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May 28, 2014 07:41
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| "signature": "sha256:0066d7751e4802e747a189c766f68718da3fe272e314ebd5e4c27d43535c1cf9" | |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "heading", | |
| "level": 1, | |
| "metadata": {}, | |
| "source": [ | |
| "Dataverse example plots" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "These were drawn from two datasets.\"[Replication data for: Asset Prices, Consumption, and the Business Cycle](http://thedata.harvard.edu/dvn/faces/study/StudyPage.xhtml;jsessionid=d814bb72587b5ac4c99920b4332a?globalId=hdl:1902.1/KSCWRAGNIJ)\" and \"[Replication data for: Consumption-Based Asset Pricing](http://thedata.harvard.edu/dvn/faces/study/StudyPage.xhtml?globalId=hdl:1902.1/UQRPVVDBHI).\" I downloaded the data, and called it into a few quick graphs. It took about five minutes. You could dress them up a good deal more; I just thought I'd show how this looks for someone who just wanted a quick look at the data." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "local({r <- getOption(\"repos\")\n", | |
| " r[\"CRAN\"] <- \"http://cran.r-project.org\" \n", | |
| " options(repos=r)\n", | |
| "})" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "if(!require(plotly)) {\n", | |
| " install_github(\"plotly\", \"ropensci\", quiet = TRUE)\n", | |
| " }" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 4 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "library(plotly)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 11 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "py <- plotly(\"ggplot2examples\", \"3gazttckd7\")#Initiate Plotly graph object " | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 12 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "library(IRdisplay)\n", | |
| "plotly_iframe <- function(url) {\n", | |
| " # set width and height from options or default square\n", | |
| " w <- \"700\"\n", | |
| " h <- \"600\"\n", | |
| " html <- paste(\"<center><iframe height=\\\"\", h, \"\\\" id=\\\"igraph\\\" scrolling=\\\"no\\\" seamless=\\\"seamless\\\"\\n\\t\\t\\t\\tsrc=\\\"\", \n", | |
| " url, \"\\\" width=\\\"\", w, \"\\\" frameBorder=\\\"0\\\"></iframe></center>\", sep=\"\")\n", | |
| " return(html)\n", | |
| "}" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 15 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1472\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1472\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 26 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1475\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1475\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 19 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1479\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1479\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 20 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1481\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1481\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 21 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1482\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1482\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 23 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1470\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1470\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 27 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "display_html(plotly_iframe(\"https://plot.ly/~MattSundquist/1483\"))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "html": [ | |
| "<center><iframe height=\"600\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\"\n", | |
| "\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/1483\" width=\"700\" frameBorder=\"0\"></iframe></center>" | |
| ], | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "prompt_number": 25 | |
| }, | |
| { | |
| "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", | |
| " /*body {\n", | |
| " background-color: #F5F5F5;\n", | |
| " }*/\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", | |
| " code{\n", | |
| " font-size: 78%;\n", | |
| " }\n", | |
| " .rendered_html code{\n", | |
| " background-color: transparent;\n", | |
| " }\n", | |
| " ul{\n", | |
| " /* color:#447adb; */ // colors text too\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; */ // colors text too\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", | |
| " }\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": 28 | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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