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Category: R

Model Diagnostics for Statistics vs Machine Learning

Published 2025-05-02 by Kevin Feasel

Christian Lorentzen talks diagnostics:

In this post, we show how different use cases require different model diagnostics. In short, we compare (statistical) inference and prediction.

As an example, we use a simple linear model for the Munich rent index dataset, which was kindly provided by the authors of Regression – Models, Methods and Applications 2nd ed. (2021). This dataset contains monthy rents in EUR (rent) for about 3000 apartments in Munich, Germany, from 1999.

Read on to learn more about this dataset and how the mindset differs if you’re thinking about inference versus prediction.

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Breaking down the Limitations of R^2

Published 2025-05-02 by Kevin Feasel

M. Fatih Tüzen explains an important regression concept:

When building a statistical model, one of the first numbers analysts and data scientists often cite is the R², or coefficient of determination. It’s widely reported in research, academic theses, and industry reports — and yet, frequently misunderstood or misused.

Does a high R² mean your model is good? Is it enough to evaluate model performance? What about its adjusted or predictive counterparts?

Read on to learn the answers to each question. H/T R-Bloggers.

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Creating Error Bars in ggplot2

Published 2025-04-29 by Kevin Feasel

Zhenguo Zhang draws a chart:

Sometimes you may want to create a plot with the following features:

  • a point to indicate the mean of a group
  • error bars to indicate the standard deviation of the group
  • and each group may have subgroups, which are represented by different colors.

In this post, I will show you how to create such a plot using the ggplot2 package in R.

Read on for the demonstration, as well as fixing a common problem of overlapping data points. H/T R-Bloggers.

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Function Generators versus Partial Application in R

Published 2025-04-25 by Kevin Feasel

Jonathan Carroll digs in:

The blog post (www.tidyverse.org) describing the latest updates to the tidyverse {scales} package neatly demonstrates the usage of the new functionality, but because the examples are written outside of actual plotting code, one feature stuck out to me in particular…

label_glue("The {x} penguin")(c("Gentoo", "Chinstrap", "Adelie"))
# The Gentoo penguin
# The Chinstrap penguin
# The Adelie penguin

Read on for a dive into what makes the actual invocation interesting. H/T R-Bloggers.

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choroplethr 4.0.0 Now in CRAN

Published 2025-04-15 by Kevin Feasel

Ari Lamstein has an announcement:

With this version, I have transferred the maintenance of choroplethr to Zhaochen He, an economics professor at Christopher Newport University. Zhao addressed the issues that led to choroplethr being archived from CRAN in February. Please join me in thanking Zhao for his contribution!

Click through for the updates, as well as what Ari views as the current challenges for the project as he hands the project over Zhaochen He. H/T R-Bloggers.

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Data Splitting and Cross-Validation in R

Published 2025-04-14 by Kevin Feasel

Nick Han has a pair of articles. First up is on data splitting and pre-processing:

Data preprocessing is a crucial step in any machine learning workflow. It ensures that your data is clean, consistent, and ready for modeling. In this blog post, we’ll walk through the process of splitting and preprocessing data in R, using the rsample package for data splitting and saving the results for future use.

H/T R-Bloggers for that one.

The second involves using cross-validation via the caret package in R:

Cross-validation is a resampling technique used to assess the performance and generalizability of machine learning models. It helps address issues like overfitting and ensures that the model’s performance is consistent across different subsets of the data. By splitting the data into multiple folds and repeating the process, cross-validation provides a robust estimate of model performance.

H/T R-Bloggers for that as well.

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What’s New in R 4.5.0

Published 2025-04-11 by Kevin Feasel

Russ Hyde checks out the changes:

R 4.5.0 (“How About a Twenty-Six”) was released on 11th April, 2025. Here we summarise some of the interesting changes that have been introduced. In previous blog posts we have discussed the new features introduced in R 4.4.0 and earlier versions (see the links at the end of this post).

The full changelog can be found at the r-release ‘NEWS’ page and if you want to keep up to date with developments in base R, have a look at the r-devel ‘NEWS’ page.

There are some nice bits of functionality on the list, so check it out.

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Avoid aggregate in R on Wide Matrices

Published 2025-04-07 by Kevin Feasel

Ali Oghabian shares some hard-earned advice:

The aggregate function can be very useful in R, allowing one to run a function (e.g. mean) within groups of rows, in each column in a matrix/data-frame and organize the results in an easy-to-read table. However, the function takes long to run for very wide matrices and data frames, where the number of the columns are large. I this post I demonstrate the issue and show a couple of nice solutions that at least for the example cuts down the time to 15% and even less, compared to the run-time of the aggregate function.

Click through for a demo. Granted, this is a matrix with 10,000 columns, so I’m not sure how this applies to narrower matrices. H/T R-Bloggers.

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Reactable Tables with Sparklines in Shiny Apps

Published 2025-04-03 by Kevin Feasel

Osheen MacOscar continues a series:

This is the third blog in a series about the {sparkline} R package for inline data visualisations. You can read the first one about getting started with the package here and the second one about embedding them in HTML tables with the {reactable} package here.

In this blog I am taking it a step further and demonstrating how to use our sparkline reactable table in a Shiny app. Thankfully {reactable} has some helpful functions that make this super easy! I will also look at using a dynamic traffic light image in a reactable table at the end.

Click through to see how it all works.

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