Question

我正在尝试使用ggplot2绘制R Shiny中的股价预测。但是，我收到了错误消息：

警告：as.data.frame.default中的错误：无法强制类‘c（“ gg”， “ ggplot”）’指向data.frame [无可用堆栈跟踪]

这是我的预测功能和服务器代码。

library(shiny)
library(shinydashboard)
library(devtools)
library(ggvis)
library(dplyr)
library(RSQLite)
library(ggplot2)
library(randomForest, quietly = TRUE)
library(lubridate)

# Forecasting Function ----------------------------------------------------

getdfnew <- function(df){
  # clean up data frame to set types appropriately
  df$Stock.Trading <- as.numeric(df$Stock.Trading)
  # Build the training/validate/test datasets.
  nobs <- nrow(df)
  ntr <- 0.2*nobs # assumes first 20% of data tunes good system
  set.seed(42)
  str(df)
  indices.train <- 1:ntr # 
  indices.apply <- (ntr+1):nobs # 

  input.variables <- c("Open",     
                       "High","Low",
                       "Close","Volume")

  input.numbers <- c("Open",     
                    "High","Low",
                    "Close","Volume")

  target.variable  <- "Stock.Trading"

  set.seed(42)
  result.rf <- randomForest::randomForest(Stock.Trading ~ .,
                                          data=df[indices.train ,c(input.variables, target.variable)], 
                                          ntree=500,
                                          mtry=3,
                                          importance=TRUE,
                                          na.action=randomForest::na.roughfix,
                                          replace=FALSE)

  # Get predicted and actual values
  predicted.training <- result.rf$predicted
  actual <- df$Stock.Trading

  # Apply model to new data -------------------------------------------------

  df.apply <- df[indices.apply, c(input.variables, target.variable)]

  # Lets say we had new data ---- data.new
  # It's essential that the new data have the same input columns and target(s)
  #  new.data <- newdataset[1:nrows(newdataset),c(crs$input, crs$target)]

  # predict for each of the validate indices the wear condition
  predicted.apply <- predict(result.rf, df.apply , type="response",
                             norm.votes=TRUE, predict.all=FALSE, proximity=FALSE, nodes=FALSE)


  predicted.all <- vector(length=nobs)
  predicted.all[1:ntr] <- predicted.training
  predicted.all[(ntr+1):nobs] <- predicted.apply

  df$Predicted <- predicted.all
  colnames(df)[8] <- "Predicted"

  return(df)
}

此标签的服务器代码：

output$rfplot <- renderTable({

    inFile <- input$file1

    if (is.null(inFile))
      return(NULL)

    df <- read.csv(inFile$datapath, header=input$header, sep=input$sep, 
                   quote=input$quote)

    dfnew <-getdfnew(df)

    x <- dfnew$Stock.Trading
    y <- dfnew$Predicted
    # browser()
    dfnew$DATE <- as.Date(parse_date_time(dfnew$Date, "%m/%d/%y"))
    p <- ggplot(dfnew, aes(DATE)) + 
      geom_line(aes(y = Stock.Trading, colour = "Actual")) + 
      geom_line(aes(y = Predicted, colour = "Predicted"))
    print(p)
  })

我的数据集如下：

Date        Open    High    Low     Close   Volume  Stock.Trading
12/30/2016  42120   42330   41700   41830   610000  25628028000
12/29/2016  43000   43220   42540   42660   448400  19188227000
12/28/2016  43940   43970   43270   43270   339900  14780670000
12/27/2016  43140   43700   43140   43620   400100  17427993000
12/26/2016  43310   43660   43090   43340   358200  15547803000

任何帮助将不胜感激！

无法将类别“ c（“ gg”，“ ggplot”）”强制转换为data.frame

0 个答案: