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Category: R

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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Practical healthyR.ts Examples

Published 2024-06-21 by Kevin Feasel

Steven Sanderson provides some examples:

Today I am going to go over some quick yet practical examples of ways that you can use the healthyR.ts package. This package is designed to help you analyze time series data in a more efficient and effective manner.

Let’s just jump right into it!

Read on for a few common time series activities, such as testing for stationarity, extracting tends from noise, and performing lagged correlation.

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An Overview of gganimate

Published 2024-06-19 by Kevin Feasel

Dario Radecic shows off a neat library:

The main criticism people have when it comes to ggplot2 is the static nature of the charts it has to offer. Truth be told, it will never be an interactive visualization king like Highcharts, but it doesn’t mean animation is out of the picture.

Meet R gganimate – a natural extension of ggplot2 that allows you to visualize your data change through time or some other variable, and then render and export the chart as a set of PNGs, or a single GIF/MP4.

Click through to learn more about it. I remembered the original gganimate and was going to say, “Wow, I hadn’t heard of that library in forever.” But it turns out that Thomas Lin Pedersen built a newer version of the library and has added in quite a bit of functionality since the last time I looked. H/T R-Bloggers.

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An Intro to Vetiver in R

Published 2024-06-14 by Kevin Feasel

Colin Gillespie introduces an R package for MLOps:

Most R users are familiar with the classic workflow popularised by R for Data Science. Data scientists begin by importing and cleaning the data, then iteratively transform, model, and visualise it. Visualisation drives the modeling process, which in turn prompts new visualisations, and periodically, they summarise their work and report results.

Click through for a demonstration of how to create and deploy a model using vetiver.

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