The following post details how to make a churn model in R. It was part of an interview process for which a take home assignment was one of the stages. The company stated this should take 2hrs, which is entirely unrealistic. To minimise the time cost, my analysis is very ... [Read more...]

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Aim: Setup shinyproxy as a production level way of deploying multiple containerised shiny apps with authentication. Additionally I'll demonstrate how to incorporate containerised python web apps and deploy this behind a reverse proxy using Nginx. Conceptual Overview This is a pretty significant piece of software. It renders Shiny Server Pro ... [Read more...]

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What's in a name? That which we call a rose by any other name would smell as sweet. Normal functions Before moving to anonymous functions, let's start with what normal functions look like. In R # Define a function functionName [Read more...]

Contrasting anonymous functions in R and Python. An overview of how to use them and conventions of how anonymous functions are used in the R and Python communities. [Read more...]

Introduced at the 2016 R conference, the profvis package offers a visual way of inspecting the call stack and highlights the most memory and computationally intensive parts of your code. Run Profvis # Load library library(profvis) # Run profiler on ... [Read more...]

Introduced at the 2016 R conference, the profvis package offers a visual way of inspecting the call stack and highlights the most memory and computationally intensive parts of your code. Run Profvis # Load library library(profvis) # Run profiler on shiny app with optional arg to save output profvis({ runApp('Projects/ [Read more...]

To assess the performance of R code there's a great little package called microbenchmark. install.packages('microbenchmark') library(microbenchmark) I was particularly interested in the performance increase of a shiny application that reads in ... [Read more...]