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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import altair as alt\n",
"from IPython.display import display, HTML"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"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": 21,
"metadata": {},
"outputs": [],
"source": [
"# Read population data\n",
"pop_df = pd.read_csv('../data/geodata/us_pop_fung_2019.csv').set_index('ST')\n",
"\n",
"# Read state-level data\n",
"data_df = pd.read_json('../data/covidtracking/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. "
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Growth trends"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Compute theoretical trends of doubling every day, 3 days, week\n",
"days = {'day':[1,2,3,4,5,10,15,20, 50, 100]}\n",
"startCase = 10\n",
"logRuleDay_df = pd.DataFrame(days, columns=['day'])\n",
"logRuleDay_df['case']= startCase * pow(2,logRuleDay_df['day']-1)\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']-1)/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']-1)/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()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
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\"doubling period\": \"every week\"}, {\"index\": 7, \"day\": 20, \"case\": 65.62682848061104, \"doubling period\": \"every week\"}, {\"index\": 8, \"day\": 50, \"case\": 1280.0, \"doubling period\": \"every week\"}, {\"index\": 9, \"day\": 100, \"case\": 180894.02592031736, \"doubling period\": \"every week\"}], \"data-fbb77d4fa425f433be0b6da01b0cd6e7\": [{\"index\": 5, \"date\": \"2020-03-26T00:00:00\", \"state\": \"CA\", \"positive\": 3006.0, \"negative\": 17380.0, \"pending\": 57400.0, \"hospitalized\": null, \"death\": 65.0, \"total\": 77786, \"dateChecked\": \"2020-03-26T20:00:00Z\", \"totalTestResults\": 20386, \"deathIncrease\": 12.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 1459.0, \"positiveIncrease\": 651.0, \"totalTestResultsIncrease\": 2110.0, \"ratio\": 0.038644486154320826, \"sinceDay0\": 9}, {\"index\": 6, \"date\": \"2020-03-26T00:00:00\", \"state\": \"CO\", \"positive\": 1086.0, \"negative\": 6978.0, \"pending\": null, \"hospitalized\": 148.0, \"death\": 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\"doubles every week\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.LayerChart(...)"
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"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>"
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"output_type": "display_data"
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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",
" [19,700,'doubles every 3 days'],\n",
" [23,50, '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",
")\n",
"\n",
"#Create a layer with the lines for doubling every day and doubling every week\n",
"ruleChart = alt.Chart(logRules_df).mark_line(opacity=0.2,clip=True).encode(\n",
" alt.X('day:Q',\n",
" scale=alt.Scale(domain=[1,23])),\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",
"#chart = alt.layer(lineChart, ruleChart, labelChart)\n",
"chart.properties(width=800,height=600)\n",
"\n",
"display(chart)\n",
"display(html_credits)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-821c26db5ee1411688198eda793a8e05\"></div>\n",
"<script type=\"text/javascript\">\n",
" (function(spec, embedOpt){\n",
" const outputDiv = document.getElementById(\"altair-viz-821c26db5ee1411688198eda793a8e05\");\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.2?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function loadScript(lib) {\n",
" return new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" s.src = paths[lib];\n",
" s.async = true;\n",
" s.onload = () => resolve(paths[lib]);\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else if (typeof vegaEmbed === \"function\") {\n",
" displayChart(vegaEmbed);\n",
" } else {\n",
" loadScript(\"vega\")\n",
" .then(() => loadScript(\"vega-lite\"))\n",
" .then(() => loadScript(\"vega-embed\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\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",
"</p>"
],
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"source": [
"#make dataframe with lines to indicate doubling every day, 3 days, week\n",
"days = {'day':[1,2,3,4,5,10,15,20, 50, 100]}\n",
"startCase = 100\n",
"logRuleDay_df = pd.DataFrame (days, columns=['day'])\n",
"logRuleDay_df['case']= startCase * pow(2,logRuleDay_df['day']-1)\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']-1)/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']-1)/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",
"#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",
" [19,300, '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",
")\n",
"\n",
"#Create a layer with the lines for doubling every day and doubling every week\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=[1,30])),\n",
