COVID-19 JAWA BARAT
Arwan Zhagi
7/9/2021
COVID-19
COVID-19 merupakan suatu pandemi yang disebabkan oleh virus corona 2019. Informasi tentang pertumbuhan kasus COVID-19 selalu setiap hari diinformasikan baik pada media sosial dan media komunikasi lainnya. Pemerintah baik pada pusat dan daerah masing-masing selalu mengumpulkan, menyediakan, dan mempublikasikan pertumbuhan COVID-19 di Indonesia maupun di daerah. Data pertumbuhan COVID-19 tersebut berupa data mentah yang belum diolah, infografis, maupun suatu dasboard.
Dalam hal ini, saya akan melakukan eksplorasi dan analisis mengenai COVID-19 Jawa Barat menggunakan data pada API berikut https://covid19-public.digitalservice.id/api/v1/sebaran_v2/jabar
COVID-19 JAWA BARAT
DATA PREPARATION DAN LOAD PACKAGE
library(ggplot2)
library(hrbrthemes)## Warning: package 'hrbrthemes' was built under R version 4.0.4## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrowlibrary(scales)
library(httr)
library(sf)## Warning: package 'sf' was built under R version 4.0.4## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v tibble 3.0.4 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x readr::col_factor() masks scales::col_factor()
## x purrr::discard() masks scales::discard()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union# get data
resp_cov_jabar<-
GET("https://covid19-public.digitalservice.id/api/v1/sebaran_v2/jabar")
# status
status_code(resp_cov_jabar)## [1] 200names(resp_cov_jabar)## [1] "url" "status_code" "headers" "all_headers" "cookies"
## [6] "content" "date" "times" "request" "handle"# raw data
raw_cov_jabar<-
content(resp_cov_jabar, as="parsed", simplifyVector=TRUE)
names(raw_cov_jabar)## [1] "status_code" "data"covid_jabar_list<-
raw_cov_jabar$data
length(covid_jabar_list)## [1] 2names(covid_jabar_list)## [1] "metadata" "content"covid_jabar_list$metadata## $last_update
## [1] "2021-07-12, 04:28:16"tanggal_terakhir_update<-
covid_jabar_list$metadata[[1]]%>%
as.Date()
tanggal_terakhir_update## [1] "2021-07-12"# dataset covid jabar
covid_jabar_data<-
covid_jabar_list$content%>%
mutate(status = as_factor(status),
stage = as_factor(stage),
tanggal_konfirmasi = as.Date(tanggal_konfirmasi),
tanggal_update = as.Date(tanggal_update),
tanggal_update_nasional = as.Date(tanggal_update_nasional))
names(covid_jabar_data)## [1] "id" "kode_kab"
## [3] "nama_kab" "kode_kec"
## [5] "nama_kec" "kode_kel"
## [7] "nama_kel" "status"
## [9] "stage" "umur"
## [11] "gender" "longitude"
## [13] "latitude" "tanggal_konfirmasi"
## [15] "tanggal_update" "current_location_type"
## [17] "current_location_district_code" "current_location_subdistrict_code"
## [19] "current_location_village_code" "current_location_address"
## [21] "report_source" "tanggal_update_nasional"str(covid_jabar_data)## 'data.frame': 822518 obs. of 22 variables:
## $ id : chr "from_manual_219498" "from_manual_219500" "from_manual_219586" "from_manual_223297" ...
## $ kode_kab : chr "3271" "3271" "3271" "3271" ...
## $ nama_kab : chr "Kota Bogor" "Kota Bogor" "Kota Bogor" "Kota Bogor" ...
## $ kode_kec : chr "3271020" "3271020" "3271020" "3271020" ...
## $ nama_kec : chr "Kota Bogor Timur" "Kota Bogor Timur" "Kota Bogor Timur" "Kota Bogor Timur" ...
## $ kode_kel : chr "3271020005" "3271020005" "3271020005" "3271020005" ...
## $ nama_kel : chr "Baranangsiang" "Baranangsiang" "Baranangsiang" "Baranangsiang" ...
## $ status : Factor w/ 5 levels "CONFIRMATION",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ stage : Factor w/ 6 levels "Selesai","Diisolasi",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ umur : num 38 32 66 64 53 21 30 43 42 33 ...
## $ gender : chr "Laki-laki" "Laki-laki" "Perempuan" "Laki-laki" ...
## $ longitude : num 107 107 107 107 107 ...
## $ latitude : num -6.59 -6.59 -6.59 -6.59 -6.59 ...
## $ tanggal_konfirmasi : Date, format: "2021-01-08" "2021-01-05" ...
## $ tanggal_update : Date, format: "2021-03-26" "2021-03-26" ...
## $ current_location_type : chr "BMC Mayapada Hospital" "Rumah Sakit Citra Arafiq" "Dinkes Prov. Jabar" "Dinkes Prov. Jabar" ...
## $ current_location_district_code : chr NA NA NA NA ...
## $ current_location_subdistrict_code: chr NA NA NA NA ...
## $ current_location_village_code : chr NA NA NA NA ...
## $ current_location_address : chr NA NA NA NA ...
## $ report_source : chr NA NA NA NA ...
## $ tanggal_update_nasional : Date, format: "2021-03-05" "2021-03-05" ...# PETA Kabupaten
peta_jabar<-
st_read("Jabar_By_Kab.geojson") %>%
select(-contains("NO"))## Reading layer `Jabar_By_Kab' from data source `C:\Users\aukk\Documents\project\jds\Jabar_By_Kab.geojson' using driver `GeoJSON'
## Simple feature collection with 27 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84str(peta_jabar)## Classes 'sf' and 'data.frame': 27 obs. of 7 variables:
## $ OBJECTID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ PROVINSI : chr "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" ...
## $ KABKOT : chr "BOGOR" "SUKABUMI" "CIANJUR" "BANDUNG" ...
## $ ID_KAB : num 3201 3202 3203 3204 3205 ...
## $ Shape_Leng: num 4.45 4.05 4.89 3.09 3.48 ...
## $ Shape_Area: num 0.245 0.34 0.294 0.144 0.253 ...
## $ geometry :sfc_MULTIPOLYGON of length 27; first list element: List of 1
## ..$ :List of 3
## .. ..$ : num [1:10876, 1:2] 107 107 107 107 107 ...
## .. ..$ : num [1:4, 1:2] 107.13 107.13 107.13 107.13 -6.47 ...
## .. ..$ : num [1:3501, 1:2] 107 107 107 107 107 ...
## ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA
## ..- attr(*, "names")= chr [1:6] "OBJECTID" "PROVINSI" "KABKOT" "ID_KAB" ...# Peta kecamatan
peta_kec<-
st_read("Jabar_By_Kec.geojson") %>%
select(-contains("NO"))## Reading layer `Jabar_By_Kec' from data source `C:\Users\aukk\Documents\project\jds\Jabar_By_Kec.geojson' using driver `GeoJSON'
## Simple feature collection with 627 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84str(peta_kec)## Classes 'sf' and 'data.frame': 627 obs. of 9 variables:
## $ OBJECTID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ PROVINSI : chr "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" ...
## $ KECAMATAN : chr "AGRABINTA" "ANDIR" "ANJATAN" "ANTAPANI" ...
## $ KABKOT : chr "CIANJUR" "KOTA BANDUNG" "INDRAMAYU" "KOTA BANDUNG" ...
## $ ID_KAB : num 3203 3273 3212 3273 3212 ...
## $ ID_KEC : num 3203010 3273180 3212210 3273141 3212171 ...
## $ Shape_Leng: num 0.873 0.128 0.532 0.104 0.286 ...
## $ Shape_Area: num 0.016108 0.000341 0.006864 0.000341 0.002766 ...
## $ geometry :sfc_MULTIPOLYGON of length 627; first list element: List of 1
## ..$ :List of 7
## .. ..$ : num [1:1504, 1:2] 107 107 107 107 107 ...
## .. ..$ : num [1:7, 1:2] 107 107 107 107 107 ...
## .. ..$ : num [1:34, 1:2] 107 107 107 107 107 ...
## .. ..$ : num [1:4, 1:2] 106.8 106.8 106.8 106.8 -7.4 ...
## .. ..$ : num [1:4, 1:2] 106.81 106.81 106.81 106.81 -7.41 ...
## .. ..$ : num [1:6, 1:2] 107 107 107 107 107 ...
## .. ..$ : num [1:4, 1:2] 106.81 106.81 106.81 106.81 -7.43 ...
## ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA
## ..- attr(*, "names")= chr [1:8] "OBJECTID" "PROVINSI" "KECAMATAN" "KABKOT" ...# Peta Kelurahan
peta_kel<-
st_read("Jabar_By_Desa.geojson") %>%
select(-contains("NO"))## Reading layer `Jabar_By_Desa' from data source `C:\Users\aukk\Documents\project\jds\Jabar_By_Desa.geojson' using driver `GeoJSON'
## Simple feature collection with 5995 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84str(peta_kel)## Classes 'sf' and 'data.frame': 5995 obs. of 11 variables:
## $ OBJECTID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ PROVINSI : chr "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" ...
## $ KECAMATAN : chr "NANGGUNG" "NANGGUNG" "NANGGUNG" "NANGGUNG" ...
## $ DESA : chr "MALASARI" "BANTAR KARET" "CISARUA" "CURUG BITUNG" ...
## $ KABKOT : chr "BOGOR" "BOGOR" "BOGOR" "BOGOR" ...
## $ ID2012 : chr "3201010001" "3201010002" "3201010003" "3201010004" ...
## $ ID_KAB : num 3201 3201 3201 3201 3201 ...
## $ ID_KEC : num 3201010 3201010 3201010 3201010 3201010 ...
## $ Shape_Leng: num 0.374 0.387 0.197 0.2 0.113 ...
## $ Shape_Area: num 0.004994 0.003062 0.00152 0.000917 0.000552 ...
## $ geometry :sfc_MULTIPOLYGON of length 5995; first list element: List of 1
## ..$ :List of 1
## .. ..$ : num [1:873, 1:2] 107 107 107 107 107 ...
## ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA
## ..- attr(*, "names")= chr [1:10] "OBJECTID" "PROVINSI" "KECAMATAN" "DESA" ...DATA DENGAN STATUS TERKOFIRMASI
# konfirmasi by kab total
konfirmasi_covid_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="CONFIRMATION")
names(konfirmasi_covid_jabar_data)## [1] "id" "kode_kab"
## [3] "nama_kab" "kode_kec"
## [5] "nama_kec" "kode_kel"
## [7] "nama_kel" "status"
## [9] "stage" "umur"
## [11] "gender" "longitude"
## [13] "latitude" "tanggal_konfirmasi"
## [15] "tanggal_update" "tanggal_update_nasional"konfirmasi_covid_jabar_data_summarise<-
konfirmasi_covid_jabar_data%>%
group_by(nama_kab)%>%
summarise(Total=n())## `summarise()` ungrouping output (override with `.groups` argument)konfirmasi_covid_jabar_data_summarise## # A tibble: 28 x 2
## nama_kab Total
## <chr> <int>
## 1 Jawa Barat 2165
## 2 Kabupaten Bandung 23895
## 3 Kabupaten Bandung Barat 11919
## 4 Kabupaten Bekasi 34271
## 5 Kabupaten Bogor 23588
## 6 Kabupaten Ciamis 7459
## 7 Kabupaten Cianjur 4910
## 8 Kabupaten Cirebon 15538
## 9 Kabupaten Garut 18027
## 10 Kabupaten Indramayu 10732
## # ... with 18 more rows# stage by kab total
konfirmasi_covid_jabar_data_by_stage_summarise<-
konfirmasi_covid_jabar_data%>%
group_by(nama_kab, stage)%>%
summarise(Total=n())## `summarise()` regrouping output by 'nama_kab' (override with `.groups` argument)stage_konfirmasi_total<-
konfirmasi_covid_jabar_data_by_stage_summarise %>%
pivot_wider(names_from = stage, values_from =c(Total))
stage_konfirmasi_total## # A tibble: 28 x 4
## # Groups: nama_kab [28]
## nama_kab Selesai Diisolasi Meninggal
## <chr> <int> <int> <int>
## 1 Jawa Barat 2155 1 9
## 2 Kabupaten Bandung 19230 4371 294
## 3 Kabupaten Bandung Barat 10452 1309 158
## 4 Kabupaten Bekasi 30240 3963 68
## 5 Kabupaten Bogor 20148 3353 87
## 6 Kabupaten Ciamis 5960 1288 211
## 7 Kabupaten Cianjur 4463 433 14
## 8 Kabupaten Cirebon 12138 3178 222
## 9 Kabupaten Garut 9942 7619 466
## 10 Kabupaten Indramayu 9421 988 323
## # ... with 18 more rows# Konfirmasi by kab hari terupdate
tanggal_terupdate<- as_date(max(konfirmasi_covid_jabar_data$tanggal_update_nasional))
tanggal_terupdate## [1] "2021-07-10"konfirmasi_covid_jabar_data_hari<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="CONFIRMATION", tanggal_update_nasional==tanggal_terupdate)
konfirmasi_covid_jabar_data_hari_summarise<-
konfirmasi_covid_jabar_data_hari%>%
group_by(nama_kab)%>%
summarise(Hari_Terakhir=n())## `summarise()` ungrouping output (override with `.groups` argument)konfirmasi_covid_jabar_data_hari_summarise## # A tibble: 21 x 2
## nama_kab Hari_Terakhir
## <chr> <int>
## 1 Kabupaten Bandung 418
## 2 Kabupaten Bandung Barat 16
## 3 Kabupaten Bekasi 25
## 4 Kabupaten Bogor 476
## 5 Kabupaten Ciamis 348
## 6 Kabupaten Cirebon 503
## 7 Kabupaten Indramayu 100
## 8 Kabupaten Karawang 933
## 9 Kabupaten Kuningan 96
## 10 Kabupaten Majalengka 324
## # ... with 11 more rows# stage by kab hari update
konfirmasi_covid_jabar_data_by_stage_hari_summarise<-
konfirmasi_covid_jabar_data_hari%>%
group_by(nama_kab, stage)%>%
summarise(Hari_Terakhir=n())## `summarise()` regrouping output by 'nama_kab' (override with `.groups` argument)stage_konfirmasi_hari<-
konfirmasi_covid_jabar_data_by_stage_hari_summarise %>%
pivot_wider(names_from = stage, values_from = Hari_Terakhir)
stage_konfirmasi_hari[is.na(stage_konfirmasi_hari)]=0
stage_konfirmasi_hari## # A tibble: 21 x 3
## # Groups: nama_kab [21]
## nama_kab Diisolasi Selesai
## <chr> <int> <int>
## 1 Kabupaten Bandung 418 0
## 2 Kabupaten Bandung Barat 16 0
## 3 Kabupaten Bekasi 25 0
## 4 Kabupaten Bogor 476 0
## 5 Kabupaten Ciamis 348 0
## 6 Kabupaten Cirebon 503 0
## 7 Kabupaten Indramayu 100 0
## 8 Kabupaten Karawang 933 0
## 9 Kabupaten Kuningan 96 0
## 10 Kabupaten Majalengka 324 0
## # ... with 11 more rows# Konfirmasi by kab pekan terakhir
tanggal_terupdate1 <-
