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Category: R

Linear Regression with R in Excel

Published 2025-07-29 by Kevin Feasel

Adam Gladstone continues a series on working with R in Excel via the ExcelRAddIn component:

In the first part of this series, I looked at using R in Excel to obtain descriptive statistics. In this second part of the series I am going to look at using R in Excel to perform linear regression, specifically using the lm() function. lm() is a real workhorse function. It can be used to carry out both single and multiple regression and different types of analysis of variance. For this demonstration I will only focus on single and multiple regression.

The workbook for this part of the series is: “Part II – R in Excel – Linear Regression.xlsx”. As before, the ‘References’ worksheet lists links to external references. The ‘Libraries’ worksheet loads additional (non-default) packages. In this demonstration, I use the datarium and broom packages. The ‘Datasets’ worksheet contains the data referenced in the worksheets.

Click through to see how you can perform ordinary least squares regression, multiple linear regression, and even logistic regression in Excel with a bit of R code. H/T R-Bloggers.

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Using R for Descriptive Statistics in Excel

Published 2025-07-28 by Kevin Feasel

Adam Gladstone shows off an Excel add-in:

The purpose of this series of posts is to demonstrate some use-cases for R in Excel using the ExcelRAddIn component (disclaimer: I am the developer of this add-in: ExcelRAddIn). The fundamental rationale for the add-in is that it allows access to the extensive R ecosystem within an Excel worksheet. Excel provides many excellent facilities for data wrangling and analysis. However, for certain types of statistical data analysis, the limitations of the built-in functions even alongside the Analysis ToolPak is not sufficient, and R provides superior facilities (for example, for performing LDA, PCA, forecasting and time series analysis to mention a few).

Click through for examples of how it all works. H/T R-Bloggers.

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Testing Plumber APIs from R

Published 2025-07-23 by Kevin Feasel

Jakub Sobolewski builds some tests:

When building Plumber APIs in R, effective testing is crucial for ensuring reliability and maintainability.

This guide explores a proven pattern for testing own Plumber APIs that maintains fast feedback loops while providing robust coverage of both business logic and API contracts.

Read on for some good advice on API testing in general, followed by its application in R. Jakub also has a link to a GitHub repo showing a concrete example of how this testing can work. H/T R-Bloggers.

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The Through-the-Door Problem in Credit Risk Modeling

Published 2025-07-10 by Kevin Feasel

Richard Vale takes us through a data challenge:

In credit risk modelling, you want to calculate the probability that a loan will default. Since different financial institutions gather different data and offer different products, there is no one-size-fits-all approach to doing this. Therefore, credit risk models are usually built using the institution’s own data. For example, if I’m building a credit risk model for XYZ Bank, I look at loans which XYZ bank has previously granted, and try to estimate the probability that a future loan will default based on principal, tenor, the borrower’s credit rating, and so on.

For those who haven’t heard of the through-the-door problem before, this is a good moment to pause and think about what is wrong with this. Why does this process contain a huge pitfall?

Click through for the answer, as well as an example of the problem and one way to get around this. H/T R-Bloggers.

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Spatial Cross-Validation in R

Published 2025-07-09 by Kevin Feasel

Jakub Nowosad wraps up a series:

This document provides an overview of two R packages, sperrorest and blockCV, that can be used for spatial cross validation, but are outside of standard machine learning frameworks like caret, tidymodels, or mlr3.

All of the examples below use the same dataset, which includes the temperature measurements in Spain, a set of covariates, and the spatial coordinates of the temperature measurements.

Click through for a pair of cross-validation packages, as well as a link to the rest of the series. H/T R-Bloggers.

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Bioconductor in the Wake of ggplot2 4.0.0

Published 2025-07-07 by Kevin Feasel

Maria Doyle lays it out:

A major update to ggplot2 (version 4.0.0) is expected around mid-to-late July 2025. It brings a significant internal change, replacing most of the S3 backend with the newer S7 object system. While this improves long-term maintainability and extensibility, it may break Bioconductor packages that depend on ggplot2, especially those that customise how plots are built or styled. Packages that use ggplot2 for typical plotting tasks, such as creating plots with ggplot() and geom_*(), are unlikely to be affected.

Click through for notes, tips on what to do, and whether the code you’re using will break with ggplot2 4.0.0. H/T R-Bloggers.

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Leaving out an Element in R

Published 2025-06-20 by Kevin Feasel

Kieran Healy shows how to get all but one element from a vector in R:

Sometimes we want to repeatedly do things with all but one row of a data frame, where we systematically drop each row in turn and do the thing. For example, jacknife cross-validation is a kind of very basic bootstrap technique. (It is computationally simpler than and predates the bootstrap.) Or in some areas “leave-one-out” summary statistics are often calculated as a quick robustness check. Sometimes we want to do this within groups, perhaps especially when the groups may be small.

Read on to see how, including several examples. H/T R-Bloggers.

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Testing Shiny Applications

Published 2025-06-13 by Kevin Feasel

Arthur Breant runs some tests:

You’ve created a fantastic mockup and your client is delighted. You’re ready to move to production with your application. But one question haunts you: how can you ensure that your application will remain stable and functional through modifications and evolutions?

The answer comes down to one word: testing.

Read on to learn how you can perform unit testing, integration testing, and end-to-end testing of Shiny applications in R. H/T R-Bloggers.

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Self-Intersecting Quadrilaterals in R

Published 2025-05-27 by Kevin Feasel

Jerry Tuttle talks shapes:

A quadrilateral is a polygon having four sides, four angles, and four vertices. A polygon means that the figure is a closed shape, meaning the last line segment connects back to the first one, effectively enclosing an area.

We usually think of quadrilaterals as squares, rectangles, parallelograms, trapezoids, rhombuses, or kites. (I was impressed that my four year-old granddaughter knew the last one, although she called it a diamond!) It could also be irregularly shaped with no name.

However, a polygon may intersect itself. 

Click through for a demonstration of a self-intersecting quadrilateral, including the R code you can use to try it out yourself.

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