covidtracking-dashboard.ipynb

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   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from IPython.display import display, HTML"
   ]
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  {
   "cell_type": "code",
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   "metadata": {},
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    "html_credits=HTML('''\n",
    "<p style=\"font-size: smaller\">Data Sources: \n",
    "  <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n",
    "<br>\n",
    "Analysis and Visualization:\n",
    "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n",
    "</p>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "data_path = '../data/covidtracking'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read population data\n",
    "pop_df = pd.read_csv(Path(data_path) / '../geodata/us_pop_fung_2019.csv').set_index('ST')\n",
    "\n",
    "# Read state-level data\n",
    "data_df = pd.read_json(Path(data_path) / 'states-daily.json')\n",
    "data_df['date'] = pd.to_datetime(data_df['date'], format=\"%Y%m%d\")\n",
    "data_df['ratio'] = data_df['positive']/data_df['total']\n",
    "\n",
    "# Compute daily differences\n",
    "tdf = data_df.sort_values(['state', 'date'], ascending=[True, False]).set_index(['state', 'date'])\n",
    "diffs_df = tdf[['positive', 'negative', 'death']].groupby(level='state').diff(periods=-1).dropna(how='all')\n",
    "tdf_diff=tdf.join(diffs_df, rsuffix='_diff').reset_index()\n",
    "\n",
    "# incidence rates\n",
    "tdf_diff = tdf_diff.set_index('state')\n",
    "tdf_diff['positive_diff_100k'] = (tdf_diff['positive_diff'] / pop_df['Population']) * 100000\n",
    "tdf_diff['death_diff_100k'] = (tdf_diff['death_diff'] / pop_df['Population']) * 100000\n",
    "tdf_diff = tdf_diff.reset_index()\n",
    "\n",
    "# \"Normalizing\" the totals\n",
    "tdf_diff['total_10'] = tdf_diff['total']/10.\n",
    "\n",
    "# Daily totals\n",
    "daily_totals = tdf_diff.groupby('date').sum()\n",
    "daily_totals.reset_index(level=0, inplace=True)\n",
    "\n",
    "# National daily totals\n",
    "nation_df = data_df.groupby('date').sum()\n",
    "nation_df['state']='All US'\n",
    "nation_df = nation_df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Covid-19 Cases in U.S.\n",
    "\n",
    "The case data from the U.S. is obtained from https://covidtracking.com, a public crowd-sourced covid-19 dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Growth trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "\n",
       "<p style=\"font-size: smaller\">Data Sources: \n",
       "  <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n",
       "<br>\n",
       "Analysis and Visualization:\n",
       "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n",
       "</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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   "source": [
    "# make dataframe for text labels on chart - hand edit these label locations\n",
    "textLabels_df = pd.DataFrame(\n",
    "    [[10,6000,'doubles every day'],\n",
    "     [28,7000,'doubles every 3 days'],\n",
    "     [34,100, 'doubles every week']],\n",
    "    columns =['labelX', 'labelY','labelText']\n",
    ")\n",
    "\n",
    "# make dataframe of states with points >=10 deaths\n",
    "death10_df = data_df.loc[data_df['death']>=10]\n",
    "\n",
    "# group death10 dataframe by state and then increasing order of date\n",
    "death10_df = death10_df.sort_values(by=['state','date'])\n",
    "\n",
    "# add US to that dataframe\n",
    "nationdeath10_df = nation_df.loc[nation_df['death']>=10]\n",
    "death10_df= pd.concat ([death10_df,nationdeath10_df])\n",
    "\n",
    "death10_df = death10_df.reset_index()\n",
    "\n",
    "# make a list of the states with 10 or more deaths\n",
    "state_list = list(set(death10_df['state']))\n",
    "\n",
    "# add a column for the number of days since the 10th death for each state\n",
    "for state, df in death10_df.groupby('state'):\n",
