Category: R

M. Fatih Tüzen fills in the gaps:

Data preprocessing is a cornerstone of any data analysis or machine learning pipeline. Raw data rarely comes in a form ready for direct analysis — it often requires cleaning, transformation, normalization, and careful handling of anomalies. Among these preprocessing tasks, dealing with missing data stands out as one of the most critical and unavoidable challenges.

Missing values appear in virtually every domain: surveys may have skipped questions, administrative registers might contain incomplete records, and clinical trials can suffer from dropout patients. Ignoring these gaps or handling them naively does not just reduce the amount of usable information; it can also introduce bias, decrease statistical power, and ultimately compromise the validity of conclusions. In other words, missing data is not just an inconvenience — it is a methodological problem that demands rigorous attention.

Quite often, we gloss over what to do with missing data when explaining or working through the data science process, in part because it’s a hard problem. This post digs into the specifics of the matter, taking us through eight separate methods. H/T R-Bloggers.

Adam Gladstone continues a series on using R in Excel:

We have already seen how to obtain descriptive statistics in Part I and how to use lm() in Part II. In this part (Part III) of the series we will look at using R in Excel to perform forecasting and time series analysis.

In the previous two parts we have seen different ways to handle the output from R function calls, unpacking and massaging the data as required. In this part we are going to focus on setting up and interacting with a number of models in the ‘forecast’ package (fpp2).

Read on for the demo. This is getting into territory that is by no means trivial to do natively in Excel.

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.

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.