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covid19.py
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import pandas as pd
import numpy as np
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table as dt
from dash.dependencies import Input, Output, State
import plotly.graph_objects as go
import plotly.express as px
import datetime
#####################################################################################################################################
# Boostrap CSS and font awesome . Option 1) Run from codepen directly Option 2) Copy css file to assets folder and run locally
#####################################################################################################################################
external_stylesheets = ['https://codepen.io/unicorndy/pen/GRJXrvP.css','https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css']
#Insert your javascript here. In this example, addthis.com has been added to the web app for people to share their webpage
external_scripts = [{
'type': 'text/javascript', #depends on your application
'src': 'insert your addthis.com js here',
}]
app = dash.Dash(__name__, external_stylesheets=external_stylesheets, external_scripts = external_scripts)
app.title = 'Covid19 - Global Dashboard Example'
#for heroku to run correctly
server = app.server
#Overwrite your CSS setting by including style locally
colors = {
'background': '#2D2D2D',
'text': '#E1E2E5',
'figure_text': '#ffffff',
'confirmed_text':'#3CA4FF',
'deaths_text':'#f44336',
'recovered_text':'#5A9E6F',
'highest_case_bg':'#393939',
}
#Creating custom style for local use
divBorderStyle = {
'backgroundColor' : '#393939',
'borderRadius': '12px',
'lineHeight': 0.9,
}
#Creating custom style for local use
boxBorderStyle = {
'borderColor' : '#393939',
'borderStyle': 'solid',
'borderRadius': '10px',
'borderWidth':2,
}
######################################
# Retrieve data
######################################
# get data directly from github. The data source provided by Johns Hopkins University.
url_confirmed = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
url_deaths = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
url_recovered = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'
# Data can also be saved locally and read from local drive
# url_confirmed = 'time_series_covid19_confirmed_global.csv'
# url_deaths = 'time_series_covid19_deaths_global.csv'
# url_recovered = 'time_series_covid19_recovered_global.csv'
df_confirmed = pd.read_csv(url_confirmed)
df_deaths = pd.read_csv(url_deaths)
df_recovered = pd.read_csv(url_recovered)
##############################################################################################################################
# Moving Singapore to the first row in the datatable (You can choose any country of interest to be display on the first row)
##############################################################################################################################
def df_move1st_sg(df_t):
#Moving Singapore to the first row in the datatable
df_t["new"] = range(1,len(df_t)+1)
df_t.loc[df_t[df_t['Country/Region'] == 'Singapore'].index.values,'new'] = 0
df_t = df_t.sort_values("new").drop('new', axis=1)
return df_t
#########################################################################################
# Data preprocessing for getting useful data and shaping data compatible to plotly plot
#########################################################################################
# Total cases
df_confirmed_total = df_confirmed.iloc[:, 4:].sum(axis=0)
df_deaths_total = df_deaths.iloc[:, 4:].sum(axis=0)
df_recovered_total = df_recovered.iloc[:, 4:].sum(axis=0)
