In this notebook, we use Plots.jl and Pluto.jl to visualise the Covid-19 confirmed cases across the world.

Note: This is the HTML rendered output of the notebook so it looses all the interactivity. You may want to directly load the pluto notebook in Julia.

Covid-19 Confirmed Cases

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md"# Covid-19 Confirmed Cases"

9.8 μs

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begin
    using Pkg
    Pkg.activate(".")
    Pkg.add(["CSV","DataFrames", "PlutoUI"])
    using CSV
    using DataFrames
    using PlutoUI
    using Plots
    using Dates
end

185 ms

Data

Download the data from Johns Hopkins University Center for Systems Science and Engineering

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md"# Data
Download the data from Johns Hopkins University Center for Systems Science and Engineering"

9.2 μs

"covid_data.csv"

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begin
    url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv";
    download(url, "covid_data.csv");
end

130 ms

Load data into dataframe

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6.2 μs

266 rows × 252 columns (omitted printing of 246 columns)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20
	String?	String	Float64	Float64	Int64	Int64
1	missing	Afghanistan	33.9391	67.71	0	0
2	missing	Albania	41.1533	20.1683	0	0
3	missing	Algeria	28.0339	1.6596	0	0
4	missing	Andorra	42.5063	1.5218	0	0
5	missing	Angola	-11.2027	17.8739	0	0
6	missing	Antigua and Barbuda	17.0608	-61.7964	0	0
7	missing	Argentina	-38.4161	-63.6167	0	0
8	missing	Armenia	40.0691	45.0382	0	0
9	Australian Capital Territory	Australia	-35.4735	149.012	0	0
10	New South Wales	Australia	-33.8688	151.209	0	0
11	Northern Territory	Australia	-12.4634	130.846	0	0
12	Queensland	Australia	-27.4698	153.025	0	0
13	South Australia	Australia	-34.9285	138.601	0	0
14	Tasmania	Australia	-42.8821	147.327	0	0
15	Victoria	Australia	-37.8136	144.963	0	0
16	Western Australia	Australia	-31.9505	115.861	0	0
17	missing	Austria	47.5162	14.5501	0	0
18	missing	Azerbaijan	40.1431	47.5769	0	0
⋮	⋮	⋮	⋮	⋮	⋮	⋮

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begin
    csv_data = CSV.File("covid_data.csv")
    data = DataFrame(csv_data)
end

5.2 ms

Rename the columns

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6.3 μs

6 rows × 252 columns (omitted printing of 244 columns)

	province	country	latitude	longitude	1/22/20	1/23/20	1/24/20	1/25/20
	String?	String	Float64	Float64	Int64	Int64	Int64	Int64
1	missing	Afghanistan	33.9391	67.71	0	0	0	0
2	missing	Albania	41.1533	20.1683	0	0	0	0
3	missing	Algeria	28.0339	1.6596	0	0	0	0
4	missing	Andorra	42.5063	1.5218	0	0	0	0
5	missing	Angola	-11.2027	17.8739	0	0	0	0
6	missing	Antigua and Barbuda	17.0608	-61.7964	0	0	0	0

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begin
    rename!(data, 1 => "province", 2 => "country", 3 => "latitude", 4 => "longitude")
    head(data)
end

295 μs

Extract the list of unique countries

x
 
md" 
Extract the list of unique countries
"

7.5 μs

all_countries

String1

"Afghanistan"

"Albania"

"Algeria"

"Andorra"

"Angola"

"Antigua and Barbuda"

"Argentina"

"Armenia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Austria"

"Azerbaijan"

"Bahamas"

"Bahrain"

"Bangladesh"

"Barbados"

"Belarus"

"Belgium"

"Belize"

"Benin"

"Bhutan"

"Bolivia"

"Bosnia and Herzegovina"

"Botswana"

"Brazil"

"Brunei"

"Bulgaria"

"Burkina Faso"

"Burma"

"Burundi"

"Cabo Verde"

"Cambodia"

"Cameroon"

"Canada"

257

"United Kingdom"

258

"Uruguay"

259

"Uzbekistan"

260

"Venezuela"

261

"Vietnam"

262

"West Bank and Gaza"

263

"Western Sahara"

264

"Yemen"

265

"Zambia"

266

"Zimbabwe"

x
 
all_countries = data[:, :country]

10.3 μs

countries

String1

"Afghanistan"

"Albania"

