{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "_Note:_ this tutorial is from the Daru visualization tutorial : http://nbviewer.jupyter.org/github/SciRuby/sciruby-notebooks/blob/master/Visualization/Visualizing%20data%20with%20daru%20DataFrame.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating Visualizations with DataFrame\n", "\n", "Using nyaplot in the background to generate interactive plots, which can be viewed in your browser.\n", "\n", "In this tutorial we'll see how we can create some interesting plots with Daru::DataFrame using the Daru::View::Plot function." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Install the spreadsheet gem version ~>1.1.1 for using spreadsheet functions.\n", "\n", "Install the mechanize gem version ~>2.7.5 for using mechanize functions.\n" ] }, { "data": { "text/plain": [ "true" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "require 'daru/view'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "if(window['d3'] === undefined ||\n", " window['Nyaplot'] === undefined){\n", " var path = {\"d3\":\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\",\"downloadable\":\"http://cdn.rawgit.com/domitry/d3-downloadable/master/d3-downloadable\"};\n", "\n", "\n", "\n", " var shim = {\"d3\":{\"exports\":\"d3\"},\"downloadable\":{\"exports\":\"downloadable\"}};\n", "\n", " require.config({paths: path, shim:shim});\n", "\n", "\n", "require(['d3'], function(d3){window['d3']=d3;console.log('finished loading d3');require(['downloadable'], function(downloadable){window['downloadable']=downloadable;console.log('finished loading downloadable');\n", "\n", "\tvar script = d3.select(\"head\")\n", "\t .append(\"script\")\n", "\t .attr(\"src\", \"http://cdn.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" ], "text/plain": [ "\"if(window['d3'] === undefined ||\\n window['Nyaplot'] === undefined){\\n var path = {\\\"d3\\\":\\\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\\\",\\\"downloadable\\\":\\\"http://cdn.rawgit.com/domitry/d3-downloadable/master/d3-downloadable\\\"};\\n\\n\\n\\n var shim = {\\\"d3\\\":{\\\"exports\\\":\\\"d3\\\"},\\\"downloadable\\\":{\\\"exports\\\":\\\"downloadable\\\"}};\\n\\n require.config({paths: path, shim:shim});\\n\\n\\nrequire(['d3'], function(d3){window['d3']=d3;console.log('finished loading d3');require(['downloadable'], function(downloadable){window['downloadable']=downloadable;console.log('finished loading downloadable');\\n\\n\\tvar script = d3.select(\\\"head\\\")\\n\\t .append(\\\"script\\\")\\n\\t .attr(\\\"src\\\", \\\"http://cdn.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": "display_data" }, { "data": { "text/plain": [ ":nyaplot" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set a default plotting library\n", "Daru::View.plotting_library = :nyaplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scatter Plot\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ " Daru::DataFrame(100x2) \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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ab
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.........
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" ], "text/plain": [ "#\n", " a b\n", " 0 0 0.34478417\n", " 1 1 0.69687349\n", " 2 2 0.09649490\n", " 3 3 0.52631267\n", " 4 4 0.62937317\n", " 5 5 0.38704789\n", " 6 6 0.16484049\n", " 7 7 0.25855979\n", " 8 8 0.67242798\n", " 9 9 0.81781846\n", " 10 10 0.35740329\n", " 11 11 0.73693421\n", " 12 12 0.89303260\n", " 13 13 0.01837073\n", " 14 14 0.63362937\n", " ... ... ..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = Daru::DataFrame.new({\n", " a: Array.new(100) {|i| i}, \n", " b: 100.times.map{rand}\n", "})\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "#\n", " a b\n", " 0 0 0.34478417\n", " 1 1 0.69687349\n", " 2 2 0.09649490\n", " 3 3 0.52631267\n", " 4 4 0.62937317\n", " 5 5 0.38704789\n", " 6 6 0.16484049\n", " 7 7 0.25855979\n", " 8 8 0.67242798\n", " 9 9 0.81781846\n", " 10 10 0.35740329\n", " 11 11 0.73693421\n", " 12 12 0.89303260\n", " 13 13 0.01837073\n", " 14 14 0.63362937\n", " ... ... ..., @options={:type=>:scatter, :x=>:a, :y=>:b}, @user_options={}, @adapter=Daru::View::Adapter::NyaplotAdapter, @chart=#[#:scatter, :options=>{:x=>:a, :y=>:b}, :data=>\"679b94f9-47f6-4e4e-89b2-49b0e019478e\"}, @xrange=[0, 99], @yrange=[0.010839114272730876, 0.9930018236532082]>], :options=>{}}>>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scatter_1 = Daru::View::Plot.new(df, type: :scatter, x: :a, y: :b)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Also you may be faced with a situation where you want to see two different scatter plots on the same graph, each with a different color.\n", "\n", "All this can be done by combining #plot with a block. The #plot method yields the corresponding Nyaplot::Plot and Nyaplot::Diagram objects for the graph, which can be used for many varied customizations. Lets see some examples:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ " Daru::DataFrame(10x4) \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", "
temperaturesalescitystaff
030.4350Pune15
123.5150Delhi20
244.5500Pune15
320.3200Delhi20
434480Pune15
524250Delhi20
631.45330Pune15
728.34400Delhi20
837420Pune15
924560Delhi20
" ], "text/plain": [ "#\n", " temperatur sales city staff\n", " 0 30.4 350 Pune 15\n", " 1 23.5 150 Delhi 20\n", " 2 44.5 500 Pune 15\n", " 3 20.3 200 Delhi 20\n", " 4 34 480 Pune 15\n", " 5 24 250 Delhi 20\n", " 6 31.45 330 Pune 15\n", " 7 28.34 400 Delhi 20\n", " 8 37 420 Pune 15\n", " 9 24 560 Delhi 20" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DataFrame denoting Ice Cream sales of a particular food chain in a city\n", "# according to the maximum recorded temperature in that city. It also lists\n", "# the staff strength present in each city.\n", "\n", "df = Daru::DataFrame.new({\n", " :temperature => [30.4, 23.5, 44.5, 20.3, 34, 24, 31.45, 28.34, 37, 24],\n", " :sales => [350, 150, 500, 200, 480, 250, 330, 400, 420, 560],\n", " :city => ['Pune', 'Delhi']*5,\n", " :staff => [15,20]*5\n", " })\n", "df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "#[#:scatter, :options=>{:x=>:temperature, :y=>:sales, :tooltip_contents=>[:city, :staff], :color=>#, :fill_by=>:city, :shape_by=>:city}, :data=>\"203a200e-5a70-4e1f-9a69-3618d9b0907e\"}, @xrange=[20.3, 44.5], @yrange=[150, 560]>], :options=>{:x_label=>\"Temperature\", :y_label=>\"Sales\", :yrange=>[100, 600], :xrange=>[15, 50], :zoom=>true, :width=>700}}>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Generating a scatter plot with tool tips, colours and different shapes.\n", "scatter_2 = Daru::View::Plot.new(df, type: :scatter, x: :temperature, y: :sales).chart\n", "scatter_2.tap do |plot, diagram|\n", " plot.x_label \"Temperature\"\n", " plot.y_label \"Sales\"\n", " plot.yrange [100, 600]\n", " plot.xrange [15, 50]\n", " plot.diagrams[0].tooltip_contents([:city, :staff])\n", " plot.diagrams[0].color(Nyaplot::Colors.qual) # set the color scheme for this diagram. See Nyaplot::Colors for more info.\n", " plot.diagrams[0].fill_by(:city) # Change color of each point WRT to the city that it belongs to.