# modified deaths dataset for mortality rate calculation
df_deaths_confirmed=df_deaths.copy()
df_deaths_confirmed['confirmed'] = df_confirmed.iloc[:,-1]
#Sorted - df_deaths_confirmed_sorted is different from others, as it is only modified later. Careful of it dataframe structure
df_deaths_confirmed_sorted = df_deaths_confirmed.sort_values(by=df_deaths_confirmed.columns[-2], ascending=False)[['Country/Region',df_deaths_confirmed.columns[-2],df_deaths_confirmed.columns[-1]]]
df_recovered_sorted = df_recovered.sort_values(by=df_recovered.columns[-1], ascending=False)[['Country/Region',df_recovered.columns[-1]]]
df_confirmed_sorted = df_confirmed.sort_values(by=df_confirmed.columns[-1], ascending=False)[['Country/Region',df_confirmed.columns[-1]]]
#Single day increase
df_deaths_confirmed_sorted['24hr'] = df_deaths_confirmed_sorted.iloc[:,-2] - df_deaths.sort_values(by=df_deaths.columns[-1], ascending=False)[df_deaths.columns[-2]]
df_recovered_sorted['24hr'] = df_recovered_sorted.iloc[:,-1] - df_recovered.sort_values(by=df_recovered.columns[-1], ascending=False)[df_recovered.columns[-2]]
df_confirmed_sorted['24hr'] = df_confirmed_sorted.iloc[:,-1] - df_confirmed.sort_values(by=df_confirmed.columns[-1], ascending=False)[df_confirmed.columns[-2]]
#Aggregate the countries with different province/state together
df_deaths_confirmed_sorted_total = df_deaths_confirmed_sorted.groupby('Country/Region').sum()
df_deaths_confirmed_sorted_total=df_deaths_confirmed_sorted_total.sort_values(by=df_deaths_confirmed_sorted_total.columns[0], ascending=False).reset_index()
df_recovered_sorted_total = df_recovered_sorted.groupby('Country/Region').sum()
df_recovered_sorted_total=df_recovered_sorted_total.sort_values(by=df_recovered_sorted_total.columns[0], ascending=False).reset_index()
df_confirmed_sorted_total = df_confirmed_sorted.groupby('Country/Region').sum()
df_confirmed_sorted_total=df_confirmed_sorted_total.sort_values(by=df_confirmed_sorted_total.columns[0], ascending=False).reset_index()
#Modified recovery csv due to difference in number of rows. Recovered will match ['Province/State','Country/Region']column with Confirmed ['Province/State','Country/Region']
df_recovered['Province+Country'] = df_recovered[['Province/State','Country/Region']].fillna('nann').agg('|'.join,axis=1)
df_confirmed['Province+Country'] = df_confirmed[['Province/State','Country/Region']].fillna('nann').agg('|'.join,axis=1)
df_recovered_fill = df_recovered
df_recovered_fill.set_index("Province+Country")
df_recovered_fill.set_index("Province+Country").reindex(df_confirmed['Province+Country'])
df_recovered_fill = df_recovered_fill.set_index("Province+Country").reindex(df_confirmed['Province+Country']).reset_index()
#split Province+Country back into its respective columns
new = df_recovered_fill["Province+Country"].str.split("|", n = 1, expand = True)
df_recovered_fill['Province/State']=new[0]
df_recovered_fill['Country/Region']=new[1]
df_recovered_fill['Province/State'].replace('nann','NaN')
#drop 'Province+Country' for all dataset
df_confirmed.drop('Province+Country',axis=1,inplace=True)
df_recovered.drop('Province+Country',axis=1,inplace=True)
df_recovered_fill.drop('Province+Country',axis=1,inplace=True)
# Data preprocessing for times series countries graph display
# create temp to store sorting arrangement for all confirm, deaths and recovered.
df_confirmed_sort_temp = df_confirmed.sort_values(by=df_confirmed.columns[-1], ascending=False)
df_confirmed_t = df_move1st_sg(df_confirmed_sort_temp)
df_confirmed_t['Province+Country'] = df_confirmed_t[['Province/State','Country/Region']].fillna('nann').agg('|'.join,axis=1)
