msund/gist:a0dcfb3e783c03a07215

## gistfile1.txt
{
 "metadata": {
  "name": "",
  "signature": "sha256:accbf07ab64ce1adbac7177a8bfba0c1021ed33763bd3996723564b3a71e6a37"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a <- 1\n",
      "a\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "[1] 1"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "install.packages(\"devtools\")  # so we can install from github\n",
      "library(\"devtools\")\n",
      "install_github(\"ropensci/plotly\")  # plotly is part of ropensci\n",
      "library(plotly)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Installing package into \u2018/Users/matthewsundquist/Library/R/3.1/library\u2019\n",
        "(as \u2018lib\u2019 is unspecified)\n"
       ]
      },
      {
       "ename": "ERROR",
       "evalue": "Error in contrib.url(repos, \"source\"): trying to use CRAN without setting a mirror\n",
       "output_type": "pyerr",
       "traceback": [
        "Error in contrib.url(repos, \"source\"): trying to use CRAN without setting a mirror\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Downloading github repo ropensci/plotly@master\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Installing plotly\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "'/Library/Frameworks/R.framework/Resources/bin/R' --vanilla CMD INSTALL  \\\n",
        "  '/private/var/folders/vm/4bcx1bc16rd_566rnf2nwypw0000gn/T/RtmpjRzcx2/devtools24a1e87bb1b/ropensci-plotly-c747575'  \\\n",
        "  --library='/Users/matthewsundquist/Library/R/3.1/library' --install-tests \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Reloading installed plotly\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py <- plotly(username=\"r_user_guide\", key=\"mw5isa4yqp\")  # open plotly connection"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x <- seq(from=0, to=10, by=0.1)\n",
      "y <- 2 * x + 4.5 + runif(x)\n",
      "qplot(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_plot <- qplot(x, y)\n",
      "py$ggplotly(my_plot, session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Loading required package: IRdisplay\n"
       ]
      },
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/971\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}