Author: R Views

Feed: R-bloggers. Author: R Views. This is a second post in a series on enterprise dashboards. See our previous post, Enterprise-ready dashboards with Shiny Databases. We have been living with spreadsheets for so long that most office workers think it is obvious that spreadsheets generated with programs like Microsoft Excel make it easy to understand data and communicate insights. Everyone in a business, from the newest intern to the CEO, has had some experience with spreadsheets. But using Excel as the de facto analytic standard is problematic. Relying exclusively on Excel produces environments where it is almost impossible to organize ... Read More

Feed: R-bloggers. Author: R Views. rstudio::conf 2018 and the New York R Conference are both behind us, but we are rushing headlong into the season for conferences focused on the R Language and its applications. The European R Users Meeting (eRum) begins this coming Monday, May 14th, in Budapest with three days of workshops and talks. Headlined by R Core member Martin Mächler and fellow keynote speakers Achim Zeileis, Nathalie Villa-Vialaneix, Stefano Maria Iacus, and Roger Bivand, the program features an outstanding array of accomplished speakers including RStudio’s own Barbara Borges Ribeiro, Andrie de Vries, and Lionel Henry. Second only ... Read More

Feed: R-bloggers. Author: R Views. In a previous post, we reviewed how to import the Fama French 3-Factor data, wrangle that data, and then regress our portfolio returns on the factors. Please have a look at that previous post, as the following work builds upon it. For more background on Fama French, see the original article published in The Journal of Financial Economics, Common risk factors in the returns on stocks and bonds. Today, we will explore the rolling Fama French model and the explanatory power of the 3 factors in different time periods. In the financial world, we often ... Read More

Feed: R-bloggers. Author: R Views. By my count, just over 200 new packages made it to CRAN and stuck during March. The trend for specialized, and sometimes downright esoteric science packages continues. I counted 40 new packages in this class. Most, but not all of these, are focused on bio-science applications. For example, the foreSIGHT package profiled below focuses on climate science. I was also pleased to see two new packages (not from RStudio) in the Data Science category, h2o4gpu and onnx, built on the reticulate package for interfacing with Python. I hope this also becomes a trend. The following ... Read More

Feed: R-bloggers. Author: R Views. I was surprised by greta. I had assumed that the tensorflow and reticulate packages would eventually enable R developers to look beyond deep learning applications and exploit the TensorFlow platform to create all manner of production-grade statistical applications. But I wasn’t thinking Bayesian. After all, Stan is probably everything a Bayesian modeler could want. Stan is a powerful, production-level probability distribution modeling engine with a slick R interface, deep documentation, and a dedicated development team. But greta lets users write TensorFlow-based Bayesian models directly in R! What could be more charming? greta removes the barrier ... Read More

Feed: R-bloggers. Author: R Views. RStudio recently announced the reticulate package, which is designed to help R users inter-operate with Python code. I was immediately excited by this announcement. In a past life, I worked with a team at the National Renewable Energy Lab (NREL) on vehicle simulations. Their models could predict MPG for vehicles based on driving routes. At the time, I had wanted to build a web app that would allow users to predict MPG for different vehicles based on their daily commutes. Unfortunately the model was written in Python, but my web application experience was limited to ... Read More

Feed: R-bloggers. Author: R Views. Introduction Companies using Google BigQuery for production analytics often run into the following problem: the company has a large user hit table that spans many years. Since queries are billed based on the fields accessed, and not on the date-ranges queried, queries on the table are billed for all available days and are increasingly wasteful. A solution is to partition the table by date, so that users can query a particular range of dates; saving costs and decreasing query duration. Partitioning an un-partitioned table can be expensive if done the brute-force way. This article explores ... Read More

Feed: R-bloggers. Author: R Views. Sometimes it appears to me that the invisible hand economists speak of guides the market for new R packages. Eight of the 129 new packages that stuck to CRAN in December fall under Computational Methods, a category I have only recently begun using. All of them made it into the list below of my “Top 40” picks. One day, I would like to go back and reexamine the categories I have been using to see if package developers really do respond to some idea that is “in the air” or whether the variation in categories ... Read More

Feed: R-bloggers. Author: R Views. Any programming environment should be optimized for its task, and not all tasks are alike. For example, if you are exploring uncharted mountain ranges, the portability of a tent is essential. However, when building a house to weather hurricanes, investing in a strong foundation is important. Similarly, when beginning a new data science programming project, it is prudent to assess how much effort should be put into ensuring the code is reproducible. Note that it is certainly possible to go back later and “shore up” the reproducibility of a project where it is weak. This ... Read More

Feed: R-bloggers. Author: R Views. In a recent post, I mentioned three avenues for working with TensorFlow from R:* The keras package, which uses the Keras API for building scaleable, deep learning models * The tfestimators package, which wraps Google’s Estimators API for fitting models with pre-built estimators* The tensorflow package, which provides an interface to Google’s low-level TensorFlow API In this post, Edgar and I use the linear_classifier() function, one of six pre-built models currently in the tfestimators package, to train a linear classifier using data from the titanic package. library(tfestimators) library(tensorflow) library(tidyverse) library(titanic) The titanic_train data set contains ... Read More