This is a POSIXct method for the date_format() generic.
date_format() formats a date-time (POSIXct) using a format string.
If format is NULL, a default format of "%Y-%m-%dT%H:%M:%S%Ez[%Z]" is
used. This matches the default format that date_time_parse_complete()
parses. Additionally, this format matches the de-facto standard extension to
RFC 3339 for creating completely unambiguous date-times.
{shinytesters}: Updating Inputs in testServer | Ashley's Blog
I have been working on a project where we have been unable to use {shinytest2}, and therefore have had a heavy reliance on the testServer function on testing the reactive functionality in each of the modules. Whilst it is great for checking the server-side logic based on dummy inputs being...
Build your own universe of packages similar to the 'tidyverse'
package with this meta-package creator. Create a
package-verse, or meta package, by supplying a custom name for the
collection of packages and the vector of desired package names to include–
and optionally supply a destination directory, an indicator of whether to
keep the created package directory, and/or a vector of verbs implement via
the 'usethis' package.
quarto R package v1.5.0: Streamlined Workflows for R Users – Quarto
The quarto R package 1.5.0 brings powerful new features for passing R values to Quarto metadata, inserting Markdown in HTML tables, working with R scripts, building paths from Quarto projects, and automating Quarto CLI from R.
posit::conf | R & Python Data Science Conference - Posit
Connect with a vibrant global community of R and Python data scientists at Posit's annual conference. Learn, share, and grow your expertise in data science.
Introducing fodr: a package for French open data in R
Nowadays, more and more government organisations subscribe to the open data movement and some have done so in France, in the hopes that new services or insights would come from the analysis of this data.
shinymgr: A Framework for Building, Managing, and Stitching Shiny Modules into Reproducible Workflows
The R package shinymgr provides a unifying framework that allows Shiny developers to create, manage, and deploy a master Shiny application comprised of one or more "apps", where an "app" is a tab-based workflow that guides end-users through a step-by-step analysis. Each tab in a given "app" consists of one or more Shiny modules. The shinymgr app builder allows developers to "stitch" Shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Apps developed using shinymgr can be incorporated into R packages or deployed on a server, where they are accessible to end-users. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an RMarkdown or Quarto report for rapid reporting. In short, developers use the shinymgr framework to write Shiny modules and seamlessly combine them into Shiny apps, and end-users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination. A comprehensive overview of the package is provided by 12 learnr tutorials.
Guest Blog: Reproducible Data Pipelines In R With {targets} - ESIP
Reproducibility is a huge challenge in science, especially as datasets grow larger and workflows become more complex. Enter targets — an R package that helps
A data workflow is the series of steps that turn raw data into something meaningful — think downloading, cleaning, analyzing and visualizing. You might already do this in R with a mix of scripts and notebooks. Some steps in your data workflow may also be manual and require no coding, such as data processing in Excel or uploading model output data to OneDrive.
A data pipeline, on the other hand, is an automated version of that workflow. It ensures that every step happens in order, only the necessary steps are rerun when data changes, and guarantees the results are reproducible every time. A well-structured pipeline ensures that anyone revisiting the analysis — including your future self — can rerun, verify and build on the work without extra effort or missing pieces.
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