    "    death10_df.loc[df.index,'sinceDay0'] = range(0, len(df))\n",
    "death10_df = death10_df.astype({'sinceDay0': 'int32'})\n",
    "\n",
    "#Now create plotlines for each state since 10 deaths\n",
    "lineChart = alt.Chart(death10_df,title='US States: Cumulative Deaths Since 10th Death').mark_line(interpolate='basis').encode(\n",
    "    alt.X('sinceDay0:Q', axis=alt.Axis(title='Days Since 10th Death')),\n",
    "    alt.Y('death:Q',\n",
    "         axis = alt.Axis(title='Cumulative Deaths'),\n",
    "         scale=alt.Scale(type='log')),\n",
    "    tooltip=['state', 'sinceDay0', 'death', 'positive'],\n",
    "    color = 'state'\n",
    ").properties(width=800,height=500)\n",
    "\n",
    "## Create a layer with the lines for doubling every day and doubling every week\n",
    "\n",
    "# Compute theoretical trends of doubling every day, 3 days, week\n",
    "days = {'day':[1,2,3,4,5,10,15,20, max(death10_df.sinceDay0)+5]}\n",
    "startCase = 10\n",
    "logRuleDay_df = pd.DataFrame(days, columns=['day'])\n",
    "logRuleDay_df['case']= startCase * pow(2,logRuleDay_df['day'])\n",
    "logRuleDay_df['doubling period']='every day'\n",
    "\n",
    "logRule3Days_df = pd.DataFrame(days, columns=['day'])\n",
    "logRule3Days_df['case']= startCase * pow(2,(logRule3Days_df['day'])/3)\n",
    "logRule3Days_df['doubling period']='three days'\n",
    "\n",
    "logRuleWeek_df = pd.DataFrame(days, columns=['day'])\n",
    "logRuleWeek_df['case']= startCase * pow(2,(logRuleWeek_df['day'])/7)\n",
    "logRuleWeek_df['doubling period']='every week'\n",
    "\n",
    "logRules_df = pd.concat([logRuleDay_df, logRule3Days_df, logRuleWeek_df])\n",
    "logRules_df = logRules_df.reset_index()\n",
    "\n",
    "\n",
    "ruleChart = alt.Chart(logRules_df).mark_line(opacity=0.2,clip=True).encode(\n",
    "    alt.X('day:Q',\n",
    "            scale=alt.Scale(domain=[1,max(death10_df.sinceDay0)+5])),\n",
    "    alt.Y('case', scale=alt.Scale(type='log',domain=[10,10000]),\n",
    "         ),\n",
    "    color = 'doubling period',\n",
    "    tooltip = ['doubling period'])        \n",
    "\n",
    "# create a layer for the state labels\n",
    "# 1) make dataframe with each state's max days\n",
    "# 2) make a chart layer with text of state name to right of each state's rightmost point\n",
    "stateLabels_df = death10_df[death10_df['sinceDay0'] == death10_df.groupby(['state'])['sinceDay0'].transform(max)]\n",
    "labelChart = alt.Chart(stateLabels_df).mark_text(align='left', baseline='middle', dx=10).encode(\n",
    "    x='sinceDay0',\n",
    "    y='death',\n",
    "    text='state',\n",
    "    color='state')\n",
    "\n",
    "#now put the text labels layer on top of state labels Chart\n",
    "labelChart = labelChart + alt.Chart(textLabels_df).mark_text(align='right', baseline='bottom', dx=0, size=18,opacity=0.5).encode(\n",
    "    x='labelX',\n",
    "    y='labelY',\n",
    "    text='labelText')\n",
    "\n",
    "\n",
    "## Create some tooltip behavior - show Y values on mouseover\n",
    "# Step 1: Selection that chooses nearest point based on value on x-axis\n",
    "nearest = alt.selection(type='single', nearest=True, on='mouseover',\n",
    "                            fields=['sinceDay0'])\n",
    "\n",
    "# Step 2: Transparent selectors across the chart. This is what tells us\n",
    "# the x-value of the cursor\n",
    "selectors = alt.Chart().mark_point().encode(\n",
    "    x=\"sinceDay0:Q\",\n",
    "    opacity=alt.value(0),\n",
    ").add_selection(\n",
    "    nearest\n",
    ")\n",
    "\n",
    "# Step 3: Add text, show values in column when it's the nearest point to \n",
    "# mouseover, else show blank\n",
    "text = lineChart.mark_text(align='center', dx=3, dy=-20).encode(\n",
    "    text=alt.condition(nearest, 'death', alt.value(' '))\n",
    ")\n",
    "\n",
    "\n",
    "#Finally, lets show the chart!\n",
    "\n",
    "chart = alt.layer(lineChart, ruleChart, labelChart, selectors, text, data=death10_df)\n",
    "\n",
    "display(chart)\n",
