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@mwaskom
Created January 21, 2015 18:53
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Implementing Karl Broman's interactive car crash dotplot with seaborn/ipython interact
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"signature": "sha256:cccfaa9e87bd3eda70c995ceb8e4be3a947ca6244fb31566bbe06fb76cc75f59"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a replication of Karl Broman's [replication](https://kbroman.wordpress.com/2014/11/03/car-crash-stats-revisited-my-measurement-errors/) of a 538 article about car crash statistics."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import seaborn as sns\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext rpy2.ipython"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R -o crashes\n",
"source(\"crash_data.R\")\n",
"abbrev <- crashes$abbrev\n",
"\n",
"# 538 data\n",
"crashes <- read.csv(\"bad-drivers.csv\")\n",
"\n",
"rownames(crashes) <- crashes[,1]\n",
"crashes[,3:6] <- crashes[,3:6]*crashes[,2]/100\n",
"colnames(crashes)[-1] <- c(\"total\", \"speeding\", \"alcohol\", \"not_distracted\", \"no_previous\", \"ins_premium\", \"ins_losses\")\n",
"crashes <- crashes[,-1]\n",
"crashes$abbrev <- abbrev"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.html.widgets import interact"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"crashes.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total</th>\n",
" <th>speeding</th>\n",
" <th>alcohol</th>\n",
" <th>not_distracted</th>\n",
" <th>no_previous</th>\n",
" <th>ins_premium</th>\n",
" <th>ins_losses</th>\n",
" <th>abbrev</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 18.8</td>\n",
" <td> 7.332</td>\n",
" <td> 5.640</td>\n",
" <td> 18.048</td>\n",
" <td> 15.040</td>\n",
" <td> 784.55</td>\n",
" <td> 145.08</td>\n",
" <td> AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 18.1</td>\n",
" <td> 7.421</td>\n",
" <td> 4.525</td>\n",
" <td> 16.290</td>\n",
" <td> 17.014</td>\n",
" <td> 1053.48</td>\n",
" <td> 133.93</td>\n",
" <td> AK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 18.6</td>\n",
" <td> 6.510</td>\n",
" <td> 5.208</td>\n",
" <td> 15.624</td>\n",
" <td> 17.856</td>\n",
" <td> 899.47</td>\n",
" <td> 110.35</td>\n",
" <td> AZ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 22.4</td>\n",
" <td> 4.032</td>\n",
" <td> 5.824</td>\n",
" <td> 21.056</td>\n",
" <td> 21.280</td>\n",
" <td> 827.34</td>\n",
" <td> 142.39</td>\n",
" <td> AR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 12.0</td>\n",
" <td> 4.200</td>\n",
" <td> 3.360</td>\n",
" <td> 10.920</td>\n",
" <td> 10.680</td>\n",
" <td> 878.41</td>\n",
" <td> 165.63</td>\n",
" <td> CA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
" total speeding alcohol not_distracted no_previous ins_premium \\\n",
"0 18.8 7.332 5.640 18.048 15.040 784.55 \n",
"1 18.1 7.421 4.525 16.290 17.014 1053.48 \n",
"2 18.6 6.510 5.208 15.624 17.856 899.47 \n",
"3 22.4 4.032 5.824 21.056 21.280 827.34 \n",
"4 12.0 4.200 3.360 10.920 10.680 878.41 \n",
"\n",
" ins_losses abbrev \n",
"0 145.08 AL \n",
"1 133.93 AK \n",
"2 110.35 AZ \n",
"3 142.39 AR \n",
"4 165.63 CA "
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This breaks what we are trying to do below\n",
"#crashes[\"abbrev\"] = pd.Categorical(crashes.abbrev.values, ordered=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@interact\n",
"def plot_drivers(sort_on=crashes.columns.tolist()):\n",
" \n",
" # Make the PairGrid\n",
" g = sns.PairGrid(crashes.sort(sort_on, ascending=False),\n",
" y_vars=[\"abbrev\"], x_vars=crashes.columns[:-3],\n",
" size=10, aspect=.3)\n",
" \n",
" # Draw a stripplot (this is really more of a dotplot) on each column\n",
" g.map(sns.stripplot, size=10, orient=\"h\", palette=\"husl\")\n",
"\n",
" # Use the same x axis limits on all columns\n",
" g.set(xlim=(0, 25), xlabel=\"Crashes\")\n",
"\n",
" # Add semantically meaningful titles to the columns\n",
" titles = [\"Total crashes\", \"Speeding crashes\", \"Alcohol crashes\",\n",
" \"Not distracted crashes\", \"No previous crashes\"]\n",
" for ax, title in zip(g.axes.flat, titles):\n",
" ax.set(title=title)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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0ijcB0bVm1TLtqtJ0fESSp7gTkXqmPlBk9tIUR0pmiMxSGOaqKkNZbXR8RJKn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\nex6+ld/+p6spj87zF9VWHgepu5hJ9RtrWpKOsic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vBEZjjNtjjBcDPw/8WQjhjBzGpYKV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gr8dkTcfIx/7lJEOAjTUDM/0taZH2nrzI90bMxQefgvY0illEaBzwE/AZ4T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\ngfK/ANuEBt6kCyV8AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x110acce10>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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