msund/gist:d3e00e5e27dff31a7b6d Secret

## gistfile1.txt
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The R Notebook: <br> Collaborative R Online. <br>Interactive ggplot2."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<a href=\"http://dominodatalab.com\"><b>Domino's</b></a> new R Notebook lets you write, share, deploy, anad publish R Notebooks. Like this one.  <a href=\"https://plot.ly/\"><b>Plotly</b></a> lets you make beautiful, interactive ggplot2 and R plots inside and outside Notebooks. This Notebook shows how it works. To learn more, see <a href=\"http://www.dominodatalab.com/on_premise\">Domino On-Premise</a> and <a href=\"https://plot.ly/product/enterprise/\">Plotly Enterprise</a>.\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<img src=\"http://i.imgur.com/82RSf2N.gif\" /></a>\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "To execute this Notebook or build your own head to the <a href=\"https://app.dominodatalab.com/nick/plotly#\">Domino repo</a>. <a href=\"https://plot.ly/ggplot2\">Sign-up</a> for Plotly or use our sandbox account.\n",
      "\n",
      "<br>\n",
      "\n",
      "\n",
      "[1. Histograms](#1.-Histograms)\n",
      "<br>\n",
      "[2. 3D Plots](#2.-3D-Plots)\n",
      "<br>\n",
      "[3. Error Bars](#3.-Error-Bars)\n",
      "<br>\n",
      "[4. Anscombe's Quartet](#4.-Anscombe's-Quartet)\n",
      "<br>\n",
      "[5. Heatmaps](#5.-Heatmaps)\n",
      "<br>\n",
      "[6. Barchart](#6.-Barcharts)\n",
      "<br>\n",
      "[7. Boxplot](#7.-Boxplots)\n",
      "<br>\n",
      "[8. Dendrogram](#8.-Dendrogram)\n",
      "<br>\n",
      "[9. Embedding: Knitr, Shiny, & HTML](#8.-Embedding:-Knitr,-Shiny,-&-HTML)\n",
      "<br>\n",
      "[10. Sharing & Exporting](#9.-Sharing-&-Exporting)\n",
      "\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. Histograms"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "suppressMessages(library(devtools))\n",
      "suppressMessages(install_github(\"ropensci/plotly\"))\n",
      "suppressMessages(library(ggplot2))\n",
      "suppressMessages(library(plyr))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "suppressMessages(library(plotly)) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py <- plotly(username=\"r_user_guide\", key=\"mw5isa4yqp\")  # open plotly connection"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat <- data.frame(xx = c(runif(100,20,50),runif(100,40,80),runif(100,0,30)),yy = rep(letters[1:3],each = 100))\n",
      "plot <- ggplot(dat, aes(x=xx, fill=yy)) + geom_histogram(alpha=0.2, position=\"identity\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "suppressMessages(py$ggplotly(plot, session=\"notebook\"))  # send to plotly, embed in Notebook"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1254\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also copy and paste this code into your R terminal to make the same plot. \n",
      "\n",
      "\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<div style=\"overflow:auto;\"><div class=\"geshifilter\"><pre class=\"r geshifilter-R\" style=\"font-family:monospace;\"><a href=\"http://inside-r.org/r-doc/utils/install.packages\"><span style=\"color: #003399; font-weight: bold;\">install.packages</span></a><span style=\"color: #009900;\">&#40;</span><span style=\"color: #0000ff;\">&quot;devtools&quot;</span><span style=\"color: #009900;\">&#41;</span>  <span style=\"color: #666666; font-style: italic;\"># so we can install from github</span>\n",
      "<a href=\"http://inside-r.org/r-doc/base/library\"><span style=\"color: #003399; font-weight: bold;\">library</span></a><span style=\"color: #009900;\">&#40;</span><span style=\"color: #0000ff;\">&quot;devtools&quot;</span><span style=\"color: #009900;\">&#41;</span>\n",
      "install_github<span style=\"color: #009900;\">&#40;</span><span style=\"color: #0000ff;\">&quot;ropensci/plotly&quot;</span><span style=\"color: #009900;\">&#41;</span>  <span style=\"color: #666666; font-style: italic;\"># plotly is part of ropensci</span>\n",
      "<a href=\"http://inside-r.org/r-doc/base/library\"><span style=\"color: #003399; font-weight: bold;\">library</span></a><span style=\"color: #009900;\">&#40;</span>plotly<span style=\"color: #009900;\">&#41;</span>\n",