\n", " plot.diagrams[0].shape_by(:city) # Shape each point WRT to the city that it belongs to.\n", "end\n", "\n", "# Move the mouse pointer over the points to see the tool tips." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[#:scatter, :options=>{:x=>:temperature, :y=>:sales, :tooltip_contents=>[:city, :staff], :color=>#, :fill_by=>:city, :shape_by=>:city}, :data=>\"203a200e-5a70-4e1f-9a69-3618d9b0907e\"}, @xrange=[20.3, 44.5], @yrange=[150, 560]>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Array of diagrams\n", "scatter_2.diagrams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bar Graph\n", "Generating a bar graph requires passing `:bar` into the `:type` option." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ " Daru::DataFrame(5x2) \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", "
nameage
3Shivaji Maharaj53
2Rana Pratap57
4Krishnadevaraya58
1Akbar The Great63
0Emperor Asoka72
" ], "text/plain": [ "#\n", " name age\n", " 3 Shivaji Ma 53\n", " 2 Rana Prata 57\n", " 4 Krishnadev 58\n", " 1 Akbar The 63\n", " 0 Emperor As 72" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# A Bar Graph denoting the age at which various Indian Kings died.\n", "\n", "df = Daru::DataFrame.new({\n", " name: ['Emperor Asoka', 'Akbar The Great', 'Rana Pratap', 'Shivaji Maharaj', 'Krishnadevaraya'],\n", " age: [72,63,57,53,58] \n", " }, order: [:name, :age])\n", "\n", "df.sort!([:age])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "#[#:bar, :options=>{:x=>:name, :y=>:age}, :data=>\"83973487-ae3d-484f-8f3b-176f0378d1af\"}, @xrange=[\"Shivaji Maharaj\", \"Rana Pratap\", \"Krishnadevaraya\", \"Akbar The Great\", \"Emperor Asoka\"], @yrange=[0, 72]>], :options=>{:x_label=>\"Name\", :y_label=>\"Age\", :yrange=>[20, 80], :width=>700, :xrange=>[\"Shivaji Maharaj\", \"Rana Pratap\", \"Krishnadevaraya\", \"Akbar The Great\", \"Emperor Asoka\"]}}>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Daru::View::Plot.new(df, type: :bar, x: :name, y: :age).chart.tap do |plot|\n", " plot.x_label \"Name\"\n", " plot.y_label \"Age\"\n", " plot.yrange [20,80]\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**It is also possible to simply pass in the `:x ` parameter if you want to the frequency of occurence of each element in a Vector.**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", 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\n", "\n" ], "text/plain": [ "#[#:bar, :options=>{:value=>:a}, :data=>\"bdf062c8-d220-4e6c-a339-1a9a8f597908\"}, @xrange=[\"A\", \"C\", \"T\", \"G\"], @yrange=[0, 1000]>], :options=>{:yrange=>[0, 350], :y_label=>\"Frequency\", :x_label=>\"Letter\", :width=>700, :xrange=>[\"A\", \"C\", \"T\", \"G\"]}}>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = ['A', 'C', 'G', 'T']\n", "v = 1000.times.map { a.sample }\n", "\n", "puts \"v : \", v\n", "\n", "df = Daru::DataFrame.new({\n", " a: v\n", " })\n", "Daru::View::Plot.new(df, type: :bar, x: :a).chart.tap do |plot|\n", " plot.yrange [0,350]\n", " plot.y_label \"Frequency\"\n", " plot.x_label \"Letter\"\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Box Plots\n", "\n", "A box plot can be generated of the numerical vectors in the DataFrame by simply passing `:box` to the `:type` argument.