Transformasi Data Covid di Indonesia

bersatulawancovid  <- c("cuci tangan", "pakai masker", "jaga jarak")
bersatulawancovid 

## [1] "cuci tangan"  "pakai masker" "jaga jarak"

Rekapitulasi data COVID-19 Indonesia tersedia dalam API publik yang beralamat di https://data.covid19.go.id/public/api/update.json.maka kita akan langsung mengambil data pada laman website tersebut.

library(httr)

resp <- GET ("https://data.covid19.go.id/public/api/update.json")

library(httr)

resp <- GET("https://data.covid19.go.id/public/api/update.json")
status_code (resp)

## [1] 200

Status Code - 2

dan sekarang kita akan mencoba mengetahui status permintaan dengan cara mengakses elemen dari resp. Status code tersebut tersedia dengan nama status_code dan dapat diakses dengan menggunakan operator $ pada obyek resp.

library(httr)

resp <- GET("https://data.covid19.go.id/public/api/update.json")
resp$status_code

## [1] 200

identical(resp$status_code, status_code(resp))

## [1] TRUE

Mengekstrak isi Respon

library(httr)

resp <- GET("https://data.covid19.go.id/public/api/update.json")
cov_id_raw <- content(resp, as = "parsed", simplifyVector = TRUE)

Mengekstrak isi Respon - Pt 2

length(cov_id_raw)

## [1] 2

names(cov_id_raw)

## [1] "data"   "update"

cov_id_update <- cov_id_raw$update

Analisa Data

sekarang kita akan menganalisa data diantaranya :

Kapan tanggal pembaharuan data penambahan kasus? Berapa jumlah penambahan kasus sembuh? Berapa jumlah penambahan kasus meninggal? Berapa jumlah total kasus positif hingga saat ini? Berapa jumlah total kasus meninggal hingga saat ini?

lapply(cov_id_update, names)

## $penambahan
## [1] "jumlah_positif"   "jumlah_meninggal" "jumlah_sembuh"    "jumlah_dirawat"  
## [5] "tanggal"          "created"         
## 
## $harian
##  [1] "key_as_string"        "key"                  "doc_count"           
##  [4] "jumlah_meninggal"     "jumlah_sembuh"        "jumlah_positif"      
##  [7] "jumlah_dirawat"       "jumlah_positif_kum"   "jumlah_sembuh_kum"   
## [10] "jumlah_meninggal_kum" "jumlah_dirawat_kum"  
## 
## $total
## [1] "jumlah_positif"   "jumlah_dirawat"   "jumlah_sembuh"    "jumlah_meninggal"

cov_id_update$penambahan$tanggal

## [1] "2021-02-19"

cov_id_update$penambahan$jumlah_sembuh

## [1] 10783

cov_id_update$penambahan$jumlah_meninggal

## [1] 183

cov_id_update$total$jumlah_positif

## [1] 1263299

cov_id_update$total$jumlah_meninggal

## [1] 34152

COVID-19 Di Sekitar Jawa Timur, Apa Kabar ?

sebelum melangkah lebih jauh mari kita jawab pertanyaan - pertanyaan berikut :

Berapa jumlah total kasus COVID-19 di Jawa Timur? Berapa persentase kematian akibat COVID-19 di Jawa Timur? Berapa persentase tingkat kesembuhan dari COVID-19 di Jawa Timur?

library(httr)

resp_jatim <- GET("https://data.covid19.go.id/public/api/prov_detail_JAWA_TIMUR.json")
cov_jatim_raw <- content(resp_jatim, as = "parsed", simplifyVector = TRUE)

names(cov_jatim_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_jatim_raw$kasus_total

## [1] 122807

cov_jatim_raw$meninggal_persen

## [1] 6.674701

cov_jatim_raw$sembuh_persen

## [1] 84.87627

Memperoleh Informasi yang Lebih Lengkap

Informasi umum mengenai COVID-19 di Jawa Timur telah Anda dapatkan. Namun informasi akan lebih lengkap jika Anda memiliki data perkembangan COVID-19 dari waktu ke waktu, apakah Anda setuju?

