EDA - Covid-19 Indonesia Analysis
Arya
June 13, 2020
Explanation
The COVID-19 pandemic, also known as the coronavirus pandemic, is an ongoing pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in Wuhan , China, in December 2019. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January, and a pandemic on 11 March.
Covid 19 Infection in Indonesia
# Convert Case Rate to numeric
covid.id$Case.Fatality.Rate <- as.numeric(gsub('%', '', covid.id$Case.Fatality.Rate)) * 0.01
covid.id$Case.Recovered.Rate <- as.numeric(gsub('%', '', covid.id$Case.Recovered.Rate)) * 0.01## 'data.frame': 2837 obs. of 36 variables:
## $ Date : Date, format: "2020-03-01" "2020-03-01" ...
## $ Location.ISO.Code : Factor w/ 35 levels "ID-AC","ID-BA",..: 10 8 10 8 35 10 8 35 10 8 ...
## $ Location : Factor w/ 35 levels "Aceh","Bali",..: 6 10 6 10 8 6 10 8 6 10 ...
## $ New.Cases : int 3 3 2 0 2 2 0 0 2 1 ...
## $ New.Deaths : int 0 0 0 0 0 0 1 0 0 0 ...
## $ New.Recovered : int 0 0 0 0 0 0 0 0 0 0 ...
## $ New.Active.Cases : int 3 3 2 0 2 2 -1 0 2 1 ...
## $ Total.Cases : int 3 3 5 3 2 7 3 2 9 4 ...
## $ Total.Deaths : int 0 0 0 0 0 0 1 0 0 1 ...
## $ Total.Recovered : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Total.Active.Cases : int 3 3 5 3 2 7 2 2 9 3 ...
## $ Location.Level : Factor w/ 2 levels "Country","Province": 2 2 2 2 1 2 2 1 2 2 ...
## $ Province : Factor w/ 35 levels "","Aceh","Bali",..: 7 10 7 10 1 7 10 1 7 10 ...
## $ Country : Factor w/ 1 level "Indonesia": 1 1 1 1 1 1 1 1 1 1 ...
## $ Continent : Factor w/ 1 level "Asia": 1 1 1 1 1 1 1 1 1 1 ...
## $ Island : Factor w/ 8 levels "","Jawa","Kalimantan",..: 2 2 2 2 1 2 2 1 2 2 ...
## $ Time.Zone : Factor w/ 4 levels "","UTC+07:00",..: 2 2 2 2 1 2 2 1 2 2 ...
## $ Special.Status : Factor w/ 4 levels "","Daerah Istimewa",..: 4 1 4 1 1 4 1 1 4 1 ...
## $ Total.Regencies : int 1 18 1 18 416 1 18 416 1 18 ...
## $ Total.Cities : int 5 9 5 9 98 5 9 98 5 9 ...
## $ Total.Districts : int 44 627 44 627 7230 44 627 7230 44 627 ...
## $ Total.Urban.Villages : int 267 645 267 645 8488 267 645 8488 267 645 ...
## $ Total.Rural.Villages : int NA 5312 NA 5312 74953 NA 5312 74953 NA 5312 ...
## $ Area..km2. : int 664 35378 664 35378 1916907 664 35378 1916907 664 35378 ...
## $ Population : int 10846145 45161325 10846145 45161325 265185520 10846145 45161325 265185520 10846145 45161325 ...
## $ Population.Density : num 16334 1277 16334 1277 138 ...
## $ Longitude : num 107 108 107 108 114 ...
## $ Latitude : num -6.205 -6.92 -6.205 -6.92 -0.789 ...
## $ New.Cases.per.Million : num 0.28 0.07 0.18 0 0.01 0.18 0 0 0.18 0.02 ...
## $ Total.Cases.per.Million : num 0.28 0.07 0.46 0.07 0.01 0.65 0.07 0.01 0.83 0.09 ...
## $ New.Deaths.per.Million : num 0 0 0 0 0 0 0.02 0 0 0 ...
## $ Total.Deaths.per.Million : num 0 0 0 0 0 0 0.02 0 0 0.02 ...
## $ Case.Fatality.Rate : num 0 0 0 0 0 ...
## $ Case.Recovered.Rate : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Growth.Factor.of.New.Cases : num NA NA 0.67 0 NA 1 1 0 1 NA ...