\n", "\n", "To demonstrate, I'll prepare some data using the distribution gem to get a bunch of normally distributed random variables. We'll then plot in a Box plot after creating a DataFrame with the data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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:d=>0.14505768415616277}, {:a=>-0.829881237317033, :b=>-3.037351546646291, :c=>-0.6128693610487363, :d=>-0.7788456085121429}, {:a=>0.1957098939928723, :b=>-1.7553626325089096, :c=>0.5152808833921596, :d=>0.554422862190734}, {:a=>1.7942143223087066, :b=>0.24276790288588312, :c=>2.2736357545395776, :d=>2.6324786190013185}, {:a=>1.839111678231645, :b=>0.29888959778955604, :c=>2.3230228460548097, :d=>2.6908451817011385}, {:a=>-1.0428002995770989, :b=>-3.3035003744713736, :c=>-0.8470803295348088, :d=>-1.0556403894502284}, {:a=>-1.3384047302740363, :b=>-3.6730059128425454, :c=>-1.1722452033014399, :d=>-1.4399261493562472}, {:a=>-1.1264347208345862, :b=>-3.4080434010432326, :c=>-0.939078192918045, :d=>-1.1643651370849621}, {:a=>-0.04894137694053139, :b=>-2.061176721175664, :c=>0.24616448536541546, :d=>0.23637620997730918}, {:a=>0.10969938734213491, :b=>-1.8628757658223314, :c=>0.42066932607634844, :d=>0.4426092035447754}, {:a=>0.7028760411686078, :b=>-1.1214049485392403, :c=>1.0731636452854687, :d=>1.2137388535191902}, {:a=>0.2908750711611964, :b=>-1.6364061610485046, :c=>0.619962578277316, :d=>0.6781375925095553}, {:a=>-0.8536431222453108, :b=>-3.0670539028066384, :c=>-0.639007434469842, :d=>-0.809736058918904}, {:a=>0.3774274794571718, :b=>-1.5282156506785354, :c=>0.715170227402889, :d=>0.7906557232943234}, {:a=>0.6647989749590101, :b=>-1.1690012813012376, :c=>1.0312788724549111, :d=>1.1642386674467131}, {:a=>-0.04324784899356504, :b=>-2.0540598112419564, :c=>0.25242736610707844, :d=>0.24377779630836544}, {:a=>0.20500175158357078, :b=>-1.7437478105205364, :c=>0.5255019267419279, :d=>0.5665022770586421}, {:a=>0.680878437815977, :b=>-1.1489019527300288, :c=>1.0489662815975747, :d=>1.18514196916077}, {:a=>-0.6298515676242159, :b=>-2.7873144595302697, :c=>-0.3928367243866376, :d=>-0.5188070379114806}, {:a=>0.09324002213306072, :b=>-1.8834499723336742, :c=>0.4025640243463668, :d=>0.4212120287729789}, {:a=>-0.531631369050414, :b=>-2.6645392113130173, :c=>-0.28479450595545547, :d=>-0.3911207797655382}, {:a=>1.6977451638463303, :b=>0.12218145480791298, :c=>2.1675196802309635, :d=>2.5070687130002294}, {:a=>1.6657905744915873, :b=>0.08223821811448406, :c=>2.132369631940746, :d=>2.4655277468390633}, {:a=>0.19701844686507372, :b=>-1.7537269414186578, :c=>0.5167202915515812, :d=>0.5561239809245959}, {:a=>0.35537987440746244, :b=>-1.555775156990672, :c=>0.6909178618482087, :d=>0.7619938367297012}, {:a=>-0.595021059642325, :b=>-2.7437763245529063, :c=>-0.35452316560655756, :d=>-0.47352737753502255}, {:a=>0.5975327651718575, :b=>-1.2530840435351782, :c=>0.9572860416890432, :d=>1.0767925947234147}, {:a=>-0.0059075291206308195, :b=>-2.0073844114007886, :c=>0.29350171796730606, :d=>0.29232021214317994}, {:a=>-0.32064869709456145, :b=>-2.400810871368202, :c=>-0.05271356680401762, :d=>-0.11684330622292993}, {:a=>1.8976360598403197, :b=>0.3720450748003996, :c=>2.3873996658243515, :d=>2.7669268777924154}, {:a=>2.0595372157033025, :b=>0.5744215196291278, :c=>2.565490937273633, :d=>2.9773983804142934}, {:a=>0.16655949142051998, :b=>-1.79180063572435, :c=>0.483215440562572, :d=>0.516527338846676}, {:a=>0.16192373003953098, :b=>-1.7975953374505862, :c=>0.47811610304348406, :d=>0.5105008490513903}, {:a=>-1.480338012774993, :b=>-3.850422515968741, :c=>-1.3283718140524923, :d=>-1.624439416607491}, {:a=>-0.39415510070290816, :b=>-2.492693875878635, :c=>-0.13357061077319904, :d=>-0.21240163091378067}, {:a=>-0.7993210096855387, :b=>-2.999151262106923, :c=>-0.5792531106540926, :d=>-0.7391173125912003}, {:a=>-0.6349741479692209, :b=>-2.793717684961526, :c=>-0.3984715627661431, :d=>-0.5254663923599872}, {:a=>1.2094553127287309, :b=>-0.4881808590890866, :c=>1.630400844001604, :d=>1.8722919065473502}]>}, :extension=>[]}>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "require 