26 October, 2020
- Load datasets
- Preprocessing datasets
- COVID-19 worldwide spread
- COVID-19 spread by countries
- COVID-19 spread by countries population
Get list of files in datasets container:
## [1] "COVID19_line_list_data.csv" "COVID19_open_line_list.csv"
## [3] "covid_19_data.csv" "time_series_covid_19_confirmed.csv"
## [5] "time_series_covid_19_confirmed_US.csv" "time_series_covid_19_deaths.csv"
## [7] "time_series_covid_19_deaths_US.csv" "time_series_covid_19_recovered.csv"
Load covid_19_data.csv dataset:
## # A tibble: 100 x 8
## SNo ObservationDate Province.State Country.Region Last.Update Confirmed Deaths Recovered
## <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 54847 07/02/2020 Anguilla UK 2020-07-03 04… 3 0 3
## 2 88850 08/17/2020 California US 2020-08-18 04… 629415 11296 0
## 3 92115 08/21/2020 Omsk Oblast Russia 2020-08-22 04… 8893 145 6800
## 4 83707 08/10/2020 Dnipropetrovsk O… Ukraine 2020-08-11 04… 1571 33 1132
## 5 39638 06/11/2020 Delaware US 2020-06-12 05… 10106 414 0
## 6 1154 02/10/2020 <NA> Singapore 2020-02-10T19… 45 0 2
## 7 8766 03/25/2020 Ohio US 2020-03-25 23… 704 11 0
## 8 116140 09/23/2020 <NA> Jamaica 2020-09-24 04… 5395 76 1444
## 9 35811 06/05/2020 Texas US 2020-06-06 02… 72548 1812 0
## 10 99925 09/01/2020 Andhra Pradesh India 2020-09-02 04… 445139 4053 339876
## # … with 90 more rows
Get datasets list:
## [1] "countries.csv" "__MACOSX/" "__MACOSX/._countries.csv"
Load countries.csv dataset:
## # A tibble: 169 x 14
## iso_alpha2 iso_alpha3 iso_numeric name official_name ccse_name density fertility_rate land_area
## <chr> <chr> <int> <chr> <chr> <chr> <int> <dbl> <int>
## 1 AF AFG 4 Afgh… Islamic Repu… Afghanis… 60 4.6 652860
## 2 AL ALB 8 Alba… Republic of … Albania 105 1.6 27400
## 3 DZ DZA 12 Alge… People's Dem… Algeria 18 3.1 2381740
## 4 AD AND 20 Ando… Principality… Andorra 164 NA 470
## 5 AO AGO 24 Ango… Republic of … Angola 26 5.6 1246700
## 6 AG ATG 28 Anti… Antigua and … Antigua … 223 2 440
## 7 AR ARG 32 Arge… Argentine Re… Argentina 17 2.3 2736690
## 8 AM ARM 51 Arme… Republic of … Armenia 104 1.8 28470
## 9 AU AUS 36 Aust… Australia Australia 3 1.8 7682300
## 10 AT AUT 40 Aust… Republic of … Austria 109 1.5 82409
## # … with 159 more rows, and 5 more variables: median_age <dbl>, migrants <dbl>, population <int>,
## # urban_pop_rate <dbl>, world_share <dbl>
Set area column, processing province_state columns, and format dates
columns:
## # A tibble: 116,805 x 5
## area country province_state observation_date confirmed
## <fct> <chr> <chr> <date> <dbl>
## 1 Rest of World India Maharashtra 2020-09-23 1242770
## 2 Rest of World Brazil Sao Paulo 2020-09-23 945422
## 3 US US California 2020-09-23 796436
## 4 US US Texas 2020-09-23 742913
## 5 US US Florida 2020-09-23 690499
## 6 Rest of World South Africa <NA> 2020-09-23 665188
## 7 Rest of World Argentina <NA> 2020-09-23 664799
## 8 Rest of World India Andhra Pradesh 2020-09-23 639302
## 9 Rest of World India Tamil Nadu 2020-09-23 552674
## 10 Rest of World India Karnataka 2020-09-23 533850
## # … with 116,795 more rows
Get unmatched countries:
## # A tibble: 60 x 2
## country n
## <chr> <dbl>
## 1 UK 47036878
## 2 Mainland China 18897448
## 3 South Korea 2717610
## 4 Czech Republic 2692681
## 5 Ivory Coast 1637533
## 6 West Bank and Gaza 1493451
## 7 Kosovo 890826
## 8 Tajikistan 869268
## 9 Hong Kong 410886
## 10 Malawi 390307
## # … with 50 more rows
Correct top of unmached countries.
