Visualize the anomalies in one or multiple time series

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.

Value

Returns a ggplot object.

Details

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

See also

plot_anomaly_decomposition()

Examples

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)
#> frequency = 7 days
#> trend = 91 days
#### MULTIPLE TIME SERIES #### tidyverse_cran_downloads %>% time_decompose(count, method = "stl") %>% anomalize(remainder, method = "iqr") %>% time_recompose() %>% plot_anomalies(time_recomposed = TRUE, ncol = 3)