My new package, tidyquant, is now available on CRAN. tidyquant integrates the best quantitative resources for collecting and analyzing quantitative data, xts, quantmod and TTR, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each. While this post aims to introduce tidyquant to the R community, it just scratches the surface of the features and benefits. We’ll go through a simple stock visualization using ggplot2, which which shows off the integration. The package is open source, and you can view the code on the tidyquant github page.

Use parallel processing to speed up your R code, using tidyverse multidplyr.

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 can result in large gains. In this post, I’ll perform a quantitative stock analysis on the entire list of Russell 2000 stock components using the R programming language. Building on the methodology from my S&P Analysis Post, I develop screening and ranking metrics to identify the top stocks with amazing growth and most consistency. I use R for the analysis including the rvest library for web scraping the list of Russell 2000 stocks, quantmod to collect historical prices for all 2000+ stock components, purrr to map modeling functions, and various other tidyverse libraries such as ggplot2, dplyr, and tidyr to visualize and manage the data workflow. Last, I use plotly to create an interactive visualization used in the screening process. Whether you are familiar with quantitative stock analysis, just beginning, or just interested in the R programming language, you’ll gain both knowledge of data science in R and immediate insights into the best Russell 2000 stocks, quantitatively selected for future returns!

Develop quantitative trading strategies in R. Analyze every stock in the S&P 500 to screen risk versus reward.

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 Component Analysis (PCA) to visualize customer groups. The third and final post performs Network Visualization (Graph Drawing) using the igraph and networkD3 libraries as a method to visualize the customer connections and relationship strengths.

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 problem from a different angle than k-means, we can get different insights. We’ll compare both the k-means results with the PCA visualization. Let’s see what happens when we apply PCA.

In this machine learning with R tutorial, use k means clustering to segment customers into distinct groups based on purchasing habits.

In this post, we will be discussing orderSimulatoR, which enables fast and easy R order simulation for customer and product learning. The basic premise is to simulate data that you’d retrieve from a SQL query of an ERP system. The data can then be merged with products and customers tables to data mine. I’ll go through the basic steps to create an order data set that combines customers and products, and I’ll wrap up with some visualizations to show how you can use order data to expose trends. You can get the scripts and the Cannondale bikes data set at the orderSimulatoR GitHub repository. In case you are wondering what simulated orders look like, click here to scroll to the end result.

Just because you’re a business professional does not mean you can’t or you shouldn’t pursue furthering yourself in analytics. Businesses view strategic decision making as a competitive advantage. You should too! Learning the basics behind data science not only adds value to your organization, it increases your value and thus your demand too.

Getting up and running in data science is tough. It’s easy to get overwhelmed, and your biggest asset is time (don’t waste it). Here’s some resources to help speed you along. I’ll continually update these as I get time. Feel free to comment or email me if I’m missing something.