Analisis Data COVID19 di Indonesia

Willibrordus Bayu 12/15/2021

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Data Preparation

library(httr)
resp_jabar <- GET("https://data.covid19.go.id/public/api/prov_detail_JAWA_BARAT.json")
cov_jabar_raw <- content(resp_jabar, as = "parsed", simplifyVector = TRUE)
names(cov_jabar_raw)
##  [1] "last_date"            "provinsi"             "kasus_total"         
##  [4] "kasus_tanpa_tgl"      "kasus_dengan_tgl"     "meninggal_persen"    
##  [7] "meninggal_tanpa_tgl"  "meninggal_dengan_tgl" "sembuh_persen"       
## [10] "sembuh_tanpa_tgl"     "sembuh_dengan_tgl"    "list_perkembangan"   
## [13] "data"
cov_jabar_raw$kasus_total
## [1] 708335
cov_jabar_raw$meninggal_persen
## [1] 2.081642
cov_jabar_raw$sembuh_persen
## [1] 97.79963

Memperoleh Informasi yang Lebih Lengkap

cov_jabar <- cov_jabar_raw$list_perkembangan
str(cov_jabar)
## 'data.frame':    650 obs. of  9 variables:
##  $ tanggal                     : num  1.58e+12 1.58e+12 1.58e+12 1.58e+12 1.58e+12 ...
##  $ KASUS                       : int  1 1 1 1 0 0 0 0 0 1 ...
##  $ MENINGGAL                   : int  1 0 0 0 0 0 0 0 1 0 ...
##  $ SEMBUH                      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ DIRAWAT_OR_ISOLASI          : int  0 1 1 1 0 0 0 0 -1 1 ...
##  $ AKUMULASI_KASUS             : int  1 2 3 4 4 4 4 4 4 5 ...
##  $ AKUMULASI_SEMBUH            : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ AKUMULASI_MENINGGAL         : int  1 1 1 1 1 1 1 1 2 2 ...
##  $ AKUMULASI_DIRAWAT_OR_ISOLASI: int  0 1 2 3 3 3 3 3 2 3 ...
head(cov_jabar)
##        tanggal KASUS MENINGGAL SEMBUH DIRAWAT_OR_ISOLASI AKUMULASI_KASUS
## 1 1.583194e+12     1         1      0                  0               1
## 2 1.583280e+12     1         0      0                  1               2
## 3 1.583366e+12     1         0      0                  1               3
## 4 1.583453e+12     1         0      0                  1               4
## 5 1.583539e+12     0         0      0                  0               4
## 6 1.583626e+12     0         0      0                  0               4
##   AKUMULASI_SEMBUH AKUMULASI_MENINGGAL AKUMULASI_DIRAWAT_OR_ISOLASI
## 1                0                   1                            0
## 2                0                   1                            1
## 3                0                   1                            2
## 4                0                   1                            3
## 5                0                   1                            3
## 6                0                   1                            3

Menjinakkan Data

library(dplyr)
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
new_cov_jabar <-
  cov_jabar %>% 
  select(-contains("DIRAWAT_OR_ISOLASI")) %>% 
  select(-starts_with("AKUMULASI")) %>% 
  rename(
    kasus_baru = KASUS,
    meninggal = MENINGGAL,
    sembuh = SEMBUH
    ) %>% 
  mutate(
    tanggal = as.POSIXct(tanggal / 1000, origin = "1970-01-01"),
    tanggal = as.Date(tanggal)
  )
str(new_cov_jabar)  
## 'data.frame':    650 obs. of  4 variables:
##  $ tanggal   : Date, format: "2020-03-03" "2020-03-04" ...
##  $ kasus_baru: int  1 1 1 1 0 0 0 0 0 1 ...
##  $ meninggal : int  1 0 0 0 0 0 0 0 1 0 ...
##  $ sembuh    : int  0 0 0 0 0 0 0 0 0 0 ...
library(ggplot2)
library(hrbrthemes)
## Warning: package 'hrbrthemes' was built under R version 4.1.2

