
+}
+\arguments{
+\item{dataset}{Dataset name}
+
+\item{var_num}{The numeric variable to analyze (Y)}
+
+\item{var_date}{The time variable (X). Can be Date or Numeric.}
+
+\item{agg_level}{Aggregation level ("day", "week", "month", "quarter", "year")}
+
+\item{agg_fun}{Aggregation function ("mean", "sum", "count")}
+
+\item{ma_n}{Window size for Moving Average}
+
+\item{fc_periods}{Number of periods to forecast}
+
+\item{methods}{Vector of methods: "ma", "ets", "acf"}
+
+\item{data_filter}{Filter expression}
+
+\item{envir}{Environment}
+}
+\value{
+A list of results
+}
+\description{
+Time Series Analysis (Smart Aggregation for Date & Numeric)
+}

radiant.data/inst/app/tools/data/init_load_data.R → radiant.data/inst/app/tools/data/ init_load_data.R

@@ -55,21 +55,24 @@ download_handler_disease_apply <- function(base_url, id, token, dest_file) {
 # -------------------------------------------------------------------------
 SYSTEM_REGISTRY <- list(
   "cric" = list(
-    api_base = "https://ds.cixincloud.com/data-search-api",
+    # api_base = "https://ds.cixincloud.com/data-search-api",
+    api_base = "http://10.120.120.34:8300/data-search-api",
     handler  = download_handler_cric,
     file_ext = ".xlsx",
     sys_description = "大数据检索系统"
   ),
 
   "disease_batch" = list(
-    api_base = "https://ds.cixincloud.com/disease-api",
+    # api_base = "https://ds.cixincloud.com/disease-api",
+    api_base = "http://10.120.120.34:8300/disease-api",
     handler  = download_handler_disease_batch,
     file_ext = ".xls",# 注意这里是 xls
     sys_description = "专病库系统"
   ),
 
   "disease_apply" = list(
-    api_base = "https://ds.cixincloud.com/disease-api",
+    # api_base = "https://ds.cixincloud.com/disease-api",
+    api_base = "http://10.120.120.34:8300/disease-api",
     handler  = download_handler_disease_apply,
     file_ext = ".xlsx",
     sys_description = "专病库系统"

radiant.data/inst/app/www/style.css

@@ -177,7 +177,7 @@ body {
 
 .navbar {
   margin-bottom: 5px;
-  background-color: black;
+  background-color: #325aa5 !important;
 }
 
 .navbar .nav>li>a {

radiant.model/R/coxp.R

@@ -5,9 +5,9 @@ coxp <- function(dataset,
                  time,
                  status,
                  evar,
+                 form = NULL,
                  int = "",
                  check = "",
-                 form,
                  data_filter = "",
                  arr = "",
                  rows = NULL,
@@ -35,6 +35,32 @@ coxp <- function(dataset,
   df_name <- if (is_string(dataset)) dataset else deparse(substitute(dataset))
   dataset <- get_data(dataset, vars, filt = data_filter, arr = arr, rows = rows, envir = envir)
   
+  if (!time %in% colnames(dataset))
+    return("时间变量不存在于数据集中" %>% add_class("coxp"))
+  if (!is.numeric(dataset[[time]]))
+    return("时间变量必须是数值型" %>% add_class("coxp"))
+  
+  if (!status %in% colnames(dataset))
+    return("状态变量不存在于数据集中" %>% add_class("coxp"))
+  
+  surv_vec <- dataset[[status]]
+  if (is.factor(surv_vec) || is.character(surv_vec)) {
+    lev <- unique(surv_vec)
+    if (length(lev) != 2)
+      return("状态变量必须是二分类（两个水平）" %>% add_class("coxp"))
+    dataset[[status]] <- as.numeric(factor(surv_vec, levels = lev)) - 1L
+  } else if (!all(unique(surv_vec) %in% c(0, 1))) {
+    return("状态变量必须只包含 0 和 1" %>% add_class("coxp"))
+  }
+  
+  n_event <- sum(dataset[[status]])
+  if (n_event < 1)
+    return("数据中未发生任何事件（status = 1）" %>% add_class("coxp"))
+  
+  evar_missing <- setdiff(evar, colnames(dataset))
+  if (length(evar_missing) > 0)
+    return(paste("解释变量不存在：", paste(evar_missing, collapse = ", ")) %>% add_class("coxp"))
+  
   ## 状态变量检查与转换
   surv_status <- dataset[[status]]
   if (!is.numeric(surv_status)) {
@@ -175,14 +201,25 @@ predict.coxp <- function(object, pred_data = NULL, pred_cmd = "",
   if (is.null(pred_data)) {
     newdata <- envir$.model_frame %||% object$model$model
   } else {
-    # 获取预测数据集
-    newdata <- get_data(pred_data, vars = NULL, envir = envir)
-    
-    # 变量存在性校验
+    model_vars <- all.vars(object$model$terms)  
     model_evar <- object$evar                  
-    pred_cols  <- colnames(newdata)
-    missing_vars <- setdiff(model_evar, pred_cols)
     
