arunsrinivasan

Update: The timings are now updated with runs from R v3.2.2 along with the new 'on=' syntax

A small note on this tweet from @KevinUshey and this tweet from @ChengHLee:

The number of rows, while is important, is only one of the factors that influence the time taken to perform the join. From my benchmarking experience, the two features that I found to influence join speed, especially on hash table based approaches (ex: dplyr), much more are:

• The number of unique groups.
• The number of columns to perform the join based on - note that this is also related to the previous point as in most cases, more the columns, more the number of unique groups.

That is, these features influence join speed in spite of having the same number of rows.

jimhester

.Rproj.user
.Rhistory
.RData
*.Rproj
*.html

kevinushey

.Rproj.user
.Rhistory
.RData
*.Rproj
*.html

larrydahooster

import React, { Component, PropTypes } from 'react';
import { connect } from 'react-redux';
import { toggleSidebar } from '../actions/sidebarActionCreators';
import { bindActionCreators } from 'redux';
import MainNav from '../components/MainNav';

class Sidebar extends Component {
    render() {
        const { dispatch, sidebar, subNav } = this.props;

idibidiart

Please go here for latest post and feel free to comment

[new post] (https://gist.github.com/idibidiart/49a095b6bc528638f34f)

ryanflorence

function createDisabledUntilClientRendersComponent(component, displayName='ClientComponent') {
  return React.createClass({

    displayName,

    propTypes: {
      disabled: React.PropTypes.bool
    },

    getInitialState() {

debergalis

Brief notes on Meteor for CS 294-101. Many of the key architectural ideas in Meteor are described at https://www.meteor.com/projects.

1. BSD as example of great system design. Application primitives: processes run by a scheduler, sockets, sys calls, virtual memory, mbufs. What makes something a platform. Unix vs Multics.

2. History of application architectures. Mainframes (e.g. IBM 360 / 3270), client-server (e.g. Win32), web (e.g. LAMP), cloud-client. Oscillation of where the software runs. Thin vs thick clients, data vs presentation on the wire. Changes driven by massive forces (cheap CPUs, ubiquitous internet, mobile). New architecture for each era.

3. What it takes to make modern UI/UX. Mobile. Live updating. Collaboration. No refresh button. All drive the need for “realtime” or “reactive” system. Very different from HTTP era.

4. Four questions: 1 — how do we move data around; 2 — where does it come from; 3 — where do we put it; 4 — how do we use it?

cpsievert

# devtools::install_github('jalapic/engsoccerdata')
library(engsoccerdata)
library(dplyr)
library(plotly)
library(htmlwidgets)

england$Date <- as.Date(england$Date, format = "%Y-%m-%d")

#Get Data into Format Needed
df<-rbind(

ryanflorence

// this wrapper component was never a Route! Just another way
// the old version "recreated react rendering"
<Route component={Wrapper}>
  <Route path="/foo" component={Foo}/>
  <Route path="/bar" component={Bar}/>
  <Route path="/baz" component={Baz}/>
</Route>

// whenever one of those paths matched, you'd get something like
// <Wrapper><Foo/></Wrapper>

oganm

library(dplyr)
library(magrittr)

baseR = c('base','compiler','datasets','graphics','grDevices','grid','methods','parallel','splines','stats','stats4','tcltk','utils')

explicit = function(fileIn,fileOut,
                    ignore = baseR){

    file = readLines(fileIn)