df_confirmed_t=df_confirmed_t.drop(['Province/State','Country/Region','Lat','Long'],axis=1).T
df_deaths_t = df_deaths.reindex(df_confirmed_sort_temp.index)
df_deaths_t = df_move1st_sg(df_deaths_t)
df_deaths_t['Province+Country'] = df_deaths_t[['Province/State','Country/Region']].fillna('nann').agg('|'.join,axis=1)
df_deaths_t=df_deaths_t.drop(['Province/State','Country/Region','Lat','Long'],axis=1).T
# take note use reovered_fill df
df_recovered_t = df_recovered_fill.reindex(df_confirmed_sort_temp.index)
df_recovered_t = df_move1st_sg(df_recovered_t)
df_recovered_t['Province+Country'] = df_recovered_t[['Province/State','Country/Region']].fillna('nann').agg('|'.join,axis=1)
df_recovered_t=df_recovered_t.drop(['Province/State','Country/Region','Lat','Long'],axis=1).T
df_confirmed_t.columns = df_confirmed_t.iloc[-1]
df_confirmed_t = df_confirmed_t.drop('Province+Country')
df_deaths_t.columns = df_deaths_t.iloc[-1]
df_deaths_t = df_deaths_t.drop('Province+Country')
df_recovered_t.columns = df_recovered_t.iloc[-1]
df_recovered_t = df_recovered_t.drop('Province+Country')
df_confirmed_t.index=pd.to_datetime(df_confirmed_t.index)
df_deaths_t.index=pd.to_datetime(df_confirmed_t.index)
df_recovered_t.index=pd.to_datetime(df_confirmed_t.index)
# Highest 10 plot data preprocessing
# getting highest 10 countries with confirmed case
name = df_confirmed_t.columns.str.split("|", 1)
df_confirmed_t_namechange=df_confirmed_t.copy()
# name0 = [x[0] for x in name]
name1 = [x[1] for x in name]
df_confirmed_t_namechange.columns = name1
df_confirmed_t_namechange=df_confirmed_t_namechange.groupby(df_confirmed_t_namechange.columns,axis=1).sum()
df_confirmed_t_namechange10 = df_confirmed_t_namechange.sort_values(by=df_confirmed_t_namechange.index[-1], axis=1, ascending=False).iloc[:,:10]
df_confirmed_t_stack = df_confirmed_t_namechange10.stack()
df_confirmed_t_stack=df_confirmed_t_stack.reset_index(level=[0,1])
df_confirmed_t_stack.rename(columns={"level_0": "Date",'level_1':'Countries', 0: "Confirmed"}, inplace=True)
# getting highest 10 countries with deceased case
name = df_deaths_t.columns.str.split("|", 1)
df_deaths_t_namechange=df_deaths_t.copy()
# name0 = [x[0] for x in name]
name1 = [x[1] for x in name]
df_deaths_t_namechange.columns = name1
df_deaths_t_namechange=df_deaths_t_namechange.groupby(df_deaths_t_namechange.columns,axis=1).sum()
df_deaths_t_namechange10 = df_deaths_t_namechange.sort_values(by=df_deaths_t_namechange.index[-1], axis=1, ascending=False).iloc[:,:10]
df_deaths_t_stack = df_deaths_t_namechange10.stack()
df_deaths_t_stack=df_deaths_t_stack.reset_index(level=[0,1])
df_deaths_t_stack.rename(columns={"level_0": "Date",'level_1':'Countries', 0: "Deceased"}, inplace=True)
# Recreate required columns for map data
map_data = df_confirmed[["Province/State", "Country/Region", "Lat", "Long"]]
map_data['Confirmed'] = df_confirmed.loc[:, df_confirmed.columns[-1]]
map_data['Deaths'] = df_deaths.loc[:, df_deaths.columns[-1]]
map_data['Recovered'] = df_recovered_fill.loc[:, df_recovered_fill.columns[-1]]
map_data['Recovered']=map_data['Recovered'].fillna(0).astype(int) #too covert value back to int and fillna with zero
#last 24 hours increase
map_data['Deaths_24hr']=df_deaths.iloc[:,-1] - df_deaths.iloc[:,-2]
map_data['Recovered_24hr']=df_recovered_fill.iloc[:,-1] - df_recovered_fill.iloc[:,-2]
map_data['Confirmed_24hr']=df_confirmed.iloc[:,-1] - df_confirmed.iloc[:,-2]
map_data.sort_values(by='Confirmed', ascending=False,inplace=True)
#Moving Singapore to the first row in the datatable
map_data["new"] = range(1,len(map_data)+1)
map_data.loc[map_data[map_data['Country/Region'] == 'Singapore'].index.values,'new'] = 0
map_data = map_data.sort_values("new").drop('new', axis=1)
