From f34fe389809cca472b97c012cbf6553107b74f27 Mon Sep 17 00:00:00 2001 From: Colin Rundel Date: Mon, 5 Feb 2024 12:07:00 -0500 Subject: [PATCH 1/2] Update shiny_r_intro.qmd --- workshops/shiny_r_intro.qmd | 27 +++++++++++---------------- 1 file changed, 11 insertions(+), 16 deletions(-) diff --git a/workshops/shiny_r_intro.qmd b/workshops/shiny_r_intro.qmd index 3398f59..8011a5c 100644 --- a/workshops/shiny_r_intro.qmd +++ b/workshops/shiny_r_intro.qmd @@ -1,35 +1,30 @@ --- -title: Shiny R intro (Details TBD) +title: Shiny R intro author: - - name: Instructor 1 name + - name: Colin Rundel affiliations: - - name: Instructor 1 affiliation - - name: Instructor 2 name (remove if single instructor) - affiliations: - - name: Instructor 2 affiliation + - name: Duke University description: | - 1-sentence summary of workshop. -categories: [add, comma, separated, categories] + Introduction to builing interactive web apps using Shiny and R +categories: [Shiny, R, reactivity, web apps] --- # Description -Full workshop description goes here. Multi-paragraph ok. +Shiny is an R package that makes it easy to build interactive web apps straight from R. This workshop will start at the beginning: designing and creating user interfaces, learning and mastering the reactive model that connects your R code to the interface, and deploying apps publicly and privately. We will wrap up with some intermediate-level tools: debugging and modularizing your apps and implementing dynamic user interfaces. In the end, you’ll be a confident Shiny user, able to design interactive apps to achieve your purpose and produce a polished and professional implementation. # Audience -This course is for you if you: - -- list at least +* are comfortable with the basics of R, such as writing functions, indexing vectors and lists, debugging simple errors, and working with data structures like data frames, -- three attributes +* are interested in creating interactive web applications, and -- for your target audience +* have no or minimal experience with Shiny for R. If you have a bit of experience, you’ll see things in a new way. If you don’t, we’ll get you started on the right footing. # Instructor(s) | | | | -|------------------|------------------|------------------------------------| -| ![](images/name-lastname.jpg) | | Instructor bio, including link to homepage. | +|-------------------------------|-----|---------------------------------------------| +| ![](images/colin-rundel.jpg) | | [Colin Rundel](https://rundel.github.io/) is Associate Professor of the Practice at Duke University in the department of Statistical Science where he has been teaching since 2012. His work focuses on teaching statistical computing to both undergraduate and graduate students in both R and Python. 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