inst/doc/anomalize_quick_start_guide.R

In anomalize: Tidy Anomaly Detection

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = F,
  fig.align = "center"
)

## ----eval = T, message = F, warning = F---------------------------------------
library(tidyverse)
library(tibbletime)
library(anomalize)

# NOTE: timetk now has anomaly detection built in, which 
#  will get the new functionality going forward.

anomalize <- anomalize::anomalize
plot_anomalies <- anomalize::plot_anomalies

## -----------------------------------------------------------------------------
tidyverse_cran_downloads

## -----------------------------------------------------------------------------
tidyverse_cran_downloads_anomalized <- tidyverse_cran_downloads %>%
    time_decompose(count, merge = TRUE) %>%
    anomalize(remainder) %>%
    time_recompose()

tidyverse_cran_downloads_anomalized %>% glimpse()

## ----fig.height=8, fig.width=6------------------------------------------------
tidyverse_cran_downloads_anomalized %>%
    plot_anomalies(ncol = 3, alpha_dots = 0.25)

## -----------------------------------------------------------------------------
lubridate_daily_downloads <- tidyverse_cran_downloads %>%
    filter(package == "lubridate") %>%
    ungroup()

lubridate_daily_downloads

## -----------------------------------------------------------------------------
lubridate_daily_downloads_anomalized <- lubridate_daily_downloads %>% 
    time_decompose(count) %>%
    anomalize(remainder) %>%
    time_recompose()

lubridate_daily_downloads_anomalized %>% glimpse()

## ----fig.width=5, fig.height=6------------------------------------------------
p1 <- lubridate_daily_downloads_anomalized %>%
    plot_anomaly_decomposition() +
    ggtitle("Freq/Trend = 'auto'")

p1

## -----------------------------------------------------------------------------
get_time_scale_template()

## ----fig.show="hold", fig.height=6, fig.align="default"-----------------------
# Local adjustment via time_decompose
p2 <- lubridate_daily_downloads %>%
    time_decompose(count,
                   frequency = "auto",
                   trend     = "14 days") %>%
    anomalize(remainder) %>%
    plot_anomaly_decomposition() +
    ggtitle("Trend = 14 Days (Local)")

# Show plots
p1
p2

## -----------------------------------------------------------------------------
# Globally change time scale template options
time_scale_template() %>%
    mutate(trend = ifelse(time_scale == "day", "14 days", trend)) %>%
    set_time_scale_template()

get_time_scale_template()

## ----fig.width=5, fig.height=6------------------------------------------------
p3 <- lubridate_daily_downloads %>%
    time_decompose(count) %>%
    anomalize(remainder) %>%
    plot_anomaly_decomposition() +
    ggtitle("Trend = 14 Days (Global)")

p3

## -----------------------------------------------------------------------------
# Set time scale template to the original defaults
time_scale_template() %>%
    set_time_scale_template()

# Verify the change
get_time_scale_template()

## ----fig.height=5, fig.width=5------------------------------------------------
p4 <- lubridate_daily_downloads %>%
    time_decompose(count) %>%
    anomalize(remainder, alpha = 0.05, max_anoms = 0.2) %>%
    time_recompose() %>%
    plot_anomalies(time_recomposed = TRUE) +
    ggtitle("alpha = 0.05")

p4

## ----fig.show="hold", fig.align="default"-------------------------------------
p5 <- lubridate_daily_downloads %>%
    time_decompose(count) %>%
    anomalize(remainder, alpha = 0.025, max_anoms = 0.2) %>%
    time_recompose() %>%
    plot_anomalies(time_recomposed = TRUE) +
    ggtitle("alpha = 0.025")

p4 
p5

## ----fig.show="hold", fig.align="default"-------------------------------------
p6 <- lubridate_daily_downloads %>%
    time_decompose(count) %>%
    anomalize(remainder, alpha = 0.3, max_anoms = 0.2) %>%
    time_recompose() %>%
    plot_anomalies(time_recomposed = TRUE) +
    ggtitle("20% Anomalies")

p7 <- lubridate_daily_downloads %>%
    time_decompose(count) %>%
    anomalize(remainder, alpha = 0.3, max_anoms = 0.05) %>%
    time_recompose() %>%
    plot_anomalies(time_recomposed = TRUE) +
    ggtitle("5% Anomalies")

p6
p7