    "display(html_credits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "<p style=\"font-size: smaller\">Data Sources: \n",
       "  <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n",
       "<br>\n",
       "Analysis and Visualization:\n",
       "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n",
       "</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make dataframe for text labels on chart - hand edit these label locations\n",
    "textLabels_df = pd.DataFrame(\n",
    "    [[9,30000,'doubles every day'],\n",
    "     [28,31000,'doubles every 3 days'],\n",
    "     [32,1000, 'doubles every week']],\n",
    "    columns =['labelX', 'labelY','labelText']\n",
    ")\n",
    "\n",
    "# make dataframe with only points >=100 positives\n",
    "positive100_df = data_df.loc[data_df['positive']>=100]\n",
    "\n",
    "## add US to that dataframe\n",
    "nationpos100_df = nation_df.loc[nation_df['positive']>=100]\n",
    "positive100_df= pd.concat ([positive100_df,nationpos100_df])\n",
    "\n",
    "# group positive100 dataframe by state and then increasing order of date\n",
    "positive100_df = positive100_df.sort_values(by=['state','date'])\n",
    "positive100_df = positive100_df.reset_index()\n",
    "\n",
    "# make a list of the states with 10 or more deaths (don't really need this)\n",
    "# state_list = list(set(positive100_df['state']))\n",
    "\n",
    "# add a column for the number of days since the 100th case for each state\n",
    "for state, df in positive100_df.groupby('state'):\n",
    "    positive100_df.loc[df.index,'sinceDay0'] = range(0, len(df))\n",
    "positive100_df = positive100_df.astype({'sinceDay0': 'int32'})\n",
    "\n",
    "    \n",
    "# Now create plotlines for each state since 10 deaths\n",
    "lineChart = alt.Chart(positive100_df, title=\"US States: total cases since 100th case\").mark_line(interpolate='basis').encode(\n",
    "    alt.X('sinceDay0:Q', axis=alt.Axis(title='Days since 100th case')),\n",
    "    alt.Y('positive:Q',\n",
    "          axis = alt.Axis(title='Cumulative positive cases'),\n",
    "          scale=alt.Scale(type='log')),\n",
    "    tooltip=['state', 'sinceDay0', 'death', 'positive'],\n",
    "    color = 'state'\n",
    ").properties(width=800,height=500)\n",
    "\n",
    "## Create a layer with the lines for doubling every day and doubling every week\n",
    "# make dataframe with lines to indicate doubling every day, 3 days, week \n",
    "\n",
    "days = {'day':[1,2,3,4,5,10,15,20, max(positive100_df.sinceDay0)+5]}\n",
    "startCase = 100\n",
    "logRuleDay_df = pd.DataFrame (days, columns=['day'])\n",
    "logRuleDay_df['case']= startCase * pow(2,logRuleDay_df['day'])\n",
    "logRuleDay_df['doubling period']='every day'\n",
    "\n",
    "logRule3Days_df = pd.DataFrame (days, columns=['day'])\n",
    "logRule3Days_df['case']= startCase * pow(2,(logRule3Days_df['day'])/3)\n",
    "logRule3Days_df['doubling period']='three days'\n",
    "\n",
    "logRuleWeek_df = pd.DataFrame (days, columns=['day'])\n",
    "logRuleWeek_df['case']= startCase * pow(2,(logRuleWeek_df['day'])/7)\n",
    "logRuleWeek_df['doubling period']='every week'\n",
    "\n",
    "logRules_df = pd.concat([logRuleDay_df, logRule3Days_df, logRuleWeek_df])\n",
    "logRules_df = logRules_df.reset_index()\n",
    "\n",
    "ruleChart = alt.Chart(logRules_df).mark_line(opacity=0.2,clip=True).encode(\n",
    "    alt.X('day:Q',\n",
    "            scale=alt.Scale(domain=[1, max(positive100_df.sinceDay0)+5])),\n",
    "    alt.Y('case', scale=alt.Scale(domain=[100,200000], type='log'),\n",
    "         ),\n",
    "    color = 'doubling period')\n",
    "\n",
    "# create a layer for the state labels\n",
    "# 1) make dataframe with each state's max days\n",
    "# 2) make a chart layer with text of state name to right of each state's rightmost point\n",
    "stateLabels_df = positive100_df[positive100_df['sinceDay0'] == positive100_df.groupby(['state'])['sinceDay0'].transform(max)]\n",
    "labelChart = alt.Chart(stateLabels_df).mark_text(align='left', baseline='middle', dx=10).encode(\n",
