Press "Enter" to skip to content

Category: R

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed