How-To

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. In such a situation, are availability, accessibility and user access management requirements will be often more modest. Commonly,in business a modelling or analytical solution can be packaged in Shiny application facilitating periodical re-run of models with different parameters and updated data sets. Such solutions can be conveniently utilised to facilitated development of monthly or quarterly reports. If the app is used once per month/quarter by a narrow user group the need to deploy it on the server is not well articulated. In that particular case we are mostly interested in ensuring that we can: ...

Background The post covers installation of Hortonworks Sandbox (HD) on Mac using Docker. In software development, sandbox describes a testing environment that can be used to isolate untested code changes from a production code. Hortonworks Sandbox provides such an environment with the Hortonworks Data Platform installed. Hortonworks Data Platform is an open source framework facilitating distributed storage and processing large volumes of data. Deploying system for distributed processing within a single computer may seem like a counter-intuitive idea but it’s actually a very common practice. Most frequent use cases involve various learning / professional development activities where one may be interested in learning new technology or simply exploring available interfaces. Other frequent use case pertains to various demos, where there may be a need to demonstrate product capabilities and accessing proper, production environment could be cumbersome. ...

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. Executing the code below will generate nice text plot straight in the R console: ...

After developing the Shiny App sourcing live labour market data from NOMIS. I wanted to accommodate a convenient way of managing rows in the legend. In particular, I wanted to account for the situation where end-user may select a number of geographies that will only conveniently fit into two or more rows. After transposing the data to long format, guessing the number of elements in the legend is relatively simple as it will correspond to the number of unique geographies passed via the subset command. ...

In an unlikely scenario that beautiful Shiny apps do not meet your analytical requirements and developing a full-blown user interface. in RGtk2 may seem to be a little too much, there is a third, often overlooked solution, - package svDialogs by Philippe Grosjean. The package in a convenient way enables user to create various interface gadgets. For example the code: 1 2 3 4 require(svDialogs) # Let's keep some data in one place user_figure <- svDialogs::dlg_input() would result in the following window being presented to the user: ...