" alt.Y('case', scale=alt.Scale(domain=[100,100000], 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=800,height=600)\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": 4,
"metadata": {},
"outputs": [
{
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{\"date\": \"2020-03-18T00:00:00\", \"positive\": 7730.0, \"negative\": 66225.0, \"pending\": 2538.0, \"hospitalized\": 0.0, \"death\": 112.0, \"total\": 76493, \"totalTestResults\": 73955, \"deathIncrease\": 22.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 18621.0, \"positiveIncrease\": 2007.0, \"totalTestResultsIncrease\": 20628.0, \"ratio\": 8.606150771718424, \"positive_diff\": 2007.0, \"negative_diff\": 17217.0, \"death_diff\": 18.0, \"positive_diff_100k\": 27.52218681129882, \"death_diff_100k\": 0.1743011549896832, \"total_10\": 7649.3}, {\"date\": \"2020-03-19T00:00:00\", \"positive\": 11719.0, \"negative\": 89119.0, \"pending\": 3025.0, \"hospitalized\": 0.0, \"death\": 160.0, \"total\": 103863, \"totalTestResults\": 100838, \"deathIncrease\": 48.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 22894.0, \"positiveIncrease\": 3989.0, \"totalTestResultsIncrease\": 26883.0, \"ratio\": 8.455627941743812, \"positive_diff\": 3989.0, \"negative_diff\": 22894.0, 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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=15).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 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": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most recent test date 2020-03-26 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": 47,
"metadata": {},
"outputs": [
{
"data": {
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" const outputDiv = document.getElementById(\"altair-viz-082e2e3156bc46868daf6ffe834160f4\");\n",
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" const paths = {\n",
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" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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" function displayChart(vegaEmbed) {\n",
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" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\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",
"</p>"
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"source": [
"viz_df = most_recent_df.sort_values('total/100k', ascending=False)\n",
"chart = alt.Chart(viz_df, title=\"Cases 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": 7,
"metadata": {},
"outputs": [
{
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\"dateChecked\": \"2020-03-22T20:00:00Z\", \"totalTestResults\": 4779, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 951.0, \"positiveIncrease\": 0.0, \"totalTestResultsIncrease\": 951.0, \"ratio\": 0.011927181418706842, \"positive_diff\": 0.0, \"negative_diff\": 951.0, \"death_diff\": null, \"positive_diff_100k\": 0.0, \"death_diff_100k\": null, \"total_10\": 477.9}, {\"state\": \"NM\", \"date\": \"2020-03-21T00:00:00\", \"positive\": 57.0, \"negative\": 3771.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 3828, \"dateChecked\": \"2020-03-21T20:00:00Z\", \"totalTestResults\": 3828, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 0.0, \"positiveIncrease\": 14.0, \"totalTestResultsIncrease\": 14.0, \"ratio\": 0.014890282131661442, \"positive_diff\": 14.0, \"negative_diff\": 0.0, \"death_diff\": null, \"positive_diff_100k\": 0.6676748556987718, \"death_diff_100k\": null, \"total_10\": 382.8}, {\"state\": \"NM\", \"date\": \"2020-03-20T00:00:00\", \"positive\": 43.0, \"negative\": 3771.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 3814, \"dateChecked\": \"2020-03-20T20:00:00Z\", \"totalTestResults\": 3814, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 1009.0, \"positiveIncrease\": 8.0, \"totalTestResultsIncrease\": 1017.0, \"ratio\": 0.011274252753015208, \"positive_diff\": 8.0, \"negative_diff\": 1009.0, \"death_diff\": null, \"positive_diff_100k\": 0.3815284889707268, \"death_diff_100k\": null, \"total_10\": 381.4}, {\"state\": \"NM\", \"date\": \"2020-03-19T00:00:00\", \"positive\": 35.0, \"negative\": 2762.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 2797, \"dateChecked\": \"2020-03-19T20:00:00Z\", \"totalTestResults\": 2797, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 436.0, \"positiveIncrease\": 7.0, \"totalTestResultsIncrease\": 443.0, \"ratio\": 0.012513407222023596, \"positive_diff\": 7.0, \"negative_diff\": 436.0, \"death_diff\": null, \"positive_diff_100k\": 0.3338374278493859, \"death_diff_100k\": null, \"total_10\": 279.7}, {\"state\": \"NM\", \"date\": \"2020-03-18T00:00:00\", \"positive\": 28.0, \"negative\": 2326.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 2354, \"dateChecked\": \"2020-03-18T20:00:00Z\", \"totalTestResults\": 2354, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 1077.0, \"positiveIncrease\": 5.0, \"totalTestResultsIncrease\": 1082.0, \"ratio\": 0.0118946474086661, \"positive_diff\": 5.0, \"negative_diff\": 1077.0, \"death_diff\": null, \"positive_diff_100k\": 0.23845530560670422, \"death_diff_100k\": null, \"total_10\": 235.4}, {\"state\": \"NM\", \"date\": \"2020-03-17T00:00:00\", \"positive\": 23.0, \"negative\": 1249.