And updated matching:
## # A tibble: 55 x 2
## country n
## <chr> <dbl>
## 1 Ivory Coast 1637533
## 2 West Bank and Gaza 1493451
## 3 Kosovo 890826
## 4 Tajikistan 869268
## 5 Hong Kong 410886
## 6 Malawi 390307
## 7 Mali 310458
## 8 South Sudan 264326
## 9 Guinea-Bissau 248246
## 10 Sierra Leone 211625
## # … with 45 more rows
Much better :)
Analyze COVID-19 worldwide spread.
View spread statistics:
## # A tibble: 246 x 9
## observation_date active_total active_total_de… confirmed_total confirmed_total… recovered_total
## <date> <dbl> <chr> <dbl> <chr> <dbl>
## 1 2020-09-23 8914289 -0.10% 31779835 0.83% 21890442
## 2 2020-09-22 8923075 0.40% 31517087 0.87% 21624434
## 3 2020-09-21 8887511 0.81% 31245797 1.00% 21394593
## 4 2020-09-20 8815987 0.07% 30935011 0.80% 21159459
## 5 2020-09-19 8810095 0.43% 30688150 0.93% 20922189
## 6 2020-09-18 8772567 0.90% 30406197 1.09% 20683110
## 7 2020-09-17 8694289 1.10% 30078889 1.06% 20439713
## 8 2020-09-16 8599363 0.65% 29764055 0.70% 20225219
## 9 2020-09-15 8544111 0.66% 29557942 1.26% 20078979
## 10 2020-09-14 8488492 0.66% 29190841 1.00% 19775100
## # … with 236 more rows, and 3 more variables: recovered_total_delta <chr>, deaths_total <dbl>,
## # deaths_total_delta <chr>
Get daily dynamics of new infected and recovered cases.
World daily spread:
## # A tibble: 7 x 5
## observation_date confirmed_total_per_… deaths_total_per_d… recovered_total_per… active_total_per_…
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2020-09-17 314834 5414 214494 94926
## 2 2020-09-04 304626 5636 204681 94309
## 3 2020-08-21 270751 5554 170679 94518
## 4 2020-08-13 275227 6001 164494 104732
## 5 2020-07-23 281417 9953 169899 101565
## 6 2020-07-22 282312 7000 176997 98315
## 7 2020-07-19 214569 4029 87836 122704
Analyze COVID-19 spread y countries.
Calculate number of infected, recovered, fatal, and active (infected cases minus recovered and fatal) cases grouped by country:
Get countries ordered by total active cases:
## # A tibble: 30,055 x 10
## country observation_date active_total active_total_de… confirmed_total confirmed_total…
## <chr> <date> <dbl> <chr> <dbl> <chr>
## 1 US 2020-09-23 4061408 0.32% 6933548 0.54%
## 2 India 2020-09-23 968377 -0.77% 5646010 1.50%
## 3 Spain 2020-09-23 512146 2.23% 693556 1.65%
## 4 Brazil 2020-09-23 406432 -6.87% 4591364 0.00%
## 5 France 2020-09-23 380511 -0.07% 508456 0.26%
## 6 United… 2020-09-23 368047 1.71% 412245 1.52%
## 7 Russia 2020-09-23 177165 0.29% 1117487 0.57%
## 8 Argent… 2020-09-23 124937 3.26% 664799 1.94%
## 9 Peru 2020-09-23 108489 -16.35% 776546 1.00%
## 10 Ukraine 2020-09-23 100937 1.70% 189488 1.94%
## # … with 30,045 more rows, and 4 more variables: recovered_total <dbl>,
## # recovered_total_delta <chr>, deaths_total <dbl>, deaths_total_delta <chr>
Get daily dynamics of new infected and recovered cases by countries.