      "&nbsp;\n",
      "py <span style=\"\">&lt;-</span> plotly<span style=\"color: #009900;\">&#40;</span>username=<span style=\"color: #0000ff;\">&quot;r_user_guide&quot;</span><span style=\"color: #339933;\">,</span> key=<span style=\"color: #0000ff;\">&quot;mw5isa4yqp&quot;</span><span style=\"color: #009900;\">&#41;</span>  <span style=\"color: #666666; font-style: italic;\"># open plotly connection</span>\n",
      "&nbsp;\n",
      "dat <span style=\"\">&lt;-</span> <a href=\"http://inside-r.org/r-doc/base/data.frame\"><span style=\"color: #003399; font-weight: bold;\">data.frame</span></a><span style=\"color: #009900;\">&#40;</span>xx = <a href=\"http://inside-r.org/r-doc/base/c\"><span style=\"color: #003399; font-weight: bold;\">c</span></a><span style=\"color: #009900;\">&#40;</span><a href=\"http://inside-r.org/r-doc/stats/runif\"><span style=\"color: #003399; font-weight: bold;\">runif</span></a><span style=\"color: #009900;\">&#40;</span><span style=\"color: #cc66cc;\">100</span><span style=\"color: #339933;\">,</span><span style=\"color: #cc66cc;\">20</span><span style=\"color: #339933;\">,</span><span style=\"color: #cc66cc;\">50</span><span style=\"color: #009900;\">&#41;</span><span style=\"color: #339933;\">,</span><a href=\"http://inside-r.org/r-doc/stats/runif\"><span style=\"color: #003399; font-weight: bold;\">runif</span></a><span style=\"color: #009900;\">&#40;</span><span style=\"color: #cc66cc;\">100</span><span style=\"color: #339933;\">,</span><span style=\"color: #cc66cc;\">40</span><span style=\"color: #339933;\">,</span><span style=\"color: #cc66cc;\">80</span><span style=\"color: #009900;\">&#41;</span><span style=\"color: #339933;\">,</span><a href=\"http://inside-r.org/r-doc/stats/runif\"><span style=\"color: #003399; font-weight: bold;\">runif</span></a><span style=\"color: #009900;\">&#40;</span><span style=\"color: #cc66cc;\">100</span><span style=\"color: #339933;\">,</span><span style=\"color: #cc66cc;\">0</span><span style=\"color: #339933;\">,</span><span style=\"color: #cc66cc;\">30</span><span style=\"color: #009900;\">&#41;</span><span style=\"color: #009900;\">&#41;</span><span style=\"color: #339933;\">,</span>yy = <a href=\"http://inside-r.org/r-doc/base/rep\"><span style=\"color: #003399; font-weight: bold;\">rep</span></a><span style=\"color: #009900;\">&#40;</span><span style=\"color: #000000; font-weight: bold;\">letters</span><span style=\"color: #009900;\">&#91;</span><span style=\"color: #cc66cc;\">1</span><span style=\"\">:</span><span style=\"color: #cc66cc;\">3</span><span style=\"color: #009900;\">&#93;</span><span style=\"color: #339933;\">,</span>each = <span style=\"color: #cc66cc;\">100</span><span style=\"color: #009900;\">&#41;</span><span style=\"color: #009900;\">&#41;</span>\n",
      "<a href=\"http://inside-r.org/r-doc/graphics/plot\"><span style=\"color: #003399; font-weight: bold;\">plot</span></a> <span style=\"\">&lt;-</span> <a href=\"http://inside-r.org/packages/cran/ggplot\"><span style=\"\">ggplot</span></a><span style=\"color: #009900;\">&#40;</span>dat<span style=\"color: #339933;\">,</span> aes<span style=\"color: #009900;\">&#40;</span>x=xx<span style=\"color: #339933;\">,</span> fill=yy<span style=\"color: #009900;\">&#41;</span><span style=\"color: #009900;\">&#41;</span> <span style=\"\">+</span> geom_histogram<span style=\"color: #009900;\">&#40;</span>alpha=<span style=\"color: #cc66cc;\">0.2</span><span style=\"color: #339933;\">,</span> position=<span style=\"color: #0000ff;\">&quot;identity&quot;</span><span style=\"color: #009900;\">&#41;</span>\n",
      "&nbsp;\n",
      "py<span style=\"\">$</span>ggplotly<span style=\"color: #009900;\">&#40;</span><a href=\"http://inside-r.org/r-doc/graphics/plot\"><span style=\"color: #003399; font-weight: bold;\">plot</span></a><span style=\"color: #009900;\">&#41;</span></pre></div></div>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. 3D Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "library(reshape)\n",
      "volcano3d <- melt(volcano)\n",
      "names(volcano3d) <- c(\"x\", \"y\", \"z\")\n",
      " \n",
      "# Basic plot\n",
      "v <- ggplot(volcano3d, aes(x, y, z = z))\n",
      "v + geom_tile(aes(fill = z)) + stat_contour()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "\n",
