We all know R is the first choice for statistical analysis and data visualisation, but what about big data munging? tidyverse (or we’d better say hadleyverse) has been doing a lot in this field, nevertheless it is often the case this kind of activities being handled from some other coding language. Moreover, sometimes you get as an input pieces of analyses performed with other kind of languages or, what is worst, piece of databases packed in proprietary format (like .dta .xpt and other). So let’s assume you are an R enthusiast like I am, and you do with R all of your work, reporting included, wouldn’t be great to have some nitty gritty way to merge together all these languages in a streamlined workflow? Yes, we all know great products like microsoft Azure and sas viya, but you know what? They don’t come free, and this can sometime become an obstacle. Moreover all of them involve some kind of sophisticated setup to go trough. But what if we could reach some useful results just leveraging a useful r package and a cleaver setup? We actually can do this and I’ll show you how within coming paragraphs.
Let’s say you have the gift of flight (or you are riding a chopper). You are also a Spy (like in James Bond movies). You are given the topography of a long narrow valley as shown in the image and you are given a rendezvous point to meet a potential aide who has intelligence that is helpful for your objective.
Today we’re excited to announce R Notebooks, which add a powerful notebook authoring engine to R Markdown. Notebook interfaces for data analysis have compelling advantages including the close association of code and output and the ability to intersperse narrative with computation. Notebooks are also an excellent tool for teaching and a convenient way to share analyses.
R has a number of ROC/AUC packages; for example ROCR, pROC, and plotROC. But it is instructive to see how ROC plots are produced and how AUC can be calculated. Bob Horton’s article showed how elegantly the points on the ROC plot are expressed in terms of sorting and cumulative summation.
You want to get started on text mining, but most of the tutorials you start, get pretty complex very quickly? Or you can’t find a proper data set to work on? DataCamp’s latest post will walk you through 8 tips and tricks that will help you to start text mining and to stay hooked on it.
Recently, there have been significant advances in Machine Learning that enable computer systems to solve complex real-world problems. One of those advances is Google’s large scale, graph-based machine learning platform, built by the Expander team in Google Research. A technology that is behind many of the Google products and features you may use everyday, graph-based machine learning is a powerful tool that can be used to power useful features such as reminders in Inbox and smart messaging in Allo, or used in conjunction with deep neural networks to power the latest image recognition system in Google Photos.