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| import streamlit as st | |
| import pandas as pd | |
| import altair as alt | |
| # Define list of largest hospitals with latitude and longitude | |
| largest_hospitals = [ | |
| { | |
| 'name': 'Florida Hospital Orlando', | |
| 'city': 'Orlando', | |
| 'state': 'FL', | |
| 'zip_code': '32803', | |
| 'bed_count': 2411, | |
| 'lat': 28.562229, | |
| 'lng': -81.362976 | |
| }, | |
| { | |
| 'name': 'Cleveland Clinic', | |
| 'city': 'Cleveland', | |
| 'state': 'OH', | |
| 'zip_code': '44195', | |
| 'bed_count': 1730, | |
| 'lat': 41.501669, | |
| 'lng': -81.621275 | |
| }, | |
| { | |
| 'name': 'Mayo Clinic', | |
| 'city': 'Rochester', | |
| 'state': 'MN', | |
| 'zip_code': '55905', | |
| 'bed_count': 1372, | |
| 'lat': 44.020634, | |
| 'lng': -92.463476 | |
| }, | |
| { | |
| 'name': 'NewYork-Presbyterian Hospital-Columbia and Cornell', | |
| 'city': 'New York', | |
| 'state': 'NY', | |
| 'zip_code': '10032', | |
| 'bed_count': 2332, | |
| 'lat': 40.840886, | |
| 'lng': -73.942184 | |
| }, | |
| { | |
| 'name': 'UCHealth University of Colorado Hospital', | |
| 'city': 'Aurora', | |
| 'state': 'CO', | |
| 'zip_code': '80045', | |
| 'bed_count': 672, | |
| 'lat': 39.742401, | |
| 'lng': -104.834694 | |
| }, | |
| { | |
| 'name': 'Houston Methodist Hospital', | |
| 'city': 'Houston', | |
| 'state': 'TX', | |
| 'zip_code': '77030', | |
| 'bed_count': 1063, | |
| 'lat': 29.710292, | |
| 'lng': -95.399262 | |
| }, | |
| { | |
| 'name': 'Johns Hopkins Hospital', | |
| 'city': 'Baltimore', | |
| 'state': 'MD', | |
| 'zip_code': '21287', | |
| 'bed_count': 1293, | |
| 'lat': 39.297082, | |
| 'lng': -76.590726 | |
| }, | |
| { | |
| 'name': 'Massachusetts General Hospital', | |
| 'city': 'Boston', | |
| 'state': 'MA', | |
| 'zip_code': '02114', | |
| 'bed_count': 1032, | |
| 'lat': 42.363371, | |
| 'lng': -71.068635 | |
| }, | |
| { | |
| 'name': 'University of Michigan Hospitals-Michigan Medicine', | |
| 'city': 'Ann Arbor', | |
| 'state': 'MI', | |
| 'zip_code': '48109', | |
| 'bed_count': 1145, | |
| 'lat': 42.282531, | |
| 'lng': -83.728376 | |
| }, | |
| { | |
| 'name': 'Mount Sinai Hospital', | |
| 'city': 'New York', | |
| 'state': 'NY', | |
| 'zip_code': '10029', | |
| 'bed_count': 1168, | |
| 'lat': 40.789866, | |
| 'lng': -73.952348 | |
| } | |
| ] | |
| largest_hospitals_df = pd.DataFrame(largest_hospitals) | |
| # Define chart functions | |
| def stacked_bar_chart(): | |
| chart = alt.Chart(largest_hospitals_df).mark_bar().encode( | |
| y=alt.Y('state:N', sort='-x'), | |
| x=alt.X('bed_count:Q', stack='normalize'), | |
| color=alt.Color('state:N'), | |
| tooltip=['state', 'bed_count'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals by State (Stacked Bar Chart)' | |
| ) | |
| st.altair_chart(chart) | |
| def bump_chart(): | |
| chart = alt.Chart(largest_hospitals_df).transform_joinaggregate( | |
| max_bed_count='max(bed_count)', | |
| ).transform_window( | |
| rank='rank(max_bed_count)', | |
| sort=[alt.SortField('max_bed_count', order='descending')] | |
| ).transform_filter( | |
| alt.datum.rank <= 10 | |
| ).mark_line().encode( | |
| y=alt.Y('name:N', sort=alt.EncodingSortField('bed_count', order='descending')), | |
