Introduction This article draws on the past post concerned with utilisation of golem for robust deployment of analytical and reporting solutions. For this article, we will assume that we are working with defined working requirements that utilise some of the Labour Market Statistics disseminated through the nomis portal. Change Plan What we have Reporting requirements Past scripts we used to create reports with accompanying instructions What we want Stronger business continuity - we want to be able to give some access to this project and don’t be concerned with missing files, outdated unavailable documentation and questions on how to produce updated reports....

Not so uncommon problem… RStudio Connect and more modest Shiny Proxy come to mind as most obvious solutions for deploying Shiny applications in production. Application servers are ideal for deploying applications that are to be consumed on a regular basis by larger audiences. In addition to serving the application, managing dependencies and user access or logging user activity are common tasks we would expect for a publishing platform to address. Frequently, however, deployment of Shiny application is directed at smaller audiences and less frequent usage....

Is there a merit for a three-way operator in R? Background In C++20 revision added “spaceship operator”, which is defined as follows: 1 2 3 (a <=> b) < 0 # if lhs < rhs (a <=> b) > 0 # if lhs > rhs (a <=> b) == 0 # if lhs and rhs are equal/equivalent. R implementation The behaviour can be achieved in R in multiple ways. A one straightforward approach would involve making use of the ifelse statement...

TL;DR If you want to see the implemented solution, please refer to: GitHub repo. Context Shiny is a widely popular web application framework for a R. In simple tearms it enables any R programmer to develop and deploy web application. This application could be simple - an interactive document consiting of a few charts and tables or a c complex “behemoth” with multiple functionalities enabling end-users to run models, query external data, generate exportable reports and sophisticated visuals....

In Stata it is possible to use function plot in order to get a simple scatter plot in Stata console. As of Stata eight, plot is no longer supported but remains a useful tool for quickly exploring relationships between variables. Using plot on the auto data provides the following results: Now the question is: can we achieve the same level of convenience in R? Of course. The txtplot package authored by Bjoern Bornkamp provides similar functionality....