{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This page is available as an executable or viewable Jupyter Notebook:\n",
"
\n",
" \n",
" ![](\"https://mybinder.org/badge_logo.svg\")
\n",
"\n",
" \n",
" ![](\"https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.png\")
\n",
"\n",
"
\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%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 = lets_plot(data)\n",
"p += geom_density(color=\"dark_green\", alpha=.3) {x=\"rating\"; fill=\"cond\"}\n",
"p + ggsize(500, 250)"
]
}
],
"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",
"pygments_lexer": "kotlin",
"version": "1.4.20-dev-1121"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": []
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}