Blog Archives

Tech Dividends, Part 2

August 16, 2019
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Tech Dividends, Part 2

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 previous work and use it to quickly visualize research from...

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Contributors

August 13, 2019
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Below is a list of contributors to this blog. Name Role Bio Joseph Rickert Ambassador at Large Joseph is RStudio’s “Ambassador at Large” for all things R, is the chief editor of the R Views blog. He works with the rest of the RStudio team and the R Consortium to promote open source activities, the R language and the R Community. Joseph also represents RStudio...

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Plumber Logging

August 12, 2019
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Plumber Logging

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 information can be used to determine how plumber APIs are...

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Tech Dividends, Part 1

August 6, 2019
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Tech Dividends, Part 1

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 string cleaning and check out ways to customize a tooltip in...

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Validating Type I and II Errors in A/B Tests in R

July 30, 2019
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Validating Type I and II Errors in A/B Tests in R

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 better understand what “peeking” is, it helps to first understand how to...

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June 2019 “Top 40” R Packages

July 23, 2019
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June 2019 “Top 40” R Packages

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 and Medicine, Statistics, Time Series, Utilities, and...

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An R Users Guide to JSM 2019

July 18, 2019
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An R Users Guide to JSM 2019

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 sessions. While in the past it took quite...

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Three Strategies for Working with Big Data in R

July 16, 2019
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Three Strategies for Working with Big Data in R

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 how to use big data in R, as well as some examples of how...

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Dividend Sleuthing with R

July 8, 2019
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Dividend Sleuthing with R

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 used to take pains to generate free cash flow and...

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Imagine your Data Before You Collect It

June 30, 2019
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Imagine your Data Before You Collect It

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 to consider where the data came from. The sample size...

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