\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%use dataframe"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d526d855",
"metadata": {},
"outputs": [],
"source": [
"import jetbrains.datalore.plot.base.stat.regression.LinearRegression\n",
"import kotlin.random.Random"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "166e457f",
"metadata": {},
"outputs": [],
"source": [
"val rnd = Random(0)\n",
"val data = mapOf(\n",
" \"x\" to (-15..14).toList(),\n",
" \"y\" to (0..29).map { rnd.nextDouble(1.0, 5.0) },\n",
" \"sugar\" to (150..179).toList()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "75bf0748",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot(data) { x = \"x\"; y = \"y\" } + geomLollipop() + ggsize(600, 200)"
]
},
{
"cell_type": "markdown",
"id": "3a401867",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 1. Parameters `size`, `stroke` and `linewidth`"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3007532a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val plots = listOf(\n",
" letsPlot(data) { x = \"x\"; y = \"y\"; size = \"sugar\" } + \n",
" geomLollipop() + ggtitle(\"variable 'size'\"),\n",
" letsPlot(data) { x = \"x\"; y = \"y\"; size = \"sugar\"; stroke = \"sugar\" } + \n",
" geomLollipop() + ggtitle(\"variable 'size' and 'stroke'\"),\n",
" letsPlot(data) { x = \"x\"; y = \"y\"; size = \"sugar\"; linewidth = \"sugar\" } + \n",
" geomLollipop() + ggtitle(\"variable 'size' and 'linewidth'\")\n",
")\n",
" \n",
"gggrid(plots, ncol=1) + ggsize(800, 800)"
]
},
{
"cell_type": "markdown",
"id": "e5d6d73f",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 2. Parameter `fatten`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e1de6c92",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gggrid(\n",
" listOf(\n",
" letsPlot(data) { x = \"x\"; y = \"y\"} + geomLollipop() + ggtitle(\"fatten=2.5 (default)\"),\n",
" letsPlot(data) { x = \"x\"; y = \"y\"} + geomLollipop(fatten = 5) + ggtitle(\"fatten=5\"),\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "89b3fbaa",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 3. Horizontal Sticks"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f1efe926",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot(data) { x = \"y\"; y = \"x\" } + geomLollipop(dir = \"h\")"
]
},
{
"cell_type": "markdown",
"id": "f5fcee23",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 4. Sloped Baseline"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1fb4fb53",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val slope = 0.5\n",
"val intercept = 1\n",
"\n",
"val abline = letsPlot(data) { x = \"x\"; y = \"y\" } +\n",
" geomABLine(intercept = intercept, slope = slope, color = \"black\", linetype = \"dotted\", size = 1.5) + \n",
" coordFixed(ylim = -12 to 12)\n",
" \n",
"gggrid(\n",
" listOf(\n",
" abline + geomLollipop(intercept = intercept, slope = slope, shape = 21) + ggtitle(\"dir='v' (default)\"),\n",
" abline + geomLollipop(intercept = intercept, slope = slope, shape = 21, dir = \"h\") + ggtitle(\"dir='h'\"),\n",
" abline + geomLollipop(intercept = intercept, slope = slope, shape = 21, dir = \"s\") + ggtitle(\"dir='s'\"),\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "36f3fc38",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 5. Parameter `stat`"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6f895fd5",
"metadata": {},
"outputs": [
{
"data": {
"application/kotlindataframe+json": "{\"nrow\":3,\"ncol\":12,\"columns\":[\"untitled\",\"manufacturer\",\"model\",\"displ\",\"year\",\"cyl\",\"trans\",\"drv\",\"cty\",\"hwy\",\"fl\",\"class\"],\"kotlin_dataframe\":[{\"untitled\":1,\"manufacturer\":\"audi\",\"model\":\"a4\",\"displ\":1.8,\"year\":1999,\"cyl\":4,\"trans\":\"auto(l5)\",\"drv\":\"f\",\"cty\":18,\"hwy\":29,\"fl\":\"p\",\"class\":\"compact\"},{\"untitled\":2,\"manufacturer\":\"audi\",\"model\":\"a4\",\"displ\":1.8,\"year\":1999,\"cyl\":4,\"trans\":\"manual(m5)\",\"drv\":\"f\",\"cty\":21,\"hwy\":29,\"fl\":\"p\",\"class\":\"compact\"},{\"untitled\":3,\"manufacturer\":\"audi\",\"model\":\"a4\",\"displ\":2.0,\"year\":2008,\"cyl\":4,\"trans\":\"manual(m6)\",\"drv\":\"f\",\"cty\":20,\"hwy\":31,\"fl\":\"p\",\"class\":\"compact\"}]}",
"text/html": [
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
\n",
"\n",
"
DataFrame: rowsCount = 3, columnsCount = 12
\n",
" \n",
" \n",
" "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val df = DataFrame.readCSV(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "66fac357",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gggrid(\n",
" listOf(\n",
" letsPlot(df.toMap()) { x = \"class\" } + geomLollipop(stat = Stat.count()) + ggtitle(\"stat='count'\"),\n",
" letsPlot(df.toMap()) { x = \"hwy\" } + geomLollipop(stat = Stat.bin()) + ggtitle(\"stat='bin'\"),\n",
" letsPlot(df.toMap()) { x = \"hwy\" } + geomLollipop(stat = Stat.density(n=30)) + ggtitle(\"stat='density'\")\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "aa9cd80d",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 6. Lollipops in Marginal Layer"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d17028b4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letsPlot(df.toMap()) { x = \"hwy\"; y = \"cty\" } +\n",
" geomBin2D(binWidth = 1 to 1) +\n",
" ggmarginal(\"r\", size=.2,\n",
" layer = geomLollipop(stat = Stat.count(), orientation = \"y\", size = 1) { color = \"..count..\" } )"
]
},
{
"cell_type": "markdown",
"id": "7f91b9f7",
"metadata": {},
"source": [
"
\n",
"\n",
"#### 7. Lollipops and a Regression Line"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5ce3b9ac",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
" "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val xs = df[\"hwy\"].toList().map { (it as Number).toDouble() }\n",
"val ys = df[\"cty\"].toList().map { (it as Number).toDouble() }\n",
"\n",
"fun linearModel(x: Double) = LinearRegression(xs, ys, 0.95).evalX(x).y\n",
"\n",
"val intercept = linearModel(0.0)\n",
"val slope = linearModel(1.0) - intercept\n",
"\n",
"letsPlot(df.toMap()) { x = \"hwy\"; y = \"cty\" } +\n",
" geomSmooth(level = 0.99) + \n",
" geomLollipop(slope = slope, intercept = intercept,\n",
" size = 1.2, shape = 21, color = \"black\", fill = \"magenta\") +\n",
" coordFixed()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Kotlin",
"language": "kotlin",
"name": "kotlin"
},
"language_info": {
"codemirror_mode": "text/x-kotlin",
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