Visualizing Time Series
Matt Dancho
Source:vignettes/TK04_Plotting_Time_Series.Rmd
TK04_Plotting_Time_Series.Rmd
This tutorial focuses on, plot_time_series()
, a
workhorse timeseries 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 staticggplot2
plots
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
30minutes. This is a single time series.
taylor_30_min
#> # A tibble: 4,032 × 2
#> date value
#> <dttm> <dbl>
#> 1 20000605 00:00:00 22262
#> 2 20000605 00:30:00 21756
#> 3 20000605 01:00:00 22247
#> 4 20000605 01:30:00 22759
#> 5 20000605 02:00:00 22549
#> 6 20000605 02:30:00 22313
#> 7 20000605 03:00:00 22128
#> 8 20000605 03:30:00 21860
#> 9 20000605 04:00:00 21751
#> 10 20000605 04:30:00 21336
#> # ℹ 4,022 more rows
The plot_time_series()
function generates an interactive
plotly
chart by default.
 Simply provide the date variable (timebased 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 × 3
#> # Groups: id [4]
#> id date value
#> <fct> <date> <dbl>
#> 1 D10 20140703 2076.
#> 2 D10 20140704 2073.
#> 3 D10 20140705 2049.
#> 4 D10 20140706 2049.
#> 5 D10 20140707 2006.
#> 6 D10 20140708 2018.
#> 7 D10 20140709 2019.
#> 8 D10 20140710 2007.
#> 9 D10 20140711 2010
#> 10 D10 20140712 2002.
#> # ℹ 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 2column faceted plot 
.facet_scales = "free"
allows the x and yaxis 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 & SubGroups
Let’s switch to an hourly dataset with multiple groups. We can showcase:
 Log transformation to the
.value
 Use of
.color_var
to highlight subgroups.
m4_hourly %>% group_by(id)
#> # A tibble: 3,060 × 3
#> # Groups: id [4]
#> id date value
#> <fct> <dttm> <dbl>
#> 1 H10 20150701 12:00:00 513
#> 2 H10 20150701 13:00:00 512
#> 3 H10 20150701 14:00:00 506
#> 4 H10 20150701 15:00:00 500
#> 5 H10 20150701 16:00:00 490
#> 6 H10 20150701 17:00:00 484
#> 7 H10 20150701 18:00:00 467
#> 8 H10 20150701 19:00:00 446
#> 9 H10 20150701 20:00:00 434
#> 10 H10 20150701 21:00:00 422
#> # ℹ 3,050 more rows
The intent is to showcase the groups in faceted plots, but to
highlight weekly windows (subgroups) within the data while
simultaneously doing a log()
transformation to the value.
This is simple to do:

.value = log(value)
Applies the Log Transformation 
.color_var = week(date)
The date column is transformed to alubridate::week()
number. The color is applied to each of the week numbers.
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 (30min intervals)",
.y_lab = "Energy Demand (MW)",
.color_lab = "Month") +
scale_y_continuous(labels = scales::label_comma())
Box Plots (Time Series)
The plot_time_series_boxplot()
function can be used to
make box plots.
 Box plots use an aggregation, which is a key parameter defined by
the
.period
argument.
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.
 Internally the function passes a
formula
to thestats::lm()
function.  A linear regression summary can be output by toggling
show_summary = TRUE
.
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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 Ensembling and Resampling
 Machine Learning
 Deep Learning
 Scalable Modeling: 10,000+ time series
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