+    # ========== 提前校验 ==========
+    # 先获取预测数据集的所有列
+    pred_data_all <- get_data(pred_data, vars = NULL, envir = envir)
+    pred_cols <- colnames(pred_data_all)
+    # 检查模型变量是否都在预测数据中
+    missing_vars <- setdiff(model_vars, pred_cols)
+    if (length(missing_vars) > 0) {
+      return(paste0(
+        "All variables in the model must also be in the prediction data\n",
+        "Variables in the model: ", paste(model_vars, collapse = ", "), "\n",
+        "Variables not available in prediction data: ", paste(missing_vars, collapse = ", ")
+      ) %>% add_class("coxp.predict"))
+    }
+    newdata <- get_data(pred_data, vars = model_vars, envir = envir)
+    
+    missing_vars <- setdiff(model_evar, pred_cols)
     if (length(missing_vars) > 0) {
       return(paste0(
         "All variables in the model must also be in the prediction data\n",

radiant.model/R/svm.R

@@ -738,7 +738,7 @@ varimp <- function(object, rvar = NULL, lev = NULL, data = NULL, seed = 1234, np
 
 #' @export
 svm_vip_plot <- function(object, size, custom) {
-  tryCatch({
+  p <- tryCatch({
     vi_scores <- varimp(
       object,
       rvar = object$rvar,
@@ -752,14 +752,14 @@ svm_vip_plot <- function(object, size, custom) {
     
     # 检查数据有效性
     if (nrow(vi_scores) == 0 || all(is.na(vi_scores$Importance))) {
-      p <- ggplot() +
+      ggplot() +
         annotate("text", x = 0.5, y = 0.5,
                  label = "Could not compute variable importance\n(check model/data)",
                  size = 5, color = "red") +
         theme_void()
     } else {
       # 创建条形图
-      p <- ggplot(vi_scores, aes(x = reorder(Variable, Importance), y = Importance)) +
+      ggplot(vi_scores, aes(x = reorder(Variable, Importance), y = Importance)) +
         geom_col(fill = "#377eb8") +
         coord_flip() +
         labs(
@@ -777,7 +777,7 @@ svm_vip_plot <- function(object, size, custom) {
         )
     }
   }, error = function(e) {
-    p <- ggplot() +
+    ggplot() +
       annotate("text", x = 0.5, y = 0.5,
                label = paste("Error calculating importance:\n", e$message),
                size = 4, color = "red") +

radiant.model/inst/app/tools/analysis/coxp_ui.R

@@ -27,10 +27,9 @@ coxp_inputs <- reactive({
   args$arr         <- if (input$show_filter) input$data_arrange else ""
   args$rows        <- if (input$show_filter) input$data_rows   else ""
   args$dataset     <- input$dataset
-  ## 其余参数手动映射
-  for (i in r_drop(names(args))) {
-    args[[i]] <- input[[paste0("coxp_", i)]]
-  }
+  args$time        <- input$coxp_time
+  args$status      <- input$coxp_status
+  args$evar        <- input$coxp_evar
   args
 })
 