tanggal_terupdate - days(7)
konfirmasi_covid_jabar_data_pekan<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
mutate(year= year(tanggal_update_nasional))%>%
filter(status=="CONFIRMATION", year==2021, tanggal_update_nasional > tanggal_terupdate1)
konfirmasi_covid_jabar_data_pekan_summarise<-
konfirmasi_covid_jabar_data_pekan%>%
group_by(nama_kab)%>%
summarise(Satu_Minggu_Trkahir=n())## `summarise()` ungrouping output (override with `.groups` argument)konfirmasi_covid_jabar_data_pekan_summarise## # A tibble: 27 x 2
## nama_kab Satu_Minggu_Trkahir
## <chr> <int>
## 1 Kabupaten Bandung 3423
## 2 Kabupaten Bandung Barat 914
## 3 Kabupaten Bekasi 3335
## 4 Kabupaten Bogor 2199
## 5 Kabupaten Ciamis 1193
## 6 Kabupaten Cianjur 202
## 7 Kabupaten Cirebon 2617
## 8 Kabupaten Garut 2561
## 9 Kabupaten Indramayu 574
## 10 Kabupaten Karawang 4451
## # ... with 17 more rows# stage by kab pekan update
konfirmasi_covid_jabar_data_by_stage_pekan_summarise<-
konfirmasi_covid_jabar_data_pekan%>%
group_by(nama_kab, stage)%>%
summarise(Satu_Minggu_Terakhir=n())## `summarise()` regrouping output by 'nama_kab' (override with `.groups` argument)stage_konfirmasi_pekan<-
konfirmasi_covid_jabar_data_by_stage_pekan_summarise %>%
pivot_wider(names_from = stage, values_from = Satu_Minggu_Terakhir)
stage_konfirmasi_pekan[is.na(stage_konfirmasi_pekan)]=0
stage_konfirmasi_pekan## # A tibble: 27 x 4
## # Groups: nama_kab [27]
## nama_kab Diisolasi Meninggal Selesai
## <chr> <int> <int> <int>
## 1 Kabupaten Bandung 3421 2 0
## 2 Kabupaten Bandung Barat 911 3 0
## 3 Kabupaten Bekasi 3335 0 0
## 4 Kabupaten Bogor 2199 0 0
## 5 Kabupaten Ciamis 1193 0 0
## 6 Kabupaten Cianjur 202 0 0
## 7 Kabupaten Cirebon 2617 0 0
## 8 Kabupaten Garut 2561 0 0
## 9 Kabupaten Indramayu 560 14 0
## 10 Kabupaten Karawang 4420 31 0
## # ... with 17 more rowsDATA DENGAN STATUS SUSPECT
# suspect by kab total
suspect_covid_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="SUSPECT")
names(suspect_covid_jabar_data)## [1] "id" "kode_kab"
## [3] "nama_kab" "kode_kec"
## [5] "nama_kec" "kode_kel"
## [7] "nama_kel" "status"
## [9] "stage" "umur"
## [11] "gender" "longitude"
## [13] "latitude" "tanggal_konfirmasi"
## [15] "tanggal_update" "tanggal_update_nasional"suspect_covid_jabar_data_summarise<-
suspect_covid_jabar_data%>%
group_by(kode_kab)%>%
summarise(Total=n())## `summarise()` ungrouping output (override with `.groups` argument)suspect_covid_jabar_data_summarise## # A tibble: 27 x 2
## kode_kab Total
## <chr> <int>
## 1 3201 2051
## 2 3202 1861
## 3 3203 3024
## 4 3204 4866
## 5 3205 6615
## 6 3206 1579
## 7 3207 135
## 8 3208 2100
## 9 3209 158
## 10 3210 88
## # ... with 17 more rows# stage by kab total
suspect_covid_jabar_data_by_stage_summarise<-
suspect_covid_jabar_data%>%
group_by(kode_kab, stage)%>%
summarise(Total=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_suspect_total<-
suspect_covid_jabar_data_by_stage_summarise %>%
pivot_wider(names_from = stage, values_from =c(Total))
stage_suspect_total[is.na(stage_suspect_total)]=0
stage_suspect_total## # A tibble: 27 x 7
## # Groups: kode_kab [27]
## kode_kab Selesai Diisolasi Meninggal Discarded Dikarantina `NA`
## <chr> <int> <int> <int> <int> <int> <int>
## 1 3201 5 281 2 1684 79 0
## 2 3202 0 536 1 1324 0 0
## 3 3203 0 198 45 2779 2 0
## 4 3204 0 808 6 4039 13 0
## 5 3205 0 777 3 5835 0 0
## 6 3206 0 1 0 1578 0 0
## 7 3207 0 35 1 99 0 0
## 8 3208 0 253 6 1841 0 0
## 9 3209 0 60 7 90 1 0
## 10 3210 0 28 0 60 0 0
## # ... with 17 more rows# Suspect by kab hari terupdate
tanggal_terupdate<- as_date(max(suspect_covid_jabar_data$tanggal_update_nasional))
tanggal_terupdate## [1] "2021-07-11"suspect_covid_jabar_data_hari<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="SUSPECT", tanggal_update_nasional==tanggal_terupdate)
suspect_covid_jabar_data_hari_summarise<-
suspect_covid_jabar_data_hari%>%
group_by(kode_kab)%>%
summarise(Hari_Terakhir=n())## `summarise()` ungrouping output (override with `.groups` argument)suspect_covid_jabar_data_hari_summarise## # A tibble: 5 x 2
## kode_kab Hari_Terakhir
## <chr> <int>
## 1 3204 7
## 2 3214 1
## 3 3217 9
## 4 3277 13
## 5 3279 41# stage by kab hari update
suspect_covid_jabar_data_by_stage_hari_summarise<-
suspect_covid_jabar_data_hari%>%
group_by(kode_kab, stage)%>%
summarise(Hari_Terakhir=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_suspect_hari<-
suspect_covid_jabar_data_by_stage_hari_summarise %>%
pivot_wider(names_from = stage, values_from = Hari_Terakhir)
stage_suspect_hari[is.na(stage_suspect_hari)]=0
stage_suspect_hari## # A tibble: 5 x 3
## # Groups: kode_kab [5]
## kode_kab Diisolasi Discarded
## <chr> <int> <int>
## 1 3204 1 6
## 2 3214 0 1
## 3 3217 9 0
## 4 3277 12 1
## 5 3279 39 2# Suspect by kab pekan terakhir
tanggal_terupdate1 <-
tanggal_terupdate - days(7)
suspect_covid_jabar_data_pekan<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
mutate(year= year(tanggal_update_nasional))%>%
filter(status=="SUSPECT", year==2021, tanggal_update_nasional > tanggal_terupdate1)
suspect_covid_jabar_data_pekan_summarise<-
suspect_covid_jabar_data_pekan%>%
group_by(kode_kab)%>%
summarise(Satu_Minggu_Trkahir=n())## `summarise()` ungrouping output (override with `.groups` argument)suspect_covid_jabar_data_pekan_summarise## # A tibble: 15 x 2
## kode_kab Satu_Minggu_Trkahir
## <chr> <int>
## 1 3201 1
## 2 3202 4
## 3 3203 85
## 4 3204 143
## 5 3205 2
## 6 3208 39
## 7 3211 6
## 8 3212 5
## 9 3214 53
## 10 3217 155
## 11 3271 1372
## 12 3273 95
## 13 3275 184
## 14 3277 618
## 15 3279 321# stage by kab pekan update
suspect_covid_jabar_data_by_stage_pekan_summarise<-
suspect_covid_jabar_data_pekan%>%
