reverse colorbar

analysis/db/us_map/choroplethMap.py

@@ -1,26 +1,26 @@
-import plotly.figure_factory as ff
 import numpy as np
 import plotly.io as pio
 from pyprojroot import here
+import seaborn as sns
+import matplotlib.pyplot as plt
 import plotly.express as px
 import pandas as pd
 from urllib.request import urlopen
-import json
 import pathlib as pl
+import json
 
 with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
     counties = json.load(response)
 
-# TODO: Change range_color numbers in fig = px.choropleth()
-# TODO: Build the map for a specific date
-# TODO: Build line/bar graphs to check case numbers per state over a period of time
-# TODO: Draw another version of this map, but accounts for population density per county (per capita count)
-# TODO: See if rate is changing, counts over time (a 14 day sliding window count)
-# Choropleth map with time slider and hover text
-
 confirmed_df = pd.read_csv('https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/'
                            'csse_covid_19_time_series/time_series_covid19_confirmed_US.csv')
+# Resource for State_FIPS: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/home/?cid=nrcs143_013697
 loc_df = pd.read_excel(here('./data/db/original/maps/State_FIPS.xlsx'))
+# Resource for PopulationEstimates: https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/
+pop_df = pd.read_excel(here('./data/db/original/maps/PopulationEstimates.xls'))  # population dataset for 2019
+
+pop_df['fips_str'] = pop_df['FIPStxt'].apply(lambda x: f'{x:05.0f}')
+pop_df = pop_df[['fips_str', 'Area_Name', 'POP_ESTIMATE_2019']]
 
 merged_df = pd.merge(loc_df, confirmed_df, right_on='Admin2', left_on='Name')
 
@@ -33,83 +33,28 @@
 
 molten_df['date_iso'] = pd.to_datetime(molten_df['date'], format="%m/%d/%y")  # change date to ISO8601 standard format
 
-fips = molten_df['fips_str'].tolist()
-# max_val = molten_df['value'].max()
+molten_pop_df = pd.merge(molten_df, pop_df, on='fips_str')  # add population per county
+grouped_by = molten_pop_df.groupby(['fips_str', 'date_iso', 'State', 'Admin2', 'POP_ESTIMATE_2019'])['value'].sum().reset_index()
+grouped_by['total_per_cap'] = grouped_by['value'] / grouped_by['POP_ESTIMATE_2019']  # get per capita value
 
+plot_data = grouped_by[grouped_by.date_iso == '2020-04-01']  # confirmed cases on a specific day
+value = 'value'   # 'value' = raw count, 'total_per_cap' = per capita
 
-fig = px.choropleth(molten_df,
+# confirmed cases per capita/raw count
+fig = px.choropleth(plot_data,
                     geojson=counties,
-                    locations=fips,
-                    color='value',
-                    animation_frame='date',
-                    hover_data=['State', 'value'],
-                    color_continuous_scale="Viridis",
-                    range_color=(0, 300),
+                    locations=plot_data.fips_str,
+                    color=value,
+                    # animation_frame='date',
+                    hover_data=['State', 'Admin2', value, 'POP_ESTIMATE_2019'],
+                    color_continuous_scale='viridis_r',
+                    range_color=(0, plot_data[value].max()),
                     scope="usa",
                     title='Confirmed cases',
                     labels={'value': 'confirmed cases'}
                     )
 fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
-# fig.show()
-
-# save out figure
-# save out working data
 pl.Path(here("./output/maps", warn=False)).mkdir(parents=True, exist_ok=True)
 pio.write_html(fig,
                file=str(here("./output/maps/choropleth_us_cases.html", warn=False)),
-               auto_open=False)
-
-
-# ChoroplethMap using FIPS from merged data
-'''
-confirmed_df = pd.read_csv('https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/'
-                           'csse_covid_19_time_series/time_series_covid19_confirmed_US.csv')
-
-loc_df = pd.read_excel(here('./data/db/original/maps/State_FIPS.xlsx'))
-merged_df = pd.merge(loc_df, confirmed_df, right_on='Admin2', left_on='Name')
-
-merged_df['fips_str'] = merged_df['FIPS_x'].apply(lambda x: f'{x:05.0f}')  # left pad with 0 for 5 digits
-
-colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef", "#b3d2e9", "#9ecae1",
-              "#85bcdb", "#6baed6", "#57a0ce", "#4292c6", "#3082be", "#2171b5", "#1361a9",
-              "#08519c", "#0b4083", "#08306b"]
-
-molten_df = merged_df.melt(
-    id_vars=['FIPS_x', 'Name', 'State', 'UID', 'iso2', 'iso3', 'code3', 'FIPS_y', 'Admin2',
-             'Province_State', 'Country_Region', 'Lat', 'Long_', 'Combined_Key', 'fips_str'],
-    var_name=['date']
-)
-
-molten_df['date_iso'] = pd.to_datetime(molten_df['date'], format="%m/%d/%y")  # change date to ISO8601 standard format
-
-endpts = list(np.linspace(0, 3000, len(colorscale) - 1))
-fips = molten_df['fips_str'].tolist()
-values = molten_df['date_iso'].tolist()
-
-fig = ff.create_choropleth(
-    fips=fips,
-    # values=values,
-    scope=['usa',
-           # 'Alaska',
-           # 'Puerto Rico',
-           # 'American Samoa',
-           # 'Commonwealth of the Northern Mariana Islands', 'Guam',
-           # 'United States Virgin Islands'
-           ],
-    binning_endpoints=endpts,
-    colorscale=colorscale,
-    show_state_data=True,
-    show_hover=True, 
-    centroid_marker={'opacity': 0},
-    asp=2.9, 
-    title='Confirmed cases on April 22',
-    legend_title='# confirmed cases',
-    text=molten_df['fips']
-)
-
-fig.layout.template = None
-fig.show()
-'''
-
-
-
+               auto_open=True)

