walkerke

library(shiny)
library(spdep)
library(leaflet)
library(RColorBrewer)

atx <- readRDS('travis.rds')

atx$id <- 1:nrow(atx)

atx2 <- atx[!is.na(atx$income), ]

jqtrde

Libraries for a modern geospatial workflow

Distribution

• Writing, Running, and Distributing
• Tox driven development
• Publishing python libraries

Numpy

RanaivosonHerimanitra

require(data.table)
alloc_opti_data.table=function(n=1000
                     ,dataset=ese_op2013_reste
                     ,strate="strate2013"
                     ,sd=ecart_type )
{
  
  #count obs per strata to form Nh:
  dataset=data.table(dataset)
  output=dataset[,.N,by=strate]

katowulf

// assumes you add a timestamp field to each record (see Firebase.ServerValue.TIMESTAMP)
// pros: fast and done server-side (less bandwidth, faster response), simple
// cons: a few bytes on each record for the timestamp
var ref = new Firebase(...);
ref.orderByChild('timestamp').startAt(Date.now()).on('child_added', function(snapshot) {
  console.log('new record', snap.key());
});

anantn

function go() {
  var userId = prompt('Username?', 'Guest');
  checkIfUserExists(userId);
}

var USERS_LOCATION = 'https://SampleChat.firebaseIO-demo.com/users';

function userExistsCallback(userId, exists) {
  if (exists) {
    alert('user ' + userId + ' exists!');

cengel

import pandas
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
from rpy2.robjects.lib import grid
from rpy2.robjects.lib import ggplot2

## read in the distances to railroad (we calculated)
neardist = pandas.read_csv('data/NearDistance.csv')

## convert to R dataframe, via Python Dictionary data type