Tag: packages

Feed: R-bloggers. Author: Daniel Petzold. [This article was first published on RStudio Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Photo by Daniel Jerez on UnsplashData science can feel like an endless tunnel, with tremendous investment at the beginning and little visibility into where or when your results will emerge. Data science teams wrestle with many challenges, such as rapid iteration of new ideas, business alignment, productivity, transparency, and delivering durable value.As we’ve discussed ... Read More

Feed: R-bloggers. Author: Artful Analytics. [This article was first published on Artful Analytics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.How I deal with wide datasets when building a predictive modelIntroductionIn this post, I will describe a preprocessing workflow that I usewhenever I have a lot variables (wide data) and need to build apredictive model quickly.The workflow has three stages:Univariate feature selection using the{Information}packageFeature engineering using the{vtreat}packageRemoval of redundant features using the{caret}packageThe overall goal ... Read More

Feed: R-bloggers. Author: Casey Bates. In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!We’ll show you how to convert the default R Markdown document into a useful reference guide of your own. We encourage you to follow along by building out your own R Markdown guide, but if you prefer to just read along, that works, too!R Markdown is an open-source tool for producing reproducible reports in R. It enables you to keep all of your code, ... Read More

Feed: R-bloggers. Author: R-bloggers on Guy Abel. [This article was first published on r-bloggers on Guy Abel, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. IntroductionThe tidycat package includes the tidy_categorical() function to expand broom::tidy() outputs for categorical parameter estimates.Hello WorldThe tidy() function in the broom package takes the messy output of built-in functions in R, such as lm(), and turns them into tidy data frames.library(dplyr) library(broom) m1 % mutate(transmission = recode_factor(am, `0` = ... Read More

Feed: R-bloggers. Author: Philipp Probst. [This article was first published on Philipp Probst, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Several R packages that use different methods are out there for using gradient boosting methods.The three most famous ones are currently xgboost, catboost and lightgbm.I want to compare these three to find out which is the best one in their default mode without tuning.These algorithms are not pure gradient boosting algorithms but combine it ... Read More

Feed: R-bloggers. Author: R Views. [This article was first published on R Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Daniel Hanson is a full-time lecturer in the Computational Finance & Risk Management program within the Department of Applied Mathematics at the University of Washington. His appointment followed over 25 years of experience in private sector quantitative development in finance and data science.One of the most time-consuming, tedious, and thankless tasks a quantitative developer ... Read More

Feed: R-bloggers. Author: R Consortium. [This article was first published on R Consortium, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.This useR!2020 session and video was organized by Laura Acion, Yanina Bellini Saibene, Paola Corrales, and Paloma Rojas Saunero. Leonardo Collado Torres coordinated the blog post submission.On June 19th, 2020, we filmed a video for useR!2020 showcasing the communities and organizations we are involved in that are for Latin Americans or have Latin American participants ... Read More

Feed: R-bloggers. Author: Topics in R. [This article was first published on Topics in R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Overview R’s built-in copy-on-modify behavior prevents the user from having two symbols always pointing to the same object. Because pointers, as they are common in other programming languages, are essentially symbols (variable) related to an object that has already another symbol attached to it, it is clear that pointers do not ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. We have been working hard behind the scenes on the upcoming release of our new git package named gert, a joint effort from rOpenSci and the Tidyverse team. One of the main features of gert is the out-of-the-box authentication mechanism, which is provided via the new credentials package.Among other things, the credentials package makes it possible to save and load https authentication details from the git credential store, which is part of the official command-line git. Thereby credentials are automatically shared between command line git and the gert package, while ... Read More

Feed: R-bloggers. Author: Quantargo Blog. [This article was first published on Quantargo Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.All data transformation functions in dplyr can be connected through the pipe %>% operator to create powerful and yet expressive data transformation pipelines.Use the pipe operator %>% to combine multiple dplyr functions into one pipeline %>% filter(___) %>% select(___) %>% arrange(___)Using the %>% operatorThe pipe operator %>% is a special part of the tidyverse ... Read More