Category: R

Using the subset() Function in R

Published 2024-11-15 by Kevin Feasel

Steven Sanderson plays duck-duck-goose with the data:

Data manipulation is a cornerstone of R programming, and selecting specific columns from data frames is one of the most common tasks analysts face. While modern tidyverse packages offer elegant solutions, Base R’s subset() function remains a powerful and efficient tool that every R programmer should master.

This comprehensive guide will walk you through everything you need to know about using subset() to manage columns in your data frames, from basic operations to advanced techniques.

Click through for a description of the function and examples of it in action.

Describing R Models with Tilde (~)

Published 2024-11-12 by Kevin Feasel

Steven Sanderson describes a relationship:

The tilde operator (~) in R is more than just a symbol – it’s a powerful tool that forms the backbone of statistical modeling and formula creation. Whether you’re performing regression analysis, creating statistical models, or working with data visualization, understanding the tilde operator is crucial for effective R programming.

Read on to see how it works and several examples along the way.

The NOT IN Operator in R

Published 2024-11-05 by Kevin Feasel

Steven Sanderson does not want these things:

In R programming, data filtering and manipulation are needed skills for any developer. One of the most useful operations you’ll frequently encounter is checking whether elements are NOT present in a given set. While R doesn’t have a built-in “NOT IN” operator like SQL, we can easily create and use this functionality. This comprehensive guide will show you how to implement and use the “NOT IN” operator effectively in R.

Read on for examples of how to use %in% and its corollary ! (...) %in%.

Using the OR Operator in R

Published 2024-11-01 by Kevin Feasel

Steven Sanderson asks, ketchup or catsup:

The OR operator is a fundamental component in R programming that enables you to evaluate multiple conditions simultaneously. This guide will walk you through everything from basic syntax to advanced applications, helping you master logical operations in R for effective data manipulation and analysis.

Click through for several examples.

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Monitoring R Models in Production with Vetiver

Published 2024-10-31 by Kevin Feasel

Myles Mitchell continues a series on Vetiver:

In those blogs, we introduced the {vetiver} package and its use as a tool for streamlined MLOps. Using the {palmerpenguins} dataset as an example, we outlined the steps of training a model using {tidymodels} then converting this into a {vetiver} model. We then demonstrated the steps of versioning our trained model and deploying it into production.

Getting your first model into production is great! But it’s really only the beginning, as you will now have to carefully monitor it over time to ensure that it continues to perform as expected on the latest data. Thankfully, {vetiver} comes with a suite of functions for this exact purpose!

Click through for the full story.

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Creating Lists in R

Published 2024-10-30 by Kevin Feasel

Steven Sanderson makes a list but doesn’t check it twice:

Lists are fundamental data structures in R programming that allow you to store multiple elements of different types in a single object. This comprehensive guide will walk you through everything you need to know about creating and working with lists in R.

Click through for quite a few examples.

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RandomWalker 0.2.0 Release

Published 2024-10-24 by Kevin Feasel

Steven Sanderson makes an announcement:

In the ever-evolving landscape of R programming, packages continually refine their capabilities to meet the growing demands of data analysts and researchers. Today, we’re excited to announce the release of RandomWalker version 0.2.0, a minor update that brings significant enhancements to time series analysis and random walk simulations.

RandomWalker has been a go-to package for R users in finance, economics, and other fields dealing with time-dependent data. This latest release introduces new functions and improvements that promise to streamline workflows and provide deeper insights into time series data.

Read on to see what has changed.

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Supply Chain Analysis in R via planr

Published 2024-10-22 by Kevin Feasel

Matt Dancho shows off an R package:

Supply chain management is all about balancing supply and demand to ensure that inventory levels are optimized. Overestimating demand leads to excess stock, while underestimating it causes shortages. Accurate inventory projections allow businesses to plan ahead, make data-driven decisions, and avoid costly errors like over-buying inventory or getting into a stock-outage and having no inventory to meet demand.

Read on to learn more about the package and how it works. H/T R-Bloggers.

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Looping through Column Names in R

Published 2024-10-18 by Kevin Feasel

Steven Sanderson builds a loop:

Looping through column names in R is a crucial technique for data manipulation, especially for beginners. This article will guide you through various methods to loop through column names in R, providing practical examples and insights to enhance your data analysis skills.

Read on for examples with for loops, the dynamic duo of lapply() and sapply(), and the map() function in the purrr library.

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Adding a Suffix to a Column Name in R

Published 2024-10-15 by Kevin Feasel

Steven Sanderson renames a column or three:

When working with data frames in R, you might find yourself needing to modify column names to include additional information, such as a suffix. This can be particularly useful when merging datasets or when you want to ensure that column names are unique and descriptive.

Read on to see how.

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