Author: R-Bloggers

(This article was first published on R – Networkx, and kindly contributed to R-bloggers) Python R Source Overview Welcome to the Descriptive Statistics Final Project! In this project, you will demonstrate what you have learned in this course by conducting an experiment dealing with drawing from a deck of playing cards and creating a writeup containing your findings. Be sure to check through the project rubric to self-assess and share with others who will give you feedback. Questions for Investigation This experiment will require the use of a standard deck of playing cards. This is a deck of fifty-two cards ... Read More

(This article was first published on DataScience+, and kindly contributed to R-bloggers) This short post is exactly what it seems: a showcase of all ggplot2 themes available within the ggplot2 package. I was doing such a list for myself (you know that feeling …”how would it look like with this theme? let’s try this one…”) and at the end I thought it could have be useful for my readers. At least this post will save you the time of trying all different themes just to have a sense of how they look like. Enjoy! theme_bw() theme_classic() theme_dark() theme_get() theme_gray() theme_grey() ... Read More

(This article was first published on Thinking inside the box , and kindly contributed to R-bloggers) A release of RQuantLib is now on CRAN and in Debian. It contains a lot of new code contributed by Terry Leitch over a number of pull requests. See below for full details but the changes focus on Fixed Income and Fixed Income Derivatives, and cover swap, discount curves, swaptions and more. In the blog post for the previous release 0.4.2, we noted that a volunteer was needed for a new Windows library build of QuantLib for Windows to replace the outdated version 1.6 ... Read More

(This article was first published on Dean Attali's R Blog, and kindly contributed to R-bloggers) Analyzing my (and others’) git activity – Click here to explore the data yourself Yesterday I saw this retweet from @timelyportfolio that links to a gist by @gka. The gist gave a few short scripts that can show you when your git commits take place during the day. I thought that was cool, so I took it a step (or five) further by writing it all up in R code and made nice wrapper functions for it and added interactive visualizations and made a shiny ... Read More

(This article was first published on R – Decision Science News, and kindly contributed to R-bloggers) FAST AND FRUGAL TREES R PACKAGE CREATES QUICK, ACCURATE DECISION TREES Fast and frugal trees allow you to make rapid decisions based on a few pieces of information. You can easily carry them out in your head. Surprisingly, the accuracy of these decisions rivals those made by gold-standard methods like logistic regression, especially when predicting out of sample. Intrigued? Check out this post by Nathaniel Phillips and the new R Package he’s created to create, visualize and test fast and frual trees. For all ... Read More

(This article was first published on En El Margen - R-English, and kindly contributed to R-bloggers) The banxicoR package is now available on CRAN. Much like inegiR this package aims to bring official Mexican data easily into R, in this case by scrapping iqy calls to the SIE (Sistema de Información Económica) webservice of the Bank of Mexico. Introduction The major difference with inegiR is that the Bank of Mexico does not have an API, so this package basically uses rvest to scrape the generated html. The package then does what it can to save it in a convenient data.frame ... Read More

(This article was first published on RStudio Blog, and kindly contributed to R-bloggers) I’m pleased to announce tidyr 0.6.0. tidyr makes it easy to “tidy” your data, storing it in a consistent form so that it’s easy to manipulate, visualise and model. Tidy data has a simple convention: put variables in the columns and observations in the rows. You can learn more about it in the tidy data vignette. Install it with: install.packages("tidyr") I mostly released this version to bundle up a number of small tweaks needed for R for Data Science. But there’s one nice new feature, contributed by ... Read More

(This article was first published on R – Win-Vector Blog, and kindly contributed to R-bloggers) When we teach parallel programming in R we start with the basic use of parallel (please see here for example). This is, in our opinion, a necessary step before getting into clever notation and wrapping such as doParallel and foreach. Only then do the students have a sufficiently explicit interface to frame important questions about the semantics of parallel computing. Beginners really need a solid mental model of what services are really being provided by their tools and to test edge cases early. One question ... Read More

(This article was first published on R-exercises, and kindly contributed to R-bloggers) As in any programming language, handling date and time variables can be quite frustrating, since, for example, there is no one single format for dates, there are different time zones and there are issues such as daylight saving time. Base R provides several packages for handling date and time variables, but they require mastering cumbersome syntax. In order to solve all those issues and more, R package “lubridate” was created. This package on one hand has a very easy and intuitive syntax and on the other hand has ... Read More

(This article was first published on The Lab-R-torian, and kindly contributed to R-bloggers) Background Back in 2011 I was not aware of any tool in R for Passing Bablok (PB) regression, a form of robust regression described in a series of three papers in Clinical Chemistry and Laboratory Medicine (then J Clin Chem and Biochem) available here, here and here. For reasons that are not entirely clear to me, this regression methodology is favoured by clinical chemists but seems largely ignored by other disciplines. However since reviewers clinical chemistry journals will demand the use of PB regression, it seemed expeditious ... Read More