Blog Archives

Speed Up Your Code: Parallel Processing with multidplyr

December 17, 2016
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Speed Up Your Code: Parallel Processing with multidplyr

There’s nothing more frustrating than waiting for long-running R scripts to iteratively run. I’ve recently come across a new-ish package for parallel processing that plays nicely with the tidyverse: multidplyr. The package has saved me countless ho...

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Russell 2000 Quantitative Stock Analysis in R: Six Stocks with Amazing, Consistent Growth

November 29, 2016
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Russell 2000 Quantitative Stock Analysis in R: Six Stocks with Amazing, Consistent Growth

The Russell 2000 Small-Cap Index, ticker symbol: ^RUT, is the hottest index of 2016 with YTD gains of over 18%. The index components are interesting not only because of recent performance, but because the top performers either grow to become mid-cap stocks or are bought by large-cap companies at premium prices. This means selecting the best components...

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Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes

October 22, 2016
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Quantitative Stock Analysis Tutorial: Screening the Returns for Every S&P500 Stock in Less than 5 Minutes

Quantitative trading strategies are easy to develop in R if you can manage the data workflow. In this post, I analyze every stock in the S&P500 to screen in terms of risk versus reward. I’ll show you how to use quantmod to collect daily stock pri...

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Customer Segmentation Part 3: Network Visualization

September 30, 2016
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Customer Segmentation Part 3: Network Visualization

This post is the third and final part in the customer segmentation analysis. The first post focused on K-Means Clustering to segment customers into distinct groups based on purchasing habits. The second post takes a different approach, using Pricipal C...

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Customer Segmentation Part 2: PCA for Segment Visualization

September 3, 2016
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This post is the second part in the customer segmentation analysis. The first post focused on k-means clustering in R to segment customers into distinct groups based on purchasing habits. This post takes a different approach, using Pricipal Component Analysis (PCA) in R as a tool to view customer groups. Because PCA attacks the...

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Customer Segmentation Part 1: K-Means Clustering

August 6, 2016
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Customer Segmentation Part 1: K-Means Clustering

In this post, we’ll be using k-means clustering in R to segment customers into distinct groups based on purchasing habits. k-means clustering is an unsupervised learning technique, which means we don’t need to have a target for clustering. All we need is to format the data in a way the algorithm can process, and we’ll let it...

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