| x=alt.X('bed_count:Q'), | |
| color=alt.Color('state:N'), | |
| tooltip=['name', 'bed_count', 'state'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals by Bed Count (Bump Chart)' | |
| ) | |
| st.altair_chart(chart) | |
| def radial_chart(): | |
| chart = alt.Chart(largest_hospitals_df).mark_circle().encode( | |
| x='sum(bed_count)', | |
| y='state', | |
| size='sum(bed_count)', | |
| color='state', | |
| tooltip=['state', 'bed_count'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals by State (Radial Chart)' | |
| ) | |
| st.altair_chart(chart) | |
| def trellis_area_sort_chart(): | |
| chart = alt.Chart(largest_hospitals_df).mark_area().encode( | |
| x=alt.X('year:O', title='Year'), | |
| y=alt.Y('bed_count:Q', title='Bed Count'), | |
| color=alt.Color('state:N', legend=alt.Legend(title='State')), | |
| row=alt.Row('name:N', sort='-x', title='Hospital') | |
| ).transform_calculate( | |
| year='substring(zip_code, 0, 2) + "00"' | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals by Year (Trellis Area Sort Chart)' | |
| ) | |
| st.altair_chart(chart) | |
| def wind_vector_map(): | |
| airports_df = pd.read_csv('https://raw.githubusercontent.com/hvo/datasets/master/nyc_airports.csv') | |
| airports = alt.Chart(airports_df).mark_circle(size=100).encode( | |
| longitude='lon:Q', | |
| latitude='lat:Q', | |
| tooltip=['name', 'city', 'state'] | |
| ) | |
| wind_df = pd.read_csv('https://raw.githubusercontent.com/vega/vega/master/docs/data/wind.csv') | |
| wind = alt.Chart(wind_df).mark_line().encode( | |
| x='u:Q', | |
| y='v:Q', | |
| color=alt.Color('speed:Q', scale=alt.Scale(scheme='inferno')), | |
| size='speed:Q', | |
| tooltip=['u', 'v', 'speed'] | |
| ) | |
| chart = alt.layer( | |
| alt.themes.dark(), | |
| alt.repeat(row=range(4), column=range(4), layer=0, data=largest_hospitals_df), | |
| airports, | |
| wind.transform_filter( | |
| alt.datum.zip_code == str(largest_hospitals_df.iloc[0]['zip_code']) | |
| ).transform_calculate( | |
| azimuth='atan2(v, u)', | |
| speed='sqrt(u * u + v * v)', | |
| dx='cos(azimuth * PI/180) * speed', | |
| dy='sin(azimuth * PI/180) * speed' | |
| ).mark_arrow().encode( | |
| longitude='lng:Q', | |
| latitude='lat:Q', | |
| angle='azimuth:Q', | |
| size=alt.Size('speed:Q', scale=alt.Scale(range=[0, 50])), | |
| tooltip=['u', 'v', 'speed', 'azimuth'] | |
| ) | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Wind Vectors and Airports in New York City' | |
| ) | |
| st.altair_chart(chart) | |
| def table_bubble_plot(): | |
| chart = alt.Chart(largest_hospitals_df).mark_circle().encode( | |
| x=alt.X('bed_count:Q', title='Bed Count'), | |
| y=alt.Y('state:N', sort='-x', title='State'), | |
| size=alt.Size('bed_count:Q', title='Bed Count'), | |
| color=alt.Color('state:N'), | |
| tooltip=['name', 'bed_count', 'city', 'state'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals in the US (Table Bubble Plot)' | |
| ) | |
| st.altair_chart(chart) | |
| def locations_of_us_airports(): | |
| airports_df = pd.read_csv('https://raw.githubusercontent.com/hvo/datasets/master/nyc_airports.csv') | |
| chart = alt.Chart(airports_df).mark_circle(size=100).encode( | |
| longitude='lon:Q', | |
| latitude='lat:Q', | |