## $ Growth.Factor.of.New.Deaths: num NA NA 1 1 NA 1 NA 1 1 0 ...## Date Location.ISO.Code Location New.Cases New.Deaths
## 2832 2020-06-12 ID-SG Sulawesi Tenggara 5 0
## 2833 2020-06-12 ID-JK DKI Jakarta 93 3
## 2834 2020-06-12 IDN Indonesia 1111 48
## 2835 2020-06-12 ID-MU Maluku Utara 22 2
## 2836 2020-06-12 ID-JT Jawa Tengah 44 0
## 2837 2020-06-12 ID-ST Sulawesi Tengah 0 0
## New.Recovered New.Active.Cases Total.Cases Total.Deaths Total.Recovered
## 2832 2 3 272 5 180
## 2833 120 -30 8244 520 3742
## 2834 577 486 36406 2048 13213
## 2835 1 19 261 22 40
## 2836 50 -6 1865 94 666
## 2837 0 0 159 4 95
## Total.Active.Cases Location.Level Province Country Continent
## 2832 87 Province Sulawesi Tenggara Indonesia Asia
## 2833 3982 Province DKI Jakarta Indonesia Asia
## 2834 21145 Country Indonesia Asia
## 2835 199 Province Maluku Utara Indonesia Asia
## 2836 1105 Province Jawa Tengah Indonesia Asia
## 2837 60 Province Sulawesi Tengah Indonesia Asia
## Island Time.Zone Special.Status Total.Regencies Total.Cities
## 2832 Sulawesi UTC+08:00 15 2
## 2833 Jawa UTC+07:00 Daerah Khusus Ibu Kota 1 5
## 2834 416 98
## 2835 Maluku UTC+09:00 8 2
## 2836 Jawa UTC+07:00 29 6
## 2837 Sulawesi UTC+08:00 12 1
## Total.Districts Total.Urban.Villages Total.Rural.Villages Area..km2.
## 2832 219 377 1911 38068
## 2833 44 267 NA 664
## 2834 7230 8488 74953 1916907
## 2835 116 118 1063 31983
## 2836 576 753 7809 32801
## 2837 175 175 1842 61841
## Population Population.Density Longitude Latitude New.Cases.per.Million
## 2832 2635461 69.23 122.0703 -4.1246888 1.90
## 2833 10846145 16334.31 106.8361 -6.2046990 8.57
## 2834 265185520 138.34 113.9213 -0.7892750 4.19
## 2835 1307803 40.89 127.5391 0.2120369 16.82
## 2836 36364072 1108.64 110.2011 -7.2590972 1.21
## 2837 2955567 47.79 121.2011 -1.0041367 0.00
## Total.Cases.per.Million New.Deaths.per.Million Total.Deaths.per.Million
## 2832 103.21 0.00 1.90
## 2833 760.09 0.28 47.94
## 2834 137.29 0.18 7.72
## 2835 199.57 1.53 16.82
## 2836 51.29 0.00 2.58
## 2837 53.80 0.00 1.35
## Case.Fatality.Rate Case.Recovered.Rate Growth.Factor.of.New.Cases
## 2832 0.0184 0.6618 0.83
## 2833 0.0631 0.4539 0.73
## 2834 0.0563 0.3629 1.13
## 2835 0.0843 0.1533 0.61
## 2836 0.0504 0.3571 2.32
## 2837 0.0252 0.5975 1.00
## Growth.Factor.of.New.Deaths
## 2832 1.00
## 2833 1.50
## 2834 1.17
## 2835 2.00
## 2836 1.00
## 2837 1.00A Glimpse about Covid 19 Infection in Indonesia
This dataset covers data of covid 19 from first of march to 12th of June with 2837 of total record.
