andrawaag

library(SPARQL)
library(ggplot2)
library(rworldmap)

wdqs <- "https://query.wikidata.org/bigdata/namespace/wdq/sparql"
query = "PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX p: <http://www.wikidata.org/prop/>
PREFIX v: <http://www.wikidata.org/prop/statement/>

gluc

This gist shows how to convert a nested JSON file to an R data.frame. To do this, it uses jsonlite and data.tree.

The gist contains two examples: one is a bit simpler, the second one a bit more advanced.

Example 1

In the first example, we download all the repos from Hadley Wickham's Github account from https://api.github.com/users/hadley/repos . This JSON contains a nested owner object. The code shows how to convert that in a flat data.frame in three statements:

1. line 5: download
2. line 8: convert to data.tree

shivaram

# Download Spark 1.4 from http://spark.apache.org/downloads.html
# 
# Download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv

# Launch SparkR using 
# ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3

# The SparkSQL context should already be created for you as sqlContext
sqlContext
# Java ref type org.apache.spark.sql.SQLContext id 1

leeper

# push
push <- function(x, values) (assign(as.character(substitute(x)), c(x, values), parent.frame()))

# pop
pop <- function(x) (assign(as.character(substitute(x)), x[-length(x)], parent.frame()))

# example
z <- 1:3
push(z, 4)
z

mrnohr

#!/bin/bash
# --- Version history ---
#   mrn: sleep for $freq instead of 3 seconds (forked)
#   0.4: added variable to store file path, and $2 for base file name
#        added variable to store desired reporting interval
#   0.3: added $1 to send in process ID at run time.
#   0.2: switched to $SECONDS for the loop. works.
#   0.1: didn't work well at all.
# --- Version history ---

Dulani

#!/bin/sh

# Converts a mysqldump file into a Sqlite 3 compatible file. It also extracts the MySQL `KEY xxxxx` from the
# CREATE block and create them in separate commands _after_ all the INSERTs.

# Awk is choosen because it's fast and portable. You can use gawk, original awk or even the lightning fast mawk.
# The mysqldump file is traversed only once.

# Usage: $ ./mysql2sqlite mysqldump-opts db-name | sqlite3 database.sqlite
# Example: $ ./mysql2sqlite --no-data -u root -pMySecretPassWord myDbase | sqlite3 database.sqlite

paulgambill

/**
 * Retrieves all the rows in the active spreadsheet that contain data and logs the
 * values for each row.
 * For more information on using the Spreadsheet API, see
 * https://developers.google.com/apps-script/service_spreadsheet
 */
function readRows() {
  var sheet = SpreadsheetApp.getActiveSheet();
  var rows = sheet.getDataRange();
  var numRows = rows.getNumRows();

killercup

/*
 * I add this to html files generated with pandoc.
 */

html {
  font-size: 100%;
  overflow-y: scroll;
  -webkit-text-size-adjust: 100%;
  -ms-text-size-adjust: 100%;
}

fawda123

plot.qual<-function(x,x.locs=c(0.01,0.99),y.locs=c(0,1),steps=NULL,sp.names=NULL,dt.tx=T,rsc=T,
	ln.st=NULL,rs.ln=c(3,15),ln.cl='RdYlGn',alpha=0.7,leg=T,rnks=FALSE,...){

	require(RColorBrewer)
	require(scales)
	
	if(length(x.locs) != 2 | length(y.locs) != 2) 
		stop('x and y dimensions must be two-element vectors')
	
	if(x.locs[1]<0 | x.locs[2]>1 | y.locs[1]<0 | y.locs[2]>1) 

lukaszb

#!/usr/bin/env python
from Foundation import NSUserNotification
from Foundation import NSUserNotificationCenter
from Foundation import NSUserNotificationDefaultSoundName
from optparse import OptionParser



def main():
    parser = OptionParser(usage='%prog -t TITLE -m MESSAGE')