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Category: R

A/B Testing with Survival Analysis in R

Published 2024-07-12 by Kevin Feasel

Iyar Lin combines two great flavors:

Usually when running an A/B test analysts assign users randomly to variants over time and measure conversion rate as the ratio between the number of conversions and the number of users in each variant. Users who just entered the test and those who are in the test for 2 weeks get the same weight.

This can be enough for cases where a conversion either happens or not within a short time frame after assignment to a variant (e.g. Finishing an on-boarding flow).

There are however many instances where conversions are spread over a longer time frame. One example would be first order after visiting a site landing page. Such conversions may happen within minutes, but a large churn could also happen within days after the first visit.

Read on for the scenario, as well as a simulation. I will note that, in the digital marketing industry, there’s usually a hard cap on number of days where you’re able to attribute a conversion to some action for exactly the reason Iyar mentions. H/T R-Bloggers.

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Random Walks in R with TidyDensity

Published 2024-07-11 by Kevin Feasel

Steven Sanderson goes for a walk:

A random walk is a mathematical object that describes a path consisting of a succession of random steps. It’s a cornerstone concept in fields like physics, economics, and biology. In finance, for example, the random walk hypothesis suggests that stock market prices evolve according to a random walk and thus cannot be predicted.

Read on to see how you can generate a dataset matching a random walk, as well as a comparison of techniques for generating them.

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Extracting Strings before a Space using R

Published 2024-07-10 by Kevin Feasel

Steven Sanderson grabs a name:

Hello, R users! Today, we’ll dive into a common text manipulation task: extracting strings before a space. This is a handy trick for dealing with names, addresses, or any text data where you need to isolate the first part of a string.

We’ll explore three approaches: using base R, stringr, and stringi. Each method offers its unique advantages, so you can choose the one that fits your style best.

Click through for the three examples. I will note that if you’re actually using this code to split names, well, names tend to be a lot trickier than we give them credit for. Keep in mind that people can have multi-part names (“Debbie Mae” or “van den Berg”), so unless you know the data all follows a specific pattern, don’t assume the data follows a specific pattern.

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Automating R Scripts via taskscheduleR

Published 2024-07-09 by Kevin Feasel

Steven Sanderson builds a Windows task:

Today, let’s dive into a nifty R package called taskscheduleR that can automate running your R scripts. Whether you need to execute a task every hour or just once a day, taskscheduleR has you covered. This package leverages the Windows Task Scheduler, making it a breeze to schedule and automate repetitive tasks directly from R. Let’s walk through a couple of examples from my new book, “Extending Excel with Python and R”.

Click through for those examples. Also check out Steven’s new book, that came out at the end of April.

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Creating a Dragon Curve in R

Published 2024-07-02 by Kevin Feasel

Tomaz Kastrun adds dragons to the edge of the map:

The algorithm is a fractal curve of Hausdorff dimension 2. One starts with one segment. In each iteration the number of segments is doubled by taking each segment as the diagonal of a square and replacing it by half the square (90 degrees). Alternating and doing the left and right function / direction to complement in order to get the shape.

Clickt hrough for the sample code and how the plot looks.

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SHAP and Additive Models

Published 2024-07-01 by Kevin Feasel

Michael Mayer answers a pair of related questions:

Within only a few years, SHAP (Shapley additive explanations) has emerged as the number 1 way to investigate black-box models. The basic idea is to decompose model predictions into additive contributions of the features in a fair way. Studying decompositions of many predictions allows to derive global properties of the model.

What happens if we apply SHAP algorithms to additive models? Why would this ever make sense?

Read on for the answers to these two questions.

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Random Walks and Brownian Motion in healthyR.ts

Published 2024-06-28 by Kevin Feasel

Steven Sanderson goes for a walk on the stock exchange:

In the world of time series analysis, Random Walks, Brownian Motion, and Geometric Brownian Motion are fundamental concepts used in various fields, including finance, physics, and biology. Today, we’ll explore these concepts using functions from the healthyR.ts package.

Click through to learn about each of these concepts and some examples of how you can generate time series datasets following each of them.

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Extracting Strings between Specific Characters in R

Published 2024-06-27 by Kevin Feasel

Steven Sanderson toes a bit of tag replacement:

Hello, R enthusiasts! Today, we’re jumping into a common text processing task: extracting strings between specific characters. This is a great skill for data cleaning and manipulation, especially when working with raw text data. I’m going to show you how to achieve this using base R, the stringr package, and the stringi package. Let’s go!

Read on for examples.

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An Introduction to the healthyR.ai Package

Published 2024-06-25 by Kevin Feasel

Steven Sanderson explains the purpose of a package:

The ultimate goal really is to make it easier to do data analysis and machine learning in R. The package is designed to be easy to use and to provide a wide range of functionality for data analysis. The package is also meant to help and provide some easy boilerplate functionality for machine learning. This package is in its early stages and will be updated frequently.

It also keeps with the same framework of all of the healthyverse packages in that it is meant for the user to be able to use the package without having to know a lot of R. Many rural hospitals do not have the resources to perform this sort of work, so I am working hard to build these types of things out for them for free.

Read on to see how it works, including several examples of the package in action.

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