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Software Carpentry
Software Carpentry
Teaching researchers the foundational computing skills they need to get more done in less time
·software-carpentry.org·
Software Carpentry
Yixuam Qiu
Yixuam Qiu
Yixuan's blogs on statistcs, programming, and more
·statr.me·
Yixuam Qiu
Dean Attali - R-Shiny consultant
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.
·deanattali.com·
Dean Attali - R-Shiny consultant
Performance: when algorithmics meets mathematics – Florian Privé – R(cpp) enthusiast
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...
·privefl.github.io·
Performance: when algorithmics meets mathematics – Florian Privé – R(cpp) enthusiast
Vectorization in R: Why?
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).
·noamross.net·
Vectorization in R: Why?
Profiling with RStudio – RStudio Support
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...
·support.rstudio.com·
Profiling with RStudio – RStudio Support
A guide to parallelism in R – Florian Privé – R(cpp) enthusiast
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)
·privefl.github.io·
A guide to parallelism in R – Florian Privé – R(cpp) enthusiast
Advanced R Course
Advanced R Course
This contains materials for the Advanced R course of the doctoral school of Grenoble, France.
·privefl.github.io·
Advanced R Course
R & R
R & R
Software Engineer
·randr.rocks·
R & R
Organize your data and code
Organize your data and code
Brief discussion of file/directory organization, perhaps the most important step to take towards ease of reproducibility.
·kbroman.org·
Organize your data and code
Gantt charts with R - Stack Overflow
Gantt charts with R - Stack Overflow
Has anybody used R to create a Gantt chart? P.S. I could live without the dependency arrows.
·stackoverflow.com·
Gantt charts with R - Stack Overflow
Best Practice: Development of Robust Shiny Dashboards as R Packages
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.
·inwt-statistics.com·
Best Practice: Development of Robust Shiny Dashboards as R Packages
R Shiny for Enterprise - Applison
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.
·appsilon.com·
R Shiny for Enterprise - Applison
Shiny Architecture (1/5) - Applison
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
·appsilon.com·
Shiny Architecture (1/5) - Applison
Prototype to Production - Applison
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.
·appsilon.com·
Prototype to Production - Applison
R Shiny Google Group
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.
·groups.google.com·
R Shiny Google Group
Becoming Minimalist
Becoming Minimalist
Becoming Minimalist inspires others to journey towards simple living. Own less, live more.
·becomingminimalist.com·
Becoming Minimalist
Font Awesome
Font Awesome
The world’s most popular and easiest to use icon set just got an upgrade. More icons. More styles. More Options.
·fontawesome.com·
Font Awesome
Colin Fay
Colin Fay
Mostly R, data, and code.
·colinfay.me·
Colin Fay
Tidyverse Blog
Tidyverse Blog
The tidyverse is an integrated collection of R packages designed to make data science fast, fluid, and fun.
·tidyverse.org·
Tidyverse Blog