Category: R

Feed: R-bloggers. Author: John Mount. We are thrilled to announce our (my and Nina Zumel’s) paper on the dot-pipe has been accepted by the R-Journal! A huge “thank you” to the reviewers and editors for helping us with this! You can find our article here (pdf here)! Related To leave a comment for the author, please follow the link and comment on their blog: R – Win-Vector Blog. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, ... Read More

Feed: R-bloggers. Author: Posts on Tychobra. We are pleased to announce a new demo Shiny application that uses machine learning to predict annual payments on individual insurance claims for 10 years into the future.This post describes the basics of the model behind the above Shiny app, and walks through the model fitting, prediction, and simulation ideas using a single claim as an example. Background Insurance is the business of selling promises (insurance policies) to pay for potential future claims. After insurance policies are sold, it can be many years before a claim is reported to the insurer and many more ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out. In the vaccine space (where I work) we analyze collections of high-dimensional immunological data sets from a variety of different technologies (RNA sequencing, cytometry, multiplexed antibody binding, and others). These data often arise from clinical ... Read More

Feed: R-bloggers. Author: Ivan Svetunkov. Intro One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green leap sticks. The demand on such a product will not be easy to predict, because green colour is not a popular colour in this case, and thus the data of sales will contain a lot of zeroes with seldom non-zero values. Such demand is called “intermittent”. In ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is difficult and not well understood. The issue is twofold, first, the necessary technology to complete such a task has only recently become mainstream, and second, most data scientists are inexperienced in their respective industries. However, with all the uncertainty surrounding this topic, one hedge fund has managed to navigate through these challenges and accomplish what ... Read More

Feed: R-bloggers. Author: statcompute. In the post (https://statcompute.wordpress.com/2018/09/15/how-to-avoid-for-loop-in-r), I briefly introduced the idea of vectorization and potential use cases. One might be wondering why we even need the Vectorize() function given the fact that it is just a wrapper and whether there is any material efficiency gain by vectorizing a function. It is true that the Vectorize() function is not able to improve the efficiency of any function itself that is wrapped around, e.g. vectorized. However, the vectorization can change the input format of a function that normally consumes scalar inputs before being vectorized and therefore would improve the processing ... Read More

Feed: R-bloggers. Author: Dr. Shirin Glander. Today I am very happy to announce that during my stay in London for the m3 conference, I’ll also be giving a talk at the R-Ladies London Meetup on Tuesday, October 16th, about one of my favorite topics: Interpretable Deep Learning with R, Keras and LIME. You can register via Eventbrite: https://www.eventbrite.co.uk/e/interpretable-deep-learning-with-r-lime-and-keras-tickets-50118369392 ABOUT THE TALK Keras is a high-level open-source deep learning framework that by default works on top of TensorFlow. Keras is minimalistic, efficient and highly flexible because it works with a modular layer system to define, compile and fit neural networks. It ... Read More

Feed: R-bloggers. Author: Adnan Fiaz. Nowadays whenever I do my work in R there is a constant nagging voice in the back of my head telling me “you should do this in Python”. And when I do my work in Python it’s telling me “you can do this faster in R”. So when the reticulate package came out I was overjoyed and in this blogpost I will explain to you why. re-tic-u-late (rĭ-tĭkˈyə-lĭt, -lātˌ) So what exactly does reticulate do? It’s goal is to facilitate interoperability between Python and R. It does this by embedding a Python session within the R session which enables ... Read More

Feed: R-bloggers. Author: Ilya Kipnis. This post will investigate using Principal Components as part of a momentum strategy. Recently, I ran across a post from David Varadi that I thought I’d further investigate and translate into code I can explicitly display (as David Varadi doesn’t). Of course, as David Varadi is a quantitative research director with whom I’ve done good work with in the past, I find that trying to investigate his ideas is worth the time spent. So, here’s the basic idea: in an allegedly balanced universe, containing both aggressive (e.g. equity asset class ETFs) assets and defensive assets ... Read More

Feed: R-bloggers. Author: Tim Bock. Explore 3D Correspondence Analysis! Back in the “olden days” of the 1970s it was apparently not unknown for statisticians to create 3D visualizations using tinkertoys. For some inexplicable reason, the advent of the PC led to a decline in this practice, with the result that 3D visualizations are now much less common. However, it is possible to create 3D-like visualizations digitally. Although they only show two dimensions at any one time, the user can understand the third dimension by interacting with the visualization. In this post I use Plotly’s excellent plotting package to create an interactive, 3D ... Read More