gaurav soodoku

## not_normal.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                soodoku
                / not_normal.ipynb
            
            
              Last active
              July 16, 2023 04:58
            
              
                Not Normal
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## flask_app.py
from flask import Flask, request
import uuid

app = Flask(__name__)

@app.route('/error_endpoint', methods=['POST'])
def receive_error_message():
    error_message = request.form.get('error_message')

    if error_message:

## group_interest.R
# Pareto Party

# Load libs
library(tidyverse)
library(readstata13)
library(car)
library(dplyr)
library(xtable)
library(dplyr)

## forest_lasso.ipynb

      
              3 files
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                soodoku
                / forest_lasso.ipynb
            
            
              Last active
              July 4, 2023 05:45
            
              
                Post Process RF Using Lasso
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## read_parsed_dmoz.py
import csv
import pandas as pd
import numpy as np

df = pd.read_csv('parsed-new.csv', header = None, delimiter="\t", quoting=csv.QUOTE_NONE, encoding='utf-8')

df.head()


## mturk.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                soodoku
                / mturk.ipynb
            
            
              Created
              August 31, 2018 17:40
            
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## get_unique_domain_names_from_comscore.py
#
# Get All Unique Domain Names from comScore
#
# INPUT: comScore browsing data file
#
# OUTPUT: a text file containing a list of unique domains
#
# PAREMETERS:
#   + INTERNET_USAGE_FILE: path to the comScore browsing data
#   + FINAL_OUTPUT_FILE: path to intended output file

## county_dma_2016.R

library(readr)
library(dplyr)
a_string <- read_file("nielsen_2016")
split_lines <- strsplit(a_string, "\r\n")[[1]]
split_cols  <- strsplit(split_lines, "--")
dat_frame <- ldply(split_cols)
names(dat_frame) <- c("dma", "counties")
write.csv(dat_frame, file = "dma_counties_2016.csv", row.names = F)

## state_leg_twitter_handles_shor_mccarty_scores.csv

          
            State
            Position
            Name
            @Twitter Handle
            Part Affiliation
            Boris Shor's Score

            
              AZ
              Senator
              David Bradley
              @Bradley4AZ
              Democrat
              -1.253

            
              AZ
              Senator
              Katie Hobbs
              @katiehobbs
              Democrat
              -1.684

            
              AZ
              Senator
              Ed Ableser
              @SenatorAbleser
              Democrat
              -1.606

            
              AZ
              Senator
              Barbara McGuire
              @SenBarbMcGuire
              Democrat
              -0.672

            
              AZ
              Senator
              Steve Farley
              @SteveFarleyAZ
              Democrat
              -1.413

            
              AZ
              Senator
              Adam Driggs
              @AdamDriggs
              Republican
              0.738

            
              AZ
              Senator
              Bob Worsley
              @bob_worsley
              Republican
              0.46

            
              AZ
              Senator
              Kelli Ward
              @kelliwardaz
              Republican
              1.144

            
              AZ
              Senator
              Nancy Barto
              @NancyBarto
              Republican
              0.996

## cces_2006_fairest.R

# Output = http://gbytes.gsood.com/2013/11/02/the-fairest-of-them-all/

# Uses cces_recode.R here: https://github.com/soodoku/in-n-out/scripts/cces_recode.R

# Plotting the fairest of all media
library(lattice)
png("fairmedia.png")
dotplot(t(t(table(droplevels(cces06$fairmedia[cces06$fairmedia != "Don't know"]), cces06$pid3[cces06$fairmedia != "Don't know"]))/colSums(table(droplevels(cces06$v2112[cces06$fairmedia != "Don't know"]), cces06$pid3[cces06$fairmedia != "Don't know"]))),
main = "Which network do you think provides the \n fairest coverage of national news?",
	from flask import Flask, request
	import uuid

	app = Flask(__name__)

	@app.route('/error_endpoint', methods=['POST'])
	def receive_error_message():
	error_message = request.form.get('error_message')

	if error_message:
	# Pareto Party

	# Load libs
	library(tidyverse)
	library(readstata13)
	library(car)
	library(dplyr)
	library(xtable)
	library(dplyr)
	import csv
	import pandas as pd
	import numpy as np

	df = pd.read_csv('parsed-new.csv', header = None, delimiter="\t", quoting=csv.QUOTE_NONE, encoding='utf-8')

	df.head()
	#
	# Get All Unique Domain Names from comScore
	#
	# INPUT: comScore browsing data file
	#
	# OUTPUT: a text file containing a list of unique domains
	#
	# PAREMETERS:
	# + INTERNET_USAGE_FILE: path to the comScore browsing data
	# + FINAL_OUTPUT_FILE: path to intended output file

	library(readr)
	library(dplyr)
	a_string <- read_file("nielsen_2016")
	split_lines <- strsplit(a_string, "\r\n")[[1]]
	split_cols <- strsplit(split_lines, "--")
	dat_frame <- ldply(split_cols)
	names(dat_frame) <- c("dma", "counties")
	write.csv(dat_frame, file = "dma_counties_2016.csv", row.names = F)
State	Position	Name	@Twitter Handle	Part Affiliation	Boris Shor's Score
AZ	Senator	David Bradley	@Bradley4AZ	Democrat	-1.253
AZ	Senator	Katie Hobbs	@katiehobbs	Democrat	-1.684
AZ	Senator	Ed Ableser	@SenatorAbleser	Democrat	-1.606
AZ	Senator	Barbara McGuire	@SenBarbMcGuire	Democrat	-0.672
AZ	Senator	Steve Farley	@SteveFarleyAZ	Democrat	-1.413
AZ	Senator	Adam Driggs	@AdamDriggs	Republican	0.738
AZ	Senator	Bob Worsley	@bob_worsley	Republican	0.46
AZ	Senator	Kelli Ward	@kelliwardaz	Republican	1.144
AZ	Senator	Nancy Barto	@NancyBarto	Republican	0.996

	# Output = http://gbytes.gsood.com/2013/11/02/the-fairest-of-them-all/

	# Uses cces_recode.R here: https://github.com/soodoku/in-n-out/scripts/cces_recode.R

	# Plotting the fairest of all media
	library(lattice)
	png("fairmedia.png")
	dotplot(t(t(table(droplevels(cces06$fairmedia[cces06$fairmedia != "Don't know"]), cces06$pid3[cces06$fairmedia != "Don't know"]))/colSums(table(droplevels(cces06$v2112[cces06$fairmedia != "Don't know"]), cces06$pid3[cces06$fairmedia != "Don't know"]))),
	main = "Which network do you think provides the \n fairest coverage of national news?",