| tooltip=['name', 'city', 'state'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Locations of US Airports' | |
| ) | |
| st.altair_chart(chart) | |
| def connections_among_us_airports_interactive(): | |
| airports_df = pd.read_csv('https://raw.githubusercontent.com/hvo/datasets/master/nyc_airports.csv') | |
| routes_df = pd.read_csv('https://raw.githubusercontent.com/vega/vega/master/docs/data/flights-2k.csv') | |
| source = alt.selection_multi(fields=['origin']) | |
| chart = alt.Chart(routes_df).mark_geoshape(stroke='white', strokeWidth=0.5).encode( | |
| color=alt.condition(source, alt.value('red'), alt.value('lightgray')), | |
| tooltip=['origin', 'destination', 'count'] | |
| ).transform_lookup( | |
| lookup='id', | |
| from_=alt.LookupData(airports_df, 'id', ['name', 'city', 'state']) | |
| ).project( | |
| type='albersUsa' | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Connections Among US Airports (Interactive)' | |
| ) | |
| points = alt.Chart(airports_df).mark_circle(size=100).encode( | |
| longitude='lon:Q', | |
| latitude='lat:Q', | |
| tooltip=['name', 'city', 'state', 'id'] | |
| ).transform_filter( | |
| source | |
| ) | |
| st.altair_chart(chart + points) | |
| def one_dot_per_zipcode(): | |
| chart = alt.Chart(largest_hospitals_df).mark_circle(size=50).encode( | |
| longitude='lng:Q', | |
| latitude='lat:Q', | |
| color=alt.Color('state:N', scale=alt.Scale(scheme='category10')), | |
| tooltip=['name', 'city', 'state', 'bed_count'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals in the US (One Dot per Zip Code)' | |
| ) | |
| st.altair_chart(chart) | |
| def isotype_visualization_with_emoji(): | |
| chart = alt.Chart(largest_hospitals_df).mark_image(width=50, height=50).encode( | |
| x=alt.X('bed_count:Q', axis=None), | |
| y=alt.Y('state:N', sort='-x', axis=None), | |
| url='https://raw.githubusercontent.com/twitter/twemoji/v13.0.1/assets/svg/1f628.svg', | |
| tooltip=['name', 'bed_count'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals in the US (Isotype Visualization with Emoji)' | |
| ) | |
| st.altair_chart(chart) | |
| def binned_heatmap(): | |
| chart = alt.Chart(largest_hospitals_df).mark_rect().encode( | |
| x=alt.X('bed_count:Q', bin=True), | |
| y=alt.Y('state:N', sort='-x'), | |
| color=alt.Color('count()'), | |
| tooltip=['state', 'bed_count'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals by State (Binned Heatmap)' | |
| ) | |
| st.altair_chart(chart) | |
| def facetted_scatterplot_with_marginal_histograms(): | |
| chart = alt.Chart(largest_hospitals_df).mark_circle(size=50).encode( | |
| x=alt.X('bed_count:Q', title='Bed Count'), | |
| y=alt.Y('state:N', sort='-x', title='State'), | |
| color=alt.Color('state:N'), | |
| tooltip=['name', 'bed_count', 'city', 'state'] | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals in the US (Facetted Scatterplot with Marginal Histograms)' | |
| ).facet( | |
| column=alt.Column('state:N', sort='-y', title='State'), | |
| spacing={'column': 30} | |
| ) | |
| histogram_x = largest_hospitals_df[['bed_count']].reset_index().rename(columns={'bed_count': 'value'}) | |
| histogram_x['variable'] = 'bed_count' | |
| histogram_y = largest_hospitals_df[['state']].reset_index().rename(columns={'state': 'value'}) | |
