Challenge 10

purrr

For this challenge, I’ll be using the function I wrote in challenge 9 that calculates z scores and apply it multiple times.

A common use of z-scores is in anomaly detection. In this practice, we compare the most recent value in a sequence to all the values that came before to see if that value is an anomaly or not.

We can use a built in dataset for this, called airquality, which stores time series air quality information:

data(airquality)
head(airquality)

Our original function looks as follows:

z_score <- function(baseline, value){
  mean <- mean(baseline)
  sd <- sd(baseline)
  z_score <- abs((value - mean)/sd)
  
  results = tibble(mean = mean, sd = sd, input_value = value, z_score = z_score)
  return(results)
}

We can rewrite this so that it determines the baseline and value itself, and instead takes a vector as input:

z_score <- function(vec){
  baseline = vec %>% head(-1)
  value = vec %>% tail(1)
  
  mean <- mean(baseline, na.rm=TRUE)
  sd <- sd(baseline, na.rm=TRUE)
  z_score <- abs((value - mean)/sd)
  
  results = tibble(baseline_mean = mean, baseline_sd = sd, most_recent_value = value, z_score = z_score)
  return(results)
}

Running this on a single column we get:

z_score(airquality$Temp)

We can use purrr::map to compute this for multiple columns and join them into a single dataframe:

cols = list(airquality$Ozone, airquality$Wind, airquality$Temp)

map_dfr(cols, z_score)