R

In my table I set the width of the first cell in a column to be 100px. However, when the text in one of the cell in this column is too long, the width of the column becomes more than 100px. How cou...

I need to set the column width of a DataTabe in an R Shiny app. I went through the Data Table Options in the documentation. Also, I reviewed this and this questions in Stackoverflow. Setting width

A template for deploying R Shiny apps with ShinyProxy in Docker Swarm mode - presstofan/shinyproxy-docker-swarm-demo

Build And Highlight Package Documentation With Customized Templates - ThinkR-open/chameleon

A package to help deal with devtools::check outputs - ThinkR-open/checkhelper

Repository with DockerFiles for tutorials. Contribute to ThinkR-open/docker4dev development by creating an account on GitHub.

Repository with DockerFiles for tutorials. Contribute to ThinkR-open/docker4dev development by creating an account on GitHub.

Engineering Production-Grade Shiny Apps — To be published in the R Series in 2020. - ThinkR-open/engineering-shiny-book

A Collaborative list of things to know before submitting to CRAN - ThinkR-open/prepare-for-cran

devindocker

Tools to deal with dependencies in scripts, Rmd and packages - ThinkR-open/attachment

R Engineering, training, and consulting. ThinkR has 87 repositories available. Follow their code on GitHub.

R Markdown lets you add dynamic parameters to your report using the params YAML field (see the R Markdown book for examples); parameterized reports are also used in RStudio Connect. The values of these variables can be called inside your R Markdown document using params$field_name. There are several ways to change the values of the parameters: manually change the YAML, use the params argument in rmarkdown::render(), or knit with parameters, which launches a Shiny app to select values for each. yml_params() accepts any number of named R objects to set as YAML fields. You can also pass argume...

Package your RShiny app into a docker container for local deployment. No rshiny-server or special rshiny.conf configurations necessary!

Authoring Books and Technical Documents with R Markdown - rstudio/bookdown

This document is full of supplemental resources and content from the Shiny in Production Workshop delievered at rstudio::conf 2019.

A Chrome extension for recording browser interaction and generating Puppeteer & Playwright scripts

Making the web more beautiful, fast, and open through great typography

R Shiny app for iAtlas (new, 2020 version). Contribute to CRI-iAtlas/iatlas-app development by creating an account on GitHub.

Data Scientist. Shiny Developer. Tidyverse Denizen. Scheme Enthusiast.

Docking or containerization is a new method of distribute a software/tool. Beside providing only the source code for installing, we give the users the so-called container, which contains the whole environment to run the program, including the tool and its dependencies with the exact version and all the needed configurations. By delivering such a “container”, users are always able to “reuse” the tool and reproduce the results as we did.

Write an awesome description for your new site here. You can edit this line in _config.yml. It will appear in your document head meta (for Google search results) and in your feed.xml site description.

The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.

Easily Extracting Information About Your Data. Contribute to cosimameyer/overviewR development by creating an account on GitHub.

Makes it easy to display descriptive information on a data set. Getting an easy overview of a data set by displaying and visualizing sample information in different tables (e.g., time and scope conditions). The package also provides publishable LaTeX code to present the sample information.

Blog of the MZES Social Science Data Lab

This post provides a guide to effectively and securely scale your ShinyProxy deployment of shiny apps in production with Docker Swarm and Traefik

A workflow for refactoring one-time analyses to sustainable data products

A walkthrough of using the projmgr package for GitHub-based project management via R