ESS-dashboard.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


from dash import Dash, dcc, html, Input, Output
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd
import numpy as np
import chart_studio.plotly as py
import plotly.graph_objects as go
import cufflinks as cf
import seaborn as sns
import plotly.express as px
import matplotlib as plt
import json
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

round10 = pd.read_csv('round10_ready.csv')
round9 = pd.read_csv('round9_ready.csv')
round8 = pd.read_csv('round8_ready.csv')


# In[2]:


r10 = round10.drop(['Code', 'Country'], axis=1)
r10.index = round10['Country']
r9 = round9.drop(['Code', 'Country'], axis=1)
r9.index = round9['Country']
r8 = round8.drop(['Code', 'Country'], axis=1)
r8.index = round8['Country']


# In[3]:


attrs = ['Happiness','Satisfaction with life','Religiousness','Emotional attachment to ones country','Emotional attachment to Europe','Feeling of safety walking alone after dark','Trust in the legal system','Trust in the police','Trust in politicians','Perceived state of health services in country nowadays',
'Perceived state of education in country nowadays']


# In[4]:


def create_barplots(round_data, attr):
    fig = px.bar(round_data, x='Country', y=attr,
             hover_data=['Country', attr], color=attr, color_continuous_scale="plasma"
                )
    fig.update_layout(xaxis={'categoryorder':'total descending'})
    fig.update(layout_coloraxis_showscale=False)
    fig.update_layout(plot_bgcolor = "white")
    return fig

def create_choropleths(round_data, attr):
    fig = px.choropleth(round_data, locations='Country', color=attr, scope='europe', locationmode= 'country names', 
                    hover_data=['Country', attr], color_continuous_scale="plasma")                         
    fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
    return fig

def create_pca_plots(round_data):
    sc = StandardScaler()
    r_s = sc.fit_transform(round_data)
    pca = PCA(n_components = 2)
    proj = pca.fit_transform(r_s)
    loadings = pca.components_.T * np.sqrt(pca.explained_variance_)
    fig = px.scatter(proj, x=0, y=1, text=round_data.index, color_discrete_sequence=["white"],
                labels={
                     "0": "1st primary component",
                     "1": "2nd primary component"
                 })
    fig.update_layout(plot_bgcolor = "white", margin={"r":10,"t":10,"l":10,"b":10})
    return fig

def create_pca_plots_attrs(round_data, attr):
    sc = StandardScaler()
    r_s = sc.fit_transform(round_data)
    pca = PCA(n_components = 2)
    proj = pca.fit_transform(r_s)
    loadings = pca.components_.T * np.sqrt(pca.explained_variance_)
    #print(proj)
    fig = px.scatter(proj, x=0, y=1,  color_discrete_sequence=["black"], hover_data={"Country":round_data.index}, 
                labels={
                     "0": "1st primary component",
                     "1": "2nd primary component"
                 })
    j = attrs.index(attr)
    for i, attr in enumerate(attrs):
        if i == j:

            col="#1c188e"
            wid = 3
            siz=15
        else:
            col="#d35070"
            wid = 1
            siz = 8
            
        fig.add_shape(
            type='line',
            x0=0, y0=0,
            x1=loadings[i, 0]*4.5,
            y1=loadings[i, 1]*4.5,
            line=dict(
            color=col, width=2)
        )
        fig.add_annotation(
            x=loadings[i, 0]*5,
            y=loadings[i, 1]*5,
            ax=0, ay=0,
            xanchor="center",
            yanchor="bottom",
            text=attr,
            font=dict(size=siz, color=col)
        )
    
    fig.update_layout(plot_bgcolor = "white", margin={"r":10,"t":10,"l":10,"b":10})
    return fig


# In[5]:


barfigs10 = []
barfigs9 = []
barfigs8 = []
chorofigs10 = []
chorofigs9 = []
chorofigs8 = []
pcafigs10 = []
pcafigs9 = []
pcafigs8 = []
pcafigs10attr = []
pcafigs9attr = []
pcafigs8attr = []

for i in attrs:
    barfigs10.append(create_barplots(round10, i))
    barfigs9.append(create_barplots(round9, i))
    barfigs8.append(create_barplots(round8, i))
    chorofigs10.append(create_choropleths(round10, i))
    chorofigs9.append(create_choropleths(round9, i))
    chorofigs8.append(create_choropleths(round8, i))
    pcafigs10.append(create_pca_plots(r10))
    pcafigs9.append(create_pca_plots(r9))
    pcafigs8.append(create_pca_plots(r8))
    pcafigs10attr.append(create_pca_plots_attrs(r10, i))
    pcafigs9attr.append(create_pca_plots_attrs(r9, i))
    pcafigs8attr.append(create_pca_plots_attrs(r8, i))


# In[6]:


app = Dash(__name__)
app.title = "European Social Survey"
app.layout = html.Div(children=[
    #html.H1(children="European Social Survey"),
    #html.P("The European Social Survey (ESS) is a cross-national survey which measures the attitudes, beliefs and behaviour patterns of different European populations through time."),
    html.Label(['Choose social aspect and year to explore:'],style={'font-weight': 'bold'}),
    html.Div(children=[
        dcc.Dropdown(
        options=['Happiness','Satisfaction with life','Religiousness','Emotional attachment to ones country','Emotional attachment to Europe','Feeling of safety walking alone after dark','Trust in the legal system','Trust in the police','Trust in politicians','Perceived state of health services in country nowadays',
'Perceived state of education in country nowadays'],
        value='Happiness', id='dropdown', style={'display': 'inline-block', "width": '400px'}),
        html.Div(style={'display': 'inline-block',"width": '5px'}),
    dcc.Dropdown(
        options=['2020','2018', '2016'],
        value='2020', id='dropdownyear', style={'display': 'inline-block',"width": '100px'}),
    ]),
    
    html.Br(),

    html.Div(children=[
                html.Span(children=dcc.Graph(id='bar'), style={'display': 'inline-block', "width": "50%"}),
                html.Span(children=dcc.Graph(id='choro'), style={'display': 'inline-block', "width": "50%"}),
                html.Br(),
                html.Span(children=dcc.Graph(id='pcaattr'), style={'display': 'inline-block', "width": "50%"}),
                html.Span(children=dcc.Graph(id='pca'), style={'display': 'inline-block', "width": "50%"}),
    ], style={'margin':'auto'}),
    
    
], style={'font-family':' Arial, Helvetica, sans-serif','font-size':'1em', 'width':'70%', 'margin':'auto'})

    
@app.callback(
    Output('bar', 'figure'),
    Output('choro', 'figure'),
    Output('pca', 'figure'),
    Output('pcaattr', 'figure'),
    Input('dropdown', 'value'),
    Input('dropdownyear', 'value')
)
def update_bar(attr, year):
    i = attrs.index(attr)
    if year=='2020':        
        return barfigs10[i], chorofigs10[i], pcafigs10[i], pcafigs10attr[i]
    elif year=='2018':
        return barfigs9[i], chorofigs9[i], pcafigs9[i], pcafigs9attr[i]
    else:
        return barfigs8[i], chorofigs8[i], pcafigs8[i], pcafigs8attr[i]

if __name__ == '__main__':
    app.run_server(debug=False)