Created
May 28, 2014 07:41
-
-
Save msund/84bc2cd7681ef8324687 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ | |
"metadata": { | |
"name": "", | |
"signature": "sha256:0066d7751e4802e747a189c766f68718da3fe272e314ebd5e4c27d43535c1cf9" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"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": {} | |
} | |
] | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment