Category: R

Steven Sanderson does a switcharoo:

Data manipulation is a crucial skill in R programming, and one common operation is transposing data frames – converting rows to columns and vice versa. Whether you’re cleaning data for analysis, preparing datasets for visualization, or restructuring information for machine learning models, understanding how to transpose data frames efficiently is essential. This comprehensive guide will walk you through various methods to transpose data frames in R, complete with practical examples and best practices.

Read on for a few approaches to the problem.

Tomaz Kastrun shares some code snippets:

Data engineering is important step that helps improve data usability, data exploration and data science. Preparing the data needs therefore needs to be done in a manner, that is easy to read, repeat and exchange between others engineers.

Tidyverse has a lot of data engineering functions, chaining different functions for getting most of your data. All six examples will show combinations of functions chained together for great result set.

Click through for those examples.

Steven Sanderson does the math:

Data transformation is a fundamental technique in statistical analysis and data preprocessing. When working with R, understanding how to properly transform data can help meet statistical assumptions, normalize distributions, and improve the accuracy of your analyses. This comprehensive guide will walk you through implementing and visualizing the most common data transformations in R: logarithmic, square root, and cube root transformations, using only base R functions.

Click through for examples.

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.