
Visualize Multiple Seasonality Features for One or More Time Series
Source:R/plot-seasonal_diagnostics.R
plot_seasonal_diagnostics.Rd
An interactive and scalable function for visualizing time series seasonality.
Plots are available in interactive plotly
(default) and static ggplot2
format.
Usage
plot_seasonal_diagnostics(
.data,
.date_var,
.value,
.facet_vars = NULL,
.feature_set = "auto",
.geom = c("boxplot", "violin"),
.geom_color = "#2c3e50",
.geom_outlier_color = "#2c3e50",
.title = "Seasonal Diagnostics",
.x_lab = "",
.y_lab = "",
.interactive = TRUE
)
Arguments
- .data
A
tibble
ordata.frame
with a time-based column- .date_var
A column containing either date or date-time values
- .value
A column containing numeric values
- .facet_vars
One or more grouping columns that broken out into
ggplot2
facets. These can be selected usingtidyselect()
helpers (e.gcontains()
).- .feature_set
One or multiple selections to analyze for seasonality. Choices include:
"auto" - Automatically selects features based on the time stamps and length of the series.
"second" - Good for analyzing seasonality by second of each minute.
"minute" - Good for analyzing seasonality by minute of the hour
"hour" - Good for analyzing seasonality by hour of the day
"wday.lbl" - Labeled weekdays. Good for analyzing seasonality by day of the week.
"week" - Good for analyzing seasonality by week of the year.
"month.lbl" - Labeled months. Good for analyzing seasonality by month of the year.
"quarter" - Good for analyzing seasonality by quarter of the year
"year" - Good for analyzing seasonality over multiple years.
- .geom
Either "boxplot" or "violin"
- .geom_color
Geometry color. Line color. Use keyword: "scale_color" to change the color by the facet.
- .geom_outlier_color
Color used to highlight outliers.
- .title
Plot title.
- .x_lab
Plot x-axis label
- .y_lab
Plot y-axis label
- .interactive
If TRUE, returns a
plotly
interactive plot. If FALSE, returns a staticggplot2
plot.
Details
Automatic Feature Selection
Internal calculations are performed to detect a sub-range of features to include useing the following logic:
The minimum feature is selected based on the median difference between consecutive timestamps
The maximum feature is selected based on having 2 full periods.
Example: Hourly timestamp data that lasts more than 2 weeks will have the following features: "hour", "wday.lbl", and "week".
Scalable with Grouped Data Frames
This function respects grouped data.frame
and tibbles
that were made with dplyr::group_by()
.
For grouped data, the automatic feature selection returned is a collection of all features within the sub-groups. This means extra features are returned even though they may be meaningless for some of the groups.
Transformations
The .value
parameter respects transformations (e.g. .value = log(sales)
).
Examples
if (FALSE) {
library(dplyr)
library(timetk)
# ---- MULTIPLE FREQUENCY ----
# Taylor 30-minute dataset from forecast package
taylor_30_min
# Visualize series
taylor_30_min %>%
plot_time_series(date, value, .interactive = FALSE)
# Visualize seasonality
taylor_30_min %>%
plot_seasonal_diagnostics(date, value, .interactive = FALSE)
# ---- GROUPED EXAMPLES ----
# m4 hourly dataset
m4_hourly
# Visualize series
m4_hourly %>%
group_by(id) %>%
plot_time_series(date, value, .facet_scales = "free", .interactive = FALSE)
# Visualize seasonality
m4_hourly %>%
group_by(id) %>%
plot_seasonal_diagnostics(date, value, .interactive = FALSE)
}