#############################################################################
# mapbox_access_token keys, not all mapbox function require token to function.
#############################################################################
mapbox_access_token = 'insert your token here'
###########################
# functions to create map
###########################
def gen_map(map_data,zoom,lat,lon):
return {
"data": [{
"type": "scattermapbox", #specify the type of data to generate, in this case, scatter map box is used
"lat": list(map_data['Lat']), #for markers location
"lon": list(map_data['Long']),
# "hoverinfo": "text",
"hovertext": [["Country/Region: {} <br>Province/State: {} <br>Confirmed: {} (+ {} past 24hrs)<br>Deaths: {} (+ {} past 24hrs)<br>Recovered: {} (+ {} past 24hrs)".format(i, j, k, k24, l, l24, m, m24)]
for i, j, k, l, m, k24, l24, m24 in zip(map_data['Country/Region'], map_data['Province/State'],
map_data['Confirmed'], map_data['Deaths'], map_data['Recovered'],
map_data['Confirmed_24hr'], map_data['Deaths_24hr'], map_data['Recovered_24hr'],)],
"mode": "markers",
"name": list(map_data['Country/Region']),
"marker": {
"opacity": 0.7,
"size": np.log(map_data['Confirmed'])*4,
}
},
],
"layout": dict(
autosize=True,
height=350,
font=dict(color=colors['figure_text']),
titlefont=dict(color=colors['text'], size='14'),
margin=dict(
l=0,
r=0,
b=0,
t=0
),
hovermode="closest",
plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
legend=dict(font=dict(size=10), orientation='h'),
mapbox=dict(
accesstoken=mapbox_access_token,
style='mapbox://styles/mapbox/dark-v10',
center=dict(
lon=lon,
lat=lat,
),
zoom=zoom,
)
),
}
##############################################
#Functions to create display for highest cases
##############################################
def high_cases(countryname,total,single,color_word='#63b6ff',confirmed_total=1,deaths = False,):
if deaths:
percent = (total/confirmed_total)*100
return html.P([ html.Span(countryname + ' | ' + f"{int(total):,d}",
style={'backgroundColor': colors['highest_case_bg'], 'borderRadius': '6px',}),
html.Span(' +' + f"{int(single):,d}",
style={'color': color_word,'margin':2,'fontWeight': 'bold','fontSize': 14,}),
html.Span(f' ({percent:.2f}%)',
style={'color': color_word,'margin':2,'fontWeight': 'bold','fontSize': 14,}),
],
style={
'textAlign': 'center',
'color': 'rgb(200,200,200)',
'fontsize':12,
}
)
return html.P([ html.Span(countryname + ' | ' + f"{int(total):,d}",
style={'backgroundColor': colors['highest_case_bg'], 'borderRadius': '6px',}),
html.Span(' +' + f"{int(single):,d}",
style={'color': color_word,'margin':2,'fontWeight': 'bold','fontSize': 14,}),
],
style={
'textAlign': 'center',
'color': 'rgb(200,200,200)',
'fontsize':12,
}
)
#########################################################################
#Convert datetime to Display datetime with following format - 06-Apr-2020
#########################################################################
def datatime_convert(date_str,days_to_add=0):
format_str = '%m/%d/%y' # The format
datetime_obj = datetime.datetime.strptime(date_str, format_str)
datetime_obj += datetime.timedelta(days=days_to_add)
return datetime_obj.strftime('%d-%b-%Y')
def return_outbreakdays(date_str):
format_str = '%d-%b-%Y' # The format
datetime_obj = datetime.datetime.strptime(date_str, format_str).date()
d0 = datetime.date(2020, 1, 22)
delta = datetime_obj - d0
return delta.days
noToDisplay = 8
confirm_cases = []
for i in range(noToDisplay):
confirm_cases.append(high_cases(df_confirmed_sorted_total.iloc[i,0],df_confirmed_sorted_total.iloc[i,1],df_confirmed_sorted_total.iloc[i,2]))
deaths_cases = []
for i in range(noToDisplay):
deaths_cases.append(high_cases(df_deaths_confirmed_sorted_total.iloc[i,0],df_deaths_confirmed_sorted_total.iloc[i,1],df_deaths_confirmed_sorted_total.iloc[i,3],'#ff3b4a',df_deaths_confirmed_sorted_total.iloc[i,2],True))
confirm_cases_24hrs = []
for i in range(noToDisplay):
