Two hundred and twenty-seven new packages made it to CRAN in August. Quite a few were devoted to medical or genomic applications, and this is reflected in my “Top 40” selections, listed below in nine categories: Computational Methods, Data, Genomics, Machine Learning, Medicine and Pharma, Statistics, Time Series, Utilities, and Visualization. ...

In this post, I will walk you through some examples that show off the major features of the ggforce package. The main goal is to share a few ideas about customizing visualizations that you may find useful in your everyday work. The ggforce package is an extension to ggplot2 developed ...

R/Medicine 2019 kicked off on Thursday with two outstanding workshops. It was difficult to choose between the two, but fortunately both presenters developed rich sets of materials that are available online. Alison Hill delivered R Markdown for Medicine with an elegant HTML exposition masterfully created to cultivate beginners while still ...

A common business oriented data science task is to programatically craft and send custom emails. In this post, I will show how to accomplish this with R on the RStudio Connect platform (a paid product built for the enterprise) using the blastula package.blastula provides a set of functions for ...

One hundred seventy-six new packages made it to CRAN in July. Here are my “Top 40” picks organized into twelve categories: Data, Data Science, Finance, Genomics, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Topological Data Analysis, Utilities and Visualization. Data eia v0.3.2: Provides API access to data from the US Energy ...

In this post, we will develop a framework for always-valid inference based on the paper Always Valid Inference: Continuous Monitoring of A/B Tests (2019 Johari, Pekelis, Walsh). Using an always-valid p-value allows us to continuously monitor A/B tests, and potentially stop the test early in a valid way1. In ...

In a previous post, we explored the dividend history of stocks included in the SP500, and we followed that with exploring the dividend history of some NASDAQ tickers. Today’s post is a short continuation of that tech dividend theme, with the aim of demonstrating how we can take our ...

The plumber R package is used to expose R functions as API endpoints. Due to plumber’s incredible flexibility, most major API design decisions are left up to the developer. One important consideration to be made when developing APIs is how to log information about API requests and responses. This ...

In a previous post, we explored the dividend history of stocks included in the SP500. Today, we’ll extend that analysis to cover the Nasdaq because, well, because in the previous post I said I would do that. We’ll also explore a different source for dividend data, do some ...

In this post, we seek to develop an intuitive sense of what type I (false-positive) and type II (false-negative) errors represent when comparing metrics in A/B tests, in order to gain an appreciation for “peeking”, one of the major problems plaguing the analysis of A/B test today. To ...

Approximately 136 new packages stuck to CRAN in June. (This number is difficult to nail down with certainty because packages may be removed from CRAN after sitting there for a few days.) Here are my picks for the June “Top 40” in ten categories: Computational Methods, Data, Finance, Genomics, Machine Learning, Science ...

If you are like me, and rather last minute about making a plan to get the most out of a large conference, you are just starting to think about JSM 2019 which will begin in just a few days. My plans always begin with an attempt to sleuth out the R-related ...

For many R users, it’s obvious why you’d want to use R with big data, but not so obvious how. In fact, many people (wrongly) believe that R just doesn’t work very well for big data. In this article, I’ll share three strategies for thinking about ...

Welcome to a mid-summer edition of Reproducible Finance with R. Today, we’ll explore the dividend histories of some stocks in the S&P 500. By way of history for all you young tech IPO and crypto investors out there: way back, a long time ago in the dark ages, companies ...

As data scientists, we are often presented with a dataset and are asked to use it to produce insights. We use R to wrangle, visualize, model, and produce tables and plots for sharing or publication. When we focus on the data in hand in this way, we don’t get ...

Two hundred twenty-two new packages made it to CRAN in May, and it was more of an effort than usual to select the “Top 40”. Nevertheless, here they are in nine categories, Computational Methods, Data, Machine Learning, Mathematics, Medicine, Science, Statistics, Utilities and Visualization. Computational Methods dde v1.0.0: Implements a Dormand-Prince ...

Recently, I had the opportunity to showcase tidymodels in workshops and talks. Because of my vantage point as a user, I figured it would be valuable to share what I have learned so far. Let’s begin by framing where tidymodels fits in our analysis projects. The diagram above is ...

We were recently presented with a problem where the decision maker wanted to understand how their data would naturally group together. The classic technique of k-means clustering was a natural choice; it’s well known, computationally efficient, and implemented in base R via the kmeans() function. Our problem has a ...