## [1] 2837## Date Location.ISO.Code Location
## Min. :2020-03-01 ID-JB : 104 DKI Jakarta : 104
## 1st Qu.:2020-04-13 ID-JK : 104 Jawa Barat : 104
## Median :2020-05-03 IDN : 103 Indonesia : 103
## Mean :2020-05-02 ID-BT : 99 Banten : 99
## 3rd Qu.:2020-05-23 ID-KI : 91 Kalimantan Timur : 91
## Max. :2020-06-12 ID-YO : 89 Daerah Istimewa Yogyakarta: 89
## (Other):2247 (Other) :2247
## New.Cases New.Deaths New.Recovered New.Active.Cases
## Min. : 0.0 Min. : 0.000 Min. : 0.000 Min. :-200.00
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 2.0 Median : 0.000 Median : 0.000 Median : 1.00
## Mean : 25.4 Mean : 1.432 Mean : 9.281 Mean : 14.69
## 3rd Qu.: 13.0 3rd Qu.: 0.000 3rd Qu.: 4.000 3rd Qu.: 9.00
## Max. :1241.0 Max. :60.000 Max. :715.000 Max. : 732.00
##
## Total.Cases Total.Deaths Total.Recovered Total.Active.Cases
## Min. : 1.0 Min. : 0.0 Min. : 0.0 Min. : -40.0
## 1st Qu.: 18.0 1st Qu.: 1.0 1st Qu.: 2.0 1st Qu.: 11.0
## Median : 95.0 Median : 4.0 Median : 19.0 Median : 60.0
## Mean : 748.1 Mean : 50.9 Mean : 186.4 Mean : 510.8
## 3rd Qu.: 304.0 3rd Qu.: 16.0 3rd Qu.: 86.0 3rd Qu.: 193.0
## Max. :36406.0 Max. :2048.0 Max. :13213.0 Max. :21145.0
##
## Location.Level Province Country Continent
## Country : 103 DKI Jakarta : 104 Indonesia:2837 Asia:2837
## Province:2734 Jawa Barat : 104
## : 103
## Banten : 99
## Kalimantan Timur : 91
## Daerah Istimewa Yogyakarta: 89
## (Other) :2247
## Island Time.Zone Special.Status
## Sumatera :777 : 103 :2404
## Jawa :570 UTC+07:00:1502 Daerah Istimewa : 89
## Sulawesi :448 UTC+08:00: 906 Daerah Khusus : 240
## Kalimantan :398 UTC+09:00: 326 Daerah Khusus Ibu Kota: 104
## Nusa Tenggara:215
## Maluku :165
## (Other) :264
## Total.Regencies Total.Cities Total.Districts Total.Urban.Villages
## Min. : 1.00 Min. : 1.000 Min. : 44.0 Min. : 35.0
## 1st Qu.: 7.00 1st Qu.: 1.000 1st Qu.: 103.0 1st Qu.: 99.0
## Median : 11.00 Median : 2.000 Median : 169.0 Median : 175.0
## Mean : 26.95 Mean : 6.666 Mean : 473.4 Mean : 574.1
## 3rd Qu.: 18.00 3rd Qu.: 5.000 3rd Qu.: 289.0 3rd Qu.: 377.0
## Max. :416.00 Max. :98.000 Max. :7230.0 Max. :8488.0
## NA's :77 NA's :79
## Total.Rural.Villages Area..km2. Population Population.Density
## Min. : 275 Min. : 664 Min. : 648407 Min. : 8.59
## 1st Qu.: 928 1st Qu.: 16787 1st Qu.: 2570289 1st Qu.: 47.79
## Median : 1591 Median : 42013 Median : 4340348 Median : 104.88
## Mean : 5086 Mean : 123923 Mean : 17801401 Mean : 879.33
## 3rd Qu.: 3026 3rd Qu.: 75468 3rd Qu.: 9426885 3rd Qu.: 283.77
## Max. :74953 Max. :1916907 Max. :265185520 Max. :16334.31
## NA's :104
## Longitude Latitude New.Cases.per.Million
## Min. : 96.91 Min. :-8.68220 Min. : 0.000
## 1st Qu.:106.11 1st Qu.:-6.20470 1st Qu.: 0.000
## Median :112.73 Median :-2.46175 Median : 0.470
## Mean :113.51 Mean :-2.81024 Mean : 1.828
## 3rd Qu.:120.16 3rd Qu.:-0.08647 3rd Qu.: 1.900
## Max. :138.70 Max. : 4.22562 Max. :34.150
##
## Total.Cases.per.Million New.Deaths.per.Million Total.Deaths.per.Million
## Min. : 0.01 Min. :0.0000 Min. : 0.000
## 1st Qu.: 4.03 1st Qu.:0.0000 1st Qu.: 0.190
## Median : 21.02 Median :0.0000 Median : 0.990
## Mean : 50.74 Mean :0.0819 Mean : 2.726
## 3rd Qu.: 61.42 3rd Qu.:0.0000 3rd Qu.: 2.240
## Max. :760.09 Max. :6.7100 Max. :47.940
##
## Case.Fatality.Rate Case.Recovered.Rate Growth.Factor.of.New.Cases
## Min. :0.00000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.01100 1st Qu.:0.0833 1st Qu.: 0.330
## Median :0.05170 Median :0.1803 Median : 1.000
## Mean :0.08932 Mean :0.2429 Mean : 1.340
## 3rd Qu.:0.09520 3rd Qu.:0.3538 3rd Qu.: 1.062
## Max. :2.00000 Max. :1.5000 Max. :54.000
## NA's :441
## Growth.Factor.of.New.Deaths
## Min. :0.0000
## 1st Qu.:1.0000
## Median :1.0000
## Mean :0.9412
## 3rd Qu.:1.0000
## Max. :8.0000
## NA's :322Cases in Indonesia
covid.id.country = subset(covid.id, covid.id$Location == 'Indonesia')
