{
"metadata": {
"language": "ruby",
"name": "",
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"require 'mikon'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
""
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"\"if(window['d3'] === undefined ||\\n window['Nyaplot'] === undefined){\\n var path = {\\\"d3\\\":\\\"http://d3js.org/d3.v3.min\\\"};\\n\\n\\n\\n var shim = {\\\"d3\\\":{\\\"exports\\\":\\\"d3\\\"}};\\n\\n require.config({paths: path, shim:shim});\\n\\n\\nrequire(['d3'], function(d3){window['d3']=d3;console.log('finished loading d3');\\n\\n\\tvar script = d3.select(\\\"head\\\")\\n\\t .append(\\\"script\\\")\\n\\t .attr(\\\"src\\\", \\\"https://rawgit.com/domitry/Nyaplotjs/master/release/nyaplot.js\\\")\\n\\t .attr(\\\"async\\\", true);\\n\\n\\tscript[0][0].onload = script[0][0].onreadystatechange = function(){\\n\\n\\n\\t var event = document.createEvent(\\\"HTMLEvents\\\");\\n\\t event.initEvent(\\\"load_nyaplot\\\",false,false);\\n\\t window.dispatchEvent(event);\\n\\t console.log('Finished loading Nyaplotjs');\\n\\n\\t};\\n\\n\\n});\\n}\\n\""
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"true"
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"prompt_number": 1
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{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Analyzing Iris dataset using Mikon and StatSample"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"* creative commons licensed (BY) flickr photo by Ian Sane: http://flickr.com/photos/31246066@N04/6705547029"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path = File.expand_path(\"../iris.csv\", __FILE__)\n",
"df = Mikon::DataFrame.from_csv(path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
" | sepal_length | sepal_width | petal_length | petal_width | species |
|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
|---|
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
|---|
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
|---|
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
|---|
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
|---|
| 5 | 5.4 | 3.9 | 1.7 | 0.4 | setosa |
|---|
| 6 | 4.6 | 3.4 | 1.4 | 0.3 | setosa |
|---|
| 7 | 5.0 | 3.4 | 1.5 | 0.2 | setosa |
|---|
| 8 | 4.4 | 2.9 | 1.4 | 0.2 | setosa |
|---|
| 9 | 4.9 | 3.1 | 1.5 | 0.1 | setosa |
|---|
| 10 | 5.4 | 3.7 | 1.5 | 0.2 | setosa |
|---|
| 11 | 4.8 | 3.4 | 1.6 | 0.2 | setosa |
|---|
| 12 | 4.8 | 3.0 | 1.4 | 0.1 | setosa |
|---|
| 13 | 4.3 | 3.0 | 1.1 | 0.1 | setosa |
|---|
| 14 | 5.8 | 4.0 | 1.2 | 0.2 | setosa |
|---|
| 15 | 5.7 | 4.4 | 1.5 | 0.4 | setosa |
|---|
| 16 | 5.4 | 3.9 | 1.3 | 0.4 | setosa |
|---|
| 17 | 5.1 | 3.5 | 1.4 | 0.3 | setosa |
|---|
| 18 | 5.7 | 3.8 | 1.7 | 0.3 | setosa |
|---|
| 19 | 5.1 | 3.8 | 1.5 | 0.3 | setosa |
|---|
| 20 | 5.4 | 3.4 | 1.7 | 0.2 | setosa |
|---|
| 21 | 5.1 | 3.7 | 1.5 | 0.4 | setosa |
|---|
| 22 | 4.6 | 3.6 | 1.0 | 0.2 | setosa |
|---|
| 23 | 5.1 | 3.3 | 1.7 | 0.5 | setosa |
|---|
| 24 | 4.8 | 3.4 | 1.9 | 0.2 | setosa |
|---|
| 25 | 5.0 | 3.0 | 1.6 | 0.2 | setosa |
|---|
| 26 | 5.0 | 3.4 | 1.6 | 0.4 | setosa |
|---|
| 27 | 5.2 | 3.5 | 1.5 | 0.2 | setosa |
|---|
| 28 | 5.2 | 3.4 | 1.4 | 0.2 | setosa |
|---|
| 29 | 4.7 | 3.2 | 1.6 | 0.2 | setosa |
|---|
| 30 | 4.8 | 3.1 | 1.6 | 0.2 | setosa |
|---|
| 31 | 5.4 | 3.4 | 1.5 | 0.4 | setosa |
|---|
| 32 | 5.2 | 4.1 | 1.5 | 0.1 | setosa |
|---|
| 33 | 5.5 | 4.2 | 1.4 | 0.2 | setosa |
|---|
| 34 | 4.9 | 3.1 | 1.5 | 0.1 | setosa |
|---|
| 35 | 5.0 | 3.2 | 1.2 | 0.2 | setosa |
