nikolasburk

GraphQL vs Firebase

With the variety of server-side technologies today, developers have a lot of choices when it comes to deciding what kind of backend to use for their next application.

In this article, we want to explore the differences between GraphQL and Firebase, two very popular server-side technologies.

Overview

Before diving into technical details, let's create some perspective on the two technologies and where they're coming from.

simenbrekken

import firebase from 'firebase'
import { filter, map } from 'lodash'
import { makeExecutableSchema } from 'graphql-tools'

firebase.initializeApp({
  databaseURL: 'https://grafire-b1b6e.firebaseio.com',
})

const mapSnapshotToEntity = snapshot => ({ id: snapshot.key, ...snapshot.val() })
const mapSnapshotToEntities = snapshot => map(snapshot.val(), (value, id) => ({ id, ...value }))

ololobus

Apache Spark installation + ipython/jupyter notebook integration guide for macOS

Tested with Apache Spark 2.1.0, Python 2.7.13 and Java 1.8.0_112

For older versions of Spark and ipython, please, see also previous version of text.

Install Java Development Kit

rdempsey

#!/usr/bin/env python
# encoding: utf-8
"""
python_3_email_with_attachment.py
Created by Robert Dempsey on 12/6/14.
Copyright (c) 2014 Robert Dempsey. Use at your own peril.

This script works with Python 3.x

NOTE: replace values in ALL CAPS with your own values

wch

Type: Shiny
Title: Dynamic UI
License: MIT
Author: Winston Chang <winston@rstudio.com>
AuthorUrl: http://www.rstudio.com/
Tags: dynamic-ui renderui uioutput
DisplayMode: Showcase

tomhopper

library(ggplot2)
#' create a data frame with test data.
my.df <- data.frame(index = 1:10, value = rnorm(10))

#' create the ggplot object
my.ggp <- ggplot(data = my.df, aes(x = index, y = value)) + geom_point() + geom_line()

#' get the x- and y-axis ranges actually used in the graph

# This worked in early versions of ggplot2 (probably <2.2)

jeromyanglim

This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) 
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for 
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post 
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and 
(5) discusses the implications of R Markdown.