## 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/arialnarrow
ggplot(new_cov_jabar, aes(x = tanggal, y = kasus_baru)) +
  geom_col()

Menunjukkan Melalui Gambar - Pt 2

library(ggplot2)
library(hrbrthemes)
ggplot(new_cov_jabar, aes(tanggal, kasus_baru)) +
    geom_col(fill = "salmon") +
    labs(
      x = NULL,
      y = "Jumlah kasus",
      title = "Kasus Harian Positif COVID-19 di Jawa Barat",
      subtitle = "Terjadi pelonjakan kasus di awal bulan Juli akibat klaster Secapa AD Bandung",
      caption = "Sumber data: covid.19.go.id"
    ) +
    theme_ipsum(
      base_size = 13,
      plot_title_size = 21,
      grid = "Y",
      ticks = TRUE
    ) +
    theme(plot.title.position = "plot")
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Grafik untuk Kasus Sembuh

library(ggplot2)
library(hrbrthemes)
ggplot(new_cov_jabar, aes(tanggal, sembuh)) +
  geom_col(fill = "olivedrab2") +
  labs(
    x = NULL,
    y = "Jumlah kasus",
    title = "Kasus Harian Sembuh Dari COVID-19 di Jawa Barat",
    caption = "Sumber data: covid.19.go.id"
  ) +
  theme_ipsum(
    base_size = 13, 
    plot_title_size = 21,
    grid = "Y",
    ticks = TRUE
  ) +
  theme(plot.title.position = "plot")
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Grafik untuk Kasus Meninggal

library(ggplot2)
library(hrbrthemes)
ggplot(new_cov_jabar, aes(tanggal, meninggal)) +
  geom_col(fill = "darkslategray4") +
  labs(
    x = NULL,
    y = "Jumlah kasus",
    title = "Kasus Harian Meninggal Akibat COVID-19 di Jawa Barat",
    caption = "Sumber data: covid.19.go.id"
  ) +
  theme_ipsum(
    base_size = 13, 
    plot_title_size = 21,
    grid = "Y",
    ticks = TRUE
  ) +
  theme(plot.title.position = "plot")
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Apakah Pekan ini Lebih Baik?

library(dplyr)
library(lubridate)
## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
cov_jabar_pekanan <- new_cov_jabar %>% 
  count(
    tahun = year(tanggal),
    pekan_ke = week(tanggal),
    wt = kasus_baru,
    name = "jumlah"
  )

glimpse(cov_jabar_pekanan)
## Rows: 95
## Columns: 3
## $ tahun    <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2~
## $ pekan_ke <dbl> 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24~
## $ jumlah   <int> 1, 3, 16, 41, 98, 149, 153, 155, 156, 284, 803, 175, 395, 203~

Apakah Pekan ini Lebih Baik dari Kemarin?

library(dplyr)
cov_jabar_pekanan <-
  cov_jabar_pekanan %>% 
  mutate(
    jumlah_pekanlalu = dplyr::lag(jumlah, 1),
    jumlah_pekanlalu = ifelse(is.na(jumlah_pekanlalu), 0, jumlah_pekanlalu),
    lebih_baik = jumlah < jumlah_pekanlalu
  )
glimpse(cov_jabar_pekanan)
## Rows: 95
## Columns: 5
## $ tahun            <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,~
## $ pekan_ke         <dbl> 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22~
## $ jumlah           <int> 1, 3, 16, 41, 98, 149, 153, 155, 156, 284, 803, 175, ~
## $ jumlah_pekanlalu <dbl> 0, 1, 3, 16, 41, 98, 149, 153, 155, 156, 284, 803, 17~
## $ lebih_baik       <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS~