group_by(kode_kab, stage)%>%
summarise(Satu_Minggu_Terakhir=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_suspect_pekan<-
suspect_covid_jabar_data_by_stage_pekan_summarise %>%
pivot_wider(names_from = stage, values_from = Satu_Minggu_Terakhir)
stage_suspect_pekan[is.na(stage_suspect_pekan)]=0
stage_suspect_pekan## # A tibble: 15 x 5
## # Groups: kode_kab [15]
## kode_kab Discarded Diisolasi Meninggal Dikarantina
## <chr> <int> <int> <int> <int>
## 1 3201 1 0 0 0
## 2 3202 0 4 0 0
## 3 3203 62 23 0 0
## 4 3204 78 65 0 0
## 5 3205 2 0 0 0
## 6 3208 18 21 0 0
## 7 3211 2 4 0 0
## 8 3212 5 0 0 0
## 9 3214 34 19 0 0
## 10 3217 91 64 0 0
## 11 3271 1346 2 24 0
## 12 3273 49 46 0 0
## 13 3275 184 0 0 0
## 14 3277 247 364 0 7
## 15 3279 72 249 0 0DATA DENGAN STATUS CLOSECONTACT
# closekontak by kab total
closekontak_covid_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="CLOSECONTACT")
names(closekontak_covid_jabar_data)## [1] "id" "kode_kab"
## [3] "nama_kab" "kode_kec"
## [5] "nama_kec" "kode_kel"
## [7] "nama_kel" "status"
## [9] "stage" "umur"
## [11] "gender" "longitude"
## [13] "latitude" "tanggal_konfirmasi"
## [15] "tanggal_update" "tanggal_update_nasional"closekontak_covid_jabar_data_summarise<-
closekontak_covid_jabar_data%>%
group_by(kode_kab)%>%
summarise(Total=n())## `summarise()` ungrouping output (override with `.groups` argument)closekontak_covid_jabar_data_summarise## # A tibble: 27 x 2
## kode_kab Total
## <chr> <int>
## 1 3201 12088
## 2 3202 5512
## 3 3203 7047
## 4 3204 11477
## 5 3205 18111
## 6 3206 173
## 7 3207 2801
## 8 3208 7428
## 9 3209 2384
## 10 3210 301
## # ... with 17 more rows# stage by kab total
closekontak_covid_jabar_data_by_stage_summarise<-
closekontak_covid_jabar_data%>%
group_by(kode_kab, stage)%>%
summarise(Total=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_closekontak_total<-
closekontak_covid_jabar_data_by_stage_summarise %>%
pivot_wider(names_from = stage, values_from =c(Total))
stage_closekontak_total[is.na(stage_closekontak_total)]=0
stage_closekontak_total## # A tibble: 27 x 6
## # Groups: kode_kab [27]
## kode_kab Selesai Diisolasi Meninggal Discarded Dikarantina
## <chr> <int> <int> <int> <int> <int>
## 1 3201 45 6 2 10391 1644
## 2 3202 0 0 0 3521 1991
## 3 3203 0 0 0 5788 1259
## 4 3204 0 0 0 6949 4528
## 5 3205 0 0 0 17374 737
## 6 3206 0 0 0 168 5
## 7 3207 0 0 0 2778 23
## 8 3208 0 0 0 7340 88
## 9 3209 0 0 0 577 1807
## 10 3210 0 0 0 86 215
## # ... with 17 more rows# Closekontak by kab hari terupdate
tanggal_terupdate<- as_date(max(closekontak_covid_jabar_data$tanggal_update_nasional))
tanggal_terupdate## [1] "2021-07-11"closekontak_covid_jabar_data_hari<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="CLOSECONTACT", tanggal_update_nasional==tanggal_terupdate)
closekontak_covid_jabar_data_hari_summarise<-
closekontak_covid_jabar_data_hari%>%
group_by(kode_kab)%>%
summarise(Hari_Terakhir=n())## `summarise()` ungrouping output (override with `.groups` argument)closekontak_covid_jabar_data_hari_summarise## # A tibble: 8 x 2
## kode_kab Hari_Terakhir
## <chr> <int>
## 1 3203 22
## 2 3204 42
## 3 3211 14
## 4 3214 6
## 5 3217 69
## 6 3275 200
## 7 3277 23
## 8 3279 49# stage by kab hari update
closekontak_covid_jabar_data_by_stage_hari_summarise<-
closekontak_covid_jabar_data_hari%>%
group_by(kode_kab, stage)%>%
summarise(Hari_Terakhir=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_closekontak_hari<-
closekontak_covid_jabar_data_by_stage_hari_summarise %>%
pivot_wider(names_from = stage, values_from = Hari_Terakhir)
stage_closekontak_hari[is.na(stage_closekontak_hari)]=0
stage_closekontak_hari## # A tibble: 8 x 3
## # Groups: kode_kab [8]
## kode_kab Dikarantina Discarded
## <chr> <int> <int>
## 1 3203 22 0
## 2 3204 36 6
## 3 3211 14 0
## 4 3214 0 6
## 5 3217 55 14
## 6 3275 0 200
## 7 3277 21 2
## 8 3279 49 0# Closekontak by kab pekan terakhir
tanggal_terupdate1 <-
tanggal_terupdate - days(7)
closekontak_covid_jabar_data_pekan<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
mutate(year= year(tanggal_update_nasional))%>%
filter(status=="CLOSECONTACT", year==2021, tanggal_update_nasional > tanggal_terupdate1)
closekontak_covid_jabar_data_pekan_summarise<-
closekontak_covid_jabar_data_pekan%>%
group_by(kode_kab)%>%
summarise(Satu_Minggu_Trkahir=n())## `summarise()` ungrouping output (override with `.groups` argument)closekontak_covid_jabar_data_pekan_summarise## # A tibble: 13 x 2
## kode_kab Satu_Minggu_Trkahir
## <chr> <int>
## 1 3201 3
## 2 3203 411
## 3 3204 289
## 4 3205 6
## 5 3211 45
## 6 3212 20
## 7 3214 119
## 8 3217 183
## 9 3271 4760
## 10 3273 87
## 11 3275 4879
## 12 3277 487
## 13 3279 485# stage by kab pekan update
closekontak_covid_jabar_data_by_stage_pekan_summarise<-
closekontak_covid_jabar_data_pekan%>%
group_by(kode_kab, stage)%>%
summarise(Satu_Minggu_Terakhir=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_closekontak_pekan<-
closekontak_covid_jabar_data_by_stage_pekan_summarise %>%
pivot_wider(names_from = stage, values_from = Satu_Minggu_Terakhir)
stage_closekontak_pekan[is.na(stage_closekontak_pekan)]=0
stage_closekontak_pekan## # A tibble: 13 x 3
## # Groups: kode_kab [13]
## kode_kab Dikarantina Discarded
## <chr> <int> <int>
## 1 3201 3 0
## 2 3203 178 233
## 3 3204 171 118
## 4 3205 0 6
## 5 3211 45 0
## 6 3212 1 19
## 7 3214 73 46
## 8 3217 113 70
## 9 3271 2 4758
## 10 3273 19 68
## 11 3275 0 4879
## 12 3277 325 162
## 13 3279 184 301DATA DENGAN STATUS PROBABLE
# probable by kab total
probable_covid_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="CLOSECONTACT")
names(probable_covid_jabar_data)## [1] "id" "kode_kab"
## [3] "nama_kab" "kode_kec"
## [5] "nama_kec" "kode_kel"
## [7] "nama_kel" "status"
## [9] "stage" "umur"
## [11] "gender" "longitude"
## [13] "latitude" "tanggal_konfirmasi"
## [15] "tanggal_update" "tanggal_update_nasional"probable_covid_jabar_data_summarise<-
probable_covid_jabar_data%>%
