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"
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.
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.
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 .
Comprehensive patent data can be found here
United States
NBER patent data or link
Search link for individual patent: link
Patent API
USPTO - United States patent and Trademark Office
Patent ranking by orgs
Bulk Data Storage System: repository for raw public bulk data
For Researcher
Patent Assignment Dataset details information of patent assignment since 1970 with schema and description and code
Pre-Grant Publications Data Download Tables with example code note that organizaiton here is different from Compustat and CRSP, hard to match.