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app.py
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app.py
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import os
import pathlib
import re
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from dash.dependencies import Input, Output, State
import cufflinks as cf
# Initialize app
app = dash.Dash(
__name__,
meta_tags=[
{"name": "viewport", "content": "width=device-width, initial-scale=1.0"}
],
)
server = app.server
# Adding favicon
# @app.route('/favicon.ico') # change this
# def favicon():
# return send_from_directory(os.path.join(app.root_path, 'static', 'images'),
# 'favicon.ico', mimetype='image/png')
# Load data
APP_PATH = str(pathlib.Path(__file__).parent.resolve())
df_lat_lon = pd.read_csv(
os.path.join(APP_PATH, os.path.join("data", "lat_lon_counties.csv"))
)
df_lat_lon["FIPS "] = df_lat_lon["FIPS "].apply(lambda x: str(x).zfill(5))
df_full_data = pd.read_csv(
os.path.join(
APP_PATH, os.path.join("data", "age_adjusted_death_rate_no_quotes.csv")
)
)
df_full_data["County Code"] = df_full_data["County Code"].apply(
lambda x: str(x).zfill(5)
)
df_full_data["County"] = (
df_full_data["Unnamed: 0"] + ", " + df_full_data.County.map(str)
)
# Select years
YEARS = [2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015]
BINS = [
"0-2",
"2.1-4",
"4.1-6",
"6.1-8",
"8.1-10",
"10.1-12",
"12.1-14",
"14.1-16",
"16.1-18",
"18.1-20",
"20.1-22",
"22.1-24",
"24.1-26",
"26.1-28",
"28.1-30",
">30",
]
DEFAULT_COLORSCALE = [
"#f2fffb",
"#bbffeb",
"#98ffe0",
"#79ffd6",
"#6df0c8",
"#69e7c0",
"#59dab2",
"#45d0a5",
"#31c194",
"#2bb489",
"#25a27b",
"#1e906d",
"#188463",
"#157658",
"#11684d",
"#10523e",
]
DEFAULT_OPACITY = 0.8
mapbox_access_token = "pk.eyJ1IjoicGxvdGx5bWFwYm94IiwiYSI6ImNqdnBvNDMyaTAxYzkzeW5ubWdpZ2VjbmMifQ.TXcBE-xg9BFdV2ocecc_7g"
mapbox_style = "mapbox://styles/plotlymapbox/cjvprkf3t1kns1cqjxuxmwixz"
# App layout
app.layout = html.Div(
id="root",
children=[
html.Div(
id="header",
children=[
html.Img(id="logo", src=app.get_asset_url("dash-logo.png")),
html.H4(children="Rate of US Poison-Induced Deaths"),
html.P(
id="description",
children="† Deaths are classified using the International Classification of Diseases, \
Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying \
cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 \
(undetermined intent). Click here for more information."
# ,
# href='https://www.cdc.gov/nchs/data-visualization/drug-poisoning-mortality/#ref9'
),
],
),
html.Div(
id="app-container",
children=[
html.Div(
id="left-column",
children=[
html.Div(
id="slider-container",
children=[
html.P(
id="slider-text",
children="Drag the slider to change the year:",
),
dcc.Slider(
id="years-slider",
min=min(YEARS),
max=max(YEARS),
value=min(YEARS),
marks={
str(year): {
"label": str(year),
"style": {"color": "#7fafdf"},
}
for year in YEARS
},
),
],
),
html.Div(
id="heatmap-container",
children=[
html.P(
"Heatmap of age adjusted mortality rates \
from poisonings in year {0}".format(
min(YEARS)
),
id="heatmap-title",
),
dcc.Graph(
id="county-choropleth",
figure=dict(
data=[
dict(
lat=df_lat_lon["Latitude "],
lon=df_lat_lon["Longitude"],
text=df_lat_lon["Hover"],
type="scattermapbox",
)
],
layout=dict(
mapbox=dict(
layers=[],
accesstoken=mapbox_access_token,
style=mapbox_style,
center=dict(