confirm_cases_24hrs.append(high_cases(df_confirmed_sorted_total.sort_values(by=df_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,0],
df_confirmed_sorted_total.sort_values(by=df_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,1],
df_confirmed_sorted_total.sort_values(by=df_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,2],
))
deaths_cases_24hrs = []
for i in range(noToDisplay):
deaths_cases_24hrs.append(high_cases(df_deaths_confirmed_sorted_total.sort_values(by=df_deaths_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,0],
df_deaths_confirmed_sorted_total.sort_values(by=df_deaths_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,1],
df_deaths_confirmed_sorted_total.sort_values(by=df_deaths_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,3],
'#ff3b4a',
df_deaths_confirmed_sorted_total.sort_values(by=df_deaths_confirmed_sorted_total.columns[-1], ascending=False).iloc[i,2],
True))
####################################################
# Prepare plotly figure to attached to dcc component
# Global outbreak Plot
####################################################
# Change date index to datetimeindex and share x-axis with all the plot
def draw_global_graph(df_confirmed_total,df_deaths_total,df_recovered_total,graph_type='Total Cases'):
df_confirmed_total.index = pd.to_datetime(df_confirmed_total.index)
if graph_type == 'Daily Cases':
df_confirmed_total = (df_confirmed_total - df_confirmed_total.shift(1)).drop(df_confirmed_total.index[0])
df_deaths_total = (df_deaths_total - df_deaths_total.shift(1)).drop(df_deaths_total.index[0])
df_recovered_total = (df_recovered_total - df_recovered_total.shift(1)).drop(df_recovered_total.index[0])
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_confirmed_total.index, y=df_confirmed_total,
mode='lines+markers',
name='Confirmed',
line=dict(color='#3372FF', width=2),
fill='tozeroy',))
fig.add_trace(go.Scatter(x=df_confirmed_total.index, y=df_recovered_total,
mode='lines+markers',
name='Recovered',
line=dict(color='#33FF51', width=2),
fill='tozeroy',))
fig.add_trace(go.Scatter(x=df_confirmed_total.index, y=df_deaths_total,
mode='lines+markers',
name='Deaths',
line=dict(color='#FF3333', width=2),
fill='tozeroy',))
fig.update_layout(
hovermode='x',
font=dict(
family="Courier New, monospace",
size=14,
color=colors['figure_text'],
),
legend=dict(
x=0.02,
y=1,
traceorder="normal",
font=dict(
family="sans-serif",
size=12,
color=colors['figure_text']
),
bgcolor=colors['background'],
borderwidth=5
),
paper_bgcolor=colors['background'],
plot_bgcolor=colors['background'],
margin=dict(l=0,
r=0,
t=0,
b=0
),
height=300,
)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='#3A3A3A')
return fig
####################################################
# Function to plot Highest 10 countries cases
####################################################
def draw_highest_10(df_confirmed_t_stack, df_deaths_t_stack, graphHigh10_type='Confirmed Cases'):
if graphHigh10_type=='Confirmed Cases':
fig = px.line(df_confirmed_t_stack, x="Date", y="Confirmed", color='Countries',
color_discrete_sequence = px.colors.qualitative.Light24)
else:
fig = px.line(df_deaths_t_stack, x="Date", y="Deceased", color='Countries',
title='Deceased cases', color_discrete_sequence = px.colors.qualitative.Light24)
fig.update_layout(
xaxis_title=None,
yaxis_title=None,
font=dict(
family="Courier New, monospace",
size=14,
color=colors['figure_text'],
),
legend=dict(
x=0.02,
y=1,
traceorder="normal",
font=dict(
family="sans-serif",
size=9,
color=colors['figure_text']
),
),
paper_bgcolor=colors['background'],
plot_bgcolor=colors['background'],
margin=dict(l=0,
r=0,
t=0,
b=0
),
height=300,
)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
# fig.update_xaxes(zeroline=True, zerolinewidth=2, zerolinecolor='#3A3A3A')
fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='#3A3A3A')
return fig
####################################################
# Function to plot Single Country Scatter Plot
####################################################
