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 quant, long-time R user and twitter humorist JD Long (@CMastication), Stack Overflow’s David Robinson (@drob) and ProPublica editor Olga Pierce (@olgapierce).

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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.

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Introduction Dynamic systems are usually represented by a model before they can be analyzed computationally. These dynamic systems are systems that change, evolve or have their states altered or varied with time based on a set of defined rules. Dynamic systems could be mechanical, electrical, electronic, biological, sociological, and so on. Many such systems are usually defined by a set rules that are represented as a set of nonlinear differential equations.

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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.

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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.

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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 within a database, the usual approach is to import all of the data and then create the plot.

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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 traditional plotting functions like plot(), matplot(), barplot(), etc.

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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:

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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, like me, who think that base R graphics remain a powerful tool for data exploration. The ability of R’s plot() function to quickly produce plots for all sorts of objects helps an R user attain that state of flow that makes R such a productive environment for data analysis.

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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?

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