Challenge 9

Creating a function

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

For this week’s challenge, I’ll create a function that calculates z score for a certain value based on a baseline computed from a vector.

It will take the following as input:

• baseline = vector of numbers of any length
• value = the value to compute the z score for

It will then give the following as output:

• a dataframe with mean, sd (of baseline), input_value and z_score

Function Definition

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)
}

Testing

Let’s use the following inputs to test the function:

baseline = c(4, 2, -1, 4, 9, 2, 3, 3, 1 -5)
value = 10

We expect the following results if this runs correctly:

• Mean should be: 2.4444444
• SD should be: 3.5746018
• Input value should be 10
• Z score should be the absolute value of (value - mean) / sd -> abs value of (10 - 2.4444444) / 3.5746018 -> 2.1136776

We confirm this by running the function below and confirming the results match:

results = z_score(baseline, value)
results

test_zscore <- function(baseline, value){
  
  results = z_score(baseline, value)

  expected_mean = mean(baseline)
  actual_mean = results$mean
  
  expected_sd = sd(baseline)
  actual_sd = results$sd
  
  expected_input = value
  actual_input = results$input_value
  
  expected_z = abs((value -  mean(baseline)) / sd(baseline))
  actual_z = results$z_score
  
  cat("Mean matches: ", expected_mean == actual_mean, "( Actual:", actual_mean, "Expected:", expected_mean, ")\n")
  cat("SD matches: ", expected_sd == actual_sd, "( Actual:", actual_sd, "Expected:", expected_sd, ")\n")
  cat("Input matches: ", expected_input == actual_input, "( Actual:", actual_input, "Expected:", expected_input, ")\n")
  cat("Z Score matches: ", expected_z == actual_z, "( Actual:", actual_z, "Expected:", expected_z, ")")
}

test_zscore(baseline, value)

Mean matches:  TRUE ( Actual: 2.444444 Expected: 2.444444 )
SD matches:  TRUE ( Actual: 3.574602 Expected: 3.574602 )
Input matches:  TRUE ( Actual: 10 Expected: 10 )
Z Score matches:  TRUE ( Actual: 2.113678 Expected: 2.113678 )

We can test again with a second set:

baseline = c(-12, 4, 7, -3, 1, 1, 0, 8, 23, -3, -8, 12, -14, 2, 16)
value = -6

test_zscore(baseline, value)

Mean matches:  TRUE ( Actual: 2.266667 Expected: 2.266667 )
SD matches:  TRUE ( Actual: 10.03185 Expected: 10.03185 )
Input matches:  TRUE ( Actual: -6 Expected: -6 )
Z Score matches:  TRUE ( Actual: 0.8240418 Expected: 0.8240418 )

The function seems to work as expected!