"Algeria"

"Andorra"

"Angola"

"Antigua and Barbuda"

"Argentina"

"Armenia"

"Australia"

"Austria"

"Azerbaijan"

"Bahamas"

"Bahrain"

"Bangladesh"

"Barbados"

"Belarus"

"Belgium"

"Belize"

"Benin"

"Bhutan"

"Bolivia"

"Bosnia and Herzegovina"

"Botswana"

"Brazil"

"Brunei"

"Bulgaria"

"Burkina Faso"

"Burma"

"Burundi"

"Cabo Verde"

"Cambodia"

"Cameroon"

"Canada"

"Central African Republic"

"Chad"

"Chile"

"China"

"Colombia"

"Comoros"

"Congo (Brazzaville)"

179

"United Kingdom"

180

"Uruguay"

181

"Uzbekistan"

182

"Venezuela"

183

"Vietnam"

184

"West Bank and Gaza"

185

"Western Sahara"

186

"Yemen"

187

"Zambia"

188

"Zimbabwe"

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countries = unique(all_countries)

66.9 μs

String1

"Afghanistan"

"Albania"

"Algeria"

"Andorra"

"Angola"

"Antigua and Barbuda"

"Argentina"

"Armenia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Australia"

"Austria"

"Azerbaijan"

"Bahamas"

"Bahrain"

"Bangladesh"

"Barbados"

"Belarus"

"Belgium"

"Belize"

"Benin"

"Bhutan"

"Bolivia"

"Bosnia and Herzegovina"

"Botswana"

"Brazil"

"Brunei"

"Bulgaria"

"Burkina Faso"

"Burma"

"Burundi"

"Cabo Verde"

"Cambodia"

"Cameroon"

"Canada"

257

"United Kingdom"

258

"Uruguay"

259

"Uzbekistan"

260

"Venezuela"

261

"Vietnam"

262

"West Bank and Gaza"

263

"Western Sahara"

264

"Yemen"

265

"Zambia"

266

"Zimbabwe"

x
 
data[:, :country]

8.1 μs

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549 ns

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332 ns

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386 ns

11 rows × 252 columns (omitted printing of 245 columns)

	province	country	latitude	longitude	1/22/20	1/23/20	1/24/20
	String?	String	Float64	Float64	Int64	Int64	Int64
1	Anguilla	United Kingdom	18.2206	-63.0686	0	0	0
2	Bermuda	United Kingdom	32.3078	-64.7505	0	0	0
3	British Virgin Islands	United Kingdom	18.4207	-64.64	0	0	0
4	Cayman Islands	United Kingdom	19.3133	-81.2546	0	0	0
5	Channel Islands	United Kingdom	49.3723	-2.3644	0	0	0
6	Falkland Islands (Malvinas)	United Kingdom	-51.7963	-59.5236	0	0	0
7	Gibraltar	United Kingdom	36.1408	-5.3536	0	0	0
8	Isle of Man	United Kingdom	54.2361	-4.5481	0	0	0
9	Montserrat	United Kingdom	16.7425	-62.1874	0	0	0
10	Turks and Caicos Islands	United Kingdom	21.694	-71.7979	0	0	0
11	missing	United Kingdom	55.3781	-3.436	0	0	0

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filter(x -> x.country == "United Kingdom", data)

173 ms

US_row

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US_row = findfirst(==("US"), all_countries)

15.2 μs

DataFrameRow (252 columns)

	province	country	latitude	longitude	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20
	String?	String	Float64	Float64	Int64	Int64	Int64	Int64	Int64	Int64
243	missing	US	40.0	-100.0	1	1	2	2	5	5

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data[US_row, :]

5.9 μs

Plots

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4.4 μs

We can now start to plot the data for each country

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md"
We can now start to plot the data for each country
"

7.6 μs

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@bind country Select(countries)

60.8 μs

current_country_idx

x
 
current_country_idx = findfirst(x -> x == country, all_countries)       

15.8 ms

 
scatter(Vector(data[current_country_idx, 5:end]), m=:o, alpha=0.5, ms=3, xlabel="Day", ylabel="Cumulative cases", leg=false, title="Covid-19 Confirmed Cases - $(country)")

1.5 ms

column_names

String1

"province"

"country"

"latitude"

"longitude"

"1/22/20"

"1/23/20"

"1/24/20"

"1/25/20"

"1/26/20"