lat=38.72490, lon=-95.61446
),
pitch=0,
zoom=3.5,
),
autosize=True,
),
),
),
],
),
],
),
html.Div(
id="graph-container",
children=[
html.P(id="chart-selector", children="Select chart:"),
dcc.Dropdown(
options=[
{
"label": "Histogram of total number of deaths (single year)",
"value": "show_absolute_deaths_single_year",
},
{
"label": "Histogram of total number of deaths (2005-2015)",
"value": "absolute_deaths_all_time",
},
{
"label": "Age-adjusted death rate (single year)",
"value": "show_death_rate_single_year",
},
{
"label": "Trends in age-adjusted death rate (2005-2015)",
"value": "death_rate_all_time",
},
],
value="show_death_rate_single_year",
id="chart-dropdown",
),
dcc.Graph(
id="selected-data",
figure=dict(
data=[dict(x=0, y=0)],
layout=dict(
paper_bgcolor="#F4F4F8",
plot_bgcolor="#F4F4F8",
autofill=True,
margin=dict(t=75, r=50, b=100, l=50),
),
),
),
],
),
],
),
],
)
@app.callback(
Output("county-choropleth", "figure"),
[Input("years-slider", "value")],
[State("county-choropleth", "figure")],
)
def display_map(year, figure):
cm = dict(zip(BINS, DEFAULT_COLORSCALE))
data = [
dict(
lat=df_lat_lon["Latitude "],
lon=df_lat_lon["Longitude"],
text=df_lat_lon["Hover"],
type="scattermapbox",
hoverinfo="text",
marker=dict(size=5, color="white", opacity=0),
)
]
annotations = [
dict(
showarrow=False,
align="right",
text="<b>Age-adjusted death rate<br>per county per year</b>",
font=dict(color="#2cfec1"),
bgcolor="#1f2630",
x=0.95,
y=0.95,
)
]
for i, bin in enumerate(reversed(BINS)):
color = cm[bin]
annotations.append(
dict(
arrowcolor=color,
text=bin,
x=0.95,
y=0.85 - (i / 20),
ax=-60,
ay=0,
arrowwidth=5,
arrowhead=0,
bgcolor="#1f2630",
font=dict(color="#2cfec1"),
)
)
if "layout" in figure:
lat = figure["layout"]["mapbox"]["center"]["lat"]
lon = figure["layout"]["mapbox"]["center"]["lon"]
zoom = figure["layout"]["mapbox"]["zoom"]
else:
lat = (38.72490,)
lon = (-95.61446,)
zoom = 3.5
layout = dict(
mapbox=dict(
layers=[],
accesstoken=mapbox_access_token,
style=mapbox_style,
center=dict(lat=lat, lon=lon),
zoom=zoom,
),
hovermode="closest",
margin=dict(r=0, l=0, t=0, b=0),
annotations=annotations,
dragmode="lasso",
)
base_url = "https://raw.githubusercontent.com/jackparmer/mapbox-counties/master/"
for bin in BINS:
geo_layer = dict(
sourcetype="geojson",
source=base_url + str(year) + "/" + bin + ".geojson",
type="fill",
color=cm[bin],
opacity=DEFAULT_OPACITY,
# CHANGE THIS
fill=dict(outlinecolor="#afafaf"),
)
layout["mapbox"]["layers"].append(geo_layer)
fig = dict(data=data, layout=layout)
return fig
@app.callback(Output("heatmap-title", "children"), [Input("years-slider", "value")])
def update_map_title(year):
return "Heatmap of age adjusted mortality rates \
from poisonings in year {0}".format(
year
)
@app.callback(
Output("selected-data", "figure"),
[
Input("county-choropleth", "selectedData"),
Input("chart-dropdown", "value"),
Input("years-slider", "value"),
],
)
def display_selected_data(selectedData, chart_dropdown, year):
if selectedData is None:
return dict(
data=[dict(x=0, y=0)],
layout=dict(
title="Click-drag on the map to select counties",
paper_bgcolor="#1f2630",
plot_bgcolor="#1f2630",
font=dict(color="#2cfec1"),
margin=dict(t=75, r=50, b=100, l=75),
),
)
pts = selectedData["points"]
fips = [str(pt["text"].split("<br>")[-1]) for pt in pts]
for i in range(len(fips)):
if len(fips[i]) == 4:
