How to Label Issues — The Carpentries Handbook documentation

02-AREAS
R for Reproducible Scientific Analysis
Fundamentals of Data Visualization
A guide to making visualizations that accurately reflect the data, tell a story, and look professional.
Software Carpentry
Teaching researchers the foundational computing skills they need to get more done in less time
Yixuam Qiu
Yixuan's blogs on statistcs, programming, and more
Dean Attali - R-Shiny consultant
R-Shiny developer and consultant with a MSc in Bioinformatics and a Bachelor of Computer Science. Previously a software engineer at Google, IBM, and Wish.com.
Performance: when algorithmics meets mathematics – Florian Privé – R(cpp) enthusiast
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...
Vectorization in R: Why?
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).
Profiling with RStudio – RStudio Support
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...
A guide to parallelism in R – Florian Privé – R(cpp) enthusiast
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)
Advanced R Course
This contains materials for the Advanced R course of the doctoral school of Grenoble, France.
Doing Meta-Analysis in R
This is a guide on how to conduct Meta-Analyses in R.
R & R
Software Engineer
Organize your data and code
Brief discussion of file/directory organization, perhaps the most important step to take towards ease of reproducibility.
Shiny GEM | Donald Mellenbruch
Donald Mellenbruch
SQL Server Schemas & R Tip
Delivering Data Science for the Enterprise with Shiny (R) in Kubernetes
As organizations grow and mature, so does the organization’s data. Eventually, the data may become too ‘big’ or too complicated for…
Packaging Shiny applications: A deep dive - Mango Solutions
(Or, how to write a Shiny app.R file that only contains a single line of code) This post is long...
Gantt charts with R - Stack Overflow
Has anybody used R to create a Gantt chart? P.S. I could live without the dependency arrows.
Shiny Tutorial - Business Science
Learn step-by-step how to built a wedding risk model Shiny app.
FDA's Approach to R Shiny
Best Practice: Development of Robust Shiny Dashboards as R Packages
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.
R Shiny for Enterprise - Applison
We create, maintain, and develop enterprise R Shiny dashboards. We deliver rapid development, scalability to thousands of users, and sophisticated UIUX.
Shiny Architecture (1/5) - Applison
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
Prototype to Production - Applison
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.
R Shiny Google Group
Google Groups allows you to create and participate in online forums and email-based groups with a rich experience for community conversations.
How to Build a Shiny “Truck”!
Becoming Minimalist
Becoming Minimalist inspires others to journey towards simple living. Own less, live more.
Font Awesome
The world’s most popular and easiest to use icon set just got an upgrade. More icons. More styles. More Options.
Colin Fay
Mostly R, data, and code.