    "    x='sinceDay0',\n",
    "    y='positive',\n",
    "    text='state',\n",
    "    color='state')\n",
    "\n",
    "#now put the text labels layer on top of state labels Chart\n",
    "labelChart = labelChart + alt.Chart(textLabels_df).mark_text(align='right', baseline='bottom', dx=0, size=18,opacity=0.5).encode(\n",
    "    x='labelX',\n",
    "    y='labelY',\n",
    "    text='labelText')\n",
    "\n",
    "#Create some tooltip behavior\n",
    "# Step 1: Selection that chooses nearest point based on value on x-axis\n",
    "nearest = alt.selection(type='single', nearest=True, on='mouseover',\n",
    "                            fields=['sinceDay0'])\n",
    "\n",
    "# Step 2: Transparent selectors across the chart. This is what tells us\n",
    "# the x-value of the cursor\n",
    "selectors = alt.Chart().mark_point().encode(\n",
    "    x=\"sinceDay0:Q\",\n",
    "    opacity=alt.value(0),\n",
    ").add_selection(\n",
    "    nearest\n",
    ")\n",
    "\n",
    "# Step 3: Add text, show values in Sex column when it's the nearest point to \n",
    "# mouseover, else show blank\n",
    "text = lineChart.mark_text(align='center', dx=3, dy=-20).encode(\n",
    "    text=alt.condition(nearest, 'positive', alt.value(' '))\n",
    ")\n",
    "\n",
    "\n",
    "#Finally, lets show the chart!\n",
    "\n",
    "chart = alt.layer(lineChart, ruleChart, labelChart, selectors, text, data=death10_df)\n",
    "#chart = alt.layer(lineChart, ruleChart, labelChart)\n",
    "chart.properties (width=500,height=800)\n",
    "display(chart)\n",
    "display(html_credits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daily Cumulative Totals\n",
    "\n",
    "Cumulative reported totals of positive cases and deaths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "</script>"
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       "alt.VConcatChart(...)"
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       "\n",
       "<p style=\"font-size: smaller\">Data Sources: \n",
       "  <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n",
       "<br>\n",
       "Analysis and Visualization:\n",
       "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n",
       "</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = alt.Chart(\n",
    "    daily_totals\n",
    ").mark_bar(size=10).encode(\n",
    "    alt.X('date', axis=alt.Axis(title='')\n",
    "    )\n",
    ")\n",
    "\n",
    "cumulative = base.encode(alt.Y('positive', title = 'Cumulative cases'))\n",
    "cumulative_deaths = base.encode(alt.Y('death', title = 'Cumulative deaths'))\n",
    "rates = base.encode(alt.Y('positive_diff', title='Daily cases'))\n",
    "rates_deaths = base.encode(alt.Y('death_diff', title='Daily deaths'))\n",
    "chart = alt.vconcat(\n",
    "    cumulative | rates, cumulative_deaths | rates_deaths,\n",
    "    title='Cumulative Covid-19 cases and deaths in the U.S.'\n",
    ").configure_title(\n",
    "    anchor='middle'\n",
    ")\n",
    "display(chart)\n",
    "display(html_credits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total tests and positives per 100k population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most recent test date 2020-04-03 00:00:00\n",
      "56 states/territories have data on this date.\n"
     ]
    }
   ],
   "source": [
    "most_recent_test_date = data_df['date'].max()\n",
    "most_recent_df = data_df[data_df['date'] == most_recent_test_date].set_index('state')\n",
    "print(\"Most recent test date\", most_recent_test_date)\n",
    "print(len(most_recent_df), \"states/territories have data on this date.\")\n",
    "\n",
    "most_recent_df['total/100k'] = (most_recent_df['total'] / pop_df['Population']) * 100000\n",
    "most_recent_df['positive/100k'] = (most_recent_df['positive'] / pop_df['Population']) * 100000\n",
    "most_recent_df = most_recent_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "\n",
       "<p style=\"font-size: smaller\">Data Sources: \n",
       "  <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n",
