This repository contains the code required to produce blogs for RStudio. Each blog post has its own RMarkdown file, and all required files referred to in these posts are also included in this repository.
The first blog post shows how to build interactive world maps in RShiny: https://rviews.rstudio.com/2019/10/09/building-interactive-world-maps-in-shiny/
The second blog post shows how to run Python in RStudio and how to classify clothing categories from the Fashion MNIST data using artificial neural networks: https://rviews.rstudio.com/2019/11/11/a-comparison-of-methods-for-predicting-clothing-classes-using-the-fashion-mnist-dataset-in-rstudio-and-python-part-1/
The third blog post is focused on dimension reduction using principal components analysis (PCA): https://rviews.rstudio.com/2020/03/03/predicting-clothing-classes-part-2/
The clothing categories are further classified using tree-based methods (random forests and gradient-boosted trees) in the fourth post: https://rviews.rstudio.com/2020/03/10/comparing-machine-learning-algorithms-for-predicting-clothing-classes-part-3/
The fifth and final post uses support vector machines, and wraps up by comparing the methods from parts one to four: https://rviews.rstudio.com/2020/03/24/comparing-machine-learning-algorithms-for-predicting-clothing-classes-part-4/