{
   "cells": [ 
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "import $ivy.`org.vegas-viz::vegas:0.3.9`\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "import vegas._\n","import vegas.data.External._\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# A simple bar chart with embedded data." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"A simple bar chart with embedded data.\").\n","  withData(Seq(\n","    Map(\"a\" -> \"A\", \"b\" -> 28), Map(\"a\" -> \"B\", \"b\" -> 55), Map(\"a\" -> \"C\", \"b\" -> 43),\n","    Map(\"a\" -> \"D\", \"b\" -> 91), Map(\"a\" -> \"E\", \"b\" -> 81), Map(\"a\" -> \"F\", \"b\" -> 53),\n","    Map(\"a\" -> \"G\", \"b\" -> 19), Map(\"a\" -> \"H\", \"b\" -> 87), Map(\"a\" -> \"I\", \"b\" -> 52)\n","  )).\n","  encodeX(\"a\", Ordinal).\n","  encodeY(\"b\", Quantitative).\n","  mark(Bar).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# A bar chart showing the US population distribution of age groups in 2000." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"A bar chart showing the US population distribution of age groups in 2000.\").\n","  withURL(Population).\n","  mark(Bar).\n","  filter(\"datum.year == 2000\").\n","  encodeY(\"age\", Ordinal, scale=Scale(bandSize=17)).\n","  encodeX(\"people\", Quantitative, aggregate=AggOps.Sum, axis=Axis(title=\"population\")).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas().\n","  withURL(Population).\n","  mark(Bar).\n","  addTransformCalculation(\"gender\", \"\"\"datum.sex == 2 ? \"Female\" : \"Male\"\"\"\").\n","  filter(\"datum.year == 2000\").\n","  encodeColumn(\"age\", Ord, scale=Scale(padding=4.0), axis=Axis(orient=Orient.Bottom, axisWidth=1.0, offset= -8.0)).\n","  encodeY(\"people\", Quantitative, aggregate=AggOps.Sum, axis=Axis(title=\"population\", grid=false)).\n","  encodeX(\"gender\", Nominal, scale=Scale(bandSize = 6.0), hideAxis=true).\n","  encodeColor(\"gender\", Nominal, scale=Scale(rangeNominals=List(\"#EA98D2\", \"#659CCA\"))).\n","  configFacet(cell=CellConfig(strokeWidth = 0)).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas().\n","  withURL(Unemployment).\n","  mark(Area).\n","  encodeX(\"date\", Temp, timeUnit=TimeUnit.Yearmonth, scale=Scale(nice=Nice.Month),\n","    axis=Axis(axisWidth=0, format=\"%Y\", labelAngle=0)).\n","  encodeY(\"count\", Quantitative, aggregate=AggOps.Sum).\n","  configCell(width=300, height=200).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas().\n","  withURL(Population).\n","  filter(\"datum.year == 2000\").\n","  addTransform(\"gender\", \"datum.sex == 2 ? \\\"Female\\\" : \\\"Male\\\"\").\n","  mark(Bar).\n","  encodeY(\"people\", Quant, AggOps.Sum, axis=Axis(title=\"population\")).\n","  encodeX(\"age\", Ord, scale=Scale(bandSize= 17)).\n","  encodeColor(\"gender\", Nominal, scale=Scale(rangeNominals=List(\"#EA98D2\", \"#659CCA\"))).\n","  configMark(stacked=StackOffset.Normalize).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# A trellis scatterplot showing Horsepower and Miles per gallons, faceted by binned values of Acceleration." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"A trellis scatterplot showing Horsepower and Miles per gallons, faceted by binned values of Acceleration.\").\n","  withURL(Cars).\n","  mark(Point).\n","  encodeX(\"Horsepower\", Quantitative).\n","  encodeY(\"Miles_per_Gallon\", Quantitative).\n","  encodeRow(\"Acceleration\", Quantitative, enableBin=true).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas().\n","  withURL(Movies).\n","  mark(Point).\n","  encodeX(\"IMDB_Rating\", Quantitative, bin=Bin(maxbins=10.0)).\n","  encodeY(\"Rotten_Tomatoes_Rating\", Quantitative, bin=Bin(maxbins=10.0)).\n","  encodeSize(aggregate=AggOps.Count, field=\"*\", dataType=Quantitative).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas().\n","  withURL(Cars).\n","  mark(Point).\n","  encodeX(\"Horsepower\", Quantitative).\n","  encodeY(\"Miles_per_Gallon\", Quantitative).\n","  encodeColor(field=\"Origin\", dataType=Nominal).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# A scatterplot showing horsepower and miles per gallons with binned acceleration on color." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"A scatterplot showing horsepower and miles per gallons with binned acceleration on color.\").\n","  withURL(Cars).\n","  mark(Point).\n","  encodeX(\"Horsepower\", Quantitative).\n","  encodeY(\"Miles_per_Gallon\", Quantitative).\n","  encodeColor(field=\"Acceleration\", dataType=Quantitative, bin=Bin(maxbins=5.0)).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas().\n","  withURL(Cars).\n","  mark(Area).\n","  encodeX(\"Acceleration\", Quantitative, bin=Bin()).\n","  encodeY(\"Horsepower\", Quantitative, AggOps.Mean, enableBin=false).\n","  encodeColor(field=\"Cylinders\", dataType=Nominal).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# The Trellis display by Becker et al. helped establish small multiples as a “powerful mechanism for understanding interactions in studies of how a response depends on explanatory variables”. Here we reproduce a trellis of Barley yields from the 1930s, complete with main-effects ordering to facilitate comparison." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"The Trellis display by Becker et al. helped establish small multiples as a “powerful mechanism for understanding interactions in studies of how a response depends on explanatory variables”. Here we reproduce a trellis of Barley yields from the 1930s, complete with main-effects ordering to facilitate comparison.\").\n","  withURL(Barley).\n","  mark(Point).\n","  encodeRow(\"site\", Ordinal).\n","  encodeX(\"yield\", Quantitative, aggregate=AggOps.Mean).\n","  encodeY(\"variety\", Ordinal, sortField=Sort(\"yield\", AggOps.Mean), scale=Scale(bandSize = 12.0)).\n","  encodeColor(field=\"year\", dataType=Nominal).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# A scatterplot with custom star shapes." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"A scatterplot with custom star shapes.\").\n","  withURL(Cars).\n","  mark(Point).\n","  encodeX(\"Horsepower\", Quant).\n","  encodeY(\"Miles_per_Gallon\", Quant).\n","  encodeColor(\"Cylinders\", Nom).\n","  encodeSize(\"Weight_in_lbs\", Quant).\n","  configMark(customShape=\"M0,0.2L0.2351,0.3236 0.1902,0.0618 0.3804,-0.1236 0.1175,-0.1618 0,-0.4 -0.1175,-0.1618 -0.3804,-0.1236 -0.1902,0.0618 -0.2351,0.3236 0,0.2Z\").\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# A scatterplot showing average horsepower and displacement for cars from different origins." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"A scatterplot showing average horsepower and displacement for cars from different origins.\").\n","  withURL(Cars).\n","  mark(Point).\n","  encodeX(\"Horsepower\", Quant, AggOps.Mean).\n","  encodeY(\"Displacement\", Quant, AggOps.Mean).\n","  encodeDetail(\"Origin\").\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Stock prices of 5 Tech Companies Over Time." ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"Stock prices of 5 Tech Companies Over Time.\").\n","  withURL(Stocks, formatType = DataFormat.Csv).\n","  mark(Line).\n","  encodeX(\"date\", Temp).\n","  encodeY(\"price\", Quant).\n","  encodeDetailFields(Field(field=\"symbol\", dataType=Nominal)).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Plot with hard-coded size value" ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"Plot with hard-coded size value\").\n","  withURL(Cars).\n","  mark(Circle).\n","  encodeY(\"Horsepower\", Quantitative).\n","  encodeX(\"Miles_per_Gallon\", Quantitative).\n","  encodeSize(value=201L).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Plots both mean and IQR as a background layer" ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas.layered(\"Plots both mean and IQR as a background layer\").\n","  withURL(Population).\n","  withLayers(\n","    Layer().\n","      mark(Line).\n","      encodeX(\"age\", Ordinal).\n","      encodeY(\"people\", aggregate=AggOps.Mean),\n","    Layer().\n","      mark(Area).\n","      encodeX(\"age\", Ordinal).\n","      encodeY(\"people\", aggregate=AggOps.Q1).\n","      encodeY2(\"people\", aggregate=AggOps.Q3)\n","  ).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Plot with legend on the left and a different title " ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"Plot with legend on the left and a different title \").\n","  withURL(Cars).\n","  mark(Point).\n","  encodeY(\"Horsepower\", Quantitative).\n","  encodeX(\"Miles_per_Gallon\", Quantitative).\n","  encodeColor(field=\"Origin\", dataType=Nominal, legend=Legend(orient = \"left\", title=\"Place of Origin\" )).\n","  encodeShape(field=\"Origin\", dataType=Nominal, legend=Legend(orient = \"left\", title=\"Place of Origin\",\n","    titleColor=\"red\")).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Plot to show Binning options" ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"Plot to show Binning options\").\n","  withURL(Movies).\n","  mark(Bar).\n","  encodeX(\"IMDB_Rating\", Quantitative, bin=Bin(step=2.0, maxbins=3.0)).\n","  encodeY(field=\"*\", Quantitative, aggregate=AggOps.Count).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Plot to show Binning options" ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"Plot to show Binning options\").\n","  withURL(Movies).\n","  mark(Bar).\n","  encodeX(\"Worldwide_Gross\", Quant, bin=Bin(maxbins=20.0), sortOrder=SortOrder.Desc).\n","  encodeY(field=\"*\", Quant, aggregate=AggOps.Count).\n","  show\n" ]
 }
    ,
 {
   "cell_type" : "markdown",
   "metadata": {},
   "source": [ "# Plot to show usage of encodeText" ]
 },
      
 {
   "cell_type" : "code",
   "execution_count": null,
   "outputs": [],
   "metadata": {},
   "source": [ "Vegas(\"Plot to show usage of encodeText\").\n","  withURL(Cars).\n","  addTransform(\"OriginInitial\", \"datum.Origin[0]\").\n","  mark(Text).\n","  encodeX(\"Horsepower\", Quantitative).\n","  encodeY(\"Miles_per_Gallon\", Quantitative).\n","  encodeColor(field=\"Origin\", dataType= Nominal).\n","  encodeText(field=\"OriginInitial\", dataType= Nominal).\n","  show\n" ]
 }
     ],
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       "display_name": "Scala 2.11",
       "language": "scala211",
       "name": "scala211"
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     "language_info": {
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 }