R

Create Google Analytics account Choose “Analytics Accounts” Create “Properties & Apps” In “Data Streams”, “Add stream” with your website URL Under “Tagging Instructions”, click on gtag.js. You should see something like this: <!

New Template to get you produce cool Shiny App instantly flexdashboard install.packages("flexdashboard") library(flexdashboard)

CSS = Cascading Style Sheets Selector Declaration To add CSS to Shiny: Add styling to tags Add CSS to HTML header Add style sheets with the www directory (most ideal) SASS = Systematically Awesome Style Sheets (built on top of CSS)

install once per computer install.packages("reprex") or part of `tidyverse install.packages("tidyverse") You can either call it in every session library(reprex) or put it in .Rprofile for each machine to run whenever you open R

To solve the problem of scaling: From DevOps/IT: Add memory, CPU Rstudio Connect set up for multiple machines From R/Shiny engineer: use Javascript for less CPU usage extract computations: Shiny worker, Plumber use a database

You can read more in Michael Barrowman’s post But the basic idea is that using the new pipe |> from the magrittr library is much faster than your old pipe %>%

We can leverage R to create randomized studies using shinysurveys with learnr library("shinysurveys") library("learnr") ou can also use formr to create survey with R.

More package author’s introduction, please access this link Instead of loading everything at once into your RAM, you divide your data into chunks. To quote author of the disk.frame package: “we go from”R can only deal with data that fits in RAM"

To have faster performance with Shiny App, you can pass this command to yoru script. # %>% bindCache()

Teaching statistics or data science, we can use learnr package. # library("learnr") To collect data, we can use learnrhash # library("learnrhash") Remember to adjust parameters so your Shinyapp.io can handle the number of students you have in the class.