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Midnighter/rp2_ggplot2_2.3.6_bugs.ipynb

Created April 4, 2013 12:54
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IPython Notebook showing problems with axes marks, labels, and legend using the interactive ggplot2.
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rp2_ggplot2_2.3.6_bugs.ipynb
{
"metadata": {
"name": "rpy2_ggplot2_2.3.5_bugs"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pandas.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 2,
"text": [
"'0.10.1'"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import rpy2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rpy2.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"'2.3.6'"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import IPython"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"IPython.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 6,
"text": [
"'1.0.dev'"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from rpy2.robjects import pandas2ri\n",
"pandas2ri.activate()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from rpy2.robjects.lib import ggplot2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from rpy2.interactive import ipython"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = {\"x\": range(10), \"y\": numpy.random.random_integers(100, 200, 10),\n",
" \"fac\": [random.choice(\"AB\") for i in range(10)], \"z\": numpy.random.random_sample(10)}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pandas.DataFrame(data)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ipython.ggplot(df) + \\\n",
" ggplot2.aes_string(x=\"x\", y=\"y\", group=\"fac\", colour=\"fac\") + \\\n",
" ggplot2.geom_line() + \\\n",
" ggplot2.scale_x_continuous(\"Some Integer X\") +\\\n",
" ggplot2.scale_y_continuous(\"Some Integer Y\")\n",
"# ggplot2.facet_grid(\"strain ~ .\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
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HZ2VlVVdXnz59/qdrZmbmW9/61uOPPx4cHIxhWG5u7pYtKPvRAJ3YxDKbWOabYSG/nVt4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\ncLn9k5Cdz5w9jcurmDLDdHcQslPKztMuFUDi3mD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],
"text": [
"<GGPlot - Python:0xbd3f78c / R:0xb99d080>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ipython.ggplot(df) + \\\n",
" ggplot2.aes_string(x=\"x\", y=\"z\", group=\"fac\", colour=\"fac\") + \\\n",
" ggplot2.geom_line() + \\\n",
" ggplot2.scale_x_continuous(\"Some Other X\") +\\\n",
" ggplot2.scale_y_continuous(\"Some Float Z\")\n",
"# ggplot2.facet_grid(\"strain ~ .\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"png": 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bBKPtTYobbVt8Em+zSY1nbYKWdJZRU9xo+4KRi1AQhDVr1rz22mtBQV+eudzT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\nXo1GM/SQhIQE5xeZmZl79+6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"text": [
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}
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