plot_anomalies: Visualize the anomalies in one or multiple time series

In anomalize: Tidy Anomaly Detection

Visualize the anomalies in one or multiple time series

Description

Visualize the anomalies in one or multiple time series

Usage

plot_anomalies(
  data,
  time_recomposed = FALSE,
  ncol = 1,
  color_no = "#2c3e50",
  color_yes = "#e31a1c",
  fill_ribbon = "grey70",
  alpha_dots = 1,
  alpha_circles = 1,
  alpha_ribbon = 1,
  size_dots = 1.5,
  size_circles = 4
)

Arguments

data	A tibble or tbl_time object.
time_recomposed	A boolean. If TRUE, will use the time_recompose() bands to place bands as approximate limits around the "normal" data.
ncol	Number of columns to display. Set to 1 for single column by default.
color_no	Color for non-anomalous data.
color_yes	Color for anomalous data.
fill_ribbon	Fill color for the time_recomposed ribbon.
alpha_dots	Controls the transparency of the dots. Reduce when too many dots on the screen.
alpha_circles	Controls the transparency of the circles that identify anomalies.
alpha_ribbon	Controls the transparency of the time_recomposed ribbon.
size_dots	Controls the size of the dots.
size_circles	Controls the size of the circles that identify anomalies.

Details

Plotting function for visualizing anomalies on one or more time series. Multiple time series must be grouped using dplyr::group_by().

Value

Returns a ggplot object.

See Also

plot_anomaly_decomposition()

Examples

## Not run: 
library(dplyr)
library(ggplot2)

data(tidyverse_cran_downloads)

#### SINGLE TIME SERIES ####
tidyverse_cran_downloads %>%
    filter(package == "tidyquant") %>%
    ungroup() %>%
    time_decompose(count, method = "stl") %>%
    anomalize(remainder, method = "iqr") %>%
    time_recompose() %>%
    plot_anomalies(time_recomposed = TRUE)


#### MULTIPLE TIME SERIES ####
tidyverse_cran_downloads %>%
    time_decompose(count, method = "stl") %>%
    anomalize(remainder, method = "iqr") %>%
    time_recompose() %>%
    plot_anomalies(time_recomposed = TRUE, ncol = 3)

## End(Not run)

anomalize documentation built on Nov. 2, 2023, 5:13 p.m.