# Plotting Time Series

#### Matt Dancho

> timetk: A toolkit for time series analysis in the tidyverse

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

- Generates interactive `plotly` plots (great for exploring & shiny apps)
- Consolidates 20+ lines of `ggplot2` & `plotly` code
- Scales well to many time series
- Can be converted from interactive `plotly` to static `ggplot2` plots

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

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

# Plotting Single 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
    #> # A tibble: 4,032 x 2
    #>    date                value
    #>    <dttm>              <dbl>
    #>  1 2000-06-05 00:00:00 22262
    #>  2 2000-06-05 00:30:00 21756
    #>  3 2000-06-05 01:00:00 22247
    #>  4 2000-06-05 01:30:00 22759
    #>  5 2000-06-05 02:00:00 22549
    #>  6 2000-06-05 02:30:00 22313
    #>  7 2000-06-05 03:00:00 22128
    #>  8 2000-06-05 03:30:00 21860
    #>  9 2000-06-05 04:00:00 21751
    #> 10 2000-06-05 04:30:00 21336
    #> # … with 4,022 more rows

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

- Simply provide the date variable (time-based column, `.date_var`) and the numeric variable (`.value`) that changes over time as the first 2 arguments
- When `.interactive = TRUE`, the `.plotly_slider = TRUE` adds a date slider to the bottom of the chart.

    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)
    #> # A tibble: 9,743 x 3
    #> # Groups:   id [4]
    #>    id    date       value
    #>    <fct> <date>     <dbl>
    #>  1 D10   2014-07-03 2076.
    #>  2 D10   2014-07-04 2073.
    #>  3 D10   2014-07-05 2049.
    #>  4 D10   2014-07-06 2049.
    #>  5 D10   2014-07-07 2006.
    #>  6 D10   2014-07-08 2018.
    #>  7 D10   2014-07-09 2019.
    #>  8 D10   2014-07-10 2007.
    #>  9 D10   2014-07-11 2010
    #> 10 D10   2014-07-12 2002.
    #> # … with 9,733 more rows

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:

- Groups can be added in 2 ways: by `group_by()` or by using the `...` to add groups.
- Groups are then converted to facets.
- `.facet_ncol = 2` returns a 2-column faceted plot
- `.facet_scales = "free"` allows the x and y-axis of each plot to scale independently of the other plots

    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)
    #> # A tibble: 3,060 x 3
    #> # Groups:   id [4]
    #>    id    date                value
    #>    <fct> <dttm>              <dbl>
    #>  1 H10   2015-07-01 12:00:00   513
    #>  2 H10   2015-07-01 13:00:00   512
    #>  3 H10   2015-07-01 14:00:00   506
    #>  4 H10   2015-07-01 15:00:00   500
    #>  5 H10   2015-07-01 16:00:00   490
    #>  6 H10   2015-07-01 17:00:00   484
    #>  7 H10   2015-07-01 18:00:00   467
    #>  8 H10   2015-07-01 19:00:00   446
    #>  9 H10   2015-07-01 20:00:00   434
    #> 10 H10   2015-07-01 21:00:00   422
    #> # … with 3,050 more rows

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())

# Learning More

If you are interested in learning from my advanced Time Series Analysis & Forecasting Course, then join my waitlist. The course is coming soon.

You will learn:

- Time Series Preprocessing, Noise Reduction, & Anomaly Detection
- Feature engineering using lagged variables & external regressors
- Hyperparameter Tuning
- Time series cross-validation
- Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- NEW - Deep Learning with RNNs (Competition Winner)
- and more.

Signup for the Time Series Course waitlist