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jupyter_example.ipynb
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jupyter_example.ipynb
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{
"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" ]
}
],
"metadata": {
"kernelspec": {
"display_name": "Scala 2.11",
"language": "scala211",
"name": "scala211"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala211",
"pygments_lexer": "scala",
"version": "2.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}