        "Attaching package: 'reshape'\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "The following objects are masked from 'package:plyr':\n",
        "\n",
        "    rename, round_any\n",
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "suppressMessages(py$ggplotly(session=\"notebook\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1255\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can convert our 2D plot to a 3D plot and re-embed. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<img src=\"http://i.imgur.com/qe8zRjb.gif\" /></a>\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotly_iframe <- function(url) {\n",
      "    # set width and height from options or default square\n",
      "    w <- \"600\"\n",
      "    h <- \"600\"\n",
      "    html <- paste(\"<iframe height=\\\"\", h, \"\\\" id=\\\"igraph\\\" scrolling=\\\"no\\\", seamless=\\\"seamless\\\"\\n\\t\\t\\t\\tsrc=\\\"\", \n",
      "        url, \"\\\" width=\\\"\", w, \"\\\" frameBorder=\\\"0\\\"></iframe>\", sep = \"\")\n",
      "    return(html)\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display_html( plotly_iframe(\"https://plot.ly/~MattSundquist/2444\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"600\" id=\"igraph\" scrolling=\"no\", seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/2444\" width=\"600\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. Error Bars"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set.seed(0815)\n",
      "df <- data.frame(x =1:10,\n",
      "                 F =runif(10,1,2),\n",
      "                 L =runif(10,0,1),\n",
      "                 U =runif(10,2,3))\n",
      "\n",
      "ggplot(df, aes(x = x, y = F)) +\n",
      "  geom_point() +\n",
      "  geom_errorbar(aes(ymax = U, ymin = L))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AcgBrgwDsEcBuAGcL+MWdTrf/Lj7aPXz7etruANsqG3j6jU5fnuKe+WXa7gDbKhtYm0O7E2AbwEVukRbAJQJ+XPnN47ijAFcvsPKbx7YTAFcvsFcAA3ytW1QU8M8XD/bw8O3ixVO+s9hpbdo6gOUqGzgZwIFVFPBwraELAJYDGODCAFbmCJwbYM8AlgMY4MIyD9y/vWHdRYADqjLg5sbojZcBDqjKgPcejNrqAQ6oyoBfOB5dfmrgdk9jY+ORrqAuh13e1XWlN3RF8BZ9oSu6e0K3iLoDF/QEL+jzuSrnB7xn4Cd41cBt0+HDh5tzQV0MuzyX6+4JXRG8RW/oivOdoVtEnwQu6DwfuODCVZ+rzvoBf/Bc9CbvwSFV2Ut0/wvrnr0EcEBVBpwfwD4BLAawXwDLAQxwYQArcwTODbBnlQJ8JnDuliOpf8hlTLd49XjoitD/9Vu2nwjd4VTggndf8bosHLjU7d9e8i3WHCv5Fo91lXqH5qVFLAJ4rAJYDGC/qhb4rddKvsVL/yv5Fmsvl3qH088XsagSgKmEAZzxAM54ZQbu3dyw9KS5/VNd3YHS7DD8yPm/ZDbGHairq3siKuWT2HfMzh/6PMoM3LQjalkxcNu2qVQ7xI+c/0tmY997e6LSPYm++seP2flDn0eZgU+djXIrB26blq9v/KQkO8SPnP9LZmPe1TWXo9I9if6+Xcfs/KHPo+zvwSeXvjfw1xNHo7efLcnjx48c/5JZSfr3W/lbjX27j9n5Q59HmYH7d9WfHj688vdSbTL4yPEvmZWi/n9czd9q7BsAjucPfR5lBj76XN/g7auvR82l+Zs/fuT8XzIb61oaC7Ya+waA4/lDn0eZgbc+WVf3TOvSqGt9/dpzJdlh8JEHdsj/JbOxbvebUVTSJzEAPDh/Ec+j7O/BVNoAzngAZzyAMx7AGW/CAjfM679627ZyT1H6Jixw/7x1T/2g3EOMQxMWOHr7+htOlnuGcWjiAkc1D5R7gvFo4gLvnT3lSLlnGIcmLHDPjP1r5vaVe4rSN2GB//CjqO+bDeWeovRNWOCJEsAZD+CMB3DGAzjjAZzxAM54AGc8gDMewBkP4Iz3f+VwAlSD4wxBAAAAAElFTkSuQmCC"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py$ggplotly(session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1256\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4. Anscombe's Quartet"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<img src = \"http://i.imgur.com/cP4YBgL.png\">\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anscombe_m <- data.frame()\n",
      " \n",
      "for(i in 1:4)\n",
      "  anscombe_m <- rbind(anscombe_m, data.frame(set=i, x=anscombe[,i], y=anscombe[,i+4]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "quartet <- ggplot(anscombe_m, aes(x, y)) + geom_point() + facet_wrap(~set, ncol=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py$ggplotly(quartet, session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1257\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display_html( plotly_iframe(\"https://plot.ly/~MattSundquist/2745\"))  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"600\" id=\"igraph\" scrolling=\"no\", seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/2745\" width=\"600\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5. Heatmaps"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generate data\n",
      "pp <- function (n,r=4) {\n",
