Author: R Views

Feed: R-bloggers. Author: R Views. Inside the enterprise, a dashboard is expected to have up-to-the-minute information, to have a fast response time despite the large amount of data that supports it, and to be available on any device. An end user may expect that clicking on a bar or column inside a plot will result in either a more detailed report, or a list of the actual records that make up that number. This article will cover how to use a set of R packages, along with Shiny, to meet those requirements. The code A working example for the dashboard ... Read More

Feed: R-bloggers. Author: R Views. In our previous portfolio volatility work, we covered how to import stock prices, convert to returns and set weights, calculate portfolio volatility, and calculate rolling portfolio volatility. Now we want to break that total portfolio volatility into its constituent parts and investigate how each asset contributes to the volatility. Why might we want to do that? For our own risk management purposes, we might want to ensure that our risk isn’t too concentrated in one asset. Not only might this lead to a less-diversified portfolio than we thought we had, but it also might indicate ... Read More

Feed: R-bloggers. Author: R Views. Writing padr The idea for the padr package came at work, where I was dealing with time-stamped data a lot. Aggregating data to a higher level and filling missing values took me a quite some time each time I did it. I thought there must be a better way for doing this. I did not have a clear end product in mind. I just started trying stuff and stitched small ideas together to do more complex operations. In the meantime, I worked my way through Hadley’s R Packages, and learned about the fundamentals of software ... Read More

Feed: R-bloggers. Author: R Views. It’s not even Labor Day, so it seems to be a bit early to start planning for next year’s R conferences. But, early-bird pricing for RStudio::Conf 2018 ends this Thursday. The conference which will be held in San Diego between January 31st and February 3rd promises to match and even surpass this year’s event. In addition to keynotes from Di Cook (Monash University and Iowa State University), J.J. Allaire (RStudio Founder, CEO & Principal Developer), Shiny creator Joe Cheng, and Chief Scientist Hadley Wickham, a number of knowledgeable (and entertaining) speakers have already committed including ... Read More

Feed: R-bloggers. Author: R Views. Two hundred and twenty-four new packages were added to CRAN in July. Below are my picks for the “Top 40” packages arranged in eight categories: Machine Learning, Science, Statistics, Numerical Methods, Statistics, Time Series, Utilities and Visualizations. Science and Numerical Methods are categories that I have not used before. The idea behind the Science category is to find a place for packages that appear to have been created with some particular scientific investigation or problem in mind. The Numerical Methods category is reserved for packages that, while they may be targeted to some general form ... Read More

Feed: R-bloggers. Author: R Views. This example groups stocks together in a network that highlights associations within and between the groups using only historical price data. The result is far from ground-breaking; you can already guess the output. For the most part, the stocks get grouped together into pretty obvious business sectors. Despite the obvious result, the process of teasing out latent groupings from historic price data is interesting. That’s the focus of this example. A central idea of the approach taken here comes from the great paper of Ledoit and Wolf, “Honey, I Shrunk the Sample Covariance Matrix” (http://www.ledoit.net/honey.pdf) ... Read More

Feed: R-bloggers. Author: R Views. Editor’s Note: The 2017 Chapman University DataFest was held during the weekend of April 21-23. The 2018 DataFest will be held during the weekend of April 27-29. DataFest was founded by Rob Gould in 2011 at UCLA with 40 students. In just seven years, it has grown to 31 sites in three countries. Have a look at Mine Çetinkaya-Rundel’s post Growth of DataFest over the years for the details. In recent years, it has been difficult for UCLA to keep up with the growing interest and demand from southern California universities. This year, the Chapman ... Read More

Feed: R-bloggers. Author: R Views. 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 rolling volatilities over time. Here is the final app: There will be a slight departure in form today because we will use a helpers.r file to hold our functions – those same functions that we ... Read More

Feed: R-bloggers. Author: R Views. 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 snapshots from an R-focused, statistical tourist. First Snapshot: What’s in a Vector? Yesterday, Gabe Becker showed some eye opening work he is doing with Luke Tierney and Thomas Kalibera on features that are making ... Read More

Feed: R-bloggers. Author: R Views. 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. Below twenty-five sessions are listed by their JSM Activity Number. If you are going to the conference, try to work ... Read More