Category: R

Tomaz Kastrun checks what time it is:

Ever wanted to have a clock on the wall or in the office, that is not binary. But it is QR-Code clock. Well, now you can have it.

This useless R function generates new QR Code for every given period and tells the time.

Click through for the code. I could see this being useful in scenarios where you want to avoid people copying the QR code, so you embed the time in there. Then, your reader service can check to see if the time is within some valid boundary, returning an error if not.

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.