      " x <- seq(-r*pi, r*pi, len=n)\n",
      " df <- expand.grid(x=x, y=x)\n",
      " df$r <- sqrt(df$x^2 + df$y^2)\n",
      " df$z <- cos(df$r^2)*exp(-df$r/6)\n",
      " df\n",
      "}\n",
      "p <- ggplot(pp(20), aes(x=x,y=y))\n",
      "\n",
      "p + geom_tile(aes(fill=z))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py$ggplotly(session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1258\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "6. Barcharts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df <- structure(c(106487, 495681, 1597442,\n",
      "     2452577, 2065141, 2271925, 4735484, 3555352,\n",
      "     8056040, 4321887, 2463194, 347566, 621147,\n",
      "     1325727, 1123492, 800368, 761550, 1359737,\n",
      "     1073726, 36, 53, 141, 41538, 64759, 124160,\n",
      "     69942, 74862, 323543, 247236, 112059, 16595,\n",
      "     37028, 153249, 427642, 1588178, 2738157,\n",
      "     2795672, 2265696, 11951, 33424, 62469,\n",
      "     74720, 166607, 404044, 426967, 38972, 361888,\n",
      "     1143671, 1516716, 160037, 354804, 996944,\n",
      "     1716374, 1982735, 3615225, 4486806, 3037122,\n",
      "     17, 54, 55, 210, 312, 358, 857, 350, 7368,\n",
      "     8443, 6286, 1750, 7367, 14092, 28954, 80779,\n",
      "     176893, 354939, 446792, 33333, 69911, 53144,\n",
      "     29169, 18005, 11704, 13363, 18028, 46547,\n",
      "     14574, 8954, 2483, 14693, 25467, 25215,\n",
      "     41254, 46237, 98263, 185986), .Dim = c(19,\n",
      "     5), .Dimnames = list(c(\"1820-30\", \"1831-40\",\n",
      "     \"1841-50\", \"1851-60\", \"1861-70\", \"1871-80\",\n",
      "     \"1881-90\", \"1891-00\", \"1901-10\", \"1911-20\",\n",
      "     \"1921-30\", \"1931-40\", \"1941-50\", \"1951-60\",\n",
      "     \"1961-70\", \"1971-80\", \"1981-90\", \"1991-00\",\n",
      "     \"2001-06\"), c(\"Europe\", \"Asia\", \"Americas\",\n",
      "     \"Africa\", \"Oceania\")))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.m <- melt(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.m <- rename(df.m, c(X1 = \"Period\", X2 = \"Region\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a <- ggplot(df.m, aes(x = Period, y = value/1e+06,\n",
      "     fill = Region))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b <- a + geom_bar(stat = \"identity\", position = \"stack\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py$ggplotly(b, session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1259\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "7. Boxplots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set.seed(1234)\n",
      "df <- data.frame(cond = factor( rep(c(\"A\",\"B\"), each=200) ), \n",
      "                   rating = c(rnorm(200),rnorm(200, mean=.8)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "box <- ggplot(df, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py$ggplotly(box, session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1260\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "8. Dendrogram"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "suppressMessages(devtools::install_github('talgalili/dendextend'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "suppressPackageStartupMessages(library(dendextend)) \n",
      "# Create a complex dend:\n",
      "dend <- iris[1:30,-5] %>% dist %>% hclust %>% as.dendrogram %>%\n",
      "   set(\"branches_k_color\", k=3) %>% set(\"branches_lwd\", c(1.5,1,1.5)) %>%\n",
      "   set(\"branches_lty\", c(1,1,3,1,1,2)) %>%\n",
      "   set(\"labels_colors\") %>% set(\"labels_cex\", c(.9,1.2))\n",
      "# plot the dend in usual \"base\" plotting engine:\n",
      "# plot(dend)\n",
      "# Now let's do it in ggplot2 :)\n",
      "ggd1 <- as.ggdend(dend)\n",
      "library(ggplot2)\n",
      "ggplot(ggd1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
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"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ggd1 <- as.ggdend(dend)\n",
      "library(ggplot2)\n",
      "ggplot(ggd1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": []
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py$ggplotly(session=\"notebook\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe height=\"525\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n",
