Publications by R Views
Introduction to Rolling Volatility
This is the second post in our series on portfolio volatility, variance and standard deviation. If you missed the first post and want to start at the beginning with calculating portfolio volatility, have a look here – Introduction to Volatility. We will use three objects created in that previous post, so a quick peek is recommended. Today we fo...
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Some Ideas for your Internal R Package
At RStudio, I have the pleasure of interacting with data science teams around the world. Many of these teams are led by R users stepping into the role of analytic admins. These users are responsible for supporting and growing the R user base in their organization and often lead internal R user groups. One of the most successful strategies to supp...
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Visualizing Portfolio Volatility
This is the third post in our series on portfolio volatility, variance and standard deviation. If you want to start at the beginning with calculating portfolio volatility, have a look at the first post here – Intro to Volatility. The second post on calculating rolling standard deviations is here: Intro to Rolling Volatility. Today we will visua...
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June 2017 New Package Picks
Two hundred and thirty-eight new packages were added to CRAN in June. Below are my picks for the “Top 40”, organized into six categories: Biostatistics, Data, Machine Learning, Miscellaneous, Statistics and Utilities. Some packages, including geofacet and secret, already seem to be gaining traction. Biostatistics BIGL v1.0.1: Implements resp...
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Looking for R at JSM
I am very much looking forward to attending JSM which begins this Sunday. And once again, I will be spending a good bit of my time hunting for new and interesting applications of R. In years gone by, this was a difficult game at JSM because R, R Package, Shiny, tidyverse and the like did not often turn up in a keyword search. This year, however, ...
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A Postcard from JSM
Baltimore has the reputation of being a tough town: hot in the summer and gritty, but the convention center hosting the Joint Statistical Meetings is a pretty cool place to be. There are thousands of people here and so many sessions (over 600) that it’s just impossible to get an overview of all that’s going on. So, here are couple of snapshot...
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R and Interactive Graphics
Judging from the number of JSM talks that incorporated interactive visualizations of some sort or another, it appears that interactive graphics have captured the attention of a good many statisticians. I found this a little surprising. Statisticians, on the whole, are not easily impressed by “eye candy”, and I believe that there are many, lik...
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Portfolio Volatility Shiny App
In our 3 previous posts, we walked through how to calculate portfolio volatility, then how to calculate rolling volatility, and then how to visualize rolling volatility. Today, we will wrap all of that work into a Shiny app that allows a user to construct his or her own five-asset portfolio, choose a benchmark and a time period, and visualize the...
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End-to-end visualization using ggplot2
ggplot2 is kind of a household word for R users. I’ve ended up using it for complex data munging and wrangling work, where I needed to get clarity on different aspects of the data, especially being able to get different views, slices and dices of it, but in a nice visualization. At some point along the line, I slowly stopped using more traditio...
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Visualizations with R and Databases
The Challenge Visualizations are one of R’s strengths. There are many functions and packages that create complex plots, often with one simple command. These plotting functions do two things: first, they take the raw data and run the calculations needed for a given visualization, and second, they draw the plot. If the source of the data resides ...
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