fips[i] = "0" + fips[i]
dff = df_full_data[df_full_data["County Code"].isin(fips)]
dff = dff.sort_values("Year")
regex_pat = re.compile(r"Unreliable", flags=re.IGNORECASE)
dff["Age Adjusted Rate"] = dff["Age Adjusted Rate"].replace(regex_pat, 0)
if chart_dropdown != "death_rate_all_time":
title = "Absolute deaths per county, <b>2005-2015</b>"
AGGREGATE_BY = "Deaths"
if "show_absolute_deaths_single_year" == chart_dropdown:
dff = dff[dff.Year == year]
title = "Absolute deaths per county, <b>{0}</b>".format(year)
elif "show_death_rate_single_year" == chart_dropdown:
dff = dff[dff.Year == year]
title = "Age-adjusted death rate per county, <b>{0}</b>".format(year)
AGGREGATE_BY = "Age Adjusted Rate"
dff[AGGREGATE_BY] = pd.to_numeric(dff[AGGREGATE_BY], errors="coerce")
deaths_or_rate_by_fips = dff.groupby("County")[AGGREGATE_BY].sum()
deaths_or_rate_by_fips = deaths_or_rate_by_fips.sort_values()
# Only look at non-zero rows:
deaths_or_rate_by_fips = deaths_or_rate_by_fips[deaths_or_rate_by_fips > 0]
fig = deaths_or_rate_by_fips.iplot(
kind="bar", y=AGGREGATE_BY, title=title, asFigure=True
)
fig_layout = fig["layout"]
fig_data = fig["data"]
fig_data[0]["text"] = deaths_or_rate_by_fips.values.tolist()
fig_data[0]["marker"]["color"] = "#2cfec1"
fig_data[0]["marker"]["opacity"] = 1
fig_data[0]["marker"]["line"]["width"] = 0
fig_data[0]["textposition"] = "outside"
fig_layout["paper_bgcolor"] = "#1f2630"
fig_layout["plot_bgcolor"] = "#1f2630"
fig_layout["font"]["color"] = "#2cfec1"
fig_layout["title"]["font"]["color"] = "#2cfec1"
fig_layout["xaxis"]["tickfont"]["color"] = "#2cfec1"
fig_layout["yaxis"]["tickfont"]["color"] = "#2cfec1"
fig_layout["xaxis"]["gridcolor"] = "#5b5b5b"
fig_layout["yaxis"]["gridcolor"] = "#5b5b5b"
fig_layout["margin"]["t"] = 75
fig_layout["margin"]["r"] = 50
fig_layout["margin"]["b"] = 100
fig_layout["margin"]["l"] = 50
return fig
fig = dff.iplot(
kind="area",
x="Year",
y="Age Adjusted Rate",
text="County",
categories="County",
colors=[
"#1b9e77",
"#d95f02",
"#7570b3",
"#e7298a",
"#66a61e",
"#e6ab02",
"#a6761d",
"#666666",
"#1b9e77",
],
vline=[year],
asFigure=True,
)
for i, trace in enumerate(fig["data"]):
trace["mode"] = "lines+markers"
trace["marker"]["size"] = 4
trace["marker"]["line"]["width"] = 1
trace["type"] = "scatter"
for prop in trace:
fig["data"][i][prop] = trace[prop]
# Only show first 500 lines
fig["data"] = fig["data"][0:500]
fig_layout = fig["layout"]
# See plot.ly/python/reference
fig_layout["yaxis"]["title"] = "Age-adjusted death rate per county per year"
fig_layout["xaxis"]["title"] = ""
fig_layout["yaxis"]["fixedrange"] = True
fig_layout["xaxis"]["fixedrange"] = False
fig_layout["hovermode"] = "closest"
fig_layout["title"] = "<b>{0}</b> counties selected".format(len(fips))
fig_layout["legend"] = dict(orientation="v")
fig_layout["autosize"] = True
fig_layout["paper_bgcolor"] = "#1f2630"
fig_layout["plot_bgcolor"] = "#1f2630"
fig_layout["font"]["color"] = "#2cfec1"
fig_layout["xaxis"]["tickfont"]["color"] = "#2cfec1"
fig_layout["yaxis"]["tickfont"]["color"] = "#2cfec1"
fig_layout["xaxis"]["gridcolor"] = "#5b5b5b"
fig_layout["yaxis"]["gridcolor"] = "#5b5b5b"
if len(fips) > 500:
fig["layout"][
"title"
] = "Age-adjusted death rate per county per year <br>(only 1st 500 shown)"
return fig
if __name__ == "__main__":
app.run_server(debug=True)