feat: added Dashboard notebook

f78b249f · Chandrasekhar Ramakrishnan · b8c0b9b5 · f78b249f
Commit f78b249f authored 5 years ago by Chandrasekhar Ramakrishnan
--- a/notebooks/Dashboard.ipynb
+++ b/notebooks/Dashboard.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "from IPython.display import display, HTML, Markdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ts_folder = \"../data/covid-19_jhu-csse/\"\n",
+    "rates_folder = \"../data/covid-19_rates/\"\n",
+    "out_folder = None\n",
+    "PAPERMILL_OUTPUT_PATH = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_jhu_covid_df(name):\n",
+    "    filename = os.path.join(ts_folder, f\"time_series_19-covid-{name}.csv\")\n",
+    "    df = pd.read_csv(filename)\n",
+    "    df = df.set_index(['Province/State', 'Country/Region', 'Lat', 'Long'])\n",
+    "    df.columns = pd.to_datetime(df.columns)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "jhu_frames_map = {\n",
+    "    \"confirmed\": read_jhu_covid_df(\"Confirmed\"),\n",
+    "    \"deaths\": read_jhu_covid_df(\"Deaths\"),\n",
+    "    \"recovered\": read_jhu_covid_df(\"Recovered\")\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_rates_covid_df(name):\n",
+    "    filename = os.path.join(rates_folder, f\"ts_rates_19-covid-{name}.csv\")\n",
+    "    df = pd.read_csv(filename).drop(\"Unnamed: 0\", axis=1)\n",
+    "    df = df.set_index(['Country/Region'])\n",
+    "    df.columns = pd.to_datetime(df.columns)\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "rates_frames_map = {\n",
+    "    \"confirmed\": read_rates_covid_df(\"confirmed\"),\n",
+    "    \"deaths\": read_rates_covid_df(\"deaths\"),\n",
+    "    \"recovered\": read_rates_covid_df(\"recovered\")\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile data needed for the visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute geospatial coordinates\n",
+    "country_coords_df = jhu_frames_map['confirmed'].reset_index([2,3])[['Lat', 'Long']]\n",
+    "country_coords_df = country_coords_df.groupby(level='Country/Region').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identify countries with 100 or more cases\n",
+    "case_count_ser = jhu_frames_map['confirmed'].iloc[:,-1].groupby(level='Country/Region').sum()\n",
+    "countries_over_thresh = case_count_ser[case_count_ser > 99].index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Questions About COVID-19 and Its Spread\n",
+    "\n",
+    "These plots should be taken with a large grain of salt. I am not an epidemiologist, so the analyses shown here are completely naive. There are large discrepencies in the data from different countries for a variety of reasons (rates of testing, demographics, etc.) so that make direct comparisons inaccurate. Nonetheless, I think there is a lot of interesting information in this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_ts = jhu_frames_map['confirmed'].iloc[:,-1].name.strftime(\"%b %d %Y\")\n",
+    "display(HTML(f\"<em>Data up to {data_ts}</em>\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How are cases per 100,000 distributed geographically?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import altair as alt\n",
+    "from vega_datasets import data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile the basic df\n",
+    "map_df = pd.concat([\n",
+    "    rates_frames_map['confirmed'].iloc[:,-1],\n",
+    "    rates_frames_map['deaths'].iloc[:,-1],\n",
+    "    rates_frames_map['recovered'].iloc[:,-1],\n",
+    "    country_coords_df], axis=1)\n",
+    "# Restrict to countries with 100 or more cases\n",
+    "map_df = map_df.loc[countries_over_thresh].dropna()\n",
+    "map_df = map_df.reset_index()\n",
+    "map_df.columns = ['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Lat', 'Long']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def map_of_variable(map_df, variable):\n",
+    "    # Data generators for the background\n",
+    "    sphere = alt.sphere()\n",
+    "    graticule = alt.graticule()\n",
+    "\n",
+    "    # Source of land data\n",
+    "    source = alt.topo_feature(data.world_110m.url, 'countries')\n",
+    "\n",
+    "    # Layering and configuring the components\n",
+    "    p = alt.layer(\n",
+    "        alt.Chart(sphere).mark_geoshape(fill='#cae6ef'),\n",
+    "        alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),\n",
+    "        alt.Chart(source).mark_geoshape(fill='#dddddd', stroke='#aaaaaa'),\n",
+    "        alt.Chart(map_df).mark_circle(opacity=0.6).encode(\n",
+    "            longitude='Long:Q',\n",
+    "            latitude='Lat:Q',\n",
+    "            size=alt.Size(f'{variable}:Q', title=\"Cases\"),\n",
+    "            color=alt.value('steelblue'),\n",
+    "            tooltip=[\"Country/Region:N\", \"Confirmed:Q\", \"Deaths:Q\", \"Recovered:Q\"]\n",
+    "        )\n",
+    "    ).project(\n",
+    "        'naturalEarth1'\n",
+    "    ).properties(width=600, height=400, title=f\"{variable} cases per 100,000\"\n",
+    "    ).configure_view(stroke=None)\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(map_of_variable(map_df, 'Confirmed'))\n",
+    "display(HTML('''\n",
+    "<p style=\"font-size: smaller\">Data Source: \n",
+    "  <a href=\"https://github.com/CSSEGISandData/COVID-19\">JHU CSSE</a> and\n",
+    "  <a href=\"https://data.worldbank.org/indicator/SP.POP.TOTL\">World Bank</a>\n",
+    "</p>'''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bars = alt.Chart(map_df).mark_bar().encode(\n",
+    "    x='Confirmed:Q',\n",