       "<br>\n",
       "Analysis and Visualization:\n",
       "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n",
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   "source": [
    "viz_df = most_recent_df.sort_values('total/100k', ascending=False)\n",
    "chart = alt.Chart(viz_df, title=\"Cases (orange points) and tests(blue bars) per 100k\").encode(alt.X('state', sort=None))\n",
    "tests = chart.mark_bar().encode(alt.Y('total/100k', axis=alt.Axis(title='COVID-19 Tests/100k, Positive Cases/100k')))\n",
    "positives = chart.mark_point(color='orange', filled=True, size=100, opacity=1).encode(alt.Y('positive/100k'))\n",
    "display(alt.layer(tests, positives))\n",
    "display(html_credits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counts and rates by state\n",
    "\n",
    "Taking a look at the three states with the highest per-capita incidence of covid-19. The red and yellow curves represent the total tests and total positive tests respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 1065.0, \"positiveIncrease\": 105.0, \"totalTestResultsIncrease\": 1170.0, \"ratio\": 0.10933660933660934, \"positive_diff\": 105.0, \"negative_diff\": 1065.0, \"death_diff\": 0.0, \"positive_diff_100k\": 1.3788768929517459, \"death_diff_100k\": 0.0, \"total_10\": 244.2}, {\"state\": \"WA\", \"date\": \"2020-03-10T00:00:00\", \"positive\": 162.0, \"negative\": 1110.0, \"pending\": null, \"hospitalizedCurrently\": null, \"hospitalizedCumulative\": null, \"inIcuCurrently\": null, \"inIcuCumulative\": null, \"onVentilatorCurrently\": null, \"onVentilatorCumulative\": null, \"recovered\": null, \"hash\": \"06768336fc25d73cd5c617780d60df98257b9efc\", \"dateChecked\": \"2020-03-10T20:00:00Z\", \"death\": 24.0, \"hospitalized\": null, \"total\": 1272, \"totalTestResults\": 1272, \"posNeg\": 1272, \"fips\": 53, \"deathIncrease\": 2.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 0.0, \"positiveIncrease\": 26.0, \"totalTestResultsIncrease\": 26.0, \"ratio\": 0.12735849056603774, \"positive_diff\": 26.0, \"negative_diff\": 0.0, \"death_diff\": 2.0, \"positive_diff_100k\": 0.3414361830166228, \"death_diff_100k\": 0.026264321770509448, \"total_10\": 127.2}, {\"state\": \"WA\", \"date\": \"2020-03-09T00:00:00\", \"positive\": 136.0, \"negative\": 1110.0, \"pending\": null, \"hospitalizedCurrently\": null, \"hospitalizedCumulative\": null, \"inIcuCurrently\": null, \"inIcuCumulative\": null, \"onVentilatorCurrently\": null, \"onVentilatorCumulative\": null, \"recovered\": null, \"hash\": \"d72514f02a70119a149f4bc9d6ec6cb789a7e255\", \"dateChecked\": \"2020-03-09T20:00:00Z\", \"death\": 22.0, \"hospitalized\": null, \"total\": 1246, \"totalTestResults\": 1246, \"posNeg\": 1246, \"fips\": 53, \"deathIncrease\": 4.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 470.0, \"positiveIncrease\": 34.0, \"totalTestResultsIncrease\": 504.0, \"ratio\": 0.10914927768860354, \"positive_diff\": 34.0, 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0.026264321770509448, \"total_10\": 80.2}, {\"state\": \"WA\", \"date\": \"2020-03-07T00:00:00\", \"positive\": 102.0, \"negative\": 370.0, \"pending\": 66.0, \"hospitalizedCurrently\": null, \"hospitalizedCumulative\": null, \"inIcuCurrently\": null, \"inIcuCumulative\": null, \"onVentilatorCurrently\": null, \"onVentilatorCumulative\": null, \"recovered\": null, \"hash\": \"bba4f8c850e820bd03b106bdf1e40f53bca745cd\", \"dateChecked\": \"2020-03-07T21:00:00Z\", \"death\": 16.0, \"hospitalized\": null, \"total\": 538, \"totalTestResults\": 472, \"posNeg\": 472, \"fips\": 53, \"deathIncrease\": 2.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 0.0, \"positiveIncrease\": 23.0, \"totalTestResultsIncrease\": 23.0, \"ratio\": 0.1895910780669145, \"positive_diff\": 23.0, \"negative_diff\": 0.0, \"death_diff\": 2.0, \"positive_diff_100k\": 0.30203970036085864, \"death_diff_100k\": 0.026264321770509448, \"total_10\": 53.8}, {\"state\": \"WA\", \"date\": \"2020-03-06T00:00:00\", \"positive\": 79.0, \"negative\": 370.0, \"pending\": null, \"hospitalizedCurrently\": null, \"hospitalizedCumulative\": null, \"inIcuCurrently\": null, \"inIcuCumulative\": null, \"onVentilatorCurrently\": null, \"onVentilatorCumulative\": null, \"recovered\": null, \"hash\": \"acf1e359af09f765bfb51daff2037013e6dcb7ef\", \"dateChecked\": \"2020-03-06T21:00:00Z\", \"death\": 14.0, \"hospitalized\": null, \"total\": 449, \"totalTestResults\": 449, \"posNeg\": 449, \"fips\": 53, \"deathIncrease\": 3.