Membuat Grafik Sederhana Covid-19
8 min readMar 31, 2020
Dengan Menggunakan RStudio
Assalamuálaikum teman-teman yang suka data
Dalam suasana duka karena wabah ini, sambil belajar dan bekerja dari rumah, bisa kita sedikit mencoba membuat grafik tentang penyebaran wabah virus corona ini, langsung saja ya..
Hasil akhirnya nanti paling tidak berisi beberapa grafis seperti berikut,
Oke, pertama kita siapkan terlebih dahulu RStudio. Kemudian klik File dan sorot pada File -> New File -> RMarkdown seperti gambar berikut,
kemudian akan muncul jendela berikut,
kemudian isilah title, author, dan klik pada HTML kemudian klik OK
sehingga tampil seperti berikut,
untuk sementara kita biarkan terlebih dahulu seperti itu.
Kemudian kita mengambil sumber koding dan data pada Github ini,
yang beralamat di https://github.com/AntoineSoetewey/coronavirus_dashboard (thank you Antoine) kemudian lihat pada file coronavirus-dashboard.Rmd
dan sedikit hasil modifikasi kodingnya untuk negara Indonesia adalah seperti berikut,
---
title: "Coronavirus in Indonesia"
author: "RB Fajriya Hakim"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_datasets()
# 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.Region == "Indonesia") %>%
dplyr::group_by(Country.Region, 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.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
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.Region == "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
)
```### 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 = -80
) %>%
plotly::add_annotations(
x = as.Date("2020-03-11"),
y = 3,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -90,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-18"),
y = 14,
text = paste(
" ",
"",
""
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = FALSE,
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 >= "2020-03-02") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)#----------------------------------------
# Plotting the datadaily_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.Region == "Indonesia" |
Country.Region == "Malaysia" |
Country.Region == "Thailand" |
Country.Region == "Singapore") %>%
dplyr::group_by(Country.Region, 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.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
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.Region == "Indonesia") %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(Country.Region, Province.State, 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$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~Long, lat = ~Lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "Country.Region", "Province.State")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```About
=======================================================================**The Coronavirus Dashboard: the case of Indonesia**This Coronavirus dashboard: the case of Indonesia provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Indonesia. This dashboard is built with R using the R Markdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.**Code**The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.**Data**The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```The data and dashboard are refreshed on a daily basis.The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.**Update**The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.*Thanks to [www.statsandr.com](https://www.statsandr.com/) (blog) or [www.antoinesoetewey.com](https://www.antoinesoetewey.com/) (personal website)*.
copy-lah koding diatas seluruhnya.. untuk detailnya bisa anda pelajari sendiri ya… :))
Kemudian kita kembali dahulu ke RStudio kita, blok semua koding yang ada pada script, kemudian delete semuanya,
setelah kosong maka timpakan koding yang telah di copy tadi, seperti berikut
sebelum dijalankan maka instal terlebih dahulu beberapa package yang diperlukan,
install.packages(c("devtools", "flexdashboard", "leaflet", "leafpop")) letakkan perintah instal package tersebut pada console
kemudian tekan enter, dan biarkan semua package terinstal terlebih dahulu,
jika telah selesai akan nampak seperti berikut,
kemudian kita akan menginstal package ‘coronavirus’ yang telah dibuat oleh Rami Krispin dan tersedia di https://github.com/RamiKrispin/coronavirus
dengan perintah seperti berikut,
devtools::install_github("RamiKrispin/coronavirus")paste seperti berikut,
kemudian klik enter, dan tunggu berproses,
jika muncul pertanyaan seperti berikut, maka ketikkan angka ‘1’ untuk mengupdate semua package yang diperlukan, dan ditunggu sampai berproses lagi,
hingga berakhir seperti ini,
setelah selesai instalasi package maka kita kembali ke script dan tinggal kita klik “knit” diatas dibawah menu,
kemudian simpanlah terlebih dahulu script diatas,
kemudian tunggu sebentar untuk berproses dan…
jadilah visualisasi real time penyebaran coronavirus di Indonesia, jika kita klik pada menu comparison akan muncul,
dan juga map,
demikian cara sederhana dengan memanfaatkan koding, package dan data yang telah tersedia di github maka kita bisa memvisualkan grafik penyebaran virus corona.
jika ingin mempublikasikan hasil tersebut tinggal klik publish pada sisi pojok sebelah kanan, kemudian ikuti langkah-langkahnya sampai selesai. Semoga bermanfaat..
Referensi:
