{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Lets-Plot Kotlin API v.4.4.2. Frontend: Notebook with dynamically loaded JS. Lets-Plot JS v.4.0.0."
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%useLatestDescriptors\n",
"%use lets-plot\n",
"LetsPlot.getInfo()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val rand = java.util.Random()\n",
"val data = mapOf (\n",
" \"rating\" to List(200) { rand.nextGaussian() } + List(200) { rand.nextGaussian() * 1.5 + 1.5 },\n",
" \"cond\" to List(200) { \"A\" } + List(200) { \"B\" }\n",
")\n",
"\n",
"var p = letsPlot(data)\n",
"p += geomDensity(color=\"dark_green\", alpha=.3) {x=\"rating\"; fill=\"cond\"}\n",
"p + ggsize(700, 350)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Kotlin",
"language": "kotlin",
"name": "kotlin"
},
"language_info": {
"codemirror_mode": "text/x-kotlin",
"file_extension": ".kt",
"mimetype": "text/x-kotlin",
"name": "kotlin",
"nbconvert_exporter": "",
"pygments_lexer": "kotlin",
"version": "1.8.20"
}
},
"nbformat": 4,
"nbformat_minor": 2
}