'distribution'\n", "rng = Distribution::Normal.rng\n", "# Daru.lazy_update = false\n", "\n", "arr = []\n", "1000.times {arr.push(rng.call)}\n", "\n", "arr1 = arr.map{|val| val/0.8-2}\n", "arr2 = arr.map{|val| val*1.1+0.3}\n", "arr3 = arr.map{|val| val*1.3+0.3}\n", "\n", "df = Daru::DataFrame.new({ a: arr, b: arr1, c: arr2, d: arr3 })\n", "box_1 = Daru::View::Plot.new(df, type: :box)\n", "box_1.show_in_iruby" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Line Graphs\n", "\n", "Line graphs can be easily generated by passing `:line` to the `:type` option.\n", "\n", "For example, lets plot a simple line graph showing the temperature of New York City over a week." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "#[#:line, :options=>{:x=>:day, :y=>:temperature, :title=>\"Temperature in NYC\"}, :data=>\"a5abf840-3eb6-47c4-a9e1-ce5490590465\"}, @xrange=[1, 6], @yrange=[43, 59]>], :options=>{:x_label=>\"Day\", :y_label=>\"Temperature\", :yrange=>[20, 60], :xrange=>[1, 6], :legend=>true, :zoom=>true, :width=>800}}>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = Daru::DataFrame.new({\n", " temperature: [43,53,50,57,59,47],\n", " day: [1,2,3,4,5,6]\n", "})\n", "\n", "line_1 = Daru::View::Plot.new(df,type: :line, x: :day, y: :temperature).chart\n", "line_1.tap do |plot|\n", " plot.x_label \"Day\"\n", " plot.y_label \"Temperature\"\n", " plot.yrange [20,60]\n", " plot.xrange [1,6]\n", " plot.legend true\n", " plot.diagrams[0].title \"Temperature in NYC\"\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histogram\n", "\n", "Specify `:histogram` to `:type` will make a histogram from the data.\n", "\n", "Histograms dont need a X axis label (because they show the frequency of elements in each bin) so you need to specify the name of the vector you want to plot by passing its name into the `:x` option." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "#[#:histogram, :options=>{:value=>:a}, :data=>\"6c70349a-50f2-45f0-93ea-b99fa42a49e5\"}, @xrange=[0, 0.9975215098702691], @yrange=[0, 1000]>], :options=>{:yrange=>[0, 150], :y_label=>\"Frequency\", :x_label=>\"Bins\", :width=>700, :xrange=>[0, 0.9975215098702691]}}>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = 1000.times.map { rand }\n", "\n", "df = Daru::DataFrame.new({\n", " a: v\n", " })\n", "\n", "Daru::View::Plot.new(df, type: :histogram, x: :a).chart.tap do |plot|\n", " plot.yrange [0,150]\n", " plot.y_label \"Frequency\"\n", " plot.x_label \"Bins\"\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple Diagrams on the same Plot¶\n", "\n", "##### Scatter Diagrams on the same Plot\n", "\n", "Daru allows you to plot as many columns of your dataframe as you want on the same plot.\n", "\n", "This can allow you to plot data from the dataframe onto the same graph and visually compare results from observations. You can individually set the color or point shape of each diagram on the plot.\n", "\n", "As a first demostration, lets create a DataFrame of the temperatures of three different cities over the period of a week. Then, we'll plot them all on the same graph by passing options to the plot method which tell it the Vectors that are to be used for each of the diagrams." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "#[#:scatter, :options=>{:x=>:day, :y=>:nyc_temp, :title=>\"Temprature in NYC\", :color=>\"#00FF00\"}, :data=>\"d542c388-aa6d-464e-85b4-eddf3b67001c\"}, @xrange=[1, 6], @yrange=[43, 59]>, #:scatter, :options=>{:x=>:day, :y=>:chicago_temp, :title=>\"Temprature in Chicago\", :color=>\"#FFFF00\"}, :data=>\"d542c388-aa6d-464e-85b4-eddf3b67001c\"}, @xrange=[1, 6], @yrange=[20, 38]>, #:scatter, :options=>{:x=>:day, :y=>:sf_temp, :title=>\"Temprature in SF\", :color=>\"#0000FF\"}, :data=>\"d542c388-aa6d-464e-85b4-eddf3b67001c\"}, @xrange=[1, 6], @yrange=[52, 73]>], :options=>{:legend=>true, :yrange=>[0, 100], :x_label=>\"Day\", :y_label=>\"Temperature\", :zoom=>true, :width=>800, :xrange=>[1, 6]}}>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = Daru::DataFrame.new({\n", " nyc_temp: [43,53,50,57,59,47],\n", " chicago_temp: [23,30,35,20,26,38],\n", " sf_temp: [60,65,73,67,55,52],\n", " day: [1 ,2 ,3 ,4 ,5 , 6]\n", " })\n", "\n", "# As you can see, the options passed denote the x and y axes that are to be used by each diagram.\n", "# You can add as many x any y axes as you want, just make sure the relevant vectors are present\n", "# in your DataFrame!\n", "#\n", "# Heres an explanation of all the options passed:\n", "#\n", "# * type - The type of graph to be drawn. All the diagrams will be of the same type in this case.\n", "# * x1/x2/x3 - The Vector from the DataFrame that is to be treated as the X axis for each of the \n", "# three diagrams. In this case all of them need the :day Vector.\n", "# * y1/y2/y3 - The Vector from the DataFrame that is to be treated as the Y axis for each of the\n", "# three diagrams. As you can see the 1st diagram will plot nyc_temp, the 2nd chicago_temp and the\n", "# the 3rd sf_temp.\n", "# \n", "# The values yielded in the block are also slightly different in this case.\n", "# The first argument ('plot') is the same as in all the above examples (Nyaplot::Plot), but the \n", "# second argument ('diagrams') is now an Array of Nyaplot::Diagram objects. Each of the elements\n", "# in the Array represents the diagrams that you want to plot according to the sorting sequence\n", "# of the options specifying the axes.\n", "\n", "graph = Daru::View::Plot.new(df, type: :scatter, x1: :day, y1: :nyc_temp, x2: :day, y2: :chicago_temp, x3: :day, y3: :sf_temp) \n", "\n", "graph.chart.tap do |plot|\n", " nyc = plot.diagrams[0]\n", " chicago = plot.diagrams[1]\n", " sf = plot.diagrams[2]\n", " \n", " nyc.title \"Temprature in NYC\"\n", " nyc.color \"#00FF00\"\n", " \n", " chicago.title \"Temprature in Chicago\"\n", " chicago.color \"#FFFF00\"\n", " \n", " sf.title \"Temprature in SF\"\n", " sf.color \"#0000FF\"\n", " \n", " plot.legend true\n", " plot.yrange [0,100]\n", " plot.x_label \"Day\"\n", " plot.y_label \"Temperature\"\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scatter and Line Diagram on the same Plot\n", "\n", "It is also possible to plot two different kinds of diagrams on the same plot. To show you how this works, I will plot a scatter graph and a line graph on the same plot.\n", "\n", "To elaborate, we will be plotting the a set of points on a scatter plot alongwith their line of best fit." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ " Daru::DataFrame(11x3) \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", "
burgerfatcalories
0Hamburger9260
1Cheeseburger13320
2Quarter Pounder21420
3Quarter Pounder with Cheese30530
4Big Mac31560
5Arch Sandwich Special31550
6Arch Special with Bacon34590
7Crispy Chicken25500
8Fish Fillet28560
9Grilled Chicken20440
10Grilled Chicken Light5300
