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app.py
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import dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output, State
import dash_daq as daq
import dash_bootstrap_components as dbc
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.express as px
path = 'data/'
emissions = pd.read_csv(path + "emissions_with_origin.csv")
productions = pd.read_csv(path + "productions.csv")
water = pd.read_csv(path + "water_use.csv")
global_emissions = pd.read_csv(path + "Global_Emissions.csv")
emissions_bar = pd.read_csv(path +'bar_plot_2.csv' )
df3 = pd.read_csv('data/productions.csv')
df3.columns = df3.columns.str.replace('Value', 'value')
df3 = df3.groupby(['Year', 'Origin'], as_index=False).sum()
df4 = df3.groupby(['Year'], as_index=False).sum()
top10 = emissions.sort_values("Total_emissions")[-8:]
top10_vegetal = emissions[emissions.Origin == 'Vegetal'].sort_values("Total_emissions")[-8:]
top8_animal = emissions[emissions.Origin == 'Animal'].sort_values("Total_emissions")[-8:]
top20 = emissions.sort_values("Total_emissions")[-8:]
top20_vegetal = emissions[emissions.Origin == 'Vegetal'].sort_values("Total_emissions")[-8:]
top20_animal = emissions[emissions.Origin == 'Animal'].sort_values("Total_emissions")[-8:]
radio_ani_veg = dbc.RadioItems(
id='ani_veg',
className='radio',
options=[dict(label='Animal Products', value=0), dict(label='Vegan Products', value=1),
dict(label='All Products', value=2)],
value=2,
inline=True
)
dict_ = {'Apples': 'Apples', 'Bananas': 'Bananas', 'Barley': 'Barley', 'Beet Sugar': 'Sugar beet',
'Berries & Grapes': 'Berries & Grapes', 'Brassicas': 'Brassicas',
'Cane Sugar': 'Sugar cane', 'Cassava': 'Cassava', 'Citrus Fruit': 'Citrus', 'Coffee': 'Coffee beans',
'Groundnuts': 'Groundnuts', 'Maize': 'Maize', 'Nuts': 'Nuts',
'Oatmeal': 'Oats', 'Olive Oil': 'Olives', 'Onions & Leeks': 'Onions & Leeks', 'Palm Oil': 'Oil palm fruit',
'Peas': 'Peas', 'Potatoes': 'Potatoes', 'Rapeseed Oil': 'Rapeseed',
'Rice': 'Rice', 'Root Vegetables': 'Roots and tubers', 'Soymilk': 'Soybeans',
'Sunflower Oil': 'Sunflower seed', 'Tofu': 'Soybeans', 'Tomatoes': 'Tomatoes',
'Wheat & Rye': 'Wheat & Rye', 'Dark Chocolate': 'Cocoa, beans', 'Milk': 'Milk', 'Eggs': 'Eggs',
'Poultry Meat': 'Poultry Meat', 'Pig Meat': 'Pig Meat',
'Seafood (farmed)': 'Seafood (farmed)', 'Cheese': 'Cheese', 'Lamb & Mutton': 'Lamb & Mutton',
'Beef (beef herd)': 'Beef (beef herd)'}
options_veg = [dict(label=key, value=dict_[key]) for key in top20_vegetal['Food product'].tolist()[::-1] if
key in dict_.keys()]
options_an = [dict(label=val, value=val) for val in top8_animal["Food product"].tolist()[::-1]]
options_total = [dict(label=key, value=dict_[key]) for key in top20['Food product'].tolist()[::-1] if
key in dict_.keys()]
bar_colors = ['#B22222', '#2E8B57']
bar_options = [top8_animal, top10_vegetal, top10]
drop_map = dcc.Dropdown(
id='drop_map',
clearable=False,
searchable=False,
style={'margin': '4px', 'box-shadow': '0px 0px #ebb36a', 'border-color': '#5CACEE'} # בחירת מוצר גבולות
)
drop_continent = dcc.Dropdown(
id='drop_continent',
clearable=False,
searchable=False,
options=[{'label': 'World', 'value': 'world'},
{'label': 'Europe', 'value': 'europe'},
{'label': 'Asia', 'value': 'asia'},
{'label': 'Africa', 'value': 'africa'},
