timetk (version 2.9.0)

plot_anomalies: Visualize Anomalies for One or More Time Series

Description

plot_anomalies() is an interactive and scalable function for visualizing anomalies in time series data. Plots are available in interactive plotly (default) and static ggplot2 format.

plot_anomalies_decomp(): Takes in data from the anomalize() function, and returns a plot of the anomaly decomposition. Useful for interpeting how the anomalize() function is determining outliers from "remainder".

plot_anomalies_cleaned() helps users visualize the before/after of cleaning anomalies.

Usage

plot_anomalies(
  .data,
  .date_var,
  .facet_vars = NULL,
  .facet_ncol = 1,
  .facet_nrow = 1,
  .facet_scales = "free",
  .facet_dir = "h",
  .facet_collapse = FALSE,
  .facet_collapse_sep = " ",
  .facet_strip_remove = FALSE,
  .line_color = "#2c3e50",
  .line_size = 0.5,
  .line_type = 1,
  .line_alpha = 1,
  .anom_color = "#e31a1c",
  .anom_alpha = 1,
  .anom_size = 1.5,
  .ribbon_fill = "grey20",
  .ribbon_alpha = 0.2,
  .legend_show = TRUE,
  .title = "Anomaly Plot",
  .x_lab = "",
  .y_lab = "",
  .color_lab = "Anomaly",
  .interactive = TRUE,
  .trelliscope = FALSE,
  .trelliscope_params = list()
)
plot_anomalies_decomp(
  .data,
  .date_var,
  .facet_vars = NULL,
  .facet_scales = "free",
  .line_color = "#2c3e50",
  .line_size = 0.5,
  .line_type = 1,
  .line_alpha = 1,
  .title = "Anomaly Decomposition Plot",
  .x_lab = "",
  .y_lab = "",
  .interactive = TRUE
)
plot_anomalies_cleaned(
  .data,
  .date_var,
  .facet_vars = NULL,
  .facet_ncol = 1,
  .facet_nrow = 1,
  .facet_scales = "free",
  .facet_dir = "h",
  .facet_collapse = FALSE,
  .facet_collapse_sep = " ",
  .facet_strip_remove = FALSE,
  .line_color = "#2c3e50",
  .line_size = 0.5,
  .line_type = 1,
  .line_alpha = 1,
  .cleaned_line_color = "#e31a1c",
  .cleaned_line_size = 0.5,
  .cleaned_line_type = 1,
  .cleaned_line_alpha = 1,
  .legend_show = TRUE,
  .title = "Anomalies Cleaned Plot",
  .x_lab = "",
  .y_lab = "",
  .color_lab = "Legend",
  .interactive = TRUE,
  .trelliscope = FALSE,
  .trelliscope_params = list()
)

Value

A plotly or ggplot2 visualization

Arguments

.data

A tibble or data.frame that has been anomalized by anomalize()

.date_var

A column containing either date or date-time values

.facet_vars

One or more grouping columns that broken out into ggplot2 facets. These can be selected using tidyselect() helpers (e.g contains()).

.facet_ncol

Number of facet columns.

.facet_nrow

Number of facet rows (only used for .trelliscope = TRUE)

.facet_scales

Control facet x & y-axis ranges. Options include "fixed", "free", "free_y", "free_x"

.facet_dir

The direction of faceting ("h" for horizontal, "v" for vertical). Default is "h".

.facet_collapse

Multiple facets included on one facet strip instead of multiple facet strips.

.facet_collapse_sep

The separator used for collapsing facets.

.facet_strip_remove

Whether or not to remove the strip and text label for each facet.

.line_color

Line color.

.line_size

Line size.

.line_type

Line type.

.line_alpha

Line alpha (opacity). Range: (0, 1).

.anom_color

Color for the anomaly dots

.anom_alpha

Opacity for the anomaly dots. Range: (0, 1).

.anom_size

Size for the anomaly dots

.ribbon_fill

Fill color for the acceptable range

.ribbon_alpha

Fill opacity for the acceptable range. Range: (0, 1).

.legend_show

Toggles on/off the Legend

.title

Plot title.

.x_lab

Plot x-axis label

.y_lab

Plot y-axis label

.color_lab

Plot label for the color legend

.interactive

If TRUE, returns a plotly interactive plot. If FALSE, returns a static ggplot2 plot.

.trelliscope

Returns either a normal plot or a trelliscopejs plot (great for many time series) Must have trelliscopejs installed.

.trelliscope_params

Pass parameters to the trelliscopejs::facet_trelliscope() function as a list(). The only parameters that cannot be passed are:

• ncol: use .facet_ncol

• nrow: use .facet_nrow

• scales: use facet_scales

• as_plotly: use .interactive

.cleaned_line_color

Line color.

.cleaned_line_size

Line size.

.cleaned_line_type

Line type.

.cleaned_line_alpha

Line alpha (opacity). Range: (0, 1).

Examples

# Plot Anomalies
library(dplyr)

walmart_sales_weekly %>%
    filter(id %in% c("1_1", "1_3")) %>%
    group_by(id) %>%
    anomalize(Date, Weekly_Sales) %>%
    plot_anomalies(Date, .facet_ncol = 2, .ribbon_alpha = 0.25, .interactive = FALSE)

# Plot Anomalies Decomposition
library(dplyr)

walmart_sales_weekly %>%
    filter(id %in% c("1_1", "1_3")) %>%
    group_by(id) %>%
    anomalize(Date, Weekly_Sales, .message = FALSE) %>%
    plot_anomalies_decomp(Date, .interactive = FALSE)

# Plot Anomalies Cleaned
library(dplyr)

walmart_sales_weekly %>%
    filter(id %in% c("1_1", "1_3")) %>%
    group_by(id) %>%
    anomalize(Date, Weekly_Sales, .message = FALSE) %>%
    plot_anomalies_cleaned(Date, .facet_ncol = 2, .interactive = FALSE)