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msund/gist:84cfa98832506cac9f83 Secret

Last active August 29, 2015 14:17
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Tableau with R
Raw
gistfile1.txt
{
"metadata": {
"name": "",
"signature": "sha256:256e909a9ab82084dc261c8c90e967302d48bad2217432f74f7c768a16f19975"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Plotting with Tableau,<br> R, & Plotly"
]
},
{
"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": [
"# grab data from Tableau\n",
"df <- read.csv(\"http://public.tableausoftware.com/views/WomenManagers/WomanPower.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# verify, show first 5 rows\n",
"df[1:5,]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" Country Latitude..generated. Longitude..generated. X..of.women.managers\n",
"1 Albania 40.6540 20.076 22.5%\n",
"2 Algeria 28.6045 2.640 4.9%\n",
"3 Argentina -33.1660 -64.310 31.0%\n",
"4 Aruba 12.5560 -70.024 41.0%\n",
"5 Australia -24.5780 133.582 36.2%"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ggplot(df, aes(x=Latitude..generated.)) + geom_histogram()"
],
"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": 6,
"text": []
},
{
"metadata": {},
"output_type": "display_data",
"png": "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"
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"suppressMessages(py$ggplotly(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/1568\" width=\"100%\" frameBorder=\"0\"></iframe>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 7
},
{
"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": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"display_html( plotly_iframe(\"https://plot.ly/~MattSundquist/8740\")) # edit in GUI, embed"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe height=\"600\" id=\"igraph\" scrolling=\"no\", seamless=\"seamless\"\n",
"\t\t\t\tsrc=\"https://plot.ly/~MattSundquist/8740\" width=\"600\" frameBorder=\"0\"></iframe>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 9
},
{
"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": 10
}
],
"metadata": {}
}
]
}
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