{'label': 'North america', 'value': 'north america'},
{'label': 'South america', 'value': 'south america'}],
value='world',
style={'margin': '4px', 'box-shadow': '0px 0px #ebb36a', 'border-color': '#5CACEE'}
)
slider_map = daq.Slider(
id='slider_map',
handleLabel={"showCurrentValue": True, "label": "Year"},
marks={str(i): str(i) for i in [1990, 1995, 2000, 2005, 2010, 2015, 2020]},
min=1990,
size=300,
color='#4B9072'
)
fig_water = px.sunburst(water, path=['Origin', 'Category', 'Product'], values='Water Used', color='Category',
color_discrete_sequence=px.colors.sequential.haline_r).update_traces(
hovertemplate='%{label}<br>' + 'Water Used: %{value} L')
fig_water = fig_water.update_layout({'margin': dict(t=0, l=0, r=0, b=10),
'paper_bgcolor': '#F9F9F8',
'font_color': '#363535'
})
fig_gemissions = px.sunburst(global_emissions, path=['Emissions', 'Group', 'Subgroup'],
values='Percentage of food emissions',
color='Group', color_discrete_sequence=px.colors.sequential.Peach_r).update_traces(
hovertemplate='%{label}<br>' + 'Global Emissions: %{value}%', textinfo="label + percent entry")
fig_gemissions = fig_gemissions.update_layout({'margin': dict(t=0, l=0, r=0, b=10),
'paper_bgcolor': '#F9F9F8',
'font_color': '#363535'})
fig2 = go.Figure(
data=go.Scatter(x=df3['Year'], y=df3['value'])
)
fig3 = go.Figure(layout=dict(height=300, font_color='#363535', paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=20))
# ------------------------------------------------------ APP ------------------------------------------------------
app = dash.Dash(__name__)
server = app.server
app.layout = html.Div([
html.Div([
html.H1(children='Information Visualization Project'),
html.Label(
'Food products that have the biggest impact on environment, whose productions emit more greenhouse gases.',
style={'color': 'rgb(242, 235, 233)'}),
html.Img(src=app.get_asset_url('bgu.png'),
style={'position': 'relative', 'width': '5%', 'left': '-40px', 'top': '-118px'}),
], className='side_bar'),
html.Div([
html.Div([
html.Div([
html.Label("Choose the Product's Origin:"),
html.Br(),
html.Br(),
radio_ani_veg
], className='box', style={'margin': '10px', 'padding-top': '15px', 'padding-bottom': '15px', }),
html.Div([
html.Div([
html.Div([
html.Label(id='title_bar'),
html.Br(),
html.Label(' (kg CO2 per kg of product)',style={'padding-bottom': '200px','font-size': '17px'}),
html.Br(),
html.Br(),html.Img(src=app.get_asset_url('len.png'),style={'width': '25%','left': '-40px', 'top': '-118px'}),
dcc.Graph(id='bar_fig'),
html.Div([
html.P(id='comment')
], className='box_comment'),
], className='box', style={'padding-bottom': '15px'}),
html.Div([
html.Img(src=app.get_asset_url('blue3.png'),
style={'width': '100%', 'position': 'relative', 'opacity': '100%',
'border-radius': '75'
'px','height':'350px'}),
]),
html.Div([
html.Br(),
html.Label(id='title_line'),
dcc.Graph(id='fig1', figure=fig2)
], className='box',
style={'width': '90%', 'position': 'relative', 'padding-top': '20px', 'padding-bottom': '20px',
'border-radius': '75px'}),
], style={'width': '40%'}),
html.Div([
html.Div([
html.Label(id='choose_product', style={'margin': '10px', 'font-size': '20px'}),
drop_map,
], className='box'),
html.Div([
html.Div([
html.Div([
html.Br(), html.Br(), html.Br(), html.Br(),
html.Div([
