R-bloggers

Are you interested in guest posting? Publish at DataScience+ via your RStudio editor. Category Advanced Modeling Tags Data Visualisation R Programming spatial Ten months after part 1 of spatial regression in R (oh my gosh where did these months go?), here is a (hopefully long-awaited) second part this time using INLA, ...

In our last post, we took a quick look at building a portfolio based on the historical averages method for setting return expectations. Beginning in 1987, we used the first five years of monthly return data to simulate a thousand possible portfolio weights, found the average weights that met our risk-return ...

This is the third in a series of posts (previous posts: Part 1, Part 2) in which I look at the following paper: Kissler, Tedijanto, Goldstein, Grad, and Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, Science, vol. 368, pp. 860-868, 22 May 2020 (released online 14 April 2020).  The paper is also available […]

This article talks about why balancing your data set before you build a model is important. The post Why balancing your data set is important? appeared first on Hi! I am Nagdev.

D20 and Random Events In the game Dungeons and Dragons, the success or failure of an event is determined by rolling a 20 sided die (D20): higher is better. If you need to roll 11 or higher you have a 50% chance of success. If another event requires 10 or better you now have ...

This talk was presented virtually at eRum 2020 and useR 2020. Learn more about Appsilon‘s ML wildlife preservation project here. Yes, R programmers can make machine learning models, too! In this presentation, we will discuss using the latest techniques in computer vision as an important part of “AI for Good” efforts, ...

Photo by Zoltan Tasi on Unsplash In a recent series of blog posts, we introduced the idea of Serious Data Science to help tackle the challenges of effectively implementing data science in an organization. We then focused on the importance of delivering insights that are credible with your stakeholders and ...

English French German The pointblank package is now at version 0.4 and is chock full of goodies. There are new functions that allow us to do really interesting things with data validation. Some of the existing functions gained new superpowers. The docs? Better than ever! They’ve been totally revised and ...

One hundred eighty-four new packages stuck to CRAN in May. The following are my “Top 40” picks in eleven categories: Data, Finance, Genomics, Marketing, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization. Data covid19nytimes v0.1.3: Provides accesses the NY Times Covid-19 county-level data for the US that is ...

R Consortium interview with Dr. Heather Turner, Chair of Forwards, the R Foundation taskforce for underrepresented groups in the R Community In January, R Consortium posted about a crowd-funding campaign... The post R Communities in South Africa appeared first on R Consortium.

Make your R data analysis beautiful and your R programming workflow smoother with these tips, tricks, and shortcuts that'll turn you into an R markdown pro! The post R Markdown Tips, Tricks, and Shortcuts appeared first on Dataquest.

R package gower was accepted on CRAN on 23 june 2020. This release fixes an edge case, affecting cases with a small number of records and a large number of threads. Thanks to Max Austin for kindly reporting the issue. … Continue reading →

Here is a fun post about using colour palettes in R. It starts with a computer game… After a few years of sporadically playing Super Mario World 2 – Yoshi’s Island on the Retropie, I made it to the final level. In the background, as Bowser approached, I noticed that those ...

Recently I wrote about the challenges of trying to learn too much from a small pilot study, even if it is a randomized controlled trial. There are limitations on how much you can learn about a treatment effect given the small sample size and relatively high variability of the estimate. ...

") training_loop(ds_train) } ``` After two hundred epochs, overall loss is at 2.67, with the MSE component at 1.8 and FNN at 0.09. Obtaining the attractor from the test set We use the test set to inspect the latent code: # A tibble: 6,242 x 10 V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 1 0.439 0.401 ...