Category: R

Steven Sanderson handles missing information in the best way possible—by ignoring it:

Missing values are a common challenge in data analysis, and R provides robust tools for handling them. The na.rm parameter is one of R’s most essential features for managing NA values in your data. This comprehensive guide will walk you through everything you need to know about using na.rm effectively in your R programming journey.

Read on for several examples of how na.rm works.

Colin Gillespie and Myles Mitchell share some updates:

The latest release of Posit Package Manager introduces several enhancements, including:

• Python Git Builders: Build Python packages (wheels) directly from Git.
• Blocklists: Easily block specific packages or versions.
• Improved Documentation: Clearer and more accessible information.

Read on for one more big change to Posit Package Manager, as well as how diffify fits into the mix.

Steven Sanderson finds the column with the maximum value for each row in an R data frame:

Finding the column with the maximum value for each row is a useful operation when you want to identify the dominant category, highest measurement, or most significant feature in your dataset. This can provide valuable insights and help in decision-making processes.

R offers several ways to accomplish this task, ranging from base R functions to powerful packages like dplyr and data.table. We’ll explore each approach in detail, providing code examples and explanations along the way.

Click through for several examples.

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.

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.

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.

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.

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.