" ], "text/plain": [ "#\n", " burger fat calories\n", " 0 Hamburger 9 260\n", " 1 Cheeseburg 13 320\n", " 2 Quarter Po 21 420\n", " 3 Quarter Po 30 530\n", " 4 Big Mac 31 560\n", " 5 Arch Sandw 31 550\n", " 6 Arch Speci 34 590\n", " 7 Crispy Chi 25 500\n", " 8 Fish Fille 28 560\n", " 9 Grilled Ch 20 440\n", " 10 Grilled Ch 5 300" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = Daru::DataFrame.new({\n", " burger: [\"Hamburger\",\"Cheeseburger\",\"Quarter Pounder\",\"Quarter Pounder with Cheese\",\"Big Mac\",\"Arch Sandwich Special\",\"Arch Special with Bacon\",\"Crispy Chicken\",\"Fish Fillet\",\"Grilled Chicken\",\"Grilled Chicken Light\"],\n", " fat: [9,13 ,21 ,30 ,31 ,31 ,34 ,25 ,28 ,20 ,5],\n", " calories: [260,320,420,530,560,550,590,500,560,440,300]\n", " },\n", " order: [:burger, :fat, :calories])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now write a small algorithm to compute the slope of the line of best fit by placing the fat content as the X co-ordinates and calories as Y co-ordinates.\n", "\n", "The line of best fit will be a line graph of red color and the fat and calorie contents will be plotted as usual using a scatter plot." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n" ], "text/plain": [ "#[#:scatter, :options=>{:x=>:fat, :y=>:calories, :tooltip_contents=>[:burger]}, :data=>\"f22e46df-81b7-41e5-b332-37ddf471f6b4\"}, @xrange=[5, 34], @yrange=[260, 590]>, #:line, :options=>{:x=>:fat, :y=>:y_coords, :color=>\"#FF0000\"}, :data=>\"f22e46df-81b7-41e5-b332-37ddf471f6b4\"}, @xrange=[5, 34], @yrange=[252.5085518814139, 592.7156974534398]>], :options=>{:x_label=>\"Fat\", :y_label=>\"Calories\", :xrange=>[0, 50], :zoom=>true, :width=>700, :yrange=>[252.5085518814139, 592.7156974534398]}}>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Algorithm for computing the line of best fit\n", "\n", "sum_x = df[:fat].sum\n", "sum2_x = (df[:fat]*df[:fat]).sum \n", "sum_xy = (df[:fat]*df[:calories]).sum\n", "mean_x = df[:fat].mean\n", "mean_y = df[:calories].mean\n", "\n", "slope = (sum_xy - sum_x * mean_y) / (sum2_x - sum_x * mean_x)\n", "yint = mean_y - slope * mean_x\n", "\n", "# Assign the computed Y co-ordinates of the line of best fit to a column \n", "# in the DataFrame called :y_coords\n", "df[:y_coords] = df[:fat].map {|f| f*slope + yint }\n", "\n", "# As you can see the options passed into plot are slightly different this time.\n", "#\n", "# Instead of passing Vector names into :x1, :x2... separately, this time we pass\n", "# the relevant names of the X and Y axes co-ordinates as an Array into the :x and \n", "# :y options.This is a simpler and easier way to plot multiple diagrams.\n", "# \n", "# As is demonstrated in the previous example, the first argument yields a Nyaplot::Plot\n", "# object and the second an Array of Nyaplot::Diagram objects. The diagrams are ordered\n", "# according to the types specified in the `:type` option.\n", "\n", "graph = Daru::View::Plot.new(df, type: [:scatter, :line], x: [:fat, :fat], y: [:calories, :y_coords]) \n", "graph.chart.tap do |plot|\n", " plot.x_label \"Fat\"\n", " plot.y_label \"Calories\"\n", " plot.xrange [0,50]\n", " \n", " scatter = plot.diagrams[0]\n", " line = plot.diagrams[1]\n", " \n", " line.color \"#FF0000\" #set color of the line to 'red'\n", " scatter.tooltip_contents [:burger] # set tool tip to :burger\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Ruby 2.4.1", "language": "ruby", "name": "ruby" }, "language_info": { "file_extension": ".rb", "mimetype": "application/x-ruby", "name": "ruby", "version": "2.4.1" } }, "nbformat": 4, "nbformat_minor": 2 }