|---|
| 36 | 5.5 | 3.5 | 1.3 | 0.2 | setosa |
|---|
| 37 | 4.9 | 3.1 | 1.5 | 0.1 | setosa |
|---|
| 38 | 4.4 | 3.0 | 1.3 | 0.2 | setosa |
|---|
| 39 | 5.1 | 3.4 | 1.5 | 0.2 | setosa |
|---|
| 40 | 5.0 | 3.5 | 1.3 | 0.3 | setosa |
|---|
| 41 | 4.5 | 2.3 | 1.3 | 0.3 | setosa |
|---|
| 42 | 4.4 | 3.2 | 1.3 | 0.2 | setosa |
|---|
| 43 | 5.0 | 3.5 | 1.6 | 0.6 | setosa |
|---|
| 44 | 5.1 | 3.8 | 1.9 | 0.4 | setosa |
|---|
| 45 | 4.8 | 3.0 | 1.4 | 0.3 | setosa |
|---|
| 46 | 5.1 | 3.8 | 1.6 | 0.2 | setosa |
|---|
| 47 | 4.6 | 3.2 | 1.4 | 0.2 | setosa |
|---|
| 48 | 5.3 | 3.7 | 1.5 | 0.2 | setosa |
|---|
| 49 | 5.0 | 3.3 | 1.4 | 0.2 | setosa |
|---|
| 50 | 7.0 | 3.2 | 4.7 | 1.4 | versicolor |
|---|
| ... | ... | ... | ... | ... | ... |
|---|
| 149 | 5.9 | 3.0 | 5.1 | 1.8 | virginica |
|---|
"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"#, @dtype=:float64>, #, @dtype=:float64>, #, @dtype=:float64>, #, @dtype=:float64>, #], @name=\"e04b0a91-2369-49cf-a1f2-b5af42bc7a22\", @index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot = df.plot(type: :scatter, x: :sepal_length, y: :petal_length, fill_by: :species, color: :qual)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"#[#:scatter, :options=>{:x=>:sepal_length, :y=>:petal_length, :color=>[\"rgb(179,226,205)\", \"rgb(253,205,172)\", \"rgb(203,213,232)\", \"rgb(244,202,228)\", \"rgb(230,245,201)\", \"rgb(255,242,174)\", \"rgb(241,226,204)\", \"rgb(204,204,204)\"], :fill_by=>:species}, :data=>\"e04b0a91-2369-49cf-a1f2-b5af42bc7a22\"}, @xrange=[4.3, 7.9], @yrange=[1.0, 6.9]>], :options=>{:x_label=>:sepal_length, :y_label=>:petal_length, :zoom=>true, :width=>700, :xrange=>[4.3, 7.9], :yrange=>[1.0, 6.9]}}>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"require 'statsample'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"false"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then draw a regression line using `Statsample::Regression`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lr = Statsample::Regression.simple(df[:sepal_length], df[:petal_length])\n",
"puts lr.summary\n",
"a, b = lr.a, lr.b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"= Regression of sepal_length over petal_length\n",
" Table 2\n",
"+----------+--------+\n",
"| Variable | Value |\n",
"+----------+--------+\n",
"| r | 0.872 |\n",
"| r^2 | 0.760 |\n",
"| a | -7.095 |\n",
"| b | 1.858 |\n",
"| s.e | 0.867 |\n",
"+----------+--------+\n",
"\n",
"\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"[-7.095381478279314, 1.8575096654214456]"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = (df[:sepal_length].min.round..df[:sepal_length].max.round).to_a\n",
"y = x.map{|v| b*v+a}\n",
"plot.add(:line, x, y)\n",
"plot"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
"\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"#[#:scatter, :options=>{:x=>:sepal_length, :y=>:petal_length, :color=>[\"rgb(127,201,127)\", \"rgb(190,174,212)\", \"rgb(253,192,134)\", \"rgb(255,255,153)\", \"rgb(56,108,176)\", \"rgb(240,2,127)\", \"rgb(191,91,23)\", \"rgb(102,102,102)\"], :fill_by=>:species}, :data=>\"57a65d78-ecc2-4842-a37d-69ad38a8f9d1\"}, @xrange=[4.3, 7.9], @yrange=[1.0, 6.9]>, #:line, :options=>{:x=>\"data0\", :y=>\"data1\"}, :data=>\"4b5665e9-adc5-4127-af0f-931653704356\"}, @xrange=[4, 8], @yrange=[0.33465718340646866, 7.764695845092251]>], :options=>{:x_label=>:sepal_length, :y_label=>:petal_length, :zoom=>true, :width=>700, :xrange=>[4.3, 7.9], :yrange=>[1.0, 6.9]}}>"
]
}
],
"prompt_number": 14
}
],
"metadata": {}
}
]
}