Purpose of this notebook

The main purpose of this notebook is to create my own corona dashboard by pulling together interesting data visualizations I found elsewhere and adding some self-made visualizations.

I mainly want to play around with different types of visualizations and learn how to use them.

I attributed all sources correctly. Most dashboards were adopted from the amazing collection of https://covid19dashboards.com/.

Other interesting dashboards are:

https://avatorl.org/covid-19/

Last updated of dataset: 02 May, 2020

Overview of latest stats

based on: https://covid19dashboards.com/covid-overview/

New cases

Compare country trajectories

Covid-19 deaths per million inhabitants

source: https://covid19dashboards.com/covid-compare-permillion/

Covid-19 confirmed cases per million inhabitants

source: https://covid19dashboards.com/covid-compare-permillion/

Flatten the curve

Let's have a look at the proportion of

confirmed
death
recovered

cases over time

#collapse
alt.Chart(df_melted.query("country.isin(@flatten_countries)")).mark_circle(
    opacity=0.8,
    stroke='black',
    strokeWidth=1
).encode(
    alt.X('date:T', axis=alt.Axis(labelAngle=45)),
    alt.Y('country:N'),
    alt.Size('value:Q',
        scale=alt.Scale(range=[0, 2000]),
        legend=alt.Legend(title='confirmed cases')
    ),
    alt.Color('country:N', legend=None)
).properties(
    width=850,
    height=820
).transform_filter(
    alt.datum.Entity != 'All natural disasters'
)

Guessing the number of infected cases https://covid19dashboards.com/covid-infected/

number of people tested

The effects of the lockdown

Take a look at raw numbers of new cases and see if a country lockdown had an effect

lockdowns = {'Spain': '3/15/20',
            'Italy': '3/10/20',
            'Wuhan': '1/23/20',
            'France': '3/15/20',
            'Ireland': '3/12/20',
            'Belgium': '3/15/20',
            'Germany': '3/16/20'}

#for curr_country in lockdowns:
curr_country = 'Germany'
alt.Chart(new_cases_melted_df.loc[new_cases_melted_df['country'] == curr_country]).mark_bar().encode(
    x='date:T',
    y='new_cases:Q',
     #color='country:N',
    column='country:N',
    color=alt.condition(
            alt.datum.Year == '2020',  # If the year is 1810 this test returns True,
    alt.value('orange'),     # which sets the bar orange.
    alt.value('steelblue')   # And if it's not true it sets the bar steelblue.
    )
).properties(
title='Number of new cases by Country',
width=250,
height=140

).properties(width=600)


Country	New Cases	Total Cases		Deaths		Fatality
	Apr. 22 May. 02		(+NEW) since Apr, 27
Spain		216,582	(+7,117)	25,100	(+1,579)	11.6%
Italy		209,328	(+9,914)	28,710	(+1,733)	13.7%
United Kingdom		183,500	(+25,152)	28,205	(+3,747)	15.4%
France		168,518	(+2,555)	24,763	(+1,467)	14.7%
Germany		164,967	(+6,209)	6,812	(+686)	4.1%
China		83,959	(+41)	4,637	(+0)	5.5%
Sweden		22,082	(+3,156)	2,669	(+395)	12.1%
Austria		15,558	(+284)	596	(+47)	3.8%
South Korea		10,793	(+41)	250	(+6)	2.3%
Denmark		9,605	(+709)	475	(+48)	4.9%
Norway		7,809	(+210)	211	(+6)	2.7%

	date	Deaths per Million	Log of Deaths per Million
Country
Austria	2020-05-02	68.227718	4.222851
China	2020-05-02	3.289778	1.190820
Denmark	2020-05-02	82.845692	4.416980
France	2020-05-02	368.352594	5.909041
Germany	2020-05-02	82.957609	4.418330
Italy	2020-05-02	483.659833	6.181382
Norway	2020-05-02	39.770925	3.683136
South Korea	2020-05-02	4.903671	1.589984
Spain	2020-05-02	541.481349	6.294309
Sweden	2020-05-02	269.304871	5.595844
United Kingdom	2020-05-02	426.175938	6.054852
United States	2020-05-02	204.552511	5.320825