shoyer

rafapereirabr

This gist shows in two steps how to tilt and stack maps using ggplot2 in order to create an image like this one: [![enter image description here][1]][1]

Let's load the necessary libraries and data to use a reproducible example:

# load libraries
  library(rgeos)
  library(UScensus2000tract)
  library(ggplot2)

mthomure

(ns hello-quil.noisy-spirals
  (:require [quil.core :as q]
            [hello-quil.noisy-spirals-dynamic :as d]
            [clojure.core.async :as a]
            [overtone.midi :as midi]
            [mojion.leap :as leap]))

(q/defsketch gen-art-14
  :title "100 Noisy Spirals"
  :size [d/SIZE d/SIZE]

davoclavo

emacs --daemon to run in the background. emacsclient.emacs24 <filename/dirname> to open in terminal

NOTE: "M-m and SPC can be used interchangeably".

• Undo - C-/
• Redo - C-?
• Change case: 1. Camel Case : M-c 2. Upper Case : M-u

3. Lower Case : M-l

chrishwiggins

---------- Forwarded message ----------
From: chris wiggins <chris.wiggins@[YYY].edu>
Date: Wed, Aug 1, 2012 at 7:26 PM
Subject: stats history
To: hadley@[XXX].edu
Cc: chris wiggins <chris.wiggins@[YYY].edu>


Dear Hadley:

hadley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

jkeirstead

// Predict from Gaussian Process
// All data parameters must be passed as a list to the Stan call
// Based on original file from https://code.google.com/p/stan/source/browse/src/models/misc/gaussian-process/

data {
int<lower=1> N1;
vector[N1] x1;
vector[N1] y1;
int<lower=1> N2;
vector[N2] x2;

jackrusher

(ns blue-ball
  (:use [seesaw core font graphics])
  (:require [clojure-leap.core :as leap]
            [clojure-leap.screen :as l-screen]))

;; these atoms contain the current x/y state from the Leap
(def x (atom 10))
(def y (atom 10))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Build the window/canvas

mrflip

Tuning Storm+Trident

Tuning a dataflow system is easy:

The First Rule of Dataflow Tuning:
* Ensure each stage is always ready to accept records, and
* Deliver each processed record promptly to its destination

quintona

package storm.cookbook.tfidf.functions;

import java.util.Map;

import storm.trident.operation.MultiReducer;
import storm.trident.operation.TridentCollector;
import storm.trident.operation.TridentMultiReducerContext;
import storm.trident.tuple.TridentTuple;
import backtype.storm.tuple.Values;