def draw_singleCountry_Scatter(df_confirmed_t,df_deaths_t,df_recovered_t,selected_row=0, daily_change=False):
if daily_change:
df_confirmed_t = (df_confirmed_t - df_confirmed_t.shift(1)).drop(df_confirmed_t.index[0])
df_deaths_t = (df_deaths_t - df_deaths_t.shift(1)).drop(df_deaths_t.index[0])
df_recovered_t = (df_recovered_t - df_recovered_t.shift(1)).drop(df_recovered_t.index[0])
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_confirmed_t.index, y=df_confirmed_t.iloc[:,selected_row],
mode='lines+markers',
name='Confirmed',
line=dict(color='#3372FF', width=2),
fill='tozeroy',))
fig.add_trace(go.Scatter(x=df_recovered_t.index, y=df_recovered_t.iloc[:,selected_row],
mode='lines+markers',
name='Recovered',
line=dict(color='#33FF51', width=2),
fill='tozeroy',))
fig.add_trace(go.Scatter(x=df_deaths_t.index, y=df_deaths_t.iloc[:,selected_row],
mode='lines+markers',
name='Deceased',
line=dict(color='#FF3333', width=2),
fill='tozeroy',))
new = df_recovered_t.columns[selected_row].split("|", 1)
if new[0] == 'nann':
title = new[1]
else:
title = new[1] + " - " + new[0]
fig.update_layout(
title=title + ' (Total Cases)',
hovermode='x',
font=dict(
family="Courier New, monospace",
size=14,
color="#ffffff",
),
legend=dict(
traceorder="normal",
font=dict(
family="sans-serif",
size=12,
color=colors['figure_text']
),
bgcolor=colors['background'],
borderwidth=5
),
paper_bgcolor=colors['background'],
plot_bgcolor=colors['background'],
margin=dict(l=0, r=0, t=65, b=0),
height=350,
)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='#3A3A3A')
return fig
####################################################
# Function to plot Single Country Bar with scatter Plot
####################################################
def draw_singleCountry_Bar(df_confirmed_t,df_deaths_t,df_recovered_t,selected_row=0,graph_line='Line Chart'):
df_confirmed_t = (df_confirmed_t - df_confirmed_t.shift(1)).drop(df_confirmed_t.index[0])
df_deaths_t = (df_deaths_t - df_deaths_t.shift(1)).drop(df_deaths_t.index[0])
df_recovered_t = (df_recovered_t - df_recovered_t.shift(1)).drop(df_recovered_t.index[0])
fig = go.Figure()
if graph_line=='Line Chart':
fig.add_trace(go.Bar(x=df_confirmed_t.index, y=df_confirmed_t.iloc[:,selected_row],
name='Confirmed',
marker_color='#3372FF'
))
fig.add_trace(go.Bar(x=df_recovered_t.index, y=df_recovered_t.iloc[:,selected_row],
name='Recovered',
marker_color='#33FF51'
))
fig.add_trace(go.Bar(x=df_deaths_t.index, y=df_deaths_t.iloc[:,selected_row],
name='Deceased',
marker_color='#FF3333'
))
else:
fig.add_trace(go.Scatter(x=df_confirmed_t.index, y=df_confirmed_t.iloc[:,selected_row],
mode='lines+markers',
name='Confirmed',
line=dict(color='#3372FF', width=2),
fill='tozeroy',))
fig.add_trace(go.Scatter(x=df_recovered_t.index, y=df_recovered_t.iloc[:,selected_row],
mode='lines+markers',
name='Recovered',
line=dict(color='#33FF51', width=2),
fill='tozeroy',))
fig.add_trace(go.Scatter(x=df_deaths_t.index, y=df_deaths_t.iloc[:,selected_row],
mode='lines+markers',
name='Deceased',
line=dict(color='#FF3333', width=2),
fill='tozeroy',))
new = df_recovered_t.columns[selected_row].split("|", 1)
if new[0] == 'nann':
title = new[1]
else:
title = new[1] + " - " + new[0]
fig.update_layout(
title=title + ' (Daily Cases)',
barmode='stack',
hovermode='x',
font=dict(
family="Courier New, monospace",
size=14,
color="#ffffff",
),
legend=dict(
traceorder="normal",
font=dict(
family="sans-serif",
size=12,
color=colors['figure_text']
),
bgcolor=colors['background'],
borderwidth=5
),
paper_bgcolor=colors['background'],
plot_bgcolor=colors['background'],
margin=dict(l=0, r=0, t=65, b=0),
height=350,
)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='#3A3A3A')
fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='#3A3A3A')
return fig
############################
# Bootstrap Grid Layout
############################
app.layout = html.Div(
html.Div([
# Header display
html.Div(
[
