Category: R

Feed: R-bloggers. Author: R on Just be-cause. I’ve discussed on several blog posts how Causal Inference involves making inference about unobserved quantities and distributions (e.g. we never observe (Y|do(x))). That means we can’t benchmark different algorithms on Causal Inference tasks (e.g (ATE/CATE) estimation) the same way we do in ML because we don’t have any ground truth to benchmark against. In the absence of ground truth, one of the main tools left for model comparison and performance bench-marking is simulation studies. On a recent post I used a semi synthetic dataset to benchmark different algorithms on the task of (ATE/CATE) estimation ... Read More

Feed: R-bloggers. Author: Learning Machines. A few months ago I published a quite popular post on Clustering the Bible… one well known clustering algorithm is k-means. If you want to learn how k-means works and how to apply it in a real-world example, read on… k-means (not to be confused with k-nearest neighbours or KNN: Teach R to read handwritten Digits with just 4 Lines of Code) is a simple, yet often very effective unsupervised learning algorithm to find similarities in large amounts of data and cluster them accordingly. The k stands for the number of clusters which has to ... Read More

Feed: R-bloggers. 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O'Reilly DVM","Add Senger","Aden Lesch Sr.","Admiral Senger","Agness O'Keefe","Aileen Barton","Ailene Hermann","Aiyanna Bruen PhD","Ala Schmidt DDS","Alannah Borer","Alcide Rohan","Aleena Berge","Alessandra Heaney","Alethea Blanda","Alex Armstrong","Alex Bergnaum","Alexis Cormier","Alf Lueilwitz","Alferd Ziemann","Alfred Metz","Almedia Yundt","Alpheus Wilkinson","Alphonse Champlin V","Alton Wintheiser","Alvah Bogisich","Alvera Balistreri","Alwina Wilkinson DDS","Alyssia Hickle","Amina Renner","Amit Langworth","Anabel Jakubowski PhD","Anastasia Howe","Anderson Quitzon","Andra Bosco","Andra Goodwin","Angeles McLaughlin PhD","Anie Hettinger","Anjelica Klocko","Antonio Bechtelar V","Ariane Hansen","Ariel Yundt","Arlie Brekke IV","Arlyn Dickinson","Arnett Waters","Arno Blick","Art Hyatt III","Aryan Bahringer","Asha Ankunding","Ashleigh Eichmann","Aubrey Witting","Aurthur Kirlin II","Auther Haley","Avah Schneider","Avery Bartoletti","Ayesha Carroll","Azariah Lubowitz","Azul Wehner","Barrett Turcotte DVM","Bartley Collier","Bascom Prosacco","Bea Considine","Beda Paucek","Benjamin Barton","Benji Jacobson","Bennett Gleason","Benson Schulist","Berkley Ernser ... 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Feed: R-bloggers. Author: Posts on Tychobra. R is great for report generation. Shiny allows us to easily create web apps that generate a variety of reports with R. This post details a demo Shiny app that generates an Excel report, a PowerPoint report, and a PDF report: The full Shiny app source code is available here. Also, we included a more basic Shiny app that generates an Excel report at the end of this post. Follow up posts will include similar simple Shiny apps generating PowerPoint and PDF reports. Here are some screenshots of the Shiny app that generates the ... Read More

Feed: R-bloggers. Author: Andrew Treadway. This post will compare Python’s BeautifulSoup package to R’s rvest package for web scraping. We’ll also talk about additional functionality in rvest (that doesn’t exist in BeautifulSoup) in comparison to a couple of other Python packages (including pandas and RoboBrowser). Getting started BeautifulSoup and rvest both involve creating an object that we can use to parse the HTML from a webpage. However, one immediate difference is that BeautifulSoup is just a web parser, so it doesn’t connect to webpages. rvest, on the other hand, can connect to a webpage and scrape / parse its HTML ... Read More

Feed: R-bloggers. Author: xi'an. An interesting question (with no clear motivation) on X validated wondering why a Gibbs sampler produces NAs… Interesting because multi-layered: The attached R code indeed produces NAs because it calls the Negative Binomial Neg(x¹,p) random generator with a zero success parameter, x¹=0, which automatically returns NAs. This can be escaped by returning a one (1) instead. The Gibbs sampler is based on a Bin(x²,p) conditional for X¹ and a Neg(x¹,p) conditional for X². When using the most standard version of the Negative Binomial random variate as the number of failures, hence supported on 0,1,2…. these two ... Read More

Feed: R-bloggers. Author: Thomas Bryce Kelly. So all oceanographers are familiar with the results of gridding, even if they are not so familiar with the process. Gridding is, in general, any method that will take observations and output interpolated (and sometimes extrapolated) data that is placed onto a regular, well-behaving grid. Below is a simple illustration of just such a process where we take scattered observations of temperature and construct a very regular map of temperatures. Gridding can be done to arbitrary resolution (even though the accuracy does not increase!) at the expense of computational resources: Although oceanographers and other ... Read More

Feed: R-bloggers. Author: Kristian Larsen. Category Tags Regression analysis is one of the most demanding machine learning methods in 2019. One group of regression analysis for measuring effects and to evaluate a policy program is Difference-in-Difference. This method is well suited for benchmarking and finding improvements for optimization in organizations. It can, therefore, be used to design organizations so they generate more value for employees and customers. In this article, you learn how to do difference-in-difference in R. Methodology The difference in differences (DiD) method is a statistical technique or quasi-experimental design method, and it is used primarily in the ... Read More

Feed: R-bloggers. Author: R by R(yo). The useR! Conference was held in Toulouse, France and for me thiswas my second useR! after my first in Brisbane last year. This timearound I wanted to write about my experiences and some highlightssimilar to my post on the RStudio::Conference 2019 & Tidyverse DevDayearlier this year. This blog post will be divided into 4 sections: Programming, Shiny, {Packages}, and Touring Toulouse. You can find slides and videos (in a week or so) in: As usual there were many talks that I didn’t get to go to as there arearound 3~5 tracks across different rooms ... Read More

Feed: R-bloggers. Author: Sébastien Rochette. At useR!2019 in Toulouse, ThinkR opened a quizz allowing to win a pipe knight. About a hundred of respondents won this Playmobil. In this blog post, we review the questions and respondents answers. We’ll see that the crowd as almost always right, but they do not know who the real R oracle is… The quizz showed some difficulties and tricks of the base R language. This does not reflect your capacity to be a good R-programmer at all. This is only a game with tricky code that you surely do not want to write this ... Read More