A/B test"

In explore: Simplifies Exploratory Data Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The explore package offers an easy way to do basic A/B testing.

Interactive A/B testing

For interactive A/B testing simply call abtest() without any parameters.

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A/B testing with tidy data

If you want to A/B test your own data, pass them into the ´abtest()´ function.

In this example we are using synthetic data using one of the create_data_() functions of {explore}

library(dplyr)
library(explore)
data <- create_data_buy(obs = 1000)

Describe dataset

data %>% describe()

Each observation is a customer. The ´buy´ variable contains 0/1 values (1 = the customer did buy a product)

Hypothesis age > 50

We want to test the hypothesis, that customer with age > 50 have a different buying behavior.

data %>% explore(age, target = buy)

We see a clear pattern, that people who buy differ in age from people who don´t buy. But is this difference statistically significant?

data %>% abtest(age > 50, target = buy, sign_level = 0.05)

The A/B test shows a statistically significant difference!

Hypothesis Mobile Voice vs Broadband Usage

We would like to test the hypothesis, that customer with a mobile voice product (mobilevoice_ind == 1) have different bbi_usg_gb (broadband usage in GB)

data %>% explore(mobilevoice_ind, target = bbi_usg_gb)

The boxplot shows a small difference in Broadband Usage between customers with/without Mobile Voice product. But is this difference statistically significant?

data %>% abtest(mobilevoice_ind == 1, target = bbi_usg_gb, sign_level = 0.05)

The A/B test shows a NOT statistically significant difference! The p-value is 0.09, but should be max. 0.05 (as we defined sign_level = 0.05)

A/B testing with count data

In this example we are using the Titanic datasst. use_data_titanic() is a functions of {explore} that makes it easier to use popular example datasets.

library(dplyr)
library(explore)
data <- use_data_titanic(count = TRUE)

Describe dataset

data %>% describe()

Hypothesis Female

We want to test the hypothesis, that female passengers have a higher chance to survive.

data %>% explore(Sex, target = Survived, n = n)

We see a clear pattern, but is it difference statistically significant?

data %>% abtest(Sex == "Female", target = Survived, n = n, sign_level = 0.05)

The A/B test shows a statistically significant difference!

explore documentation built on Oct. 11, 2023, 9:07 a.m.