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In this post, I talk about performance through an efficient algorithm I developed for finding closest points on a map. This algorithm uses both concepts from mathematics and algorithmics. Problem to solve This problem comes from a recent question on StackOverflow. I have two matrices, one is 200K rows long, the other is 20K. For each row (which is a point) in the first matrix, I am trying to find which row (also a point) in the second matrix is closest to the point in the first matrix. This is the first method that I tried on a sample dataset: # Test dataset: longitude and latitude pixels.l...

Here are my notes from a recent talk I gave on vectorization at a Davis R Users’ Group meeting. Thanks to Vince Buffalo, John Myles White, and Hadley Wickham for their input as I was preparing this. Feedback welcome! Beginning R users are often told to “vectorize” their code. Here, I try to explain why vectorization can be advantageous in R by showing how R works under the hood. Now, remember, premature optimization is the root of all evil (Knuth).

Getting started Using the profiler Using the flame graph Using the data viewer Profiling examples Profiling time example Profiling memory example Frequently Asked Questions Additional Resou...

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)

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

This is a guide on how to conduct Meta-Analyses in R.

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Brief discussion of file/directory organization, perhaps the most important step to take towards ease of reproducibility.

Donald Mellenbruch

As organizations grow and mature, so does the organization’s data. Eventually, the data may become too ‘big’ or too complicated for…

(Or, how to write a Shiny app.R file that only contains a single line of code) This post is long...

Has anybody used R to create a Gantt chart? P.S. I could live without the dependency arrows.

Learn step-by-step how to built a wedding risk model Shiny app.

This article describes best practice approaches for developing shiny dashboards. The creation of the dashboard in package form, as well as the use of unit tests should enable the development of robust solutions and guarantee high quality.

We create, maintain, and develop enterprise R Shiny dashboards. We deliver rapid development, scalability to thousands of users, and sophisticated UIUX.

Learn how to use the session argument as a global list for passing parameters between modules in advanced Shiny apps to simplify the objects’ flow in code., organize app content, simplify objects flow logic, decrease bugs, increase speed

We will start with a basic shiny dashboard that uses no libraries. Then we will enhance it using bootstrap and semantic UI. Then we use custom CSS.

Google Groups allows you to create and participate in online forums and email-based groups with a rich experience for community conversations.

The world’s most popular and easiest to use icon set just got an upgrade. More icons. More styles. More Options.

Mostly R, data, and code.

The tidyverse is an integrated collection of R packages designed to make data science fast, fluid, and fun.

This blog contains information on RStudio products and events, including RStudio open-source and commercial product announcements, company events, and company news.

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The primary goal of this book is to get you up to speed with tidy evaluation and how to write functions around tidyverse pipelines and grammars.