+    "    y=alt.Y(\"Country/Region:N\", sort='-x')\n",
+    ")\n",
+    "\n",
+    "text = bars.mark_text(\n",
+    "    align='left',\n",
+    "    baseline='middle',\n",
+    "    dx=3  # Nudges text to right so it doesn't appear on top of the bar\n",
+    ").encode(\n",
+    "    text=alt.Text('Confirmed:Q', format=\".3\")\n",
+    ")\n",
+    "\n",
+    "(bars + text).properties(height=900)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
+%% Cell type:code id: tags:
+
+``` python
+import pandas as pd
+import os
+from IPython.display import display, HTML, Markdown
+```
+
+%% Cell type:code id: tags:parameters
+
+``` python
+ts_folder = "../data/covid-19_jhu-csse/"
+rates_folder = "../data/covid-19_rates/"
+out_folder = None
+PAPERMILL_OUTPUT_PATH = None
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Read in the data
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def read_jhu_covid_df(name):
+    filename = os.path.join(ts_folder, f"time_series_19-covid-{name}.csv")
+    df = pd.read_csv(filename)
+    df = df.set_index(['Province/State', 'Country/Region', 'Lat', 'Long'])
+    df.columns = pd.to_datetime(df.columns)
+    return df
+
+
+jhu_frames_map = {
+    "confirmed": read_jhu_covid_df("Confirmed"),
+    "deaths": read_jhu_covid_df("Deaths"),
+    "recovered": read_jhu_covid_df("Recovered")
+}
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def read_rates_covid_df(name):
+    filename = os.path.join(rates_folder, f"ts_rates_19-covid-{name}.csv")
+    df = pd.read_csv(filename).drop("Unnamed: 0", axis=1)
+    df = df.set_index(['Country/Region'])
+    df.columns = pd.to_datetime(df.columns)
+    return df
+
+
+rates_frames_map = {
+    "confirmed": read_rates_covid_df("confirmed"),
+    "deaths": read_rates_covid_df("deaths"),
+    "recovered": read_rates_covid_df("recovered")
+}
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Compile data needed for the visualizations
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Compute geospatial coordinates
+country_coords_df = jhu_frames_map['confirmed'].reset_index([2,3])[['Lat', 'Long']]
+country_coords_df = country_coords_df.groupby(level='Country/Region').mean()
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Identify countries with 100 or more cases
+case_count_ser = jhu_frames_map['confirmed'].iloc[:,-1].groupby(level='Country/Region').sum()
+countries_over_thresh = case_count_ser[case_count_ser > 99].index
+```
+
+%% Cell type:markdown id: tags:
+
+# Questions About COVID-19 and Its Spread
+
+These plots should be taken with a large grain of salt. I am not an epidemiologist, so the analyses shown here are completely naive. There are large discrepencies in the data from different countries for a variety of reasons (rates of testing, demographics, etc.) so that make direct comparisons inaccurate. Nonetheless, I think there is a lot of interesting information in this data.
+
+%% Cell type:code id: tags:
+
+``` python
+data_ts = jhu_frames_map['confirmed'].iloc[:,-1].name.strftime("%b %d %Y")
+display(HTML(f"<em>Data up to {data_ts}</em>"))
+```
+
+%% Cell type:markdown id: tags:
+
+## How are cases per 100,000 distributed geographically?
+
+%% Cell type:code id: tags:
+
+``` python
+import altair as alt
+from vega_datasets import data
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# Compile the basic df
+map_df = pd.concat([
+    rates_frames_map['confirmed'].iloc[:,-1],
+    rates_frames_map['deaths'].iloc[:,-1],
+    rates_frames_map['recovered'].iloc[:,-1],
+    country_coords_df], axis=1)
+# Restrict to countries with 100 or more cases
+map_df = map_df.loc[countries_over_thresh].dropna()
+map_df = map_df.reset_index()
+map_df.columns = ['Country/Region', 'Confirmed', 'Deaths', 'Recovered', 'Lat', 'Long']
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def map_of_variable(map_df, variable):
+    # Data generators for the background
+    sphere = alt.sphere()
+    graticule = alt.graticule()
+
+    # Source of land data
+    source = alt.topo_feature(data.world_110m.url, 'countries')
+
+    # Layering and configuring the components
+    p = alt.layer(
+        alt.Chart(sphere).mark_geoshape(fill='#cae6ef'),
+        alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),
+        alt.Chart(source).mark_geoshape(fill='#dddddd', stroke='#aaaaaa'),
+        alt.Chart(map_df).mark_circle(opacity=0.6).encode(
+            longitude='Long:Q',
+            latitude='Lat:Q',
+            size=alt.Size(f'{variable}:Q', title="Cases"),
+            color=alt.value('steelblue'),
+            tooltip=["Country/Region:N", "Confirmed:Q", "Deaths:Q", "Recovered:Q"]
+        )
+    ).project(
+        'naturalEarth1'
+    ).properties(width=600, height=400, title=f"{variable} cases per 100,000"
+    ).configure_view(stroke=None)
+    return p
+```
+
+%% Cell type:code id: tags:
+
+``` python
+display(map_of_variable(map_df, 'Confirmed'))
+display(HTML('''
+<p style="font-size: smaller">Data Source:
+  <a href="https://github.com/CSSEGISandData/COVID-19">JHU CSSE</a> and
+  <a href="https://data.worldbank.org/indicator/SP.POP.TOTL">World Bank</a>
+</p>'''))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+bars = alt.Chart(map_df).mark_bar().encode(
+    x='Confirmed:Q',
+    y=alt.Y("Country/Region:N", sort='-x')
+)
+
+text = bars.mark_text(
+    align='left',
+    baseline='middle',
+    dx=3  # Nudges text to right so it doesn't appear on top of the bar
+).encode(
+    text=alt.Text('Confirmed:Q', format=".3")
+)
+
+(bars + text).properties(height=900)
+```