Visualizing Time Series"

knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  fig.width = 8, 
  fig.height = 4.5,
  fig.align = 'center',
  out.width='95%', 
  dpi = 100,
  collapse = TRUE,
  comment = "#>"
)
knitr::include_graphics("timetk_version_2.jpg")

This tutorial focuses on, plot_time_series(), a workhorse time-series plotting function that:

Libraries

Run the following code to setup for this tutorial.

library(tidyverse)
library(lubridate)
library(timetk)

# Setup for the plotly charts (# FALSE returns ggplots)
interactive <- FALSE

Plotting Time Series

Let's start with a popular time series, taylor_30_min, which includes energy demand in megawatts at a sampling interval of 30-minutes. This is a single time series.

taylor_30_min

The plot_time_series() function generates an interactive plotly chart by default.

taylor_30_min %>% 
  plot_time_series(date, value, 
                   .interactive = interactive,
                   .plotly_slider = TRUE)

Plotting Groups

Next, let's move on to a dataset with time series groups, m4_daily, which is a sample of 4 time series from the M4 competition that are sampled at a daily frequency.

m4_daily %>% group_by(id)

Visualizing grouped data is as simple as grouping the data set with group_by() prior to piping into the plot_time_series() function. Key points:

m4_daily %>%
  group_by(id) %>%
  plot_time_series(date, value, 
                   .facet_ncol = 2, .facet_scales = "free",
                   .interactive = interactive)

Visualizing Transformations & Sub-Groups

Let's switch to an hourly dataset with multiple groups. We can showcase:

  1. Log transformation to the .value
  2. Use of .color_var to highlight sub-groups.
m4_hourly %>% group_by(id)

The intent is to showcase the groups in faceted plots, but to highlight weekly windows (sub-groups) within the data while simultaneously doing a log() transformation to the value. This is simple to do:

  1. .value = log(value) Applies the Log Transformation
  2. .color_var = week(date) The date column is transformed to a lubridate::week() number. The color is applied to each of the week numbers.
m4_hourly %>%
  group_by(id) %>%
  plot_time_series(date, log(value),             # Apply a Log Transformation
                   .color_var = week(date),      # Color applied to Week transformation
                   # Facet formatting
                   .facet_ncol = 2, 
                   .facet_scales = "free", 
                   .interactive = interactive)

Static ggplot2 Visualizations & Customizations

All of the visualizations can be converted from interactive plotly (great for exploring and shiny apps) to static ggplot2 visualizations (great for reports).

taylor_30_min %>%
  plot_time_series(date, value, 
                   .color_var = month(date, label = TRUE),

                   # Returns static ggplot
                   .interactive = FALSE,  

                   # Customization
                   .title = "Taylor's MegaWatt Data",
                   .x_lab = "Date (30-min intervals)",
                   .y_lab = "Energy Demand (MW)",
                   .color_lab = "Month") +
  scale_y_continuous(labels = scales::comma_format())

Box Plots (Time Series)

The plot_time_series_boxplot() function can be used to make box plots.

m4_monthly %>%
    group_by(id) %>%
    plot_time_series_boxplot(
        date, value,
        .period      = "1 year",
        .facet_ncol  = 2,
        .interactive = FALSE)

Regression Plots (Time Series)

A time series regression plot, plot_time_series_regression(), can be useful to quickly assess key features that are correlated to a time series.

m4_monthly %>%
    group_by(id) %>%
    plot_time_series_regression(
        .date_var     = date,
        .formula      = log(value) ~ as.numeric(date) + month(date, label = TRUE),
        .facet_ncol   = 2,
        .interactive  = FALSE,
        .show_summary = FALSE
    )

Summary

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timetk documentation built on April 7, 2022, 5:08 p.m.