World daily spread:
## # A tibble: 30,055 x 6
## # Groups: country [178]
## country observation_date confirmed_total_p… recovered_total_p… deaths_total_pe… active_total_pe…
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 Afghani… 2020-09-23 49 34 1 14
## 2 Albania 2020-09-23 121 97 3 21
## 3 Algeria 2020-09-23 186 121 9 56
## 4 Andorra 2020-09-23 72 4 0 68
## 5 Angola 2020-09-23 127 11 4 112
## 6 Argenti… 2020-09-23 12625 8258 424 3943
## 7 Armenia 2020-09-23 210 350 4 -144
## 8 Austral… 2020-09-23 8 117 2 -111
## 9 Austria 2020-09-23 681 637 6 38
## 10 Azerbai… 2020-09-23 146 173 2 -29
## # … with 30,045 more rows
## # A tibble: 198 x 8
## country observation_date since_100_confi… since_10_deaths… recovered_total deaths_total
## <chr> <date> <date> <date> <dbl> <dbl>
## 1 US 2020-09-23 2020-03-10 2020-03-04 2670256 201884
## 2 US 2020-09-22 2020-03-10 2020-03-04 2646959 200786
## 3 US 2020-09-21 2020-03-10 2020-03-04 2615949 199865
## 4 US 2020-09-20 2020-03-10 2020-03-04 2590671 199509
## 5 US 2020-09-19 2020-03-10 2020-03-04 2577446 199282
## 6 US 2020-09-18 2020-03-10 2020-03-04 2556465 198570
## 7 US 2020-09-17 2020-03-10 2020-03-04 2540334 197633
## 8 US 2020-09-16 2020-03-10 2020-03-04 2525573 196763
## 9 US 2020-09-15 2020-03-10 2020-03-04 2495127 195781
## 10 US 2020-09-14 2020-03-10 2020-03-04 2474570 194493
## # … with 188 more rows, and 2 more variables: confirmed_deaths_rate <dbl>,
## # recovered_deaths_rate <dbl>
## # A tibble: 191 x 5
## country n_days_since_100_confirmed population confirmed_total confirmed_total_per_1M
## <chr> <dbl> <int> <dbl> <dbl>
## 1 Russia 190 145934462 1117487 7657.
## 2 Russia 189 145934462 1111157 7614.
## 3 Russia 188 145934462 1105048 7572.
## 4 Russia 187 145934462 1098958 7530.
## 5 Russia 186 145934462 1092915 7489.
## 6 Russia 185 145934462 1086955 7448.
## 7 Russia 184 145934462 1081152 7408.
## 8 Russia 183 145934462 1075485 7370.
## 9 Russia 182 145934462 1069873 7331.
## 10 Russia 181 145934462 1064438 7294.
## # … with 181 more rows
Calculate countries stats whose populations were most affected by the virus:
## # A tibble: 133 x 6
## country population confirmed_total confirmed_total_pe… n_days_since_100_con… n_days_since_10th_d…
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Qatar 2881053 124175 43101. 196 145
## 2 Bahrain 1701575 67014 39384. 197 131
## 3 Panama 4314767 107990 25028. 188 179
## 4 Kuwait 4270571 101299 23720. 193 155
## 5 Israel 8655535 204690 23648. 196 180
## 6 Peru 32971854 776546 23552. 190 180
## 7 Chile 19116201 449903 23535. 191 176
## 8 Brazil 212559417 4591364 21600. 194 187
## 9 US 331002651 6933548 20947. 197 203
## 10 Oman 5106626 95339 18670. 181 146
## # … with 123 more rows
## # A tibble: 133 x 6
## country population active_total active_total_per_… n_days_since_100_con… n_days_since_10th_d…
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 US 331002651 4061408 12270. 197 203
## 2 Spain 46754778 512146 10954. 205 199
## 3 Sweden 10099265 83880 8306. 201 188
## 4 Costa Rica 5094118 41142 8076. 186 107
## 5 Israel 8655535 58402 6747. 196 180
## 6 Belgium 11589623 77849 6717. 201 189
## 7 France 65273511 380511 5829. 206 200
## 8 Netherlands 17134872 95817 5592. 201 193
## 9 United Kin… 67886011 368047 5422. 202 193
## 10 Panama 4314767 21262 4928. 188 179
## # … with 123 more rows
## # A tibble: 133 x 6
## country population deaths_total deaths_total_per_… n_days_since_100_con… n_days_since_10th_d…
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Peru 32971854 31568 957. 190 180
## 2 Belgium 11589623 9959 859. 201 189
## 3 Spain 46754778 31034 664. 205 199
## 4 Bolivia 11673021 7731 662. 176 170
## 5 Brazil 212559417 138105 650. 194 187
## 6 Chile 19116201 12345 646. 191 176
## 7 Ecuador 17643054 11171 633. 189 185
## 8 United Kin… 67886011 41951 618. 202 193
## 9 US 331002651 201884 610. 197 203
## 10 Italy 60461826 35758 591. 213 210
## # … with 123 more rows
Select countries to monitoring:
## [1] "Belgium" "Costa Rica" "France" "Israel" "Netherlands"
## [6] "Panama" "Spain" "Sweden" "United Kingdom" "US"
## [11] "Russia" "Mainland China"
Take Care and Stay Healthy!