html.H1(children='Covid-19 (Coronavirus) Interactive Outbreak Tracker',
style={
'textAlign': 'left',
'color': colors['text'],
'backgroundColor': colors['background'],
},
className='ten columns',
),
html.Div([
html.Button(html.I(className="fa fa-info-circle"),
id='info-button',
style={
'color': colors['text'],
'fontSize':'36px'
},)
],className='two columns',),
# Preload Modal windows and set "display": "none" to hide it first
html.Div([ # modal div
html.Div([ # content div
dcc.Markdown('''
##### Dataset provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE):
https://systems.jhu.edu/
Data Sources:
* World Health Organization (WHO): https://www.who.int/
* DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
* BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
* National Health Commission of the People’s Republic of China (NHC):
http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
* China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
* Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
* Macau Government: https://www.ssm.gov.mo/portal/
* Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
* US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
* Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
* Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
* European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
* Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
* Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
* 1Point3Arces: https://coronavirus.1point3acres.com/en
* WorldoMeters: https://www.worldometers.info/coronavirus/
'''),
html.Hr(),
html.Button('Close', id='modal-close-button',
style={
'color': colors['text'],
},)
],
style={
'fontSize': 10,
'lineHeight': 0.9,
},
className='modal-content',
),
],
id='modal',
className='modal',
style={"display": "none"},
),
html.Div([html.Span('Dashboard: Covid-19 outbreak. (Updated once a day, based on consolidated last day total) Last Updated: ',
style={'color': colors['text'],
}),
html.Span(datatime_convert(df_confirmed.columns[-1],1) + ' 00:01 (UTC).',
style={'color': colors['confirmed_text'],
'fontWeight': 'bold',
}),
],className='twelve columns'
),
html.Div([html.Span('Outbreak since 22-Jan-2020: ',
style={'color': colors['text'],
}),
html.Span(str(return_outbreakdays(datatime_convert(df_confirmed.columns[-1],1))) + ' days.',
style={'color': colors['confirmed_text'],
'fontWeight': 'bold',
}),
],
className='twelve columns'
),
html.Div(children='Best viewed on Desktop. Refresh browser for latest update.',
style={
'textAlign': 'left',
'color': colors['text'],
'backgroundColor': colors['background'],
},
className='twelve columns'
)
], className="row"
),
# Top column display of confirmed, death and recovered total numbers
html.Div([
html.Div([
html.H4(children='Total Cases: ',
style={
'textAlign': 'center',
'color': colors['confirmed_text'],
}
),
html.P(f"{df_confirmed_total[-1]:,d}",
style={
'textAlign': 'center',
'color': colors['confirmed_text'],
'fontSize': 30,
}
),
html.P('Past 24hrs increase: +' + f"{df_confirmed_total[-1] - df_confirmed_total[-2]:,d}"
+ ' (' + str(round(((df_confirmed_total[-1] - df_confirmed_total[-2])/df_confirmed_total[-1])*100, 2)) + '%)',
style={
'textAlign': 'center',
'color': colors['confirmed_text'],
}
),
],
style=divBorderStyle,
className='four columns',
),
html.Div([
html.H4(children='Total Deceased: ',
style={
'textAlign': 'center',
'color': colors['deaths_text'],
}
),
html.P(f"{df_deaths_total[-1]:,d}",
style={
'textAlign': 'center',
'color': colors['deaths_text'],
'fontSize': 30,
}
),
html.P('Mortality Rate: ' + str(round(df_deaths_total[-1]/df_confirmed_total[-1] * 100, 3)) + '%',
style={
'textAlign': 'center',
'color': colors['deaths_text'],
}
),
],
style=divBorderStyle,
className='four columns'),
html.Div([
html.H4(children='Total Recovered: ',
style={
'textAlign': 'center',
'color': colors['recovered_text'],
}
),
html.P(f"{df_recovered_total[-1]:,d}",
style={
'textAlign': 'center',
