如何在R中绘制预测子集？ [英] How to plot a subset of forecast in R?

问题描述

我有一个简单的R脚本，可以基于文件创建预测。
自2014年以来就记录了数据，但我在尝试实现以下两个目标时遇到了麻烦：

• 仅绘制预测信息（从11/2017开始）。


• 以特定格式（例如6月17日）包括月份和年份。

此处是指向

I have a simple R script to create a forecast based on a file. Data has been recorded since 2014 but I am having trouble trying to accomplish below two goals:

• Plot only a subset of the forecast information (starting on 11/2017 onwards).
• Include month and year in a specific format (i.e. Jun 17).

Here is the link to the dataset and below you will find the code made by me so far.

# Load required libraries
library(forecast)
library(ggplot2)

# Load dataset
emea <- read.csv(file="C:/Users/nsoria/Downloads/AMS Globales/EMEA_Depuy_Finanzas.csv", header=TRUE, sep=';', dec=",")

# Create time series object
ts_fin <- ts(emea$Value, frequency = 26, start = c(2014,11))

# Pull out the seasonal, trend, and irregular components from the time series 
model <- stl(ts_fin, s.window = "periodic")

# Predict the next 3 bi weeks of tickets
pred <- forecast(model, h = 5)

# Plot the results
plot(pred, include = 5, showgap = FALSE, main = "Ticket amount", xlab = "Timeframe", ylab = "Quantity")

I appreciate any help and suggestion to my two points and a clean plot.

Thanks in advance.

Edit 01/10 - Issue 1: I added the screenshot output for suggested code. Plot1

Edit 01/10 - Issue 2: Once transformed with below code, it somehow miss the date count and mess with the results. Please see two screenshots and compare the last value.

Screenshot 1 Screenshot 2

解决方案

Plotting using ggplot2 w/ ggfortify, tidyverse, lubridate and scales packages

    library(lubridate)
    library(tidyverse)
    library(scales)
    library(ggfortify)

    # Convert pred from list to data frame object
    df1 <- fortify(pred) %>% as_tibble()

    # Convert ts decimal time to Date class
    df1$Date <- as.Date(date_decimal(df1$Index), "%Y-%m-%d")
    str(df1)

    # Remove Index column and rename other columns
    # Select only data pts after 2017
    df1 <- df1 %>% 
      select(-Index) %>% 
      filter(Date >= as.Date("2017-01-01")) %>% 
      rename("Low95" = "Lo 95",
             "Low80" = "Lo 80",
             "High95" = "Hi 95",
             "High80" = "Hi 80",
             "Forecast" = "Point Forecast")
    df1

    ### Updated: To connect the gap between the Data & Forecast, 
    # assign the last non-NA row of Data column to the corresponding row of other columns
    lastNonNAinData <- max(which(complete.cases(df1$Data)))
    df1[lastNonNAinData, !(colnames(df1) %in% c("Data", "Fitted", "Date"))] <- df1$Data[lastNonNAinData]

    # Or: use [geom_segment](http://ggplot2.tidyverse.org/reference/geom_segment.html)

    plt1 <- ggplot(df1, aes(x = Date)) +   
      ggtitle("Ticket amount") +
      xlab("Time frame") + ylab("Quantity") +
      geom_ribbon(aes(ymin = Low95, ymax = High95, fill = "95%")) +
      geom_ribbon(aes(ymin = Low80, ymax = High80, fill = "80%")) +
      geom_point(aes(y = Data, colour = "Data"), size = 4) +
      geom_line(aes(y = Data, group = 1, colour = "Data"), 
                linetype = "dotted", size = 0.75) +
      geom_line(aes(y = Fitted, group = 2, colour = "Fitted"), size = 0.75) +
      geom_line(aes(y = Forecast, group = 3, colour = "Forecast"), size = 0.75) +
      scale_x_date(breaks = scales::pretty_breaks(), date_labels = "%b %y") +
      scale_colour_brewer(name = "Legend", type = "qual", palette = "Dark2") +
      scale_fill_brewer(name = "Intervals") +
      guides(colour = guide_legend(order = 1), fill = guide_legend(order = 2)) +
      theme_bw(base_size = 14)
    plt1

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