html.Label(id='title_map', style={'font-size': '20px'}),
html.Br(),
], style={'width': '80%'}),
html.Div([
], style={'width': '50%'}),
html.Div([
drop_continent,
html.Br(),
html.Br(), html.Br(), html.Br(), html.Br()
], style={'width': '50%'}),
], className='row'),
dcc.Graph(id='map', style={'position': 'relative', 'top': '-50px'}),
html.Div([
slider_map
], style={'margin-left': '30%', 'position': 'relative', 'top': '-38px'}),
], className='box', style={'padding-bottom': '0px'}),
]),
html.Div([dcc.Graph(id='graph2', figure=fig3),
html.Div([
html.Label('These represent greenhouse gas emissions per kg of food product across different stages in the lifecycle of food production.'),
], className='box_comment')
], className='box'),
html.Div([
html.Img(src=app.get_asset_url('farm2.png'),
style={'width': '80%', 'position': 'relative', 'opacity': '90%',
'border-radius': '75px', 'height': '190px','left':'80px'}),
]),
], style={'width': '60%'}),
], className='row'),
html.Div([
html.Div([
html.P(['Ⓒ Final Information Visualization Project by', html.Br(),
html.A('Natalie Morad', href='https://www.linkedin.com/in/natalie-morad-44881620b',
target='_blank'), ', ',
html.A('Bar Avraham',
href='https://www.linkedin.com/in/bar-avraham-4475b1218/', target='_blank')],
style={'font-size': '17px' ,'text-align': 'center'}),
], style={'width': '100%'}),
], className='side_bar', style={'display': 'flex'}),
]
, className='main'),
]),
])
# ------------------------------------------------------ Callbacks ---------------------------------------------------
@app.callback(
[
Output('title_bar', 'children'),
Output('bar_fig', 'figure'),
Output('comment', 'children'),
Output('drop_map', 'options'),
Output('drop_map', 'value'),
Output('choose_product', 'children')
],
[
Input('ani_veg', 'value')
],
)
def bar_chart(top10_select):
################## Top10 Plot ##################
title = '1. Food products \n that cause the greatest emissions of CO2'
df = bar_options[top10_select]
if top10_select == 2:
bar_fig = dict(type='bar',
y=df.Total_emissions,
x=df["Food product"],
orientation='v',
marker_color=['#B22222' if x == 'Animal' else '#2E8B57' for x in df.Origin])
fig=go.Figure(data=bar_fig,
layout=dict(height=300, font_color='#363535', paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=20), layout_yaxis_range=[0, 60])
else:
bar_fig = dict(type='bar',
y=df.Total_emissions,
x=df["Food product"],
orientation='v',
marker_color=bar_colors[top10_select])
fig=go.Figure(data=bar_fig,
layout=dict( height=300, font_color='#363535', paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=20), layout_yaxis_range=[0, 60])
################## Dropdown Bar ##################comments
if top10_select == 0:
options_return = options_an
product_chosen = "2. Choose an animal product:"
comment = ["Each kilogram of beef produces almost 60 kg of CO2!",
html.Br()]
elif top10_select == 1:
options_return = options_veg
product_chosen = "2. Choose a vegan product:"
comment = ["Dark chocolate is the vegan product with the most CO2 emissions with almost 20 kg of CO2.",
html.Br()]
else:
options_return = options_total
product_chosen = "2. Choose an animal or vegan product:"
comment = "Beef (top1 animal-based emitter) produces around 3 times more emissions than dark chocolate (top1 vegan-based emitter)."