        "\t\t\t\tsrc=\"https://plot.ly/~r_user_guide/1261\" width=\"100%\" frameBorder=\"0\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "9. Embedding: Knitr, Shiny, & HTML"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotly graphs can be placed in iframes, [knitr](blog.plot.ly/post/106630252117/plot-with-ggplot2-and-plotly-within-knitr-reports), or [Shiny](https://github.com/chriddyp/plotly-shiny):\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "<img src=\"https://camo.githubusercontent.com/676bf9fb2ee08e462e549a872ef1448a6c47247d/687474703a2f2f692e696d6775722e636f6d2f6f73635a7445692e706e67\"></a>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "10. Sharing & Exporting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Every Plotly graph can be exported in varied programming languages and image types. For example, for the boxplot above:\n",
      "<br>\n",
      "<br>\n",
      "<br>\n",
      "- https://plot.ly/~r_user_guide/1079.png\n",
      "- https://plot.ly/~r_user_guide/1079.svg\n",
      "- https://plot.ly/~r_user_guide/1079.pdf\n",
      "- https://plot.ly/~r_user_guide/1079.eps\n",
      "- https://plot.ly/~r_user_guide/1079.py\n",
      "- https://plot.ly/~r_user_guide/1079.m\n",
      "- https://plot.ly/~r_user_guide/1079.jl\n",
      "- https://plot.ly/~r_user_guide/1079.json\n",
      "- https://plot.ly/~r_user_guide/1079.embed\n",
      "<br>\n",
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# CSS styling within IPython notebook\n",
      "display_html(getURL(\"https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    div.cell{\n",
        "        width: 850px;\n",
        "        margin-left: 10% !important;\n",
        "        margin-right: auto;\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "    }\n",
        "    .text_cell_render h1 {\n",
        "        font-weight: 200;\n",
        "        font-size: 40pt;\n",
        "        line-height: 100%;\n",
        "        color:#447adb;\n",
        "        margin-bottom: 0em;\n",
        "        margin-top: 0em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    } \n",
        "    h2 {\n",
        "        font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "        text-indent:1em;\n",
        "    }\n",
        "    .text_cell_render h2 {\n",
        "        font-weight: 200;\n",
        "        font-size: 20pt;\n",
        "        font-style: italic;\n",
        "        line-height: 100%;\n",
        "        color:#447adb;\n",
        "        margin-bottom: 1.5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    } \n",
        "    h3 {\n",
        "        font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "    }\n",
        "    .text_cell_render h3 {\n",
        "        font-weight: 300;\n",
        "        font-size: 18pt;\n",
        "        line-height: 100%;\n",
        "        color:#447adb;\n",
        "        margin-bottom: 0.5em;\n",
        "        margin-top: 2em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    }\n",
        "    h4 {\n",
        "        font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "    }\n",
        "    .text_cell_render h4 {\n",
        "        font-weight: 300;\n",
        "        font-size: 16pt;\n",
        "        color:#447adb;\n",
        "        margin-bottom: 0.5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    }\n",
        "    h5 {\n",
        "        font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "    }\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-style: normal;\n",
        "        color: #1d3b84;\n",
        "        font-size: 16pt;\n",
        "        margin-bottom: 0em;\n",
        "        margin-top: 1.5em;\n",
        "        display: block;\n",
        "        white-space: nowrap;\n",
        "    }\n",
        "    div.text_cell_render{\n",
        "        font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "        line-height: 135%;\n",
        "        font-size: 125%;\n",
        "        width:750px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "        text-align:justify;\n",
        "        text-justify:inter-word;\n",
        "    }\n",
