如何从矩阵绘制Highcharter区域范围

Question

我有一个Seasonal包的矩阵输出，我过滤掉了“预测”列，只留下时间（月份）和“lowerci”和“upperci”条目。 这通过以下方式完成： season13201101FL.forecast[,c('lowerci','upperci')]

数据样本：

           lowerci  upperci
Oct 2017 2415.8826 3083.332
Nov 2017 2217.2670 3238.572
Dec 2017 1976.0041 3181.648
Jan 2018 2048.9771 3577.373
Feb 2018 2046.3051 3834.099

这是“mts”类。 我正在使用highcharter库来绘制我的值。但是，即使我使用series.keys来映射，它似乎也没有同时使用“lowerci”和“upperci”列。：

hc <- highchart(type = "stock") %>% 
  hc_add_series(season13201101FL, id = "Original", name = "Original-FL") %>% 
  hc_add_series(season13201101FL.seasonalData, id = "Seasonally Adjusted-FL", name = "Seasonally Adjusted") %>% 
  hc_add_series(season13201101FL.forecast[,c('forecast')], id = "Forecast-FL") %>% 
  hc_add_series(season13201101FL.forecast[,c('lowerci','upperci')], id = "ForecastRange-FL", keys = c('x', 'low', 'high'), type = "arearange")
hc

结果图表显示原始，季节性调整和预测系列，但预测范围显示没有连接点的“线”，每个时间条目只有一个实际数据点。如何让highcharter看到这是一个arearange系列？

要重现，请将以下内容用作导入CSV theCSV：

date    count
2008.0027   45778
2008.0874   50460
2008.1667   62162
2008.2514   55999
2008.3333   51571
2008.418    45044
2008.5  46357
2008.5847   48498
2008.6694   45472
2008.7514   47161
2008.8361   41907
2008.918    39131
2009.0027   33810
2009.0877   34469

然后代码是：

library(shiny)
library(highcharter)
library(dplyr)
library(tidyr)
library(seasonal)

seasonData <- ts(theCSV[,-1], frequency = 12, start = c(2008,1));
seasonData.seas <- seas(seasonData);
seasonData.seasonalData <- final(seasonData.seas);
seasonData.forecast <- series(seasonData.seas, "forecast.forecasts");
seasonData.seasComp <- series(seasonData.seas, "seats.seasonal");

    hc <- highchart(type = "stock") %>% 
      hc_add_series(seasonData, id = "Original", name = "Original-FL") %>% 
      hc_add_series(seasonData.seasonalData, id = "Seasonally Adjusted-FL", name = "Seasonally Adjusted") %>% 
      hc_add_series(seasonData.forecast[,c('forecast')], id = "Forecast-FL") %>% 
      hc_add_series(seasonData.forecast[,c('lowerci','upperci')], id = "ForecastRange-FL", keys = c('x', 'low', 'high'), type = "arearange")
    hc;

Answer 1

一种方法是使用值和日期/时间值将数据预测转换为数据框。

要获取datetime值，您可以使用time和as.Date函数。然后 使用hc_add_series添加数据。

library(highcharter)
library(dplyr)
library(tidyr)
library(seasonal)

seasonData <- AirPassengers
seasonData.seas <- seas(seasonData);
seasonData.seasonalData <- final(seasonData.seas);
seasonData.forecast <- series(seasonData.seas, "forecast.forecasts");
seasonData.seasComp <- series(seasonData.seas, "seats.seasonal");


time <- seasonData.forecast %>%
  stats::time() %>%
  zoo::as.Date() %>% 
  datetime_to_timestamp()

dfforecast <- seasonData.forecast %>% 
  as.data.frame() %>% 
  mutate(time = time)

highchart(type = "stock") %>% 
  hc_add_series(seasonData, id = "Original", name = "Original-FL") %>% 
  hc_add_series(seasonData.seasonalData, id = "Seasonally Adjusted-FL", name     = "Seasonally Adjusted") %>% 
  hc_add_series(seasonData.forecast[,c('forecast')], id = "Forecast-FL") %>% 
  hc_add_series(dfforecast, hcaes(x = time, low = lowerci, high = upperci),     id = "ForecastRange-FL", type = "arearange")

hc