fototo’s gists

fototo / process recipes.py

Last active August 29, 2015 14:19 — forked from inkhorn/process recipes.py

	import os
	rfiles = os.listdir('.')
	rc = []
	for f in rfiles:
	if '.txt' in f:
	# The recipes come in 3 txt files consisting of 1 recipe per line, the
	# cuisine of the recipe as the first entry in the line, and all subsequent ingredient
	# entries separated by a tab
	infile = open(f, 'r')
	rc.append(infile.read())

fototo / markdown.css

Last active August 29, 2015 14:22 — forked from imjasonh/markdown.css

fototo / README.md

Last active August 29, 2015 14:23 — forked from e9t/README.md

fototo / us_states_hexgrid.geojson

Last active August 29, 2015 14:25 — forked from hrbrmstr/us_states_hexgrid.geojson

hexbin faceted choropleths in R

	library(rgdal)
	library(rgeos)
	library(ggplot2)
	library(readr)
	library(tidyr)
	library(dplyr)
	library(grid)

	us <- readOGR("us_states_hexgrid.geojson", "OGRGeoJSON")

fototo / sna_example.R

Last active August 29, 2015 14:25 — forked from dsparks/sna_example.R

Flag similarity network

	# Drawing a scatter plot of raster images
	doInstall <- TRUE # Change to FALSE if you don't want packages installed.
	toInstall <- c("png", "sna")
	if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
	lapply(toInstall, library, character.only = TRUE)

	# Go to https://www.gosquared.com/resources/2400-flags, download the ZIP,
	# and put the 64 x 64 files into a directory of your choosing.
	# Then setwd() to that directory:
	setwd("C:/Dropbox/isDotR_Files/flagIcons")

fototo / Rakefile

Last active September 14, 2015 00:06 — forked from jerodsanto/Rakefile

A code dump showing how we generate "The Changelog Weekly" using the Trello API

	require "rubygems"
	require "bundler"
	require_relative "lib/importer"

	Bundler.setup

	desc "Import from Trello board"
	task :import do
	Importer.new(File.dirname(__FILE__)).import ENV["ISSUE"]
	end

fototo / customer-segmentation.py

Last active September 14, 2015 23:49 — forked from glamp/customer-segmentation.py

Analysis for customer segmentation blog post

	import pandas as pd
	# http://blog.yhathq.com/static/misc/data/WineKMC.xlsx
	df_offers = pd.read_excel("./WineKMC.xlsx", sheetname=0)
	df_offers.columns = ["offer_id", "campaign", "varietal", "min_qty", "discount", "origin", "past_peak"]
	df_offers.head()

	df_transactions = pd.read_excel("./WineKMC.xlsx", sheetname=1)
	df_transactions.columns = ["customer_name", "offer_id"]
	df_transactions['n'] = 1
	df_transactions.head()

fototo / question.csv

Created November 9, 2015 06:11 — forked from hrbrmstr/question.csv

Data & code for http://rud.is/b/2015/11/08/visualizing_survey_data/

belief	y2015	y2014
Improves the security posture of my organization	0.75	0.71
Improves the security posture of the nations critical infrastructure	0.63	0.64
Reduces the cost of detecting and preventing cyber attacks	0.22	0.21
Improves situational awareness	0.60	0.54
Fosters collaboration among peers and industry groups	0.48	0.51
Enhances the timeliness of threat data	0.11	0.16
Makes threat data more actionable	0.21	0.24

fototo / K-Means.ipynb

Created November 22, 2015 10:04 — forked from narphorium/K-Means.ipynb

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fototo / copulas_example.R

Created January 3, 2016 06:28 — forked from mick001/copulas_example.R

Modelling dependence with copulas. Full article at: http://datascienceplus.com/modelling-dependence-with-copulas/

	#Load library mass and set seed
	library(MASS)
	set.seed(100)

	# We are going to use 3 random variables
	m <- 3

	# Number of samples to be drawn
	n <- 2000