cov_jatim <- cov_jatim_raw$list_perkembangan
str(cov_jatim)

## 'data.frame':    335 obs. of  9 variables:
##  $ tanggal                     : num  1.58e+12 1.58e+12 1.58e+12 1.58e+12 1.58e+12 ...
##  $ KASUS                       : int  2 0 0 0 0 0 0 0 8 7 ...
##  $ MENINGGAL                   : int  0 0 0 0 0 0 1 2 0 0 ...
##  $ SEMBUH                      : int  0 1 0 0 0 1 0 2 2 0 ...
##  $ DIRAWAT_OR_ISOLASI          : int  2 -1 0 0 0 -1 -1 -4 6 7 ...
##  $ AKUMULASI_KASUS             : int  2 2 2 2 2 2 2 2 10 17 ...
##  $ AKUMULASI_SEMBUH            : int  0 1 1 1 1 2 2 4 6 6 ...
##  $ AKUMULASI_MENINGGAL         : int  0 0 0 0 0 0 1 3 3 3 ...
##  $ AKUMULASI_DIRAWAT_OR_ISOLASI: int  2 1 1 1 1 0 -1 -5 1 8 ...

head(cov_jatim)

##        tanggal KASUS MENINGGAL SEMBUH DIRAWAT_OR_ISOLASI AKUMULASI_KASUS
## 1 1.584490e+12     2         0      0                  2               2
## 2 1.584576e+12     0         0      1                 -1               2
## 3 1.584662e+12     0         0      0                  0               2
## 4 1.584749e+12     0         0      0                  0               2
## 5 1.584835e+12     0         0      0                  0               2
## 6 1.584922e+12     0         0      1                 -1               2
##   AKUMULASI_SEMBUH AKUMULASI_MENINGGAL AKUMULASI_DIRAWAT_OR_ISOLASI
## 1                0                   0                            2
## 2                1                   0                            1
## 3                1                   0                            1
## 4                1                   0                            1
## 5                1                   0                            1
## 6                2                   0                            0

Setelah mengekstrak dan mengamati cov_jatim, Anda menemukan beberapa kejanggalan pada data tersebut. Diantaranya adalah kejanggalan data pada kolom tanggal dan format penulisan kolom yang tidak konsisten. Sekarang Anda akan mencoba melakukan beberapa tahapan untuk menjinakan data tersebut sehingga dapat diolah dan dianalisis dengan lebih mudah. sebagai berikut :

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_jatim <-
  cov_jatim %>% 
  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_jatim)

## 'data.frame':    335 obs. of  4 variables:
##  $ tanggal   : Date, format: "2020-03-18" "2020-03-19" ...
##  $ kasus_baru: int  2 0 0 0 0 0 0 0 8 7 ...
##  $ meninggal : int  0 0 0 0 0 0 1 2 0 0 ...
##  $ sembuh    : int  0 1 0 0 0 1 0 2 2 0 ...

Menunjukkan Melalui Gambar

selanjutnya komponen utama untuk membuat visualisasi antara lain adalah tabel data, kolom data, serta bentuk geometri untuk mempresentasikan data. Sebagai contoh untuk membuat scatter-plot yang diperlukan adalah bentuk geometri titik (geom_col()), line-chart memerlukan geometri garis (geom_line()), sedangkan bar-chart memerlukan bentuk geometri batang atau kolom (geom_bar() atau geom_col()).

library(ggplot2)
library(hrbrthemes)