group_by(kode_kab)%>%
summarise(Total=n())## `summarise()` ungrouping output (override with `.groups` argument)probable_covid_jabar_data_summarise## # A tibble: 27 x 2
## kode_kab Total
## <chr> <int>
## 1 3201 12088
## 2 3202 5512
## 3 3203 7047
## 4 3204 11477
## 5 3205 18111
## 6 3206 173
## 7 3207 2801
## 8 3208 7428
## 9 3209 2384
## 10 3210 301
## # ... with 17 more rows# stage by kab total
probable_covid_jabar_data_by_stage_summarise<-
probable_covid_jabar_data%>%
group_by(kode_kab, stage)%>%
summarise(Total=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_probable_total<-
probable_covid_jabar_data_by_stage_summarise %>%
pivot_wider(names_from = stage, values_from =c(Total))
stage_probable_total[is.na(stage_probable_total)]=0
stage_probable_total## # A tibble: 27 x 6
## # Groups: kode_kab [27]
## kode_kab Selesai Diisolasi Meninggal Discarded Dikarantina
## <chr> <int> <int> <int> <int> <int>
## 1 3201 45 6 2 10391 1644
## 2 3202 0 0 0 3521 1991
## 3 3203 0 0 0 5788 1259
## 4 3204 0 0 0 6949 4528
## 5 3205 0 0 0 17374 737
## 6 3206 0 0 0 168 5
## 7 3207 0 0 0 2778 23
## 8 3208 0 0 0 7340 88
## 9 3209 0 0 0 577 1807
## 10 3210 0 0 0 86 215
## # ... with 17 more rows# Probable by kab hari terupdate
tanggal_terupdate<- as_date(max(probable_covid_jabar_data$tanggal_update_nasional))
tanggal_terupdate## [1] "2021-07-11"probable_covid_jabar_data_hari<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(status=="CLOSECONTACT", tanggal_update_nasional==tanggal_terupdate)
probable_covid_jabar_data_hari_summarise<-
probable_covid_jabar_data_hari%>%
group_by(kode_kab)%>%
summarise(Hari_Terakhir=n())## `summarise()` ungrouping output (override with `.groups` argument)probable_covid_jabar_data_hari_summarise## # A tibble: 8 x 2
## kode_kab Hari_Terakhir
## <chr> <int>
## 1 3203 22
## 2 3204 42
## 3 3211 14
## 4 3214 6
## 5 3217 69
## 6 3275 200
## 7 3277 23
## 8 3279 49# stage by kab hari update
probable_covid_jabar_data_by_stage_hari_summarise<-
probable_covid_jabar_data_hari%>%
group_by(kode_kab, stage)%>%
summarise(Hari_Terakhir=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_probable_hari<-
probable_covid_jabar_data_by_stage_hari_summarise %>%
pivot_wider(names_from = stage, values_from = Hari_Terakhir)
stage_probable_hari[is.na(stage_probable_hari)]=0
stage_probable_hari## # A tibble: 8 x 3
## # Groups: kode_kab [8]
## kode_kab Dikarantina Discarded
## <chr> <int> <int>
## 1 3203 22 0
## 2 3204 36 6
## 3 3211 14 0
## 4 3214 0 6
## 5 3217 55 14
## 6 3275 0 200
## 7 3277 21 2
## 8 3279 49 0# Probable by kab pekan terakhir
tanggal_terupdate1 <-
tanggal_terupdate - days(7)
probable_covid_jabar_data_pekan<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
mutate(year= year(tanggal_update_nasional))%>%
filter(status=="CLOSECONTACT", year==2021, tanggal_update_nasional > tanggal_terupdate1)
probable_covid_jabar_data_pekan_summarise<-
probable_covid_jabar_data_pekan%>%
group_by(kode_kab)%>%
summarise(Satu_Minggu_Trkahir=n())## `summarise()` ungrouping output (override with `.groups` argument)probable_covid_jabar_data_pekan_summarise## # A tibble: 13 x 2
## kode_kab Satu_Minggu_Trkahir
## <chr> <int>
## 1 3201 3
## 2 3203 411
## 3 3204 289
## 4 3205 6
## 5 3211 45
## 6 3212 20
## 7 3214 119
## 8 3217 183
## 9 3271 4760
## 10 3273 87
## 11 3275 4879
## 12 3277 487
## 13 3279 485# stage by kab pekan update
probable_covid_jabar_data_by_stage_pekan_summarise<-
probable_covid_jabar_data_pekan%>%
group_by(kode_kab, stage)%>%
summarise(Satu_Minggu_Terakhir=n())## `summarise()` regrouping output by 'kode_kab' (override with `.groups` argument)stage_probable_pekan<-
probable_covid_jabar_data_by_stage_pekan_summarise %>%
pivot_wider(names_from = stage, values_from = Satu_Minggu_Terakhir)
stage_probable_pekan[is.na(stage_probable_pekan)]=0
stage_probable_pekan## # A tibble: 13 x 3
## # Groups: kode_kab [13]
## kode_kab Dikarantina Discarded
## <chr> <int> <int>
## 1 3201 3 0
## 2 3203 178 233
## 3 3204 171 118
## 4 3205 0 6
## 5 3211 45 0
## 6 3212 1 19
## 7 3214 73 46
## 8 3217 113 70
## 9 3271 2 4758
## 10 3273 19 68
## 11 3275 0 4879
## 12 3277 325 162
## 13 3279 184 301VISUASLISASI DIISOLASI
# Covid Diisolasi for visualisasi
covid_konfirmasi_diisolasi_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Diisolasi", status== "CONFIRMATION")%>%
group_by(kode_kab)%>%
mutate(n=n())%>%
drop_na()
covid_konfirmasi_diisolasi_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Diisolasi", status== "CONFIRMATION")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
rename(ID_KAB = kode_kab)%>%
filter(ID_KAB!="32")%>%
mutate(ID_KAB = as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_konfirmasi_diisolasi_jabar_summarise## # A tibble: 27 x 2
## ID_KAB n
## <dbl> <int>
## 1 3201 3353
## 2 3202 921
## 3 3203 433
## 4 3204 4371
## 5 3205 7619
## 6 3206 1293
## 7 3207 1288
## 8 3208 3169
## 9 3209 3178
## 10 3210 2121
## # ... with 17 more rows# Join Data dengan Peta
## Bagusnya join datanya berdasarkan koordinat latitude dan longitude responden, seperti ini:
## covid_diisolasi_jabar_data <- st_as_sf(covid_diisolasi_jabar_data, coords=c("longitude", "latitude"))
## st_crs(covid_diisolasi_jabar_data) <- st_crs(peta_jabar)
## covid_diisolasi_jabar_final <- st_join(peta_jabar, covid_diisolasi_jabar_data, left= FALSE)
## covid_diisolasi_jabar_final <- covid_diisolasi_jabar_final %>% group_by(nama_kab) %>% summarise(n= n())
# Karena kalau gitu lama, jadi menggunakan ini
covid_konfirmasi_diisolasi_jabar_final<-
full_join(peta_jabar, covid_konfirmasi_diisolasi_jabar_summarise, by= "ID_KAB")%>%
select(-contains("Shape_"))
covid_konfirmasi_diisolasi_jabar_final[is.na(covid_konfirmasi_diisolasi_jabar_final)]=0
covid_konfirmasi_diisolasi_jabar_final## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB n geometry
