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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",
"metadata": {},
"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",
"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'])\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()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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\"ratio\": 0.08065573770491803, \"sinceDay0\": 3}, {\"index\": 37, \"date\": \"2020-03-29T00:00:00\", \"state\": \"NY\", \"positive\": 59513.0, \"negative\": 112847.0, \"pending\": null, \"hospitalized\": 12075.0, \"death\": 965.0, \"total\": 172360, \"hash\": \"ba807849425122f97cff754bfca452232a1e75ce\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 172360, \"fips\": 36, \"deathIncrease\": 237.0, \"hospitalizedIncrease\": 2021.0, \"negativeIncrease\": 9231.0, \"positiveIncrease\": 7195.0, \"totalTestResultsIncrease\": 16426.0, \"ratio\": 0.34528312833604086, \"sinceDay0\": 11}, {\"index\": 38, \"date\": \"2020-03-29T00:00:00\", \"state\": \"OH\", \"positive\": 1653.0, \"negative\": 19012.0, \"pending\": null, \"hospitalized\": 403.0, \"death\": 29.0, \"total\": 20665, \"hash\": \"bf97498f0e2f4741137db5392baa1abf5648979f\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 20665, \"fips\": 39, \"deathIncrease\": 4.0, \"hospitalizedIncrease\": 59.0, 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\"sinceDay0\": 2}, {\"index\": 25, \"date\": \"2020-03-29T00:00:00\", \"state\": \"All US\", \"positive\": 139061.0, \"negative\": 692290.0, \"pending\": 65549.0, \"hospitalized\": 19730.0, \"death\": 2428.0, \"total\": 896900, \"hash\": null, \"dateChecked\": null, \"totalTestResults\": 831351, \"fips\": 1822, \"deathIncrease\": 463.0, \"hospitalizedIncrease\": 3001.0, \"negativeIncrease\": 74820.0, \"positiveIncrease\": 20827.0, \"totalTestResultsIncrease\": 95647.0, \"ratio\": 7.531325513159175, \"sinceDay0\": 18}], \"data-e4b5b6965c8c823b5ce3cb31b8c7f248\": [{\"labelX\": 10, \"labelY\": 6000, \"labelText\": \"doubles every day\"}, {\"labelX\": 19, \"labelY\": 700, \"labelText\": \"doubles every 3 days\"}, {\"labelX\": 23, \"labelY\": 50, \"labelText\": \"doubles every week\"}]}}, {\"mode\": \"vega-lite\"});\n</script>",
"text/plain": "alt.LayerChart(...)"
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n<br>\nAnalysis 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 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",
").properties(width=800,height=800)\n",
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"\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",
"\n",
"display(chart)\n",
"display(html_credits)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [
{
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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 }\n })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"name\": \"data-b56d69ef163b22abe89d736fadd4ca50\"}, \"mark\": {\"type\": \"line\", \"interpolate\": \"basis\"}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"state\"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"state\"}, {\"type\": \"quantitative\", \"field\": \"sinceDay0\"}, {\"type\": \"quantitative\", \"field\": \"death\"}, {\"type\": \"quantitative\", 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{\"type\": \"nominal\", \"field\": \"state\"}, \"text\": {\"type\": \"nominal\", \"field\": \"state\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"sinceDay0\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"positive\"}}}, {\"data\": {\"name\": \"data-fd6452152b33a51e350c04c156674fe8\"}, \"mark\": {\"type\": \"text\", \"align\": \"right\", \"baseline\": \"bottom\", \"dx\": 0, \"opacity\": 0.5, \"size\": 18}, \"encoding\": {\"text\": {\"type\": \"nominal\", \"field\": \"labelText\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"labelX\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"labelY\"}}}]}, {\"mark\": \"point\", \"encoding\": {\"opacity\": {\"value\": 0}, \"x\": {\"type\": \"quantitative\", \"field\": \"sinceDay0\"}}, \"selection\": {\"selector002\": {\"type\": \"single\", \"nearest\": true, \"on\": \"mouseover\", \"fields\": [\"sinceDay0\"]}}}, {\"data\": {\"name\": \"data-b56d69ef163b22abe89d736fadd4ca50\"}, \"mark\": {\"type\": \"text\", \"align\": \"center\", \"dx\": 3, \"dy\": -20}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"state\"}, \"text\": {\"condition\": {\"type\": \"quantitative\", \"field\": \"positive\", \"selection\": \"selector002\"}, \"value\": \" \"}, \"tooltip\": [{\"type\": \"nominal\", \"field\": \"state\"}, {\"type\": \"quantitative\", \"field\": \"sinceDay0\"}, {\"type\": \"quantitative\", \"field\": \"death\"}, {\"type\": \"quantitative\", \"field\": \"positive\"}], \"x\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Days since 100th case\"}, \"field\": \"sinceDay0\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Cumulative positive cases\"}, \"field\": \"positive\", \"scale\": {\"type\": \"log\"}}}, \"height\": 800, \"title\": \"US States: total cases since 100th case\", \"width\": 800}], \"data\": {\"name\": \"data-c6b5c328d716138dbd91ab39cd198128\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\", \"datasets\": {\"data-c6b5c328d716138dbd91ab39cd198128\": 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\"state\": \"NH\", \"positive\": 214.0, \"negative\": 4524.0, \"pending\": 285.0, \"hospitalized\": 33.0, \"death\": 2.0, \"total\": 5023, \"hash\": \"5602e725da5424f5efebbaa8e3b8ac7c71a21d53\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 4738, \"fips\": 33, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 3.0, \"negativeIncrease\": 868.0, \"positiveIncrease\": 27.0, \"totalTestResultsIncrease\": 895.0, \"ratio\": 0.04260402150109496, \"sinceDay0\": 5}, {\"index\": 34, \"date\": \"2020-03-29T00:00:00\", \"state\": \"NJ\", \"positive\": 13386.0, \"negative\": 22216.0, \"pending\": null, \"hospitalized\": null, \"death\": 161.0, \"total\": 35602, \"hash\": \"5e0cce3ce15a05bc385736f7f690e041feb4f2d9\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 35602, \"fips\": 34, \"deathIncrease\": 21.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 2830.0, \"positiveIncrease\": 2262.0, \"totalTestResultsIncrease\": 5092.0, \"ratio\": 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\"positiveIncrease\": 117.0, \"totalTestResultsIncrease\": 628.0, \"ratio\": 0.08065573770491803, \"sinceDay0\": 9}, {\"index\": 37, \"date\": \"2020-03-29T00:00:00\", \"state\": \"NY\", \"positive\": 59513.0, \"negative\": 112847.0, \"pending\": null, \"hospitalized\": 12075.0, \"death\": 965.0, \"total\": 172360, \"hash\": \"ba807849425122f97cff754bfca452232a1e75ce\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 172360, \"fips\": 36, \"deathIncrease\": 237.0, \"hospitalizedIncrease\": 2021.0, \"negativeIncrease\": 9231.0, \"positiveIncrease\": 7195.0, \"totalTestResultsIncrease\": 16426.0, \"ratio\": 0.34528312833604086, \"sinceDay0\": 21}, {\"index\": 38, \"date\": \"2020-03-29T00:00:00\", \"state\": \"OH\", \"positive\": 1653.0, \"negative\": 19012.0, \"pending\": null, \"hospitalized\": 403.0, \"death\": 29.0, \"total\": 20665, \"hash\": \"bf97498f0e2f4741137db5392baa1abf5648979f\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 20665, \"fips\": 39, \"deathIncrease\": 4.0, \"hospitalizedIncrease\": 59.0, \"negativeIncrease\": 0.0, \"positiveIncrease\": 247.0, \"totalTestResultsIncrease\": 247.0, \"ratio\": 0.07999032180014518, \"sinceDay0\": 10}, {\"index\": 39, \"date\": \"2020-03-29T00:00:00\", \"state\": \"OK\", \"positive\": 429.0, \"negative\": 1205.0, \"pending\": null, \"hospitalized\": 140.0, \"death\": 16.0, \"total\": 1634, \"hash\": \"43ef7985bd9555698d09d6a950875ade320286df\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 1634, \"fips\": 40, \"deathIncrease\": 1.0, \"hospitalizedIncrease\": 14.0, \"negativeIncrease\": 25.0, \"positiveIncrease\": 52.0, \"totalTestResultsIncrease\": 77.0, \"ratio\": 0.26254589963280295, \"sinceDay0\": 5}, {\"index\": 40, \"date\": \"2020-03-29T00:00:00\", \"state\": \"OR\", \"positive\": 548.0, \"negative\": 10878.0, \"pending\": null, \"hospitalized\": 129.0, \"death\": 13.0, \"total\": 11426, \"hash\": \"ec860456b55d5a18391c8cbf2748ea02bf908fd2\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 11426, \"fips\": 41, \"deathIncrease\": 0.0, \"hospitalizedIncrease\": 12.0, \"negativeIncrease\": 1185.0, \"positiveIncrease\": 69.0, \"totalTestResultsIncrease\": 1254.0, \"ratio\": 0.04796079117801506, \"sinceDay0\": 9}, {\"index\": 41, \"date\": \"2020-03-29T00:00:00\", \"state\": \"PA\", \"positive\": 3394.0, \"negative\": 30061.0, \"pending\": null, \"hospitalized\": 682.0, \"death\": 38.0, \"total\": 33455, \"hash\": \"ad1df7816ad6d19a996749d630bc38345291f26e\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 33455, \"fips\": 42, \"deathIncrease\": 4.