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| Visualisasi animasi Google Mobility Index Indonesia dengan gganimate |
Perkembangan Big Data demikian pesat. Data tidak terstruktur dan terstruktur bercampur menjadi sebuah awan data yang berdimensi besar, memiliki kecepatan update yang tinggi, bervariasi, serta mengandung value serta insight berharga apabila dikupas secara mendalam.
Penggunaan Big Data juga sangat diminati akhir-akhir ini seiring kehadiran cabang-cabang ilmu baru, seperti Data Science. Meski masih untrusted dan debatable karena belum ada payung hukum yang menjamin potensi Big Data menjadi Official Statistics, tetapi kenyataan empiris yang berkembang menyebutkan bahwa Big Data berpotensi menjadi sumber data Official Statistics baru.
Salah satu Big Data yang lahir di tengah masa Pandemi Covid-19 adalah data mobilitas penduduk yang diukur berdasarkan base mobilitas awal kedatangan Pandemi Covid-19. Data ini dibuat oleh Google dalam bentuk indeks dan dinamai Google Mobility Index (GMI). Sekilas, indeks ini menunjukkan tingkat mobilitas masyarakat ke beberapa tempat tujuan aktivitas, yaitu ke pasar, tempat rekreasi, toko obat, taman, tempat transit, tempat kerja, dan residential.
Pada kesempatan kali ini, kita akan mencoba mempratikkan visualisasi Google Mobility Index Indonesia menggunakan package gganimate. Keunggulan dari package gganimate adalah mampu membuat tampilan data yang dinamis karena memiliki fungsi render setiap gambar yang terbentuk (frame) menjadi sebuah visualisasi bergerak (animasi). Selain kita dapat mengatur jumlah frame, kita juga bisa mengcustome ukuran visualisasi, kecepatan gerakan, serta ketajaman visualisasi. Adapun cara melakukan interpretasi terhadap GMI dapat melihat laman berikut.
Data yang kita gunakan dalam praktik kali mencakup data Google Mobility Index (GMI) untuk wilayah Indonesia sejak 15 Februari 2020 hingga 15 Oktober 2022 berikut. Setelah datanya diunduh, praktik visualisasi menggunakan package gganimate kita terapkan dengan mengikuti langkah-langkah berikut:
library(readxl)
indo_1 <- read_excel("C:/Users/Joko Ade/Documents/gmi_id2023.xlsx")
head(indo_1)
## # A tibble: 6 x 8
## date retail_recreation grocery_pharmacy parks transit_stations
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2020-02-15 00:00:00 -2 -2 -8 1
## 2 2020-02-16 00:00:00 -3 -3 -7 -4
## 3 2020-02-17 00:00:00 -3 -4 -7 -1
## 4 2020-02-18 00:00:00 -3 -2 -4 1
## 5 2020-02-19 00:00:00 -3 -4 -3 0
## 6 2020-02-20 00:00:00 -2 -1 -1 4
## # ... with 3 more variables: workplaces <dbl>, residential <dbl>, waktu <dttm>
#Melihat variabel data Google Mobility Index Indonesia
names(indo_1)
## [1] "date" "retail_recreation" "grocery_pharmacy" "parks"
## [5] "transit_stations" "workplaces" "residential" "waktu"
#Visualisasi dengan ggplot2
library(ggplot2)
indo_1 %>%
ggplot(aes(x = waktu)) +
geom_line(aes(y = retail_recreation), col = "red") +
geom_line(aes(y = grocery_pharmacy), col = "orange") +
geom_line(aes(y = workplaces), col = "green") +
geom_point(aes(y = retail_recreation), col = "red") +
geom_point(aes(y = grocery_pharmacy), col = "orange") +
geom_point(aes(y = workplaces), col = "green") +
geom_vline(xintercept = as.numeric(ymd("2020-04-02")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2020-03-20")), y = 40, label = "PSBB DKI Jakarta", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2020-04-10")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2020-04-20")), y = 40, label = "PSBB Beberapa Wilayah", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2020-06-05")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2020-05-20")), y = 40, label = "Pelonggaran PSBB", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2020-09-14")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2020-09-04")), y = 40, label = "PSBB DKI Jakarta", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2021-01-26")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2021-01-20")), y = 40, label = "PPKM Jawa Bali", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2021-02-09")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2021-01-30")), y = 40, label = "PPKM Mikro", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2021-07-03")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2021-06-25")), y = 40, label = "PPKM Darurat", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2021-07-15")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2021-07-06")), y = 40, label = "Covid19 Delta", size = 3, angle = 90) +
geom_vline(xintercept = as.numeric(ymd("2022-05-18")), linetype = "dotted", size = 0.8, color = "blue") +
geom_text(x = as.numeric(ymd("2022-05-1")), y = 60, label = "Pelonggaran pakai masker", size = 3) +
ylim(-75, 60) +
labs(x = "periode", y = "indeks mobilitas") -> p
#Animasi ggplot dengan gganimate
library(gganimate)
p +
labs(x = "periode", y = "Indeks Mobilitas") +
transition_reveal(waktu) -> q
animate(q, nframe = 800, end_pause = 20,
width = 800, height = 600, duration = 20, res = 100)
## Rendering [-------------------------------------------------] at 8.4 fps ~ eta: 2m
## Rendering [-------------------------------------------------] at 7.6 fps ~ eta: 2m
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## Rendering [==>----------------------------------------------] at 7.9 fps ~ eta: 2m
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## [=======================================>---------] at 7.6 fps ~ eta: 18s
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 2 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
## Rendering [========================================>--------] at 7.6 fps ~ eta: 18s
## Warning: Removed 5 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 2 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
## Rendering [========================================>--------] at 7.6 fps ~ eta: 17s
## Warning: Removed 4 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing missing values (geom_point).
##
Rendering [========================================>--------] at 7.6 fps ~ eta: 16s
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Rendering [============================================>----] at 7.4 fps ~ eta: 10s
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Rendering [============================================>----] at 7.3 fps ~ eta: 8s
Rendering [=============================================>---] at 7.3 fps ~ eta: 8s
Rendering [=============================================>---] at 7.3 fps ~ eta: 7s
Rendering [=============================================>---] at 7.3 fps ~ eta: 6s
Rendering [=============================================>---] at 7.2 fps ~ eta: 6s
Rendering [===============================================>---] at 7 fps ~ eta: 6s
Rendering [=============================================>---] at 6.9 fps ~ eta: 6s
Rendering [==============================================>--] at 6.8 fps ~ eta: 6s
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Rendering [===============================================>-] at 6.3 fps ~ eta: 4s
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Rendering [===============================================>-] at 5.9 fps ~ eta: 3s
Rendering [===============================================>-] at 5.9 fps ~ eta: 2s
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Rendering [===============================================>-] at 5.6 fps ~ eta: 1s
Rendering [================================================>] at 5.3 fps ~ eta: 1s
Rendering [================================================>] at 5.2 fps ~ eta: 1s
Rendering [================================================>] at 5.1 fps ~ eta: 0s
Rendering [=================================================] at 5.1 fps ~ eta: 0s
Adapun hasilnya adalah sebagai berikut:
 |
| Visualisasi menggunakan package gganimate |
Demikian sedikit sharing kita kali ini. Semoga sedikit banyak memberi manfaat kepada pembaca blog ini. Jangan lupa untuk terus mengikuti unggahan menarik dan bermanfaat di blog sederhana ini. Selamat membaca dan mempraktikan!