## 1 1 JAWA BARAT BOGOR 3201 3353 MULTIPOLYGON (((106.9708 -6...
## 2 2 JAWA BARAT SUKABUMI 3202 921 MULTIPOLYGON (((106.7415 -6...
## 3 3 JAWA BARAT CIANJUR 3203 433 MULTIPOLYGON (((107.2302 -6...
## 4 4 JAWA BARAT BANDUNG 3204 4371 MULTIPOLYGON (((107.7331 -6...
## 5 5 JAWA BARAT GARUT 3205 7619 MULTIPOLYGON (((107.9182 -6...
## 6 6 JAWA BARAT TASIKMALAYA 3206 1293 MULTIPOLYGON (((108.3549 -7...
## 7 7 JAWA BARAT CIAMIS 3207 1288 MULTIPOLYGON (((108.4415 -7...
## 8 8 JAWA BARAT PANGANDARAN 3218 303 MULTIPOLYGON (((108.6694 -7...
## 9 9 JAWA BARAT KUNINGAN 3208 3169 MULTIPOLYGON (((108.4211 -6...
## 10 10 JAWA BARAT CIREBON 3209 3178 MULTIPOLYGON (((108.685 -6....ggplot(covid_konfirmasi_diisolasi_jabar_final) +
geom_sf(aes(fill=n)) +
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Diisolasi")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Terkonfirmasi Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
ggplot(covid_konfirmasi_diisolasi_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Terkonfirmasi Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Pasien")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))
# Covid Diisolasi for visualisasi
covid_suspect_diisolasi_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Diisolasi", status== "SUSPECT")%>%
group_by(kode_kab)%>%
mutate(n=n())
covid_suspect_diisolasi_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Diisolasi", status== "SUSPECT")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
rename(ID_KAB = kode_kab)%>%
filter(ID_KAB!="32")%>%
mutate(ID_KAB = as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_suspect_diisolasi_jabar_summarise## # A tibble: 26 x 2
## ID_KAB n
## <dbl> <int>
## 1 3201 281
## 2 3202 536
## 3 3203 198
## 4 3204 808
## 5 3205 777
## 6 3206 1
## 7 3207 35
## 8 3208 253
## 9 3209 60
## 10 3210 28
## # ... with 16 more rows# Join Data dengan Peta
covid_suspect_diisolasi_jabar_final<-
full_join(peta_jabar, covid_suspect_diisolasi_jabar_summarise, by= "ID_KAB")%>%
select(-contains("Shape_"))
covid_suspect_diisolasi_jabar_final[is.na(covid_suspect_diisolasi_jabar_final)] = 0
covid_suspect_diisolasi_jabar_final## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB n geometry
## 1 1 JAWA BARAT BOGOR 3201 281 MULTIPOLYGON (((106.9708 -6...
## 2 2 JAWA BARAT SUKABUMI 3202 536 MULTIPOLYGON (((106.7415 -6...
## 3 3 JAWA BARAT CIANJUR 3203 198 MULTIPOLYGON (((107.2302 -6...
## 4 4 JAWA BARAT BANDUNG 3204 808 MULTIPOLYGON (((107.7331 -6...
## 5 5 JAWA BARAT GARUT 3205 777 MULTIPOLYGON (((107.9182 -6...
## 6 6 JAWA BARAT TASIKMALAYA 3206 1 MULTIPOLYGON (((108.3549 -7...
## 7 7 JAWA BARAT CIAMIS 3207 35 MULTIPOLYGON (((108.4415 -7...
## 8 8 JAWA BARAT PANGANDARAN 3218 121 MULTIPOLYGON (((108.6694 -7...
## 9 9 JAWA BARAT KUNINGAN 3208 253 MULTIPOLYGON (((108.4211 -6...
## 10 10 JAWA BARAT CIREBON 3209 60 MULTIPOLYGON (((108.685 -6....ggplot(covid_suspect_diisolasi_jabar_final) +
geom_sf(aes(fill=n)) +
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Diisolasi")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Suspect Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
ggplot(covid_suspect_diisolasi_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Suspect Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Pasien")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))
# Covid Diisolasi for visualisasi
covid_closekontak_diisolasi_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Dikarantina", status== "CLOSECONTACT")%>%
group_by(kode_kab)%>%
mutate(n=n())
covid_closekontak_diisolasi_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Dikarantina", status== "CLOSECONTACT")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
rename(ID_KAB = kode_kab)%>%
filter(ID_KAB!="32")%>%
mutate(ID_KAB= as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_closekontak_diisolasi_jabar_summarise## # A tibble: 27 x 2
## ID_KAB n
## <dbl> <int>
## 1 3201 1644
## 2 3202 1991
## 3 3203 1259
## 4 3204 4528
## 5 3205 737
## 6 3206 5
## 7 3207 23
## 8 3208 88
## 9 3209 1807
## 10 3210 215
## # ... with 17 more rows# Join Data dengan Peta
covid_closekontak_diisolasi_jabar_final<-
full_join(peta_jabar, covid_closekontak_diisolasi_jabar_summarise, by= "ID_KAB")%>%
select(-contains("Shape_"))
covid_closekontak_diisolasi_jabar_final[is.na(covid_closekontak_diisolasi_jabar_final)]=0
covid_closekontak_diisolasi_jabar_final## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB n geometry
