No Clocks

Summarise text by finding relevant sentences and keywords using the Textrank algorithm - bnosac/textrank

:chart_with_upwards_trend: Financial statements in R - bergant/finstr

Data model diagrams in R. Contribute to bergant/datamodelr development by creating an account on GitHub.

Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, GitHub, licenses, Rcpp, RStudio projects, and more.

validate your data and create nice reports straight from R - Appsilon/data.validator

:bar_chart: R package for tracking performance metrics across git versions and branches. - analyticalmonk/Rperform

This contains materials for the Advanced R course of the doctoral school of Grenoble, France.

How to load geospatial data into your workspace and prepare it for visualization.

Distributed programming in R is normally taken up to speed up a process or piece of code or scale up an interface or application for multiple users.

It all starts with sandboxes. Development sandboxes are dedicated safe spaces for experimentation and creativity. A sandbox is a place where you can go to test and break things, without the ramifications of breaking the real, important things. If you’re an analytic administrator who doesn’t have access or means to get a sandbox, I recommend that you consider advocating to change that. Here are just some of the arguments for why sandboxes are a powerful tool for the R admin that you may find helpful.

In this post, I talk about parallelism in R. This post is likely biased towards the solutions I use. For example, I never use mcapply nor clusterApply; I prefer to always use foreach. In this post, we will focus on how to parallelize R code on your computer with package {foreach}. In this post, I use mainly silly examples just to show one point at a time. Basics of foreach You can install R package {foreach} with install.packages("foreach"). library(foreach)

"R in Production " is a blog that talks about Security, Automation, Scalability in the R environment. Furthermore, Software Engineering teaches us proven methodologies to bring a prototype into production and to facilitate the work process in all its aspects. (Click on the title to read more ...)

This is a guest post by Chris Paciorek, Department of Statistics, University of California at Berkeley. In this post, I’ll demonstrate that you can easily use the future package in R on a cluster of machines running in the cloud, specifically on a Kubernetes cluster. This allows you to easily doing parallel computing in R in the cloud. One advantage of doing this in the cloud is the ability to easily scale the number and type of (virtual) machines across which you run your parallel computation.

plumber

An opinionated example for implementing logging in Plumber APIs - sol-eng/plumber-logging

An opinionated example for implementing logging in Plumber APIs - sol-eng/plumber-logging

plumber