Grafik COVID-19 di Indonesia Menggunakan RStudio
Assalamu’alaikum Warohmatullahi Wabarakatuh, hallo temen temen semua setelah sekian lama tidak menulis artikel.
Karena udah beberapa bulan aku dirumah aja dan kuliah daring.. Jadi aku mau membuat grafik secara sederhana mengenai wabah COVID-19 ini. Kalian udah gak asing lagikan sama wabah COVID-19 atau Corona, pandemi yang lagi nyerang dan dimana-mana ada.
Kira-kira hasilnya seperti apa yaaaa ??
Yuk kita mulai saja!!
Pertama kita harus menyiapkan RStudio, tetapi disini aku pake RCloud jadi tidak masalah sama saja. Klik File → New File → RMarkdown seperti pada gambar dibawah.
Lalu akan muncul jendela baru
isi bagian title, author, dan klik HTML dan OK. Tampilan akan berubah seperti berikut.
Sebelum langkah selanjutnya, perlu di install terlebih dahulu beberapa package yang akan digunakan:
install.packages(c("devtools", "flexdashboard", "leaflet", "leafpop"))letakkan perintah install package tersebut pada bagian console
klik enter biarkan semua package terinstall terlebih dahulu.
Dan apabila telah selesai akan nampak seperti berikut.
Karena akan membuat grafik tentang Corona maka perlu menginstal packgae ‘coronavirus’ yang telah dibuat oleh Rami Krispin dan tersedia di link https://github.com/RamiKrispin/coronavirus
Menggunakan perintah sebagai berikut:
devtools::install_github("RamiKrispin/coronavirus")dan pastekan pada console seperti berikut:
nantinya akan muncul pertanyaan seperti gambar berikut. Maka cukup ketikkan angka ‘1’ untuk mengupdate semua package yang diperlukan. Jangan bosan ya buat menunggunya sampai berproses lagi..
Hingga berakhir seperti berikut.
Nah selanjutnya akan proses pembuatan grafiknya. Dengan mengambil sumber koding dan data pada Github karangan Antoine
dapat di akses dengan link https://github.com/AntoineSoetewey/coronavirus_dashboard kemudian dapat dilihat pada file coronavirus-dashboard.Rmd
Dengan modifikasi codinf yang digunakan untuk kasus Corona di Indonesia, digunakan coding sebagai berikut:
---
title: "Coronavirus in Indonesia"
author: "Farhatun Nissa"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---```{r setup, include=FALSE}
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_dataset()
# View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Indonesia") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(country == "Indonesia") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
#dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
# recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```Summary
=======================================================================Row {data-width=400}
-----------------------------------------------------------------------### confirmed {.value-box}```{r}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
```<!-- ### active {.value-box} --><!-- ```{r} -->
<!-- valueBox( -->
<!-- value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (", -->
<!-- round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1), -->
<!-- "%)", -->
<!-- sep = "" -->
<!-- ), -->
<!-- caption = "Active cases (% of total cases)", icon = "fas fa-ambulance", -->
<!-- color = active_color -->
<!-- ) -->
<!-- ``` -->### death {.value-box}```{r}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
```Row
-----------------------------------------------------------------------### **Daily cumulative cases by type** (Indonesia only)
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_annotations(
x = as.Date("2020-03-02"),
y = 1,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-11"),
y = 1,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```Comparison
=======================================================================Column {data-width=400}
-------------------------------------### **Daily new confirmed cases**
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::mutate(country = country) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~Indonesia,
type = "scatter",
mode = "lines+markers",
name = "Indonesia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Malaysia,
type = "scatter",
mode = "lines+markers",
name = "Malaysia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Singapore,
type = "scatter",
mode = "lines+markers",
name = "Singapore"
) %>%
plotly::add_trace(
x = ~date,
y = ~Thailand,
type = "scatter",
mode = "lines+markers",
name = "Thailand"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new confirmed cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
### **Cases distribution by type**```{r daily_summary}
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Indonesia" |
country == "Malaysia" |
country == "Singapore" |
country == "Thailand") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```Map
=======================================================================### **World map of cases** (*use + and - icons to zoom in/out*)```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
# dplyr::filter(country == "Indonesia") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(country, province, lat, long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~long, lat = ~lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "country", "province")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```About
=======================================================================**The Coronavirus Dashboard: the case of Indonesia**This [Coronavirus dashboard: the case of Indonesia](https://www.antoinesoetewey.com/files/coronavirus-dashboard.html) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Indonesia. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.
**Code**
The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.
**Data**
The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:
```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```
The data and dashboard are refreshed on a daily basis.
The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.
**Information and contact**
More information about this dashboard and how to replicate it for your own country can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).
For any question or feedback, you can [contact me](https://www.statsandr.com/contact/).
**Update**
The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.
<br>
copy semua codingan diatas…
Lalu, kembali terlebih dahulu ke RStudio kita, blok semua coding dikotak biru yang ada pada script, delete semuanya sampai kosong.
Setelah kosong kemudian paste codingan sebelumnya, seperti berikut yang ditunjukkan oleh panah merah:
Klik knit diatas bawah menu atau yang ditunjukkan panah biru, lalu akan beralih pada penyimpanan file simpanlah terlebih dahulu script diatas.
Untuk melihat hasilnya membutuhkan beberapa waktu untuk menunggu.. Hingga akhirnya didapatkan hasil grafik seperti berikut:
Visualisasi real time penyebaran coronavirus di Indonesia sudah jadi!! Jika kita klik menu comparison akan muncul grafik lain:
Dan juga dapat dilihat dari Mapnya ..
Bagaimana hasilnya? Cukup memuaskan bukan?
Dengan cara sederhana memanfaatkan coding, package, dan data yang telah disediakan di Github https://github.com/AntoineSoetewey/coronavirus_dashboard Thank you so much Antoine XoXo
Semoga dengan adanya visualisasi coronavirus di Indonesia, masyarakat lebih mempertimbangkan kembali dengan sekitarnya.
Wassalamu’alaikum Wr Wb.
Referensi :
