Author: R Views

Feed: R-bloggers. Author: R Views. Last month, I was delighted to be invited to speak, along with Hadley Wickham, at the seventy-first meeting of the TokyoR user group in Tokyo, Japan. This day-long mini-conference attracted more than 200 attendees and featured 16 talks that covered a wide range of topics, including two near-real-time analyses of World Cup Soccer games (here and here) and an analysis of wind direction with circular data and autogressive processes (here). The tone of the talks ranged from light-hearted to business-serious. The slides for most of the presentations can be found here. If you scan through ... Read More

Feed: R-bloggers. Author: R Views. Today, in honor of last week’s jobs report from the Bureau of Labor Statistics (BLS), we will visualize jobs data with ggplot2 and then, more extensively with highcharter. Our aim is to explore highcharter and its similarity with ggplot and to create some nice interactive visualizations. In the process, we will cover how to import BLS data from FRED and then wrangle it for visualization. We won’t do any modeling or statistical analysis today, though it wouldn’t be hard to extend this script into a forecasting exercise. One nice thing about today’s code flow is ... Read More

Feed: R-bloggers. Author: R Views. Approximately 144 new packages stuck to CRAN in June. That fact that 31 of these are specialized to particular scientific disciplines or analyses provides some evidence to my hypothesis that working scientists are actively adopting R. Below are my Top 40 picks for June, organized into the categories of Computational Methods, Data, Data Science, Economics, Science, Statistics, Time Series, Utilities and Visualizations. The Data packages, especially rtrek and opensensmapr, look like they have some interesting new data to explore. Computational Methods nnTensor v0.99.1: Provides methods for n-negative matrix factorization and decomposition. See Cichock et al ... Read More

Feed: R-bloggers. Author: R Views. Moving R resources from development to production can be a challenge, especially when the resource isn’t something like a shiny application or rmarkdown document that can be easily published and consumed. Consider, as an example, a customer success model created in R. This model is responsible for taking customer data and returning a predicted outcome, like the likelihood the customer will churn. Once this model is developed and validated, there needs to be some way for the model output to be leveraged by other systems and individuals within the company. Traditionally, moving this model into ... Read More

Feed: R-bloggers. Author: R Views. In our first blog post, we introduced CVXR, an R package for disciplined convex optimization, and showed how to model and solve a non-negative least squares problem using its interface. This time, we will tackle a non-parametric estimation example, which features new atoms as well as more complex constraints. Direct Standardization Consider a set of observations ((x_i,y_i)) drawn non-uniformly from an unknown distribution. We know the expected value of the columns of (X), denoted by (b in {mathbf R}^n), and want to estimate the true distribution of (y). This situation may arise, for instance, if ... Read More

Feed: R-bloggers. Author: R Views. In previous posts, we covered how to run a Monte Carlo simulation and how to visualize the results. Today, we will wrap that work into a Shiny app wherein a user can build a custom portfolio, and then choose a number of simulations to run and a number of months to simulate into the future. A link to that final Shiny app is here and here is a snapshot: We will use RMarkdown to build our Shiny application by inserting runtime: shiny into the yaml header. This will alert the server (or our laptop) that ... Read More

Feed: R-bloggers. Author: R Views. Introduction In our previous blogpost, weintroduced CVXR, an R package for disciplined convexoptimization. The package allows one to describe an optimizationproblem with Disciplined Convex Programmingrules using high level mathematical syntax. Passing this problemdefinition along (with a list of constraints, if any) to the solvefunction transforms it into a form that can be handed off toa solver. The default installation of CVXR comes with two (imported)open source solvers: ECOS and its mixed integercousin ECOS_BB via the CRAN packageECOSolveR SCS via the CRAN packagescs. CVXR (version 0.99) can also make use of several other open sourcesolvers implemented ... Read More

Feed: R-bloggers. Author: R Views. While looking over the 215 or so new packages that made it to CRAN in May, I was delighted to find several packages devoted to subjects a little bit out of the ordinary; for instance, bioacoustics analyzes audio recordings, freegroup looks at some abstract mathematics, RQEntangle computes quantum entanglement, stemmatology analyzes textual musical traditions, and treedater estimates clock rates for evolutionary models. I take this as evidence that R is expanding beyond its traditional strongholds of statistics and finance as people in other fields with serious analytic and computational requirements become familiar with the language ... Read More

Feed: R-bloggers. Author: R Views. I have frequent conversations with R champions and Systems Administrators responsible for R, in which they ask how they can measure and analyze the usage of their servers. Among the many solutions to this problem, one of the my favourites is to use an RRD database and RRDtool. From Wikipedia: RRDtool (round-robin database tool) aims to handle time series data such as network bandwidth, temperatures or CPU load. The data is stored in a circular buffer based database, thus the system storage footprint remains constant over time. RRDtool is a library written in C, with ... Read More

Feed: R-bloggers. Author: R Views. In a previous post, I showed a very simple example of using the R function tools::CRAN_package_db() to analyze information about CRAN packages. CRAN_package_db() extracts the metadata CRAN stores on all of its 12,000 plus packages and arranges it into a “database”, actually a complicated data frame in which some columns have vectors or lists as entries.It’s simple to run the function and it doesn’t take very long on my Mac Book Air.The following gives some insight into what’s contained in the data frame.Looking at a few rows and columns gives a feel for how complicated ... Read More