## 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_jatim, aes(x = tanggal, y = kasus_baru)) +
  geom_col()

Menunjukkan Melalui Gambar - Pt 2

Anda telah berhasil membuat bar-chart yang diminta. Namun grafik tersebut belum memiliki informasi yang jelas serta cenderung membosankan untuk dilihat. Sekarang silakan buat kembali grafik tersebut dengan menggunakan baris kode yang telah dimodifikasi berikut:

library(ggplot2)
library(hrbrthemes)

ggplot(new_cov_jatim, aes(tanggal, kasus_baru)) +
    geom_col(fill = "salmon") +
    labs(
      x = NULL,
      y = "Jumlah kasus",
      title = "Kasus Harian Positif COVID-19 di Jawa Timur",
      subtitle = "Terjadi pelonjakan kasus di awal bulan Januari akibat klaster penyebaran",
      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")

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
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Grafik untuk Kasus Sembuh

library(ggplot2)
library(hrbrthemes)

ggplot(new_cov_jatim, aes(tanggal, sembuh)) +
  geom_col(fill = "olivedrab2") +
  labs(
    x = NULL,
    y = "Jumlah kasus",
    title = "Kasus Harian Sembuh Dari COVID-19 di Jawa Timur",
    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")

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Grafik untuk Kasus Meninggal

library(ggplot2)
library(hrbrthemes)

ggplot(new_cov_jatim, aes(tanggal, meninggal)) +
  geom_col(fill = "darkslategray4") +
  labs(
    x = NULL,
    y = "Jumlah kasus",
    title = "Kasus Harian Meninggal Akibat COVID-19 di Jawa Timur",
    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")

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Apakah Pekan ini Lebih Baik?

Setelah mengamati grafik perkembangan kasus Anda menyadari bahwa terjadi fluktuasi pertambahan kasus harian. Dilandasi hal tersebut Anda kemudian ingin mencoba mengamati bagaimana perkembangan kasus dalam rentang waktu pekanan. Bagaimanakah caranya? Anda dapat dengan mudah bekerja dengan data tanggal apabila menggunakan paket lubridate. Adapun yang akan digunakan untuk mengekstrak informasi pekan dalam satu tahun adalah fungsi week().sebagai berikut :

library(dplyr)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

cov_jatim_pekanan <- new_cov_jatim %>% 
  count(
    tahun = year(tanggal),
    pekan_ke = week(tanggal),
    wt = kasus_baru,
    name = "jumlah"
  )

glimpse(cov_jatim_pekanan)

## Rows: 49
## Columns: 3
## $ tahun    <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020...
## $ pekan_ke <dbl> 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26...
## $ jumlah   <int> 2, 41, 102, 279, 129, 253, 303, 483, 712, 1552, 1185, 1332...

Menjawab Pertanyaan

Pertanyaan baru muncul di benak Anda setelah melakukan inspeksi terhadap data cov_jatim_pekanan tersebut: "Apakah pekan ini lebih baik dari pekan kemarin?".

Demi menjawab hal tersebut Anda melakukan kalkulasi sederhana dengan tahapan berikut:

Membuat kolom baru yang berisi jumlah kasus baru dalam satu pekan sebelumnya. Kolom ini diberi nama "jumlah_pekanlalu". Mengganti nilai NA pada kolom "jumlah_pekanlalu" dengan nilai 0 Melakukan komparasi antara kolom "jumlah" dengan kolom "jumlah_pekanlalu". Hasil komparasi ini disimpan dalam kolom baru dengan nama "lebih_baik", isinya adalah TRUE apabila jumlah kasus baru pekan ini lebih rendah dibandingkan jumlah kasus pekan lalu

library(dplyr)

cov_jatim_pekanan <-
  cov_jatim_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_jatim_pekanan)

## Rows: 49
## Columns: 5
## $ tahun            <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 20...
## $ pekan_ke         <dbl> 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24...
## $ jumlah           <int> 2, 41, 102, 279, 129, 253, 303, 483, 712, 1552, 11...
## $ jumlah_pekanlalu <dbl> 0, 2, 41, 102, 279, 129, 253, 303, 483, 712, 1552,...
## $ lebih_baik       <lgl> FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FA...

