This tutorial focuses on,
plot_time_series(), a workhorse time-series plotting function that:
plotlyplots (great for exploring & shiny apps)
Run the following code to setup for this tutorial.
library(tidyverse) library(lubridate) library(timetk) # Setup for the plotly charts (# FALSE returns ggplots) <- FALSEinteractive
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
plot_time_series() function generates an interactive
plotly chart by default.
.date_var) and the numeric variable (
.value) that changes over time as the first 2 arguments
.interactive = TRUE, the
.plotly_slider = TRUEadds a date slider to the bottom of the chart.
%>% taylor_30_min plot_time_series(date, value, .interactive = interactive, .plotly_slider = TRUE)
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.
%>% group_by(id) m4_daily #> # A tibble: 9,743 x 3 #> # Groups: id  #> 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:
group_by()or by using the
...to add groups.
.facet_ncol = 2returns 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)
Let’s switch to an hourly dataset with multiple groups. We can showcase:
.color_varto highlight sub-groups.
%>% group_by(id) m4_hourly #> # A tibble: 3,060 x 3 #> # Groups: id  #> 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:
.value = log(value)Applies the Log Transformation
.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)
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())
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)
A time series regression plot,
plot_time_series_regression(), can be useful to quickly assess key features that are correlated to a time series.
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 )
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