        "    div.output_subarea.output_text.output_pyout {\n",
        "        overflow-x: auto;\n",
        "        overflow-y: scroll;\n",
        "        max-height: 300px;\n",
        "    }\n",
        "    div.output_subarea.output_stream.output_stdout.output_text {\n",
        "        overflow-x: auto;\n",
        "        overflow-y: scroll;\n",
        "        max-height: 300px;\n",
        "    }\n",
        "    div.output_subarea.output_html.rendered_html {\n",
        "        overflow-x: scroll;\n",
        "        max-width: 100%;\n",
        "      /*  overflow-y: scroll; */\n",
        "      /*  max-height: 300px;   */\n",
        "    }\n",
        "    code{\n",
        "      font-size: 78%;\n",
        "    }\n",
        "    .rendered_html code{\n",
        "        background-color: transparent;\n",
        "        white-space: inherit;   \n",
        "    }\n",
        "    ul{\n",
        "        /* color:#447adb; */  \n",
        "        margin: 2em;\n",
        "    }\n",
        "    ul li{\n",
        "        padding-left: 0.5em; \n",
        "        margin-bottom: 0.5em; \n",
        "        margin-top: 0.5em; \n",
        "    }\n",
        "    ul li li{\n",
        "        padding-left: 0.2em; \n",
        "        margin-bottom: 0.2em; \n",
        "        margin-top: 0.2em; \n",
        "    }\n",
        "    ol{\n",
        "        /* color:#447adb; */  \n",
        "        margin: 2em;\n",
        "    }\n",
        "    ol li{\n",
        "        padding-left: 0.5em; \n",
        "        margin-bottom: 0.5em; \n",
        "        margin-top: 0.5em; \n",
        "    }\n",
        "    /*.prompt{\n",
        "        display: None;\n",
        "    } */\n",
        "    ul li{\n",
        "        padding-left: 0.5em; \n",
        "        margin-bottom: 0.5em; \n",
        "        margin-top: 0.2em; \n",
        "    }\n",
        "    a:link{\n",
        "       font-weight: bold;\n",
        "       color:#447adb;\n",
        "    }\n",
        "    a:visited{\n",
        "       font-weight: bold;\n",
        "       color: #1d3b84;\n",
        "    }\n",
        "    a:hover{\n",
        "       font-weight: bold;\n",
        "       color: #1d3b84;\n",
        "    }\n",
        "    a:focus{\n",
        "       font-weight: bold;\n",
        "       color:#447adb;\n",
        "    }\n",
        "    a:active{\n",
        "       font-weight: bold;\n",
        "       color:#447adb;\n",
        "    }\n",
        "    .rendered_html :link {\n",
        "       text-decoration: none; \n",
        "    }\n",
        "    .rendered_html :hover {\n",
        "       text-decoration: none; \n",
        "    }\n",
        "    .rendered_html :visited {\n",
        "      text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :focus {\n",
        "      text-decoration: none;\n",
        "    }\n",
        "    .rendered_html :active {\n",
        "      text-decoration: none;\n",
        "    }\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "    } \n",
        "    hr {\n",
        "      color: #f3f3f3;\n",
        "      background-color: #f3f3f3;\n",
        "      height: 1px;\n",
        "    }\n",
        "    blockquote{\n",
        "      display:block;\n",
        "      background: #f3f3f3;\n",
        "      font-family: \"Open sans\",verdana,arial,sans-serif;\n",
        "      width:610px;\n",
        "      padding: 15px 15px 15px 15px;\n",
        "      text-align:justify;\n",
        "      text-justify:inter-word;\n",
        "      }\n",
        "      blockquote p {\n",
        "        margin-bottom: 0;\n",
        "        line-height: 125%;\n",
        "        font-size: 100%;\n",
        "        text-align: center;\n",
        "      }\n",
        "   /* element.style {\n",
        "    } */  \n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ [\"$\",\"$\"], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ [\"$$\",\"$$\"], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: \"center\", // Change this to \"center\" to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {\".MathJax_Display\": {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 32
    }
   ],
   "metadata": {}
  }
 ]
}