'color': colors['recovered_text'],
'fontSize': 30,
}
),
html.P('Recovery Rate: ' + str(round(df_recovered_total[-1]/df_confirmed_total[-1] * 100, 3)) + '%',
style={
'textAlign': 'center',
'color': colors['recovered_text'],
}
),
],
style=divBorderStyle,
className='four columns'),
], className='row'),
# Graph of total confirmed, recovered and deaths
html.Div(
[
html.H4(children='Global Covid-19 cases',
style={
'textAlign': 'center',
'color': colors['text'],
'backgroundColor': colors['background'],
},
className='twelve columns'
),
html.Div([
dcc.RadioItems(
id='graph-type',
options=[{'label': i, 'value': i}
for i in ['Total Cases', 'Daily Cases']],
value='Total Cases',
labelStyle={'display': 'inline-block'},
style={
'fontSize': 20,
},
)
],className='six columns'
),
html.Div([
dcc.RadioItems(
id='graph-high10-type',
options=[{'label': i, 'value': i}
for i in ['Confirmed Cases', 'Deceased Cases']],
value='Confirmed Cases',
labelStyle={'display': 'inline-block'},
style={
'fontSize': 20,
},
)
],className='five columns'
),
html.Div([
dcc.Graph(
id='global-graph',
)
], className='six columns'
),
html.Div([
dcc.Graph(
id='high10-graph',
)
], className='five columns'
),
], className="row",
style={
'textAlign': 'left',
'color': colors['text'],
'backgroundColor': colors['background'],
},
),
# Highest 5 Countries Display
# 1x4 grid
html.Div([
html.Div([
html.P([html.Span('Countries with highest cases: ',
),
html.Br(),
html.Span(' + past 24hrs',
style={'color': colors['confirmed_text'],
'fontWeight': 'bold','fontSize': 14,})
],
style={
'textAlign': 'center',
'color': 'rgb(200,200,200)',
'fontsize':12,
'backgroundColor':'#3B5998',
'borderRadius': '12px',
'fontSize': 17,
}
),
html.P(confirm_cases),
],
className="three columns",
),
html.Div([
html.P([html.Span('Single day highest cases: ',
),
html.Br(),
html.Span(' + past 24hrs',
style={'color': colors['confirmed_text'],
'fontWeight': 'bold','fontSize': 14,})
],
style={
'textAlign': 'center',
'color': 'rgb(200,200,200)',
'fontsize':12,
'backgroundColor':'#3B5998',
'borderRadius': '12px',
'fontSize': 17,
}
),
html.P(confirm_cases_24hrs),
],
className="three columns",
),
html.Div([
html.P([html.Span('Countries with highest mortality: ',
),
html.Br(),
html.Span(' + past 24hrs (Mortality Rate)',
style={'color': '#f2786f',
'fontWeight': 'bold','fontSize': 14,})
],
style={
'textAlign': 'center',
'color': 'rgb(200,200,200)',
'fontsize':12,
'backgroundColor':'#ab2c1a',
'borderRadius': '12px',
'fontSize': 17,
}
),
html.P(deaths_cases),
],
className="three columns",
),
html.Div([
html.P([html.Span('Single day highest mortality: ',
),
html.Br(),
html.Span(' + past 24hrs (Mortality Rate)',
style={'color': '#f2786f',
'fontWeight': 'bold','fontSize': 14,})
],
style={
'textAlign': 'center',
'color': 'rgb(200,200,200)',
'fontsize':12,
'backgroundColor':'#ab2c1a',
'borderRadius': '12px',
'fontSize': 17,
}
),
html.P(deaths_cases_24hrs),
],
className="three columns",
),
], className="row",
style={
'textAlign': 'left',
'color': colors['text'],
'backgroundColor': colors['background'],
'padding': 20
},
),
html.Div([
html.Div(children='Global Outbreak Map - Select row from table to locate in map',
style={
'textAlign': 'center',
'color': colors['text'],
'backgroundColor': colors['background'],
},
className='six columns'
),
], className='row'
),
#Map, Table
html.Div(
[
html.Div(
[
dcc.Graph(id='map-graph',
)
], className="six columns"
),
html.Div(
[
dt.DataTable(
data=map_data.to_dict('records'),
columns=[
{"name": i, "id": i, "deletable": False, "selectable": True} for i in ['Province/State', 'Country/Region', 'Confirmed',
'Deaths', 'Recovered']
],
fixed_rows={'headers': True, 'data': 0},
style_header={
'backgroundColor': 'rgb(30, 30, 30)',
'fontWeight': 'bold'
},
style_cell={
'backgroundColor': 'rgb(100, 100, 100)',
'color': colors['text'],
'maxWidth': 0,
'fontSize':14,
},
style_table={
'maxHeight': '350px',
'overflowY': 'auto'
},
style_data={
'whiteSpace': 'normal',
'height': 'auto',