R

[Disclaimer] The package presented in this blog post is stillexperimental at the time of writing these lines (2021-02-28), so itmight face some API changes in the future.Yesterday Jacqueline’s tweeted about her need to add POST requests toa {shiny} application.I'm astounded that it doesn't seem like a Shiny app is able to acceptPOST requests. I have data I wanna pass to my server :/— Dr. Jacqueline Nolis (@skyetetra)February27, 2021This is actually one of the things I had in mind when building{brochure}: more flexibility with the HTTP requests and how they arehandled.So here is a working ex...

When using shiny in production, often you will want to have some sort of database interactions for storing and accessing data. The DBI package provides an easy way to do the database interactions with a variety of SQL database flavors. In this example, I’m going to use a SQLite in memory database for reproducibility. In practice, you will just switch to the code to use a persistent database. Let’s start by creating a table to right to.

In this guide you’ll find both original content and curated resources to: + Build R shiny apps from scratch + Select the optimal dashboard framework for your R shiny app dashboard + R packages to build interactive, professional quality data visualization + Improve the user experience and dashboard speed tricks + All the ways you can deploy and share R shiny app dashboards + Lessons from a dashboard veteran for better R shiny app dashboards

"R in Production " is a blog that talks about Security, Automation, Scalability in the R environment. Furthermore, Software Engineering teaches us proven methodologies to bring a prototype into production and to facilitate the work process in all its aspects. (Click on the title to read more ...)

Véritables outils de visualisation des données, les tables interactives permettent de trier, colorer, éditer, rechercher, paginer les données

In this guide you’ll find both original content and curated resources to: + Build R shiny apps from scratch + Select the optimal dashboard framework for your R shiny app dashboard + R packages to build interactive, professional quality data visualization + Improve the user experience and dashboard speed tricks + All the ways you can deploy and share R shiny app dashboards + Lessons from a dashboard veteran for better R shiny app dashboards

This is a tutorial post about building a webapp with user form and leaflet map visualization using R Shiny. The webapp I built aims to collect crowdsourced data of how sustainable are the eateries …

validate your data and create nice reports straight from R - Appsilon/data.validator

Support for simple features, a standardized way to encode spatial vector data. Binds to GDAL for reading and writing data, to GEOS for geometrical operations, and to PROJ for projection conversions and datum transformations. Optionally uses the s2 package for spherical geometry operations on geographic coordinates.

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

Shiny's 1.6 has a new function, bindCache(), which makes it easy to dramatically speed up reactive expressions and output rendering functions.

Together with Clara Bicalho (UC Berkeley) and Sisi Huang (WZB), I recently developed a web application that acts as a convenient interface to the DeclareDesign R package and its repository of resea…

When you write code, you’re sure to run into problems from time to time. Here are some advanced tips and tricks for handling these errors, explained accessibly.

In this article we look at how to build a shiny app with clear code, reusable and automatically tested modules. For that, we first go into the package structure and testing a shiny app before we focus on the actual modules.

In this post we’d like to introduce you to our new R package shinyMatrix. It provides you with an editable matrix input field for shiny apps.

Pedro Coutinho Silva is a software engineer at Appsilon Data Science. It is not always possible to create a dashboard that fully meets your expectations or requirements using only existing libraries. Maybe you want a specific function that needs to be custom built, or maybe you want to add your own style or company branding. Whatever the case, a moment might come when you need to expand and organize your code base, and dive into creating a custom solution for your project; but where to start?

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In this article we present our R package rsync, which serves as an interface between R and the popular Linux command line tool rsync. Rsync allows users of Unix systems to synchronize local and remote files between two locations.

Introduction Starting the viewer Sorting Filtering Searching Advanced topics Auto-refreshing Labels Restrictions and Performance Saving filters Introduction RStudio includes a data viewer th...

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If you are interested in learning Data Science with R, but not interested in spending money on books, you are definitely in a very good space. There are a number of fantastic R/Data Science books and resources available online for free from top most creators and scientists. Here are such 13 free 20 free (so […]

Customizing Package Build Options Overview There are three R package build commands used by the RStudio package development tools: R CMD check R CMD build R CMD INSTALL It's possible to c...