{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The layer `orientation`\n",
"\n",
"Some geoms treat each axis differently and, thus, can thus have two orientations.\n",
"\n",
"The `orientation` parameter specifies the axis that the layer' stat and geom should run along (x-axis by default). "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from lets_plot import *\n",
"from lets_plot.mapping import as_discrete\n",
"LetsPlot.setup_html()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### geom_bar()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"mpg = pd.read_csv(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv\")\n",
"\n",
"base = ggplot(mpg) + ggsize(800, 300)\n",
"manufacturer_mapping = as_discrete('manufacturer', order_by='..count..')\n",
"hide_x_label = theme(axis_title_x='blank')\n",
"hide_y_label = theme(axis_title_y='blank')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# orientation \"x\" (default) : \"manufacturer\" is mapped to the x-axis.\n",
"orientation_x = base + geom_bar(aes(x=manufacturer_mapping, fill='class'), color='white')\n",
"orientation_x + hide_x_label"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"orientation_x + coord_flip() + hide_x_label"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Orientation \"y\".\n",
"# The \"manufacturer\" is mapped to the y-axis.\n",
"\n",
"base_y = base + hide_y_label\n",
"base_y + geom_bar(aes(y=manufacturer_mapping, fill='class'), color='white', orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### geom_boxplot()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"base_y + geom_boxplot(aes(y='manufacturer', x='cty'), width=0.7, orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `geom_violin()`"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ggplot(mpg) + geom_violin(aes(y=\"drv\", x='cty', fill=\"drv\"), trim=False, orientation=\"y\") \\\n",
" + geom_boxplot(aes(y=\"drv\", x='cty'), fill=\"white\", alpha=0.5, width=0.3, orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Continuous variables"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#\n",
"# Density estimates.\n",
"#\n",
"\n",
"np.random.seed(0)\n",
"\n",
"cov0=[[1, -.8], \n",
" [-.8, 1]] \n",
"cov1=[[ 10, .1],\n",
" [.1, .1]]\n",
"\n",
"x0, y0 = np.random.multivariate_normal(mean=[-2,0], cov=cov0, size=200).T\n",
"x1, y1 = np.random.multivariate_normal(mean=[0,1], cov=cov1, size=200).T\n",
"\n",
"data = dict(\n",
" x = np.concatenate((x0,x1)),\n",
" y = np.concatenate((y0,y1)),\n",
" c = [\"A\"]*200 + [\"B\"]*200\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = ggplot(data, aes(\"x\", \"y\", color=\"c\")) + geom_point()\n",
"p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `geom_density()`"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_density(size=2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_density(size=2, orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `geom_histogram()`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_histogram(fill=\"rgba(0,0,0,0)\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_histogram(alpha=0, orientation=\"y\", show_legend=False)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_freqpoly(size=2)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_freqpoly(size=2, orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `geom_smooth()`"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_smooth()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_smooth() + coord_flip()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_smooth(orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `geom_boxplot()`"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_boxplot(x=-10, color=\"black\") \\\n",
" + geom_boxplot(y=-3, color=\"black\", orientation=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `geom_violin()`"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_violin(x=-10, color=\"black\", alpha=0) \\\n",
" + geom_violin(y=-3, color=\"black\", alpha=0, orientation=\"y\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p + geom_violin(aes(fill=\"c\"), y=-2, color=\"black\", \n",
" alpha=0.3, position=\"identity\", width=3, show_legend=False,\n",
" orientation=\"y\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}