Membuat Bar Chart

library(ggplot2)
library(hrbrthemes)

ggplot(cov_jabar_pekanan[cov_jabar_pekanan$tahun==2020,], aes(pekan_ke, jumlah, fill = lebih_baik)) +
    geom_col(show.legend = FALSE) +
    scale_x_continuous(breaks = 9:29, expand = c(0,0)) +
    scale_fill_manual(values = c("TRUE" = "seagreen3", "FALSE" = "salmon")) +
    labs(
      x = NULL,
      y = "Jumlah kasus",
      title = "Kasus Pekanan Positif COVID-19 di Jawa Barat",
      subtitle = "Kolom hijau menunjukan penambahan kasus baru lebih sedikit dibandingkan satu pekan sebelumnya",
      caption = "Sumber data: covid.19.go.id"
     ) +
     theme_ipsum(
       base_size = 13,
       plot_title_size = 21,
       grid = "Y",
       ticks = TRUE
     ) +
    theme(plot.title.position = "plot")
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Jumlah Kasus Aktif

library(dplyr)
cov_jabar_akumulasi <- 
  new_cov_jabar %>% 
  transmute(
    tanggal,
    akumulasi_aktif = cumsum(kasus_baru) - cumsum(sembuh) - cumsum(meninggal),
    akumulasi_sembuh = cumsum(sembuh),
    akumulasi_meninggal = cumsum(meninggal)
  )

tail(cov_jabar_akumulasi)
##        tanggal akumulasi_aktif akumulasi_sembuh akumulasi_meninggal
## 645 2021-12-07             815           692566               14742
## 646 2021-12-08             796           692646               14743
## 647 2021-12-09             804           692682               14744
## 648 2021-12-10             810           692720               14744
## 649 2021-12-11             832           692738               14744
## 650 2021-12-12             853           692738               14744

Membuat Line Chart

library(ggplot2)
ggplot(data = cov_jabar_akumulasi, aes(x = tanggal, y = akumulasi_aktif)) +
  geom_line()

Transformasi Data

library(dplyr)
library(tidyr)

dim(cov_jabar_akumulasi)
## [1] 650   4
cov_jabar_akumulasi_pivot <- 
  cov_jabar_akumulasi %>% 
  gather(
    key = "kategori",
    value = "jumlah",
    -tanggal
  ) %>% 
  mutate(
    kategori = sub(pattern = "akumulasi_", replacement = "", kategori)
  )

dim(cov_jabar_akumulasi_pivot)
## [1] 1950    3
glimpse(cov_jabar_akumulasi_pivot)
## Rows: 1,950
## Columns: 3
## $ tanggal  <date> 2020-03-03, 2020-03-04, 2020-03-05, 2020-03-06, 2020-03-07, ~
## $ kategori <chr> "aktif", "aktif", "aktif", "aktif", "aktif", "aktif", "aktif"~
## $ jumlah   <int> 0, 1, 2, 3, 3, 3, 3, 3, 2, 3, 6, 6, 4, 2, 13, 14, 17, 29, 34,~

Grafik Akumulasi Kasus Aktif

library(ggplot2)
library(hrbrthemes)

ggplot(cov_jabar_akumulasi_pivot, aes(tanggal, jumlah, colour = (kategori))) +
    geom_line(size = 0.9) + 
    scale_y_continuous(sec.axis = dup_axis(name = NULL)) +
    scale_colour_manual(
      values = c(
        "aktif" = "salmon",
        "meninggal" = "darkslategray4",
        "sembuh" = "olivedrab2"
     ),
     labels = c("Aktif", "Meninggal", "Sembuh")
    ) +
    labs(
      x = NULL,
      y = "Jumlah kasus akumulasi",
      colour = NULL,
      title = "Dinamika Kasus COVID-19 di Jawa Barat",
      caption = "Sumber data: covid.19.go.id"
     ) +
    theme_ipsum(
      base_size = 13,
      plot_title_size = 21,
      grid = "Y",
      ticks = TRUE
    ) +
    theme(
      plot.title = element_text(hjust = 0.5),
      legend.position = "top"
    )
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