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 370.0, \"positiveIncrease\": 9.0, \"totalTestResultsIncrease\": 379.0, \"ratio\": 0.1759465478841871, \"positive_diff\": 9.0, \"negative_diff\": null, \"death_diff\": 3.0, \"positive_diff_100k\": 0.11818944796729251, \"death_diff_100k\": 0.03939648265576417, \"total_10\": 44.9}, {\"state\": \"WA\", \"date\": \"2020-03-05T00:00:00\", \"positive\": 70.0, \"negative\": null, \"pending\": null, \"hospitalizedCurrently\": null, \"hospitalizedCumulative\": null, \"inIcuCurrently\": null, \"inIcuCumulative\": null, \"onVentilatorCurrently\": null, \"onVentilatorCumulative\": null, \"recovered\": null, \"hash\": \"dfe5b036415333cb6e796cd8bde4805a159d6676\", \"dateChecked\": \"2020-03-05T21:00:00Z\", \"death\": 11.0, \"hospitalized\": null, \"total\": 70, \"totalTestResults\": 70, \"posNeg\": 70, \"fips\": 53, \"deathIncrease\": 1.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 0.0, \"positiveIncrease\": 31.0, \"totalTestResultsIncrease\": 31.0, \"ratio\": 1.0, \"positive_diff\": 31.0, \"negative_diff\": null, \"death_diff\": 1.0, \"positive_diff_100k\": 0.4070969874428964, \"death_diff_100k\": 0.013132160885254724, \"total_10\": 7.0}, {\"state\": \"WA\", \"date\": \"2020-03-04T00:00:00\", \"positive\": 39.0, \"negative\": null, \"pending\": null, \"hospitalizedCurrently\": null, \"hospitalizedCumulative\": null, \"inIcuCurrently\": null, \"inIcuCumulative\": null, \"onVentilatorCurrently\": null, \"onVentilatorCumulative\": null, \"recovered\": null, \"hash\": \"e07bd12171fed21351784c462b0dab23d8cfabbd\", \"dateChecked\": \"2020-03-04T21:00:00Z\", \"death\": 10.0, \"hospitalized\": null, \"total\": 39, \"totalTestResults\": 39, \"posNeg\": 39, \"fips\": 53, \"deathIncrease\": null, \"hospitalizedIncrease\": null, \"negativeIncrease\": null, \"positiveIncrease\": null, \"totalTestResultsIncrease\": null, \"ratio\": 1.0, \"positive_diff\": null, \"negative_diff\": null, \"death_diff\": null, \"positive_diff_100k\": null, \"death_diff_100k\": null, \"total_10\": 3.9}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<p style=\"font-size: smaller\">Data Sources: \n",
       "  <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n",
       "<br>\n",
       "Analysis and Visualization:\n",
       "  <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n",
       "</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# produce the charts for a few states\n",
    "\n",
    "charts=[]\n",
    "for state in most_recent_df.sort_values('total/100k', ascending=False)['state'].to_list()[:3]: \n",
    "    state_df = tdf_diff[tdf_diff['state'] == state].copy()\n",
    "\n",
    "    base = alt.Chart(state_df, title=state).encode(alt.X('date', axis=alt.Axis(title='Date'))).properties(width=250, height=150)\n",
    "    dailies = base.mark_bar(size=6).encode(alt.Y('positive_diff', axis=alt.Axis(title='Daily positive')))\n",
    "\n",
    "    totals = base.mark_line(color='red').encode(alt.Y('total_10', axis=alt.Axis(title='Total/10'))) \n",
    "    positives = totals.mark_line(color='orange').encode(alt.Y('positive', axis=alt.Axis(title='Positive')))\n",
    "    cumulative = totals + positives\n",
    "\n",
    "    ratio = base.mark_line(color='red').encode(alt.Y('ratio', axis=alt.Axis(title='Positive/Total'), scale=alt.Scale(domain=(0,1))))\n",
    "    \n",
    "    charts.append(alt.layer(dailies, cumulative).resolve_scale(y='independent'))\n",
    "\n",
    "display(alt.hconcat(*charts))\n",
    "display(html_credits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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