Introduction As data science continues to be a sought-after field, creating a reliable and accurate model is essential. While there are various machine learning algorithms available, the process of selecting the correct algorithm can be complex....

Introduction Yesterday I spoke about building tidymodels models using my package {tidyAML} and {shiny}. I have made an update to it, and will continue to make updates to it this week. I have added all of the supported engines for regression prob...

Introduction Welcome to the {tidyAML} Model Builder, a Shiny web application that allows you to build predictive models using the tidyAML and Parsnip packages in R. Let’s dive into the code to understand how it works! Load Libraries First, we ...

Introduction I have been writing about using the {TidyDensity} package with shiny for the last few posts, and this one is the last. This post will go over the app and discuss how to change the output of the graph from a ggplot2 object into a plo...

Introduction If you’re new to data science or statistics, you may have heard about probability distributions. Probability distributions are mathematical functions that help us understand the probability of a random variable taking on a certain v...

Introduction In the previous post we allowed users to choose a distribution and a plot type. Now, we want to allow users to download a .csv file of the data that is generated. In the UI, we added a downloadButton with outputId = "download_data" ...

Introduction Shiny is an R package that allows you to create interactive web applications from R code. In this blog post, we’ll explore the different components of a Shiny application and show how they work together to create an interactive data...

Introduction In the analytics realm whether some like it or not, Excel is huge and maybe King. This is due to the fact of the shear volume of people using it. Microsoft has positioned Excel well in this situation, but, that does not mean we cann...

BRVM Introduction This R package provides a user-friendly interface for accessing data from the {BRVM}, which is a regional stock exchange serving multiple West African countries. With this package, users can easily retrieve historical stoc...

Introduction Time series analysis is a powerful tool for understanding and predicting patterns in data that vary over time. In this tutorial, we will use the AirPassengers dataset to create a bootstrapped timeseries model in R. The AirPassengers...

Introduction Cumulative mean is a statistical measure that calculates the mean of a set of numbers up to a certain point in time or after a certain number of observations. It is also known as a running average or moving average. Cumulative mean ...