@@ -309,63 +308,11 @@ coxp_available <- reactive({
 
 ## 10. 模型估计
 .coxp <- eventReactive(input$coxp_run, {
-  ds <- tryCatch(
-    get_data(input$dataset, vars = c(), envir = r_data),
-    error = function(e) return(paste("数据集获取失败：", e$message))
-  )
-  if (is.character(ds)) return(ds %>% add_class("coxp"))
-  
-  time_var <- input$coxp_time
-  if (!time_var %in% colnames(ds))
-    return("时间变量不存在于数据集中" %>% add_class("coxp"))
-  if (!is.numeric(ds[[time_var]]))
-    return("时间变量必须是数值型" %>% add_class("coxp"))
-  
-  status_var <- input$coxp_status
-  if (!status_var %in% colnames(ds))
-    return("状态变量不存在于数据集中" %>% add_class("coxp"))
-  
-  surv_vec <- ds[[status_var]]
-  if (is.factor(surv_vec) || is.character(surv_vec)) {
-    lev <- unique(surv_vec)
-    if (length(lev) != 2)
-      return("状态变量必须是二分类（两个水平）" %>% add_class("coxp"))
-    ds[[status_var]] <- as.numeric(factor(surv_vec, levels = lev)) - 1L
-  } else if (!all(unique(surv_vec) %in% c(0, 1))) {
-    return("状态变量必须只包含 0 和 1" %>% add_class("coxp"))
-  }
-  n_event <- sum(ds[[status_var]])
-  if (n_event < 1)
-    return("数据中未发生任何事件（status = 1）" %>% add_class("coxp"))
-  
-  evar_missing <- setdiff(input$coxp_evar, colnames(ds))
-  if (length(evar_missing) > 0)
-    return(paste("解释变量不存在：", paste(evar_missing, collapse = ", ")) %>% add_class("coxp"))
-  
-  rhs <- if (length(input$coxp_evar) > 0) {
-    paste(input$coxp_evar, collapse = " + ")
-  } else {
-    "1"
-  }
-  form <- as.formula(paste0("Surv(", time_var, ", ", status_var, ") ~ ", rhs))
-  
-  model <- tryCatch(
-    survival::coxph(form, data = ds),
-    error = function(e) return(paste("coxph 模型失败：", e$message))
-  )
-  if (is.character(model))
-    return(model %>% add_class("coxp"))
-  
-  coxp(
-    dataset   = input$dataset,
-    time      = time_var,
-    status    = status_var,
-    evar      = input$coxp_evar,
-    data_filter = if (input$show_filter) input$data_filter else "",
-    arr       = if (input$show_filter) input$data_arrange else "",
-    rows      = if (input$show_filter) input$data_rows else NULL,
-    envir     = r_data
-  )
+  coxpi <- coxp_inputs()
+  coxpi$envir <- r_data
+  withProgress(message = i18n$t("Estimating model"), value = 1, {
+    do.call(coxp, coxpi)
+  })
 })
 
 ## 11. summary / predict / plot --------------------------------------------

radiant.model/man/coxp.Rd

@@ -9,9 +9,9 @@ coxp(
   time,
   status,
   evar,
+  form = NULL,
   int = "",
   check = "",
-  form,
   data_filter = "",
   arr = "",
   rows = NULL,

radiant.model/man/predict.svm.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/svm.R
 \name{predict.svm}
 \alias{predict.svm}
-\title{Predict method for SVM model}
+\title{Predict method for SVM model  (FIXED VERSION)}
 \usage{
 \method{predict}{svm}(
   object,
@@ -14,5 +14,5 @@
 )
 }
 \description{
-Predict method for SVM model
+Predict method for SVM model  (FIXED VERSION)
 }

radiant.multivariate/R/hclus.R

@@ -37,11 +37,6 @@ hclus <- function(dataset, vars, labels = "none", distance = "sq.euclidian",
       add_class("hclus"))
   }
 
-  anyCategorical <- sapply(dataset, function(x) is.numeric(x)) == FALSE
-  ## in case : is used
-  if (length(vars) < ncol(dataset)) vars <- colnames(dataset)
-  if (any(anyCategorical) && distance != "gower") distance <- "gower"
-
   if (labels != "none") {
     if (length(unique(dataset[[1]])) == nrow(dataset)) {
       rownames(dataset) <- dataset[[1]]
@@ -52,6 +47,11 @@ hclus <- function(dataset, vars, labels = "none", distance = "sq.euclidian",
     dataset <- select(dataset, -1)
   }
 
+  anyCategorical <- sapply(dataset, function(x) is.numeric(x)) == FALSE
+  ## in case : is used
+  if (length(vars) < ncol(dataset)) vars <- colnames(dataset)
+  if (any(anyCategorical) && distance != "gower") distance <- "gower"
+
   if (standardize) {
     dataset <- mutate_if(dataset, is.numeric, ~ as.vector(scale(.)))
   }
@@ -59,7 +59,7 @@ hclus <- function(dataset, vars, labels = "none", distance = "sq.euclidian",
   if (distance == "sq.euclidian") {
     d <- dist(dataset, method = "euclidean")^2
   } else if (distance == "gower") {
-    d <- sapply(1:nrow(dataset), function(i) gower::gower_dist(dataset[i, ], dataset)) %>%
+    d <- sapply(1:nrow(dataset), function(i) gower::gower_dist(dataset[i, , drop = FALSE], dataset)) %>%
       as.dist()
   } else {
     d <- dist(dataset, method = distance)

radiant.quickgen/inst/app/tools/analysis/quickgen_chat_ui.R

@@ -49,7 +49,7 @@ output$chat_history_area <- renderUI({
     return(create_no_data_ui())  
   }
   
-  dify_base_url <- "http://122.112.232.121:8079/chat/pmo9kEMGQVAdv8YR"
+  dify_base_url <- "http://10.120.120.35/chat/pmo9kEMGQVAdv8YR"
   dify_url <- paste0(
     dify_base_url,
     "?showSidebar=true",