Tag: SQL

Feed: R-bloggers. Author: rOpenSci - open tools for open science. Part of rOpenSci’s mission is to create technical infrastructure in the form of carefully vetted R software tools that lower barriers to working with data sources on the web. Our open peer software review system for community-contributed tools is a key component of this. As the rOpenSci community grows and more package authors submit their work for peer review, we need to expand our editorial board to maintain a speedy process. As our recent post shows, package submissions have grown every year since we started this experiment, and we see ... Read More

Feed: R-bloggers. Author: . As I’ve been blogging more about statistics, R, and research in general, I’ve been trying to increase my online presence, sharing my blog posts in groups of like-minded people. Those efforts seem to have paid off, based on my view counts over the past year: And based on read counts, here are my top 10 blog posts, most of which are stats-related: Beautiful Asymmetry – none of us is symmetrical, and that’s okay  Statistical Sins: Stepwise Regression – just step away from stepwise regression Statistics Sunday: What Are Degrees of Freedom? (Part 1) – and read Part ... Read More

Feed: R-bloggers. Author: filip Schouwenaars. Immediate and personalized feedback has been central to the learning experience on DataCamp since we launched the first courses. If students submit code that contains a mistake, they are told where they made a mistake, and how they can fix this. You can play around with it in our free Introduction to R course. The screenshot below is from our Intermediate R course. To check submissions and generate feedback, every exercise on DataCamp features a so-called Submission Correctness Test, or SCT. The SCT is a script of custom tests that assesses the code students submitted ... Read More

Feed: R-bloggers. Author: sindri. Datasets often arrive to us in a form that is different from what we need for our modelling or visualisations functions who in turn don’t necessary require the same format. Reshaping data.frames is a step that all analysts need but many struggle with. Practicing this meta-skill will in the long-run result in more time to focus on the actual analysis. The solutions to this set will rely on data.table, mostly melt() and dcast() which are originally from the reshape2 package. However, you can also get practice out if it using your favourite base-R, tidyverse or any ... Read More

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. By Bryce Merkl Sasaki, Editor-in-Chief, Neo4j | June 22, 2018  “The future of graph technology is already here. It’s everywhere, because we can model anything in a way that’s more close to how it is in real life,” said Alicia Powers, Senior Vice President at New York City Economic Development Corporation (NYCEDC).For Alicia, it’s all about the visualization. While working as a researcher at the New York City Economic Development Corporation, she took on a side project to build a recommendation engine to see different connections in eating and how it relates ... Read More

Feed: R-bloggers. Author: Blog-rss on stevenmortimer.com. I recently wrote a blog post here comparing the number of CRAN downloads an R package gets relative to its number of stars on GitHub. What I didn’t really think about during my analysis was whether or not scraping CRAN was a violation of its Terms and Conditions. I simply copy and pasted some code from R-bloggers that seemed to work and went on my merry way. In hindsight, it would have been better to check whether or not the scraping was allowed and maybe find a better way to get the information I ... Read More

Feed: R-bloggers. Author: Dr. Shirin Glander. Last week I published a blog post about how easy it is to train image classification models with Keras. What I did not show in that post was how to use the model for making predictions. This, I will do here. But predictions alone are boring, so I’m adding explanations for the predictions using the lime package. I have already written a few blog posts (here, here and here) about LIME and have given talks (here and here) about it, too. Neither of them applies LIME to image classification models, though. And with the ... Read More

Feed: R-bloggers. Author: Chuck Powell. Presenting data results in the most informative and compelling manner is part of the role of the data scientist. It's all well and good to master the arcana of some algorithm, to manipulate and master the numbers and bend them to your will to produce a “solution” that is both accurate and useful. But, those activities are typically in pursuit of informing some decision or at least providing information that serves a purpose. So taking those results and making them compelling and understandable by your audience is part of your job! This article will focus ... Read More

Feed: R-bloggers. Author: arthur charpentier. My previous post was explaining how mathematically it was possible to parallelize computation to estimate the parameters of a linear regression. More speficially, we have a matrix which is matrix and a -dimensional vector, and we want to compute observations, we’ve seen that it was to possible to compute “something” using the first rows, then the next rows, etc. Then, finally, we “aggregate” the objects created to get our overall estimate. Parallelizing on multiple cores Let us see how it works from a computational point of view, to run each computation on a different core ... Read More

Feed: Blog. Author: Stella11; Unlike their failed counterparts, solutions are successful solely because systems, users, and organizations have come to depend upon them. Solutions are not necessarily successful because they were well crafted, intelligently designed, or adhere to any other theoretical standards or best practices.In essence, performance becomes a problem when a solution’s success starts to overwhelm its bearing capacity. Some applications reach this point almost immediately, either because they were more successful than anticipated, or because their design and architecture were poor. Consequently, genuinely successful applications require attention because they are continually changing and adapting to meet business needs, ... Read More