Membuat Bar Chart

library(ggplot2)
library(hrbrthemes)

ggplot(cov_jatim_pekanan, 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 Timur",
    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")

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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Pola dan Dinamika

Ada yang akhirnya sembuh, namun tak sedikit pula yang meninggal akibat COVID-19. Sementara itu penambahan kasus baru terus terjadi di masyarakat. Hal ini mungkin memicu pertanyaan lain di diri Anda: "Hingga saat ini ada berapa kasus yang masih aktif?". Aktif dalam artian sedang dalam perawatan atau isolasi.

library(dplyr)

cov_jatim_akumulasi <- 
  new_cov_jatim %>% 
  transmute(
    tanggal,
    akumulasi_aktif = cumsum(kasus_baru) - cumsum(sembuh) - cumsum(meninggal),
    akumulasi_sembuh = cumsum(sembuh),
    akumulasi_meninggal = cumsum(meninggal)
  )

tail(cov_jatim_akumulasi)

##        tanggal akumulasi_aktif akumulasi_sembuh akumulasi_meninggal
## 330 2021-02-10            8998           102399                8033
## 331 2021-02-11            9611           102803                8060
## 332 2021-02-12            9715           103400                8135
## 333 2021-02-13            9948           103708                8154
## 334 2021-02-14           10118           104031                8177
## 335 2021-02-15           10342           104222                8194

Membuat Line Chart

library(ggplot2)

ggplot(data = cov_jatim_akumulasi, aes(x = tanggal, y = akumulasi_aktif)) +
  geom_line()

Kesimpulan apa yang dapat Anda tarik dari grafik tersebut?

Kabar Buruk dan Kabar Baik

library(ggplot2)

ggplot(data = cov_jatim_akumulasi, aes(x = tanggal)) +
  geom_line(aes(x = tanggal, y = akumulasi_aktif), color = "blue") +
  geom_line(aes(x = tanggal, y = akumulasi_sembuh), color = "green") +
  geom_line(aes(x = tanggal, y = akumulasi_meninggal), color = "red")

Transformasi Data

library(dplyr)
library(tidyr)

dim(cov_jatim_akumulasi)

## [1] 335   4

cov_jatim_akumulasi_pivot <- 
  cov_jatim_akumulasi %>% 
  gather(
    key = "kategori",
    value = "jumlah",
    -tanggal
  ) %>% 
  mutate(
    kategori = sub(pattern = "akumulasi_", replacement = "", kategori)
  )

dim(cov_jatim_akumulasi_pivot)

## [1] 1005    3

glimpse(cov_jatim_akumulasi_pivot)

## Rows: 1,005
## Columns: 3
## $ tanggal  <date> 2020-03-18, 2020-03-19, 2020-03-20, 2020-03-21, 2020-03-2...
## $ kategori <chr> "aktif", "aktif", "aktif", "aktif", "aktif", "aktif", "akt...
## $ jumlah   <int> 2, 1, 1, 1, 1, 0, -1, -5, 1, 8, 19, 28, 28, 28, 34, 31, 79...

cov_jatim_akumulasi_pivot <-
  cov_jatim_akumulasi %>%
  pivot_longer(
    cols = -tanggal,
    names_to = "kategori",
    names_prefix = "akumulasi_",
    values_to = "jumlah"
  )

dim(cov_jatim_akumulasi_pivot)

## [1] 1005    3

glimpse(cov_jatim_akumulasi_pivot)

## Rows: 1,005
## Columns: 3
## $ tanggal  <date> 2020-03-18, 2020-03-18, 2020-03-18, 2020-03-19, 2020-03-1...
## $ kategori <chr> "aktif", "sembuh", "meninggal", "aktif", "sembuh", "mening...
## $ jumlah   <int> 2, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 2, 0, -1, ...

Dinamika Kasus Covid di Jawa Timur

library(ggplot2)
library(hrbrthemes)

ggplot(cov_jatim_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 Timur",
    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"
  )

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## font family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

Sekian dan Terimakasih