Category: R

Theo Roe performs a product comparison:

Positron is the new beta Data Science IDE from Posit. Though Posit have stressed that maintenance and development of RStudio will continue, I want to use this blog to explore if Positron is worth the switch. I’m coming at this from the R development side but there will of course be some nuances from other languages in use within Positron that require some thought.

Read on for Theo’s perspective. Knowing that it’s using the same underlying framework as Visual Studio Code, I kind of wish this were an extension for VS Code rather than a separate app.

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Steven Sanderson looks for the missing columns:

When working with real-world datasets in R, it’s common to encounter missing values, often represented as NA. These missing values can impact the quality and reliability of your analyses. One important step in data preprocessing is identifying columns that consist entirely of missing values. By detecting these columns, you can decide whether to remove them or take appropriate action based on your specific use case. In this article, we’ll explore how to find columns with all missing values using base R functions.

Click through to see how you can do this. It’s not quite as simple as missing rows (complete_cases()) but it’s also not too much of an ordeal, either.

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Steven Sanderson fills in the blanks:

Interpolation is a method of estimating missing values based on the surrounding known values. It’s particularly useful when dealing with time series data or any dataset where the missing values are not randomly distributed.

There are various interpolation methods, but we’ll focus on linear interpolation in this article. Linear interpolation assumes a straight line between two known points and estimates the missing values along that line.

Read on to see how you can perform linear interpolation in R.

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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.

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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.

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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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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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