"1/27/20"

"1/28/20"

"1/29/20"

"1/30/20"

"1/31/20"

"2/1/20"

"2/2/20"

"2/3/20"

"2/4/20"

"2/5/20"

"2/6/20"

"2/7/20"

"2/8/20"

"2/9/20"

"2/10/20"

"2/11/20"

"2/12/20"

"2/13/20"

"2/14/20"

"2/15/20"

"2/16/20"

"2/17/20"

"2/18/20"

"2/19/20"

"2/20/20"

"2/21/20"

"2/22/20"

"2/23/20"

"2/24/20"

"2/25/20"

"2/26/20"

243

"9/16/20"

244

"9/17/20"

245

"9/18/20"

246

"9/19/20"

247

"9/20/20"

248

"9/21/20"

249

"9/22/20"

250

"9/23/20"

251

"9/24/20"

252

"9/25/20"

 
column_names = names(data)

61.2 μs

Daily Cumulative Cases

Next, we generate the timer based plot for selected countries

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md"## Daily Cumulative Cases
​
Next, we generate the timer based plot for selected countries
"

10.2 μs

Date1

2020-01-22

2020-01-23

2020-01-24

2020-01-25

2020-01-26

2020-01-27

2020-01-28

2020-01-29

2020-01-30

2020-01-31

2020-02-01

2020-02-02

2020-02-03

2020-02-04

2020-02-05

2020-02-06

2020-02-07

2020-02-08

2020-02-09

2020-02-10

2020-02-11

2020-02-12

2020-02-13

2020-02-14

2020-02-15

2020-02-16

2020-02-17

2020-02-18

2020-02-19

2020-02-20

2020-02-21

2020-02-22

2020-02-23

2020-02-24

2020-02-25

2020-02-26

2020-02-27

2020-02-28

2020-02-29

2020-03-01

239

2020-09-16

240

2020-09-17

241

2020-09-18

242

2020-09-19

243

2020-09-20

244

2020-09-21

245

2020-09-22

246

2020-09-23

247

2020-09-24

248

2020-09-25

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# We will need to convert the date from string to date format
begin
    date_strings = String.(names(data)[5:end])
    date_format = Dates.DateFormat("m/d/Y")
    parse(Date, date_strings[1], date_format)
    dates = parse.(Date, date_strings, date_format) .+ Year(2000)
end

218 μs

6 rows × 252 columns (omitted printing of 242 columns)

	province	country	latitude	longitude	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20
	String?	String	Float64	Float64	Int64	Int64	Int64	Int64	Int64	Int64
1	Brazil	Brazil	-14.235	-51.9253	0	0	0	0	0	0
2	India	India	20.5937	78.9629	0	0	0	0	0	0
3	Italy	Italy	41.8719	12.5674	0	0	0	0	0	0
4	Mexico	Mexico	23.6345	-102.553	0	0	0	0	0	0
5	Spain	Spain	40.4637	-3.74922	0	0	0	0	0	0
6	US	US	40.0	-100.0	1	1	2	2	5	5

 
begin
    filter_countries = ["US","India", "Brazil", "Italy", "Spain", "Mexico"]
    certain_countries = filter(x -> x.country in filter_countries  , data)
end

139 ms

Add the Clock to animate the plot

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md"
Add the Clock to animate the plot
"

8.6 μs

speed:

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@bind today Clock(0.3)

58.4 μs

Date: 2020-02-12

data source: Johns Hopkins CSSE

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13.7 μs

plot_country (generic function with 1 method)

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function plot_country(name, row)
    df = Vector(data[row, 5:today])
    plot!(df, m=:o, alpha=0.5, ms=2, xlabel="Day", ylabel="Cumulative cases", label=name)   
end

34.5 μs

certain_countries_plot

 
certain_countries_plot = begin
    plot(xlabel="day", ylabel="cumulative cases",size=(680,500), leg=:topleft)
    for country in filter_countries
        current_country = findfirst(x -> x == country, all_countries)       
        plot_country(country, current_country)  
    end
    title!("Confirmed cases of Covid-19 Confirmed of $(dates[today])")
end

55.5 ms

And, that's it folks

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md"
And, that's it folks
"

7.7 μs

📈 Plot Covid-19 Confirmed Cases using Julia

Covid-19 Confirmed Cases

Data

Plots

Daily Cumulative Cases

Date: 2020-02-12

See also