octocat

# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

dotike

#!/bin/sh

##############################################################################
# This code known is distributed under the following terms:
# 
# Copyright (c) 2013 Isaac (.ike) Levy <ike@blackskyresearch.net>.
# All rights reserved.
# 
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions

seeRead

import re
import pprint
import urlparse
import urllib2
#http://blog.ianbicking.org/2008/12/10/lxml-an-underappreciated-web-scraping-library/
from lxml.html import parse, tostring, fromstring #for better css selectors than Beautiful Soup
from lxml.html.diff import htmldiff
from lxml import cssselect, etree
from urlparse import urlparse
from pytz import timezone

fredbenenson

SELECT *
FROM
  (SELECT
    month,
    amount,
    pledge_count,
    SUM(1) OVER(PARTITION BY month ORDER BY pledge_count DESC ROWS UNBOUNDED PRECEDING) as row
  FROM
    (SELECT
      TO_CHAR(CONVERT_TIMEZONE('UTC', 'America/New_York', backings.pledged_at), 'YYYY-MM-01') as month,

cartazio

PSA :

just use GHC for OSX https://ghcformacosx.github.io

the rest of these directions are preserved for historical purposes

TLDR version, if you have homebrew

xcode-select --install ; brew tap homebrew/versions ;   brew tap homebrew/dupes \

cjbayesian

###################################################
##
## Functions for calculating AUC and plotting ROC
## Corey Chivers, 2013
## corey.chivers@mail.mcgill.ca
##
###################################################


## Descrete integration for AUC calc

gislars

get the FileGDB API http://www.esri.com/apps/products/download/
extract it somewhere on your system and remember the path :)

mkdir build   #directory where we are playing around
cd build
git clone https://github.com/OSGeo/gdal.git
cd gdal
./configure --with-fgdb=/path/to/your/FileGDB_API
make
(make install) optional, I'm just using it locally

benmarwick

# Speed tests of different ways to read in large numbers of CSV files
# specifically read.csv.sql, read.csv (optimised) and fread


library(sqldf)
setwd("~/Downloads/wordcounts")
files <- sample(list.files(".", pattern="*.csv|CSV$"), 10000)

############# read.csv.sql ###################
system.time(

wch

data_sets <- c("mtcars", "morley", "rock")

shinyServer(function(input, output) {

  # Drop-down selection box for which data set
  output$choose_dataset <- renderUI({
    selectInput("dataset", "Data set", as.list(data_sets))
  })

  # Check boxes

codegordi

# best practices for web scraping in R #

# function should be used with ldply
# eg: 

ldply(urls, scrape)

# add a try to ignore broken links/ unresponsive pages
# eg: