Category: R

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: Bluecology blog. [This article was first published on Bluecology 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.When we perform a statistical test we’d like to have confidence what theType I error rate is what we say it is (rate of false positivefindings). Often scientists choose a rate (AKA ‘alpha’) of 0.05 forrejecting the null hypothesis.We’d like some guarantees that our statistical test will actually have atype I rate of 5% ... 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: Data Enthusiast's Blog. [This article was first published on Data Enthusiast's 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.IntroductionThere are often situations when you need to perform repetitive plotting tasks. For example, you’d like to plot the same kind of data (e.g. the same economic indicator) for several states, provinces, or cities. Here are some ways you can address this:But what if the data is too complex to fit into ... 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: Thinking inside the box. A smashing new RcppSimdJson release 0.1.0 containing several small updates to upstream simdjson (now at 0.4.6) in part triggered by very excisting work by Brendan who added actual parser from file and string—and together with Daniel upstream worked really hard to make Windows builds as well as complete upstream tests on our beloved (ahem) MinGW platform possible. So this version will, once the builders have caught up, give everybody on Windows a binary—with a JSON parser running circles around the (arguably more feature-rich and possibly easier-to-use) alternatives. Dave just tweeted a benchmark snippet ... Read More

Feed: R-bloggers. Author: Thinking inside the box. An updated version of the AsioHeaders package arrived on CRAN today (after a we days of “rest” in the incoming directory of CRAN). Asio provides a cross-platform C++ library for network and low-level I/O programming. It is also included in Boost – but requires linking when used as part of Boost. This standalone version of Asio is a header-only C++ library which can be used without linking (just like our BH package with parts of Boost).This release brings a new upstream version. Its changes required a corresponding change in one of (only) three ... Read More