| histogram_y['variable'] = 'state' | |
| chart_x = alt.Chart(histogram_x).mark_bar().encode( | |
| x=alt.X('value:Q', bin=True), | |
| y=alt.Y('count()'), | |
| color=alt.Color('variable:N', scale=alt.Scale(range=['#675193', '#ca8861'])), | |
| tooltip=['value'] | |
| ).properties( | |
| width=700, | |
| height=100 | |
| ) | |
| chart_y = alt.Chart(histogram_y).mark_bar().encode( | |
| y=alt.Y('value:N', sort='-x'), | |
| x=alt.X('count()'), | |
| color=alt.Color('variable:N', scale=alt.Scale(range=['#675193', '#ca8861'])), | |
| tooltip=['value'] | |
| ).properties( | |
| width=100, | |
| height=500 | |
| ) | |
| st.altair_chart(chart | chart_x, use_container_width=True) | |
| st.altair_chart(chart_y, use_container_width=True) | |
| def ridgeline_plot(): | |
| chart = alt.Chart(largest_hospitals_df).transform_joinaggregate( | |
| count='count()', | |
| groupby=['state'] | |
| ).transform_window( | |
| rank='rank(count)', | |
| sort=[alt.SortField('count', order='descending')] | |
| ).transform_filter( | |
| alt.datum.rank <= 5 | |
| ).transform_density( | |
| density='bed_count', | |
| as_=['bed_count', 'density'], | |
| extent=[0, 3000], | |
| groupby=['state'] | |
| ).mark_area().encode( | |
| y=alt.Y('state:N', sort='-x'), | |
| x=alt.X('bed_count:Q', title='Bed Count'), | |
| color=alt.Color('state:N'), | |
| row=alt.Row('rank:O', sort='descending', title=None) | |
| ).properties( | |
| width=700, | |
| height=500, | |
| title='Largest Hospitals by State (Ridgeline Plot)' | |
| ) | |
| st.altair_chart(chart) | |
| def create_sidebar(): | |
| chart_functions = { | |
| 'Stacked Bar Chart with Text Overlay': stacked_bar_chart, | |
| 'Bump Chart': bump_chart, | |
| 'Radial Chart': radial_chart, | |
| 'Trellis Area Sort Chart': trellis_area_sort_chart, | |
| 'Wind Vector Map': wind_vector_map, | |
| 'Table Bubble Plot': table_bubble_plot, | |
| 'Locations of US Airports': locations_of_us_airports, | |
| 'Connections Among U.S. Airports Interactive': connections_among_us_airports_interactive, | |
| 'One Dot Per Zipcode': one_dot_per_zipcode, | |
| 'Isotype Visualization with Emoji': isotype_visualization_with_emoji, | |
| 'Binned Heatmap': binned_heatmap, | |
| 'Facetted Scatterplot with Marginal Histograms': facetted_scatterplot_with_marginal_histograms, | |
| 'Ridgeline Plot': ridgeline_plot | |
| } | |
| st.sidebar.title('Charts') | |
| for chart_name, chart_function in chart_functions.items(): | |
| chart_button = st.sidebar.button(f'{chart_name} {emoji(chart_name)}') | |
| if chart_button: | |
| chart_function() | |
| def emoji(chart_name): | |
| emojis = { | |
| 'Stacked Bar Chart with Text Overlay': '📊', | |
| 'Bump Chart': '📈', | |
| 'Radial Chart': '🎡', | |
| 'Trellis Area Sort Chart': '📉', | |
| 'Wind Vector Map': '🌬️', | |
| 'Table Bubble Plot': '💬', | |
| 'Locations of US Airports': '✈️', | |
| 'Connections Among U.S. Airports Interactive': '🛫', | |
| 'One Dot Per Zipcode': '📍', | |
| 'Isotype Visualization with Emoji': '😀', | |
| 'Binned Heatmap': '🗺️', | |
| 'Facetted Scatterplot with Marginal Histograms': '🔳', | |
| 'Ridgeline Plot': '🏔️' | |
| } | |
| return emojis.get(chart_name, '') | |
| create_sidebar() | |