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 4807.0, \"positiveIncrease\": 643.0, \"totalTestResultsIncrease\": 5450.0, \"ratio\": 0.10144970856374234, \"sinceDay0\": 11}, {\"index\": 42, \"date\": \"2020-03-29T00:00:00\", \"state\": \"PR\", \"positive\": 127.0, \"negative\": 841.0, \"pending\": 817.0, \"hospitalized\": null, \"death\": 5.0, 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\"pending\": null, \"hospitalized\": 129.0, \"death\": 16.0, \"total\": 3789, \"hash\": \"b85a9aaa3da2d62f633714b4d08f409109f52bb4\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 3789, \"fips\": 45, \"deathIncrease\": 3.0, \"hospitalizedIncrease\": 0.0, \"negativeIncrease\": 607.0, \"positiveIncrease\": 235.0, \"totalTestResultsIncrease\": 842.0, \"ratio\": 0.2042755344418052, \"sinceDay0\": 8}, {\"index\": 46, \"date\": \"2020-03-29T00:00:00\", \"state\": \"TN\", \"positive\": 1537.0, \"negative\": 19037.0, \"pending\": null, \"hospitalized\": 133.0, \"death\": 7.0, \"total\": 20574, \"hash\": \"c928e23a1549ab46b5a78ea8b86ea8fa727ca05f\", \"dateChecked\": \"2020-03-29T20:00:00Z\", \"totalTestResults\": 20574, \"fips\": 47, \"deathIncrease\": 1.0, \"hospitalizedIncrease\": 15.0, \"negativeIncrease\": 2072.0, \"positiveIncrease\": 164.0, \"totalTestResultsIncrease\": 2236.0, \"ratio\": 0.07470593953533586, \"sinceDay0\": 10}, {\"index\": 47, \"date\": 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week\"}]}}, {\"mode\": \"vega-lite\"});\n</script>",
"text/plain": "alt.LayerChart(...)"
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n<br>\nAnalysis and Visualization:\n <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n</p>"
}
],
"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'])\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",
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"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",
").properties(width=800,height=800)\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=[100,200000], type='log'),\n",
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" ),\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",
"metadata": {},
"outputs": [
{
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"text/plain": "alt.VConcatChart(...)"
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n<br>\nAnalysis 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": [
"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",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Most recent test date 2020-03-29 00:00:00\n56 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",
"metadata": {},
"outputs": [
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"text/plain": "alt.LayerChart(...)"
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n<br>\nAnalysis and Visualization:\n <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n</p>"
}
],
"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",
"metadata": {},
"outputs": [
{
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null, \"positive_diff_100k\": 0.0, \"death_diff_100k\": null, \"total_10\": 0.5}, {\"state\": \"LA\", \"date\": \"2020-03-07T00:00:00\", \"positive\": 0.0, \"negative\": null, \"pending\": null, \"hospitalized\": null, \"death\": null, \"total\": 0, \"hash\": \"1e11659401041270af62f339cbe0863507fc1a3a\", \"dateChecked\": \"2020-03-07T21:00:00Z\", \"totalTestResults\": 0, \"fips\": 22, \"deathIncrease\": null, \"hospitalizedIncrease\": null, \"negativeIncrease\": null, \"positiveIncrease\": null, \"totalTestResultsIncrease\": null, \"ratio\": null, \"positive_diff\": null, \"negative_diff\": null, \"death_diff\": null, \"positive_diff_100k\": null, \"death_diff_100k\": null, \"total_10\": 0.0}]}}, {\"mode\": \"vega-lite\"});\n</script>",
"text/plain": "alt.HConcatChart(...)"
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "\n<p style=\"font-size: smaller\">Data Sources: \n <a href=\"https://covidtracking.com\">The COVID Tracking Project</a>\n<br>\nAnalysis and Visualization:\n <a href=\"https://renkulab.io/projects/covid-19/covid-19-public-data\">Covid-19 Public Data Collaboration Project</a>\n</p>"
}
],
"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=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)"
]
},
{
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}
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