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 1272, \"dateChecked\": \"2020-03-17T20:00:00Z\", \"totalTestResults\": 1272, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 683.0, \"positiveIncrease\": 6.0, \"totalTestResultsIncrease\": 689.0, \"ratio\": 0.018081761006289308, \"positive_diff\": 6.0, \"negative_diff\": 683.0, \"death_diff\": null, \"positive_diff_100k\": 0.28614636672804505, \"death_diff_100k\": null, \"total_10\": 127.2}, {\"state\": \"NM\", \"date\": \"2020-03-16T00:00:00\", \"positive\": 17.0, \"negative\": 566.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 583, \"dateChecked\": \"2020-03-16T20:00:00Z\", \"totalTestResults\": 583, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 84.0, \"positiveIncrease\": 4.0, \"totalTestResultsIncrease\": 88.0, \"ratio\": 0.029159519725557463, \"positive_diff\": 4.0, \"negative_diff\": 84.0, \"death_diff\": null, \"positive_diff_100k\": 0.1907642444853634, \"death_diff_100k\": null, \"total_10\": 58.3}, {\"state\": \"NM\", \"date\": \"2020-03-15T00:00:00\", \"positive\": 13.0, \"negative\": 482.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 495, \"dateChecked\": \"2020-03-15T20:00:00Z\", \"totalTestResults\": 495, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 245.0, \"positiveIncrease\": 3.0, \"totalTestResultsIncrease\": 248.0, \"ratio\": 0.026262626262626262, \"positive_diff\": 3.0, \"negative_diff\": 245.0, \"death_diff\": null, \"positive_diff_100k\": 0.14307318336402253, \"death_diff_100k\": null, \"total_10\": 49.5}, {\"state\": \"NM\", \"date\": \"2020-03-14T00:00:00\", \"positive\": 10.0, \"negative\": 237.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 247, \"dateChecked\": \"2020-03-14T20:00:00Z\", \"totalTestResults\": 247, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 47.0, \"positiveIncrease\": 0.0, \"totalTestResultsIncrease\": 47.0, \"ratio\": 0.04048582995951417, \"positive_diff\": 0.0, \"negative_diff\": 47.0, \"death_diff\": null, \"positive_diff_100k\": 0.0, \"death_diff_100k\": null, \"total_10\": 24.7}, {\"state\": \"NM\", \"date\": \"2020-03-13T00:00:00\", \"positive\": 10.0, \"negative\": 190.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 200, \"dateChecked\": \"2020-03-13T20:00:00Z\", \"totalTestResults\": 200, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 35.0, \"positiveIncrease\": 5.0, \"totalTestResultsIncrease\": 40.0, \"ratio\": 0.05, \"positive_diff\": 5.0, \"negative_diff\": 35.0, \"death_diff\": null, \"positive_diff_100k\": 0.23845530560670422, \"death_diff_100k\": null, \"total_10\": 20.0}, {\"state\": \"NM\", \"date\": \"2020-03-12T00:00:00\", \"positive\": 5.0, \"negative\": 155.0, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 160, \"dateChecked\": \"2020-03-12T20:00:00Z\", \"totalTestResults\": 160, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, 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\"positive\": 0.0, \"negative\": 48.0, \"pending\": 0.0, \"hospitalized\": null, \"death\": null, \"total\": 48, \"dateChecked\": \"2020-03-08T20:00:00Z\", \"totalTestResults\": 48, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 0.0, \"positiveIncrease\": 0.0, \"totalTestResultsIncrease\": 0.0, \"ratio\": 0.0, \"positive_diff\": 0.0, \"negative_diff\": 0.0, \"death_diff\": null, \"positive_diff_100k\": 0.0, \"death_diff_100k\": null, \"total_10\": 4.8}, {\"state\": \"NM\", \"date\": \"2020-03-07T00:00:00\", \"positive\": 0.0, \"negative\": 48.0, \"pending\": 0.0, \"hospitalized\": null, \"death\": null, \"total\": 48, \"dateChecked\": \"2020-03-07T21:00:00Z\", \"totalTestResults\": 48, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 32.0, \"positiveIncrease\": 0.0, \"totalTestResultsIncrease\": 32.0, \"ratio\": 0.0, \"positive_diff\": 0.0, \"negative_diff\": 32.0, \"death_diff\": null, \"positive_diff_100k\": 0.0, \"death_diff_100k\": null, \"total_10\": 4.8}, {\"state\": \"NM\", \"date\": \"2020-03-06T00:00:00\", \"positive\": 0.0, \"negative\": 16.0, \"pending\": 0.0, \"hospitalized\": null, \"death\": null, \"total\": 16, \"dateChecked\": \"2020-03-06T21:00:00Z\", \"totalTestResults\": 16, \"deathIncrease\": null, \"hospitalizedIncrease\": null, \"negativeIncrease\": null, \"positiveIncrease\": null, \"totalTestResultsIncrease\": null, \"ratio\": 0.0, \"positive_diff\": null, \"negative_diff\": null, \"death_diff\": null, \"positive_diff_100k\": null, \"death_diff_100k\": null, \"total_10\": 1.6}]}}, {\"mode\": \"vega-lite\"});\n",
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"</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 ['NY', 'WA', 'NM']: \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=10).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)"
]
}
],
"metadata": {
"hide_input": true,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
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"name": "python",
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