analysis/db/us_map/graphs.py

@@ -0,0 +1,47 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+from pyprojroot import here
+import plotly.io as pio
+import seaborn as sns
+from urllib.request import urlopen
+import pathlib as pl
+
+
+confirmed_df = pd.read_csv('https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/'
+                           'csse_covid_19_time_series/time_series_covid19_confirmed_US.csv')
+loc_df = pd.read_excel(here('./data/db/original/maps/State_FIPS.xlsx'))
+
+merged_df = pd.merge(loc_df, confirmed_df, right_on='Admin2', left_on='Name')
+
+merged_df['fips_str'] = merged_df['FIPS_x'].apply(lambda x: f'{x:05.0f}')  # left pad with 0 for 5 digits
+molten_df = merged_df.melt(
+    id_vars=['FIPS_x', 'Name', 'State', 'UID', 'iso2', 'iso3', 'code3', 'FIPS_y', 'Admin2',
+             'Province_State', 'Country_Region', 'Lat', 'Long_', 'Combined_Key', 'fips_str'],
+    var_name=['date']
+)
+
+molten_df['date_iso'] = pd.to_datetime(molten_df['date'], format="%m/%d/%y")  # change date to ISO8601 standard format
+# state = molten_df.loc[molten_df.Province_State == 'Virginia', molten_df.Province_State == 'New York']
+# molten_df['date_iso'] = molten_df.loc[molten_df.date_iso == '2020-04-01', molten_df.date_iso == '2020-04-05']
+state1 = 'Washington'
+state2 = 'California'
+subset = molten_df.loc[(molten_df.Province_State.isin([state1, state2])),
+                       ['Province_State', 'Admin2', 'value', 'date_iso']]
+# subset = molten_df[['Province_State', 'Admin2', 'value', 'date_iso']]
+# subset = subset[subset.date_iso == '2020-04-01']   # for violinplot to show on certain date
+grouped_counts = subset.groupby(['date_iso', 'Province_State', 'Admin2'])['value'].sum().reset_index()
+
+# Noninteractive graps
+'''
+# ax = sns.lineplot(x="date_iso", y="value", hue='Province_State', data=grouped_counts)  # show cases per state monthly
+# ax = sns.stripplot(x="date_iso", y="value", hue='Province_State', data=grouped_counts)
+# ax = sns.violinplot(x='date_iso', y='value', hue='Province_State', data=grouped_counts, palette="Set2", split=True,
+#                     scale="count", inner="quartile")
+# ax = sns.countplot(x="date_iso", hue='Province_State', data=grouped_counts)  # works better if there are certain dates
+# plt.tight_layout()
+# plt.show()
+'''
+