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, there is quite a bit of low hanging fruit.
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 response surface methods for drug synergy analysis, including generalized and classical Loewe formulations and the Highest Single Agent methodology. There are vignettes on Methodology and Synergy Analysis.
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.
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 support a corporate R user group is the creation of an internal R package.
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 focus on two tasks:
Calculate the rolling standard deviation of SPY monthly returns.
The R Consortium is undertaking a multi-year effort to survey the whole R world. In a rather low-key blog post at the end of last month, the R Consortium’s technical committee, the Infrastructure Steering Committee (ISC), launched its prototype survey of R users. The idea is to use the information gleaned from the exploratory questions in this first survey to craft a more refined version that can be sent out annually.
This is the beginning of a series on portfolio volatility, variance, and standard deviation. I realize that it’s a lot more fun to fantasize about analyzing stock returns, which is why television shows and websites constantly update the daily market returns and give them snazzy green and red colors. But good ol’ volatility is quite important in its own right, especially to finance geeks, aspiring finance geeks, and institutional investors.
Introduction Control theory is an interdisciplinary branch of mathematics and engineering that has the objective of controlling physical systems. A control system is a device or a collection of devices that manage, command, direct or regulate the behavior of other devices or systems. Control systems engineering is a major application of control theory, and involves the study, design/modeling of automatic control systems. Control theory has also been applied to several other fields such as finance, sociology, psychology, physiology and other sciences that analyze dynamic systems.
Shiny applications of all stripes (including flexdashboard with runtime Shiny) are revolutionary in that they put the power of R directly in the end user’s hands without needing to interact directly with the language. A common way end-users wish to interact with their data is via a dashboard that they can manipulate on the fly. Flexdashboard streamlines the process of turning an R-based analysis into a dashboard, so R users can create good-looking output for their analyses - and deploy this output to the web - with very little additional effort.
Two hundred and twenty-nine new packages were submitted to CRAN in May. Here are my picks for the “Top 40”, organized into five categories: Data, Data Science and Machine Learning, Education, Miscellaneous, Statistics and Utilities.
Data angstroms v0.0.1: Provides helper functions for working with Regional Ocean Modeling System (ROMS) output.
bikedata v0.0.1: Download and aggregate data from public bicycle systems from around the world. There is a vignette.
datasauRushttps://CRAN.R-project.org/package=datasauRus v0.