plot(
covid.id.country[c('Date', 'Total.Cases')],
type='l',
col='#AAAAAA',
main="Number of cases",
)
lines(
covid.id.country[c('Date', 'Total.Active.Cases')],
type='l',
col='#177cff',
)
lines(
covid.id.country[c('Date', 'Total.Deaths')],
type='l',
col='#ff9587'
)
lines(
covid.id.country[c('Date', 'Total.Recovered')],
type='l',
col='#32a834'
)
> Red = Total Death
> Green = Total Recovered
> Blue = Total Active case
> Gray = Total Case
Province
# Filter data with province only
covid.id.province <- covid.id[covid.id$Location.Level == 'Province',]Province with most cases
covid.id.province.cases.table <- head(sort(xtabs(New.Cases~Location, covid.id.province), decreasing = T))
as.data.frame(covid.id.province.cases.table)## Location Freq
## 1 DKI Jakarta 8244
## 2 Jawa Timur 7372
## 3 Sulawesi Selatan 2573
## 4 Jawa Barat 2489
## 5 Jawa Tengah 1865
## 6 Kalimantan Selatan 1693
Province with most recovered
covid.id.province.recovered.table <- head(sort(xtabs(New.Recovered~Location, covid.id.province), decreasing = T))
as.data.frame(covid.id.province.recovered.table)## Location Freq
## 1 DKI Jakarta 3742
## 2 Jawa Timur 1853
## 3 Jawa Barat 1053
## 4 Sulawesi Selatan 829
## 5 Jawa Tengah 666
## 6 Sumatera Selatan 577
Province with high Fatality Rate
covid.id.province.fatality.rate.table <- aggregate(Case.Fatality.Rate~Location, covid.id.province, mean)
covid.id.province.fatality.rate.table[order(-covid.id.province.fatality.rate.table$Case.Fatality.Rate),]## Location Case.Fatality.Rate
## 34 Sumatera Utara 0.299305682
## 6 DKI Jakarta 0.236179808
## 18 Kepulauan Riau 0.216776000
## 5 Daerah Istimewa Yogyakarta 0.184276404
## 4 Bengkulu 0.177856757
## 9 Jawa Barat 0.176357692
## 3 Banten 0.161752525
## 31 Sulawesi Utara 0.149113699
## 19 Lampung 0.138407595
## 17 Kepulauan Bangka Belitung 0.125494667
## 10 Jawa Tengah 0.121798851
## 11 Jawa Timur 0.116532184
## 26 Riau 0.113028947
## 29 Sulawesi Tengah 0.092167089
## 13 Kalimantan Selatan 0.078722667
## 28 Sulawesi Selatan 0.072895238
## 32 Sumatera Barat 0.055858228
## 7 Gorontalo 0.050984375
## 14 Kalimantan Tengah 0.043950633
## 27 Sulawesi Barat 0.043119481
## 1 Aceh 0.042769620
## 12 Kalimantan Barat 0.037638158
## 30 Sulawesi Tenggara 0.031821127
## 24 Papua 0.027775904
## 21 Maluku Utara 0.024700000
## 20 Maluku 0.023045783
## 22 Nusa Tenggara Barat 0.022537838
## 33 Sumatera Selatan 0.020050649
## 15 Kalimantan Timur 0.014656044
## 16 Kalimantan Utara 0.011907792
## 23 Nusa Tenggara Timur 0.006720000
## 2 Bali 0.004988158
## 25 Papua Barat 0.001669231
## 8 Jambi 0.000000000Province with high Recovery Rate
covid.id.province.recovered.rate.table <- aggregate(Case.Recovered.Rate~Location, covid.id.province, mean)
covid.id.province.recovered.rate.table[order(-covid.id.province.recovered.rate.table$Case.Recovered.Rate),]## Location Case.Recovered.Rate
