Joshua Rosenberg jrosen48

The Generality Widget

The goal of this document is to describe how and why we created The Generality Widget and how we made it, focusing on:

	from facebook_scraper import get_posts
	import pandas as pd
	import time
	import json

	# param
	n_pages_to_iterate = 100 # number of pages to scrape within one FB page

	# reading data with page names
	district_data = pd.read_csv("2020-2021-critical-race-posts-schools-districts.csv")

	from facebook_scraper import *
	import pandas as pd

	set_user_agent("Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)")

	page = pd.DataFrame()

	for post in get_posts('245937932251091/posts/2129192957258903', pages=10 ,options={"comments": True}, credentials = ("[email protected]","025021")):
	page = page.append(post, ignore_index = True)

	library(tidyverse)
	library(googlesheets4)

	# this is an example Google Sheet
	d <- read_sheet("https://docs.google.com/spreadsheets/d/1kz2LlLgXkN_HaBEAiFETl59b09u9AwMIgrwQx6DCv8A/edit#gid=0", col_names = FALSE)

	prep_otter_transcript <- function(d, length_less_than_100_min) {

	d <- d %>% rename(x1 = 1)

	# apply for access to Twitter's academic program: https://developer.twitter.com/en/solutions/academic-research

	library(academictwitteR)
	library(rtweet)
	library(tidytags) # must be installed first with: remotes::install_github("ropensci/tidytags")

	bearer_token <- "xxx" # Insert bearer token from developer.twitter.com from the your academic account

	trial <- get_all_tweets("#BAmazonUnion OR #UnionizeAmazon", "2020-01-01T00:00:00Z", "2021-05-01T00:00:00Z", bearer_token, data_path = NULL)

	library(tidyverse)
	library(tidytuesdayR)
	library(janitor)

	tt_output <- tt_load_gh(last_tuesday())
	list_of_d <- tt_download(tt_output)

	d <- list_of_d$animal_complaints

	d %>%

	library(tidyverse)
	library(readxl)
	d <- read_excel("Downloads/Public-Dataset-Age.xlsx")
	d %>%
	mutate(month = lubridate::month(DATE, label = TRUE)) %>%
	group_by(month, AGE_RANGE) %>%
	summarize(total_cases = sum(NEW_ARCASES)) %>%
	filter(AGE_RANGE != "Pending") %>%
	mutate(total_cases_prop = total_cases / sum(total_cases)) %>%
	ggplot(aes(y = total_cases_prop, x = AGE_RANGE, fill = as.factor(month))) +

	library(shiny)

	ui <- fluidPage(

	titlePanel("How Many Reviewers?"),

	sidebarLayout(
	sidebarPanel(
	numericInput("n_papers", "Number of Anticipated Papers", 100),
	numericInput("n_reviews", "Number of Reviews Per Paper", 3),

	library(shiny)
	library(tidyverse)

	d <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv")

	states <- d %>%
	pull(state) %>%
	unique() %>%
	sort()

	#
	# This is a Shiny web application. You can run the application by clicking
	# the 'Run App' button above.
	#
	# Find out more about building applications with Shiny here:
	#
	# http://shiny.rstudio.com/
	#

	library(shiny)