return title, \
fig.update_layout(legend= {'itemsizing': 'constant'}),\
comment, \
options_return, \
options_return[0]['value'], \
product_chosen
@app.callback(
[
Output('slider_map', 'max'),
Output('slider_map', 'value'),
],
[
Input('drop_map', 'value')
]
)
def update_slider(product):
year = productions[productions['Item'] == product]['Year'].max()
return year, year
@app.callback(
[
Output('title_map', 'children'),
Output('map', 'figure')
],
[
Input('drop_map', 'value'),
Input('slider_map', 'value'),
Input('drop_continent', 'value')
],
[State("drop_map", "options")]
)
def update_map(drop_map_value, year, continent, opt):
################## Emissions datset ##################
the_label = [x['label'] for x in opt if x['value'] == drop_map_value]
data_emissions = emissions[emissions["Food product"] == the_label[0]]
################## Choroplet Plot ##################
prod1 = productions[(productions['Item'] == drop_map_value) & (productions['Year'] == year)]
title = ' Production quantities of {}, by country and year'.format(prod1['Item'].unique()[0] ) # font_color = '#363535',
data_slider = []
data_each_yr = dict(type='choropleth',
locations=prod1['Area'],
locationmode='country names',
autocolorscale=False,
z=np.log(prod1['Value'].astype(float)),
zmin=0,
zmax=np.log(productions[productions['Item'] == drop_map_value]['Value'].max()),
# colorscale=["#ffe2bd", "#006837"],
colorscale=["#FFFF00", "#CD5C5C"],
marker_line_color='rgba(0,0,0,0)',
colorbar={'title': 'Tonnes (log)'}, # Tonnes in logscale
colorbar_lenmode='fraction',
colorbar_len=0.7,
colorbar_x=1,
colorbar_xanchor='left',
colorbar_y=0.5,
name='')
data_slider.append(data_each_yr)
layout = dict(geo=dict(scope=continent,
projection={'type': 'natural earth'},
bgcolor='rgba(0,0,0,0)'),
margin=dict(l=0,
r=0,
b=0,
t=0,
pad=0),
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
fig_choropleth = go.Figure(data=data_slider, layout=layout)
fig_choropleth.update_geos(showcoastlines=False, showsubunits=True, showframe=True)
return title, \
fig_choropleth
@app.callback(
[Output('title_line', 'children'),
Output('fig1', 'figure')]
,
Input('ani_veg', 'value')
)
def update_graph(dropdown_line):
# font_color = '#363535',
if dropdown_line == 0:
prod1 = df3[(df3['Origin'] == 'Animal') & (df3['Year'] >= 1990)]
title = '3. Production quantities of animal, by year'
layout = go.Layout(
dict(xaxis_title="Year", yaxis_title="Production quantities", height=300, font_color='#363535',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=20))
fig = go.Figure(
data=go.Scatter(x=prod1['Year'], y=prod1['value'], line_color='#B22222', fill='tozeroy', line_width=4
), layout=layout
)
fig.update_layout(yaxis_range=[0, 10000000000])
elif dropdown_line == 1:
prod1 = df3.loc[df3['Origin'] == 'Vegetal']
# print(prod1['Year'].unique())
title = '3.Production quantities of vegetal, by year'
layout = go.Layout(
dict(xaxis_title="Year", yaxis_title="Production quantities", height=300, font_color='#363535',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=20))
fig = go.Figure(
data=go.Scatter(x=prod1['Year'], y=prod1['value'], line_color='#2E8B57', fill='tozeroy', line_width=4
), layout=layout
)
fig.update_layout(yaxis_range=[0, 10000000000])
else:
title = '3.Production quantities of all productions, by year'
prod1 = df4[( df3['Year'] >= 1990)]
# prod1 = df4
layout = go.Layout(
dict(xaxis_title="Year", yaxis_title="Production quantities", height=300, font_color='#363535',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=20))
fig = go.Figure(
data=go.Scatter(x=prod1['Year'], y=prod1['value'], line_color='#5CACEE', fill='tozeroy', line_width=4
), layout=layout
)
return title, \
fig
@app.callback(
Output('graph2', 'figure'),
Input('drop_map', 'value')
, Input('ani_veg', 'value')
)
def update_bar_chart(name, origin):
if origin == 1:
data = emissions_bar
color= '#2E8B57'
elif origin == 0:
data = emissions_bar
color='#B22222'
else:
if emissions_bar.loc[emissions_bar['Food product'] == name].values.tolist()[0][-1]=='Vegetal':
color = '#2E8B57'
else:
color = '#B22222'
data = emissions_bar
x = ['Land use change', 'Animal Feed', 'Farm', 'Processing', 'Transport', 'Packging', 'Retail']
y = data.loc[data['Food product'] == name].values.tolist()
data = [go.Bar(
x=x,
y=y[0],
marker_color=color
)]
title = 'Emissions measured as kg of CO2 per kg of {}'.format(name) # font_color = '#363535',
fig = go.Figure(data=data, layout=dict(height=190, width=1000,font_color='#363535', paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)', margin=dict(l=20, r=20, t=30, b=20),
margin_pad=10,yaxis_range=[0, 40],title=title))
return fig
if __name__ == '__main__':
app.run_server(debug=True)