Using R, ggplot2, Plotly, and the Plotly Cloud for Analytical Applications
In this visualization, Steve Miller from Inquidia Consulting monitors performance of the stock market in 2016 using daily index data available from Russell. The graphs track the current value of an initial $1 investment starting end of day December 31, 2015. Eighteen separate portfolios dimensioned by company size and a growth-value continuum are shown. The challenge is to provide an interpretable dashboard that includes all 18 traces in a way that can easily be compared.
The data are downloaded daily from the Russell website using a Python script in a Jupyter notebook. The visualization is a trellis developed in Jupyter, R, ggplot2, and plotly – and ultimately stored in the plotly cloud. Plotly offers state-of-the-art visual capabilities built on a foundation of D3. The plotly package in R provides the code-saving capacity of generating powerful interactive graphics directly from R's well-traveled ggplot syntax.
The six trellis panels showcase indexes comprised of companies ranging large to small from top to bottom, left to right. Within each trellis, traces are grouped by growth-value. With this “design”, traces can be readily compared across panels, since each panel has identical axes. The combination of trellis, groupings and size sortings makes comparison of all eighteen tracings feasible. Hopefully the reader can make sense of what is now a rather unenthusiastic stock performance environment.
See the visualization on the plotly cloud, here: https://plot.ly/~stevemiller/41.embed
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