## 5 Daerah Istimewa Yogyakarta 0.55786854
## 1 Aceh 0.55127595
## 2 Bali 0.50413816
## 26 Riau 0.48160658
## 18 Kepulauan Riau 0.43164267
## 17 Kepulauan Bangka Belitung 0.30889067
## 19 Lampung 0.27837089
## 12 Kalimantan Barat 0.27609079
## 7 Gorontalo 0.27515000
## 30 Sulawesi Tenggara 0.26557183
## 22 Nusa Tenggara Barat 0.26272838
## 28 Sulawesi Selatan 0.24073810
## 32 Sumatera Barat 0.23874177
## 14 Kalimantan Tengah 0.23854304
## 29 Sulawesi Tengah 0.23010633
## 6 DKI Jakarta 0.22840288
## 15 Kalimantan Timur 0.22114945
## 27 Sulawesi Barat 0.21930000
## 20 Maluku 0.21784940
## 34 Sumatera Utara 0.20812500
## 11 Jawa Timur 0.20644368
## 9 Jawa Barat 0.20129519
## 16 Kalimantan Utara 0.19886883
## 31 Sulawesi Utara 0.18905205
## 23 Nusa Tenggara Timur 0.18695231
## 21 Maluku Utara 0.17406829
## 10 Jawa Tengah 0.17073678
## 3 Banten 0.15330909
## 33 Sumatera Selatan 0.15119481
## 24 Papua 0.13732651
## 4 Bengkulu 0.13165270
## 13 Kalimantan Selatan 0.09862800
## 25 Papua Barat 0.09472436
## 8 Jambi 0.08536667What about Jakarta
Jakarta is the capital city of Indonesia and currently (or 13 June 2020) Jakarta has the most active cases in Indonesia.
Jakarta confirmed its first case of covid-19 as early as first of march.
## [1] "2020-03-01"Number of cases
plot(
covid.id.jakarta[c('Date', 'Total.Cases')],
type='l',
col='#AAAAAA',
main="Number of cases",
)
lines(
covid.id.jakarta[c('Date', 'Total.Active.Cases')],
type='l',
col='#177cff',
)
lines(
covid.id.jakarta[c('Date', 'Total.Deaths')],
type='l',
col='#ff9587'
)
lines(
covid.id.jakarta[c('Date', 'Total.Recovered')],
type='l',
col='#32a834'
)
> Desription
> - Red = Total Death
> - Green = Total Recovered
> - Blue = Total Active case
> - Gray = Total Case
After a month of no sign of slowing, Jakarta Goverment has decided to implement PSBB or (Social Distrancing in masive scale) in April 10th.
Is PSBB in Jakarta effective?
Since PSBB has been conducted in April, the number of active cases has been slowing down and recovery cases has risen gradually at the same time. This proofs that PSBB is taking a positive effect.
Jawa Timur
Meanwhile Jawa Timur has a different story.
Number of cases
plot(
covid.id.jawatimur[c('Date', 'Total.Cases')],
type='l',
col='#AAAAAA',
main="Number of cases",
)
lines(
covid.id.jawatimur[c('Date', 'Total.Active.Cases')],
type='l',
col='#177cff',
)
lines(
covid.id.jawatimur[c('Date', 'Total.Deaths')],
type='l',
col='#ff9587'
)
lines(
covid.id.jawatimur[c('Date', 'Total.Recovered')],
type='l',
col='#32a834'
)
> Desription
> - Red = Total Death
> - Green = Total Recovered
> - Blue = Total Active case
> - Gray = Total Case
Number of cases in Jawa Timur has risen significantly since Mudik Season and making it the second place of top cases in indonesia.
Conclusion
The outbreak of covid-19 was first identified in Wuhan, China, in December 2019. And the first case of covid-19 in Indonesia was detected in first of march. With Jakarta leading the number of infected cases of covid-19 and Sumatera Utara with the highest fatality rate. The goverment has implement some policy to help reduce number of cases, and some of it’s PSBB or Social Distancing in Masive Scale. PSBB has been conducted since April in Jakarta and the number of active cases has been slowing down and recovery cases has risen gradually at the same time. Altough the good news, some other region in Indonesia has a different kind story especially since the Mudik season where everyone go back to his/her hometown. Where in Jawa Timur the cases of covid-19 has risen significantly, making it the second place of total cases of covid-19 in Indonesia.