plot_anomalies {anomalize} | R Documentation |
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 )
data |
A |
time_recomposed |
A boolean. If |
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. |
Plotting function for visualizing anomalies on one or more time series.
Multiple time series must be grouped using dplyr::group_by()
.
Returns a ggplot
object.
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)