`tidyquant`

, version 0.2.0, is now available on CRAN. If your not already familiar, `tidyquant`

integrates the best quantitative resources for collecting and analyzing quantitative data, `xts`

, `zoo`

, `quantmod`

and `TTR`

, with the tidy data infrastructure of the `tidyverse`

allowing for seamless interaction between each. I’ll briefly touch on some of the updates. The package is open source, and you can view the code on the tidyquant github page.

### tidyquant: Bringing Quantitative Financial Analysis to the tidyverse

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.

### Speed Up Your Code: Parallel Processing with multidplyr

### 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 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!

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

### 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 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.

### Customer Segmentation Part 2: PCA for Segment Visualization

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.