R

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.

Use siuba library in Python to preserve all your skills from dplyr package in R pip install siuba You can see TidyTuesday for tutorial Or Learn Siuba

Connect from R to Wharton Research Data Services to set up connection from R to WRDS (here) library(RPostgres) library(tidyverse) # I've set up wrds connection before hand. # Please use your username and password here.

Flipbooks can be used for: Education Reference Communication Transparency library(flipbookr) cars %>% filter(speed > 4) %>% ggplot() + aes(x = speed) + #BREAK aes(y = dist) + #BREAK geom_point( alpha = .

Use jaysire package to build flexible behavioral experiments in R. Why? Better workflow Free Cutomizable remotes::install_github("djnavarro/jaysire")

Check where your package is installed find.package("dplyr") ## [1] "C:/Program Files/R/R-4.0.3/library/dplyr" All projects use the same library path. Then for each project, you need different library dependencies. Intializing a project renv::init() A .