Dashboard Visualisasi Corona Menggunakan R
Covid-19 Masih aja nangkring di Indonesia nih, yuk bikin dashboard visualisasi dengan memanfaatkan data yang Ada!
Halo manteman, Its been a while! dah lama ini babang penulis ga nyorat nyoret di kertas medium, kayak yang seperti diatas ya kita mau ngapain sekarang. Langsung aja kali ya.
Alat dan bahan
Pertama udah pasti dong kita butuhin yang namanya Perangkat Lunak R Studio. Nah kalo udah punya alatnya siapin bahannya ya, namanya package pastiin dah dibeli bahan-bahannya, emm maksudnya dah diinstall. Ini bahan bahannya :
- Install package coronavirus from Rami Krispin
- Install package devtools
- Install package flexdashboard
- Install package leaflet
- Install package leafpop
- Install package plotly
- Install package tidyr
- Install package dplyr
Sudah kan ? Ayok Masak!
Buka r Markdown, kalo ga inget dia ada disitu kayak gambar :
Lalu pake yang HTML …
dah, klik OK.
Selanjutnya, kita bakal biki 4 tab utama ya, ada Tab Summary, Tab Comparison, Tab Map, sama Tab About. Untungnya, ada mas Antoine Soetewey nih yang baik hati ngebagiin codingannya untuk kita ngebikin dashboard. Manteman bisa cek ke Githubnya mas Antoine ya. Btw, terimakasih mas Antoine!
Sudah di cek ? ambil codingannya di file “coronavirus-dashboard.Rmd”, yang ini nih. yang di blok biru
nah, ada kan ? oke sekarang modifikasi codingannya biar bikin versi kita sendiri, kita bakal make untuk yang Indonesia. Ini codingannya.
---
title: "Dashboard Corona Virus 19 Indonesia"
author: "Rintaldi Ghazian Hindami"
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$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "#b4821f"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(country == "Indonesia") %>%
dplyr::group_by(country, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(country == "Indonesia") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
#dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
# recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```Summary
=======================================================================Row {data-width=400}
-----------------------------------------------------------------------### confirmed {.value-box}```{r}
valueBox(
value = paste(format(sum(df$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 itu semua, lalu letakkan di R Markdown nya
Eits, jangan di run dulu. Jangan lupa untuk nge Install packages “coronavirus” dari mas Rami Krispin, packagenya bisa didapet di githubnya. Makasih mas Rami! Jalankan codingan di bawah ini pada console, nggak usah dimasukin di R Markdown kalian.
devtools::install_github("RamiKrispin/coronavirus")Nah kalo kondisi ini keluar maka ketikan 1 untuk mengupdate semua.
setelah tekan 1 tunggu sampe penginstallan benar benar done ya.
setelah semua Done, barulah untuk mengeksekusi codingan yang ada pada R Markdown tadi. Tekan tombol Knit
silahkan menunggu dan jadi deh
nah hasil yang dihasilkan diatas untuk kasus indonesia ya, jika ingin mengganti negara mana yang mentemen inginkan tinggal temen temen ganti di bagian sini,
paham ya ? lalu untuk tab comparisson
Saya memvisualisasikan untuk negara negara tentangga dibandingkan dengan indonesia, tapi kalo temen temen mau ngebandingin negara yang bukan seperti yang saya tampilkan nanti temen temen tinggal ganti nama negaranya, edit nya dibagian sini
nah keliatan ada nama negara-negaranya kan ? temen temen ganti aja disitu, inget ya namanya harus sama persis yang ada di datasets Johnhopskin. misal kalo mau ganti Thailand dengan Zimbabwe. “y” dan “name” nya diganti, ya kayak gitu lah.
trus ada dasrboard interaktif untuk sebaran Covid-19 di dunia.
nah itu bisa diotak atik seenak hati mentemen, kalo mau liat sebaran matinya aja, tinggal di uncheck recovered sama confirmednya. gampang kan ?
WELL WELL WELL!
Gampang kok cara buatnya, kamu yang baru belajar ngoding juga bisa, codingannya disediain kok sama Mas Antoine di githubnya, sebebas kreatif kalian.
Itu aja ya, semoga bermanfaat.
Until Next time
Ciao Manteman!
Sumber inspirasi :
