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Category: R

An Introduction to healthyR

Published 2024-06-14 by Kevin Feasel

Steven Sanderson covers a package:

This article will introduce you to the healthyR package. healthyR is a package that provides functions for analyzing and visualizing health-related data. It is designed to make it easier for health professionals and researchers to work with health data in R. It is an experimental package that is still under active development, so some functions may change in the future along with the package structure and scope.

Unfortunately, the package needs some love and attention. Which I am trying to give it. Given that information, I will be updating the package to include more functions and improve the existing ones. I will also be updating the documentation and adding more examples to help users get started with the package.

So let’s get started!

Read on for that overview, including an explanation of why the package exists and several examples of how to use it.

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Checking for Patterns in Text with R

Published 2024-06-10 by Kevin Feasel

Steven Sanderson looks for a sub-string:

When working with text data in R, one common task is to check if a character or substring is present within a larger string. R offers multiple ways to accomplish this, ranging from base R functions to packages like stringr and stringi. In this post, we’ll explore how to use grepl() from base R, str_detect() from stringr, and stri_detect_fixed() from stringi to achieve this.

Click through for three separate ways of doing this.

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Distribution Parameter Wrangling in TidyDensity

Published 2024-06-06 by Kevin Feasel

Steven Sanderson introduces a new set of functions:

Greetings, fellow data enthusiasts! Today, we’re thrilled to unveil a fresh wave of functionalities in the ever-evolving TidyDensity package. Buckle up, as we delve into the realm of distribution statistics!

This update brings a bounty of new functions that streamline the process of extracting key parameters from various probability distributions. These functions adhere to the familiar naming convention util_distribution_name_stats_tbl(), making them easily discoverable within your R workflow.

Read on for the list and an example of how to use them.

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Building a Custom Color Palette for ggplot2

Published 2024-06-04 by Kevin Feasel

Tomaz Kastrun pulls out the color swatches:

A simple, yet effective way to set your colour palette in R using ggplot library.

Click through for the demonstration. Tomaz keeps the text very light in this post, so I’ll do a little vamping of my own. Creating a custom palette is neat, but do make sure that your custom palette works for users with color vision deficiency (CVD). Taking Tomaz’s bar chart into Coblis (an amazing tool I continue to use quite regularly), here’s what it looks like for people with protanopia—that is, no red cones in their eyes:

It’s not awful, particularly because Tomaz changed the fill but not the border color, so you get a funky striation effect.

But the real kicker is if you switch to the monochromatic option in Coblis.

Granted, I know of exactly one person with monochromacy, so if you want to be fair, this isn’t one I’d check for on a webpage. But the large majority of technical books have grayscale images because it saves money on printing, so if this were your sweet-looking color scheme and you’re adding the image into a book, readers would need to focus particularly hard on the bars to figure anything out.

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healthR.data Package Updates

Published 2024-05-29 by Kevin Feasel

Steven Sanderson has an update for us:

I’m excited to share the latest updates to the healthyR.data R package! This release brings new functionality and minor improvements, all aimed at making your data management tasks easier and more efficient. Here’s a breakdown of what’s new:

Read on for information on four new functions and a couple of bugfixes.

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Splitting a Number into Component Digits in R

Published 2024-05-24 by Kevin Feasel

Steven Sanderson does a bit of splitting:

Splitting numbers into individual digits can be a handy trick in data analysis and manipulation. Today, we’ll explore how to achieve this using base R functions, specifically gsub() and strsplit(). Let’s walk through the process step by step, explain the syntax of each function, and provide some examples for clarity.

Click through for a pair of examples.

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Removing Elements from a Vector in R

Published 2024-05-22 by Kevin Feasel

Steven Sanderson wants to leave one of these things out:

Working with vectors is one of the fundamental aspects of R programming. Sometimes, you need to remove specific elements from a vector to clean your data or prepare it for analysis. This post will guide you through several methods to achieve this, using base R, dplyr, and data.table. We’ll look at examples for both numeric and character vectors and explain the code in a straightforward manner. By the end, you’ll have a clear understanding of how to manipulate your vectors efficiently. Let’s dive in!

Read on for three pairs of examples, one for numeric vectors and one for character vectors.

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Accuracy is Not Enough for Classification

Published 2024-05-22 by Kevin Feasel

I have a new video:

In this video, I explain why accuracy is not the be-all, end-all measure for classification. After that, I introduce the confusion matrix, a mechanism for tracking predicted versus actual values. Then, I talk about a variety of measures and how we can derive them from the confusion matrix.

The trickiest part of the confusion matrix measures is just remembering which measures comport to which combinations in the matrix. The second-trickiest part of the confusion matrix is that R and Python invert them, so reading across the top row in R is equivalent to reading down the first column in Python.

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