## 1 1 JAWA BARAT BOGOR 3201 1644 MULTIPOLYGON (((106.9708 -6...
## 2 2 JAWA BARAT SUKABUMI 3202 1991 MULTIPOLYGON (((106.7415 -6...
## 3 3 JAWA BARAT CIANJUR 3203 1259 MULTIPOLYGON (((107.2302 -6...
## 4 4 JAWA BARAT BANDUNG 3204 4528 MULTIPOLYGON (((107.7331 -6...
## 5 5 JAWA BARAT GARUT 3205 737 MULTIPOLYGON (((107.9182 -6...
## 6 6 JAWA BARAT TASIKMALAYA 3206 5 MULTIPOLYGON (((108.3549 -7...
## 7 7 JAWA BARAT CIAMIS 3207 23 MULTIPOLYGON (((108.4415 -7...
## 8 8 JAWA BARAT PANGANDARAN 3218 656 MULTIPOLYGON (((108.6694 -7...
## 9 9 JAWA BARAT KUNINGAN 3208 88 MULTIPOLYGON (((108.4211 -6...
## 10 10 JAWA BARAT CIREBON 3209 1807 MULTIPOLYGON (((108.685 -6....ggplot(covid_closekontak_diisolasi_jabar_final) +
geom_sf(aes(fill=n)) +
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Diisolasi")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Closecontact Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
ggplot(covid_closekontak_diisolasi_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Closecontact Pasien Aktif COVID-19",
subtitle = "Pasien Dikarantina KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Pasien")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))
# Covid Diisolasi for visualisasi
covid_probable_diisolasi_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Diisolasi", status== "PROBABLE")%>%
group_by(kode_kab)%>%
mutate(n=n())
covid_probable_diisolasi_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Diisolasi", status== "PROBABLE")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
rename(ID_KAB = kode_kab)%>%
filter(ID_KAB!="32")%>%
mutate(ID_KAB = as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_probable_diisolasi_jabar_summarise## # A tibble: 17 x 2
## ID_KAB n
## <dbl> <int>
## 1 3201 16
## 2 3202 52
## 3 3203 19
## 4 3204 4
## 5 3205 1
## 6 3207 2
## 7 3208 16
## 8 3209 3
## 9 3211 1
## 10 3212 5
## 11 3214 1
## 12 3216 173
## 13 3217 10
## 14 3218 2
## 15 3275 89
## 16 3277 7
## 17 3279 14# Join Data dengan Peta
covid_probable_diisolasi_jabar_final<-
full_join(peta_jabar, covid_probable_diisolasi_jabar_summarise, by= "ID_KAB")%>%
select(-contains("Shape_"))
covid_probable_diisolasi_jabar_final[is.na(covid_probable_diisolasi_jabar_final)]=0
covid_probable_diisolasi_jabar_final## Simple feature collection with 27 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB n geometry
## 1 1 JAWA BARAT BOGOR 3201 16 MULTIPOLYGON (((106.9708 -6...
## 2 2 JAWA BARAT SUKABUMI 3202 52 MULTIPOLYGON (((106.7415 -6...
## 3 3 JAWA BARAT CIANJUR 3203 19 MULTIPOLYGON (((107.2302 -6...
## 4 4 JAWA BARAT BANDUNG 3204 4 MULTIPOLYGON (((107.7331 -6...
## 5 5 JAWA BARAT GARUT 3205 1 MULTIPOLYGON (((107.9182 -6...
## 6 6 JAWA BARAT TASIKMALAYA 3206 0 MULTIPOLYGON (((108.3549 -7...
## 7 7 JAWA BARAT CIAMIS 3207 2 MULTIPOLYGON (((108.4415 -7...
## 8 8 JAWA BARAT PANGANDARAN 3218 2 MULTIPOLYGON (((108.6694 -7...
## 9 9 JAWA BARAT KUNINGAN 3208 16 MULTIPOLYGON (((108.4211 -6...
## 10 10 JAWA BARAT CIREBON 3209 3 MULTIPOLYGON (((108.685 -6....ggplot(covid_probable_diisolasi_jabar_final) +
geom_sf(aes(fill=n)) +
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Diisolasi")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Probable Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
ggplot(covid_probable_diisolasi_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Probable Pasien Aktif COVID-19",
subtitle = "Pasien Diisolasi KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Pasien")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))
VISUALISASI MENINGGAL
# Covid Disolasi for visualisasi
covid_konfirmasi_meninggal_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Meninggal", status== "CONFIRMATION")%>%
group_by(kode_kab)%>%
mutate(n=n())
covid_konfirmasi_meninggal_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Meninggal", status== "CONFIRMATION")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
filter(kode_kab !="32")%>%
rename(ID_KAB = kode_kab)%>%
mutate(ID_KAB = as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_konfirmasi_meninggal_jabar_summarise## # A tibble: 28 x 2
## ID_KAB n
## <dbl> <int>
## 1 NA 1
## 2 3201 87
## 3 3202 66
## 4 3203 14
## 5 3204 294
## 6 3205 466
## 7 3206 126
## 8 3207 211
## 9 3208 15
## 10 3209 222
## # ... with 18 more rows# Join data dengan peta
covid_konfirmasi_meninggal_jabar_final<-
full_join(peta_jabar, covid_konfirmasi_meninggal_jabar_summarise, by= "ID_KAB")
covid_konfirmasi_meninggal_jabar_final[is.na(covid_konfirmasi_meninggal_jabar_final)]=0
covid_konfirmasi_meninggal_jabar_final## Simple feature collection with 28 features and 7 fields (with 1 geometry empty)
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB Shape_Leng Shape_Area n
## 1 1 JAWA BARAT BOGOR 3201 4.450488 0.24474031 87
## 2 2 JAWA BARAT SUKABUMI 3202 4.046777 0.34048048 66
## 3 3 JAWA BARAT CIANJUR 3203 4.890595 0.29407572 14
## 4 4 JAWA BARAT BANDUNG 3204 3.093005 0.14360665 294
## 5 5 JAWA BARAT GARUT 3205 3.480848 0.25302534 466
## 6 6 JAWA BARAT TASIKMALAYA 3206 3.811662 0.22094821 126
## 7 7 JAWA BARAT CIAMIS 3207 3.131695 0.13047727 211
## 8 8 JAWA BARAT PANGANDARAN 3218 2.491501 0.09307806 55
## 9 9 JAWA BARAT KUNINGAN 3208 1.921314 0.09702479 15
## 10 10 JAWA BARAT CIREBON 3209 2.756094 0.08733455 222
## geometry
## 1 MULTIPOLYGON (((106.9708 -6...
## 2 MULTIPOLYGON (((106.7415 -6...
## 3 MULTIPOLYGON (((107.2302 -6...
## 4 MULTIPOLYGON (((107.7331 -6...
## 5 MULTIPOLYGON (((107.9182 -6...
## 6 MULTIPOLYGON (((108.3549 -7...
## 7 MULTIPOLYGON (((108.4415 -7...
## 8 MULTIPOLYGON (((108.6694 -7...
## 9 MULTIPOLYGON (((108.4211 -6...
## 10 MULTIPOLYGON (((108.685 -6....ggplot(covid_konfirmasi_meninggal_jabar_final) +
geom_sf(aes(fill= n))+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Meninggal")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Terkonfirmasi Pasien COVID-19",
subtitle = "Pasien Meninggal KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data## Warning: Removed 1 rows containing missing values (geom_label).
ggplot(covid_konfirmasi_meninggal_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Terkonfirmasi Pasien COVID-19",
subtitle = "Pasien Meninggal KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Kasus")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))
# Covid Disolasi for visualisasi
covid_suspect_meninggal_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Meninggal", status== "SUSPECT")%>%
group_by(kode_kab)%>%
mutate(n=n())
covid_suspect_meninggal_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Meninggal", status== "SUSPECT")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
filter(kode_kab !="32")%>%
rename(ID_KAB = kode_kab)%>%
mutate(ID_KAB = as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_suspect_meninggal_jabar_summarise## # A tibble: 20 x 2
## ID_KAB n
## <dbl> <int>
## 1 3201 2
## 2 3202 1
## 3 3203 45
## 4 3204 6
## 5 3205 3
## 6 3207 1
## 7 3208 6
## 8 3209 7
## 9 3212 3
## 10 3213 3
## 11 3214 8
## 12 3215 6
## 13 3216 7
## 14 3217 2
## 15 3218 6
## 16 3271 171
## 17 3275 373
## 18 3276 1
## 19 3277 14
## 20 3279 7# Join data dengan peta
covid_suspect_meninggal_jabar_final<-
full_join(peta_jabar, covid_suspect_meninggal_jabar_summarise, by= "ID_KAB")
covid_suspect_meninggal_jabar_final[is.na(covid_suspect_meninggal_jabar_final)]=0
covid_suspect_meninggal_jabar_final## Simple feature collection with 27 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB Shape_Leng Shape_Area n
## 1 1 JAWA BARAT BOGOR 3201 4.450488 0.24474031 2
## 2 2 JAWA BARAT SUKABUMI 3202 4.046777 0.34048048 1
## 3 3 JAWA BARAT CIANJUR 3203 4.890595 0.29407572 45
## 4 4 JAWA BARAT BANDUNG 3204 3.093005 0.14360665 6
## 5 5 JAWA BARAT GARUT 3205 3.480848 0.25302534 3
## 6 6 JAWA BARAT TASIKMALAYA 3206 3.811662 0.22094821 0
## 7 7 JAWA BARAT CIAMIS 3207 3.131695 0.13047727 1
## 8 8 JAWA BARAT PANGANDARAN 3218 2.491501 0.09307806 6
## 9 9 JAWA BARAT KUNINGAN 3208 1.921314 0.09702479 6
## 10 10 JAWA BARAT CIREBON 3209 2.756094 0.08733455 7
## geometry
## 1 MULTIPOLYGON (((106.9708 -6...
## 2 MULTIPOLYGON (((106.7415 -6...
## 3 MULTIPOLYGON (((107.2302 -6...
## 4 MULTIPOLYGON (((107.7331 -6...
## 5 MULTIPOLYGON (((107.9182 -6...
## 6 MULTIPOLYGON (((108.3549 -7...
## 7 MULTIPOLYGON (((108.4415 -7...
## 8 MULTIPOLYGON (((108.6694 -7...
## 9 MULTIPOLYGON (((108.4211 -6...
## 10 MULTIPOLYGON (((108.685 -6....ggplot(covid_suspect_meninggal_jabar_final) +
geom_sf(aes(fill= n))+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Meninggal")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Suspect Pasien COVID-19",
subtitle = "Pasien Meninggal KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
ggplot(covid_suspect_meninggal_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Suspect Pasien COVID-19",
subtitle = "Pasien Meninggal KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Kasus")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))
# Covid Disolasi for visualisasi
covid_probable_meninggal_jabar_data<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Meninggal", status== "PROBABLE")%>%
group_by(kode_kab)%>%
mutate(n=n())
covid_probable_meninggal_jabar_summarise<-
covid_jabar_data%>%
select(-c(report_source,contains("current_")))%>%
filter(stage=="Meninggal", status== "PROBABLE")%>%
group_by(kode_kab)%>%
summarise(n=n())%>%
filter(kode_kab !="32")%>%
rename(ID_KAB = kode_kab)%>%
mutate(ID_KAB = as.numeric(ID_KAB))## `summarise()` ungrouping output (override with `.groups` argument)covid_probable_meninggal_jabar_summarise## # A tibble: 20 x 2
## ID_KAB n
## <dbl> <int>
## 1 3201 4
## 2 3202 120
## 3 3203 52
## 4 3204 77
## 5 3205 5
## 6 3208 21
## 7 3209 16
## 8 3212 121
## 9 3213 1
## 10 3214 15
## 11 3215 89
## 12 3216 9
## 13 3217 44
## 14 3218 10
## 15 3271 55
## 16 3272 10
## 17 3273 1
## 18 3275 286
## 19 3277 42
## 20 3279 32# Join data dengan peta
covid_probable_meninggal_jabar_final<-
full_join(peta_jabar, covid_probable_meninggal_jabar_summarise, by= "ID_KAB")
covid_probable_meninggal_jabar_final[is.na(covid_probable_meninggal_jabar_final)]=0
covid_probable_meninggal_jabar_final## Simple feature collection with 27 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 106.3705 ymin: -7.823398 xmax: 108.8338 ymax: -5.91377
## Geodetic CRS: WGS 84
## First 10 features:
## OBJECTID PROVINSI KABKOT ID_KAB Shape_Leng Shape_Area n
## 1 1 JAWA BARAT BOGOR 3201 4.450488 0.24474031 4
## 2 2 JAWA BARAT SUKABUMI 3202 4.046777 0.34048048 120
## 3 3 JAWA BARAT CIANJUR 3203 4.890595 0.29407572 52
## 4 4 JAWA BARAT BANDUNG 3204 3.093005 0.14360665 77
## 5 5 JAWA BARAT GARUT 3205 3.480848 0.25302534 5
## 6 6 JAWA BARAT TASIKMALAYA 3206 3.811662 0.22094821 0
## 7 7 JAWA BARAT CIAMIS 3207 3.131695 0.13047727 0
## 8 8 JAWA BARAT PANGANDARAN 3218 2.491501 0.09307806 10
## 9 9 JAWA BARAT KUNINGAN 3208 1.921314 0.09702479 21
## 10 10 JAWA BARAT CIREBON 3209 2.756094 0.08733455 16
## geometry
## 1 MULTIPOLYGON (((106.9708 -6...
## 2 MULTIPOLYGON (((106.7415 -6...
## 3 MULTIPOLYGON (((107.2302 -6...
## 4 MULTIPOLYGON (((107.7331 -6...
## 5 MULTIPOLYGON (((107.9182 -6...
## 6 MULTIPOLYGON (((108.3549 -7...
## 7 MULTIPOLYGON (((108.4415 -7...
## 8 MULTIPOLYGON (((108.6694 -7...
## 9 MULTIPOLYGON (((108.4211 -6...
## 10 MULTIPOLYGON (((108.685 -6....ggplot(covid_probable_meninggal_jabar_final) +
geom_sf(aes(fill= n))+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"),
line = element_blank(),
rect = element_blank(),
axis.text = element_blank(),
axis.ticks.length = unit(0, "cm"),
legend.justification=c(0, 0),
legend.position = c(0, 0),
panel.spacing = unit(0, "lines"),
plot.margin = unit(c(0, 0, 0, 0), "lines"),
complete = TRUE) +
viridis::scale_fill_viridis("Banyaknya Pasien Meninggal")+
geom_sf_label(aes(label = KABKOT, fill=n), size=2)+
labs(title = "Data Kasus Probable Pasien COVID-19",
subtitle = "Pasien Meninggal KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi",
x = NULL,
y = NULL)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
ggplot(covid_probable_meninggal_jabar_final, aes(x = reorder(KABKOT, n), y = n))+
geom_bar(stat = "identity", fill= "brown", alpha = 0.5)+
coord_flip()+
labs(title = "Data Kasus Probable Pasien COVID-19",
subtitle = "Pasien Meninggal KAB/KOTA di JAWA BARAT",
caption = "by : Arwan Zhagi")+
ylab("Banyaknya Kasus")+
xlab("Nama Kabupaten/Kota")+
theme(plot.title = element_text(color = "black",size = 17, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 10, face = "bold"))