R for applications in data science

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All posts with the R tag deal with applications of the statistical programming language R in the data science setting.

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Two Environment Variables for More Robust R Code

Two Environment Variables for More Robust R Code

The good, the bad, and the ugly of R's typing system and how environment variables can remedy the situation.

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R's type system is known to be flexible, which, at the same time makes the language very fragile. Luckily, there are environment variables that can make our code more robust. In this post, you will learn how to use two environment variables in order to prevent mistakes when dealing with conditionals and logical operators.

An Introduction to Forecasting

An Introduction to Forecasting

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Forecasting is a powerful technique for time-series data. Here, I investigate the most common variants of forecasting algorithms: ARMA, ARIMA, SARIMA, and ARIMAX, which are primarily based on autocorrelation and moving averages.

Performance Measures for Multi-Class Problems

Performance Measures for Multi-Class Problems

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For multi-class prediction scenarios, we can use similar performance measures as for binary classification. Here, I explain how we can obtain the (weighted) accuracy, micro- and macro-averaged F1-scores, and a generalization of the AUC to the multi-class setting.

Dimensionality Reduction for Visualization and Prediction

Dimensionality Reduction for Visualization and Prediction

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Dimensionality reduction is primarily used for exploring data and for reducing the feature space in machine learning applications. In this post, I investigate techniques such as PCA to obtain insights from a whiskey data set and show how PCA can be used to improve supervised approaches. Finally, I introduce the notion of the whiskey twilight zone.

Radar plots

Radar plots

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Radar plots are exceptional for visualizing the properties of individual objects. Here, I demonstrate how to draw radar plots in R by plotting the properties of whiskeys from several distilleries.