Adobe Analytics Anomaly Detection ggplot

aa-ggplot2.R

#Plot data using ggplot2
library(ggplot2)

#Calculate points crossing UCL or LCL
pageviews_w_forecast$outliers <- 
  ifelse(pageviews_w_forecast$pageviews > pageviews_w_forecast$upperBound.pageviews, pageviews_w_forecast$pageviews,
         ifelse(pageviews_w_forecast$pageviews < pageviews_w_forecast$lowerBound.pageviews, pageviews_w_forecast$pageviews, NA))

#Add LCL and UCL labels
LCL <- vector(mode = "character", nrow(pageviews_w_forecast))
LCL[nrow(pageviews_w_forecast)] <- "LCL"
UCL <- vector(mode = "character", nrow(pageviews_w_forecast))
UCL[nrow(pageviews_w_forecast)] <- "UCL"
pageviews_w_forecast <- cbind(pageviews_w_forecast, LCL)
pageviews_w_forecast <- cbind(pageviews_w_forecast, UCL)

#Create ggplot with actual, UCL, LCL, outliers
ggplot(pageviews_w_forecast, aes(datetime)) +
  theme_bw() + 
  #theme(text = element_text(size=20)) + 
  geom_line(aes(y = pageviews), colour = "grey40") + 
  geom_point(aes(y = pageviews), colour = "grey40", size=3) +
  geom_point(aes(y = outliers), colour = "red", size=3) + 
  geom_line(aes(y = upperBound.pageviews), colour = "green4", linetype = "dashed") + 
  geom_line(aes(y = lowerBound.pageviews), colour = "green4", linetype = "dashed") +
  xlab("\nDate\n") + 
  ylab("Page Views\n") +
  geom_text(aes(label=UCL), y = pageviews_w_forecast$upperBound.pageviews, size=4.5, hjust = -.1) +
  geom_text(aes(